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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareVenous and Arterial Thrombosis in COVID-19 Era; Risk and Management
English0205Kharaba AymanEnglish Hussein MohamedEnglish Albeihany AmalEnglishSince its discovery in December 2019, SARS-CoV-2 has resulted in more than 23 million infected cases worldwide with more than 800.000 deaths with a mortality rate of about 3.47%. The respiratory system seems to be the primary target system for SARS-CoV-2, and infected patients may develop acute lung injury and Adult respiratory distress syndrome (ARDS). However, severe COVID-19 is a multi-systemic disease associated with a hypercoagulable state affecting the microvascular, venous, and arterial system. Severe COVID-19 infection is characterized by coagulation derangement and hyperinflammatory state that may lead to overt disseminated intravascular coagulopathy (DIC), a status termed Covid-19 Induced Coagulopathy (CIC). This can result in multi-organ involvement with microvascular, arterial, and venous thrombosis. In this article, we review the incidence of thrombosis in severe covid-19 patients, the mechanisms of Covid-19 induced thrombosis, and recommendations regarding anticoagulation in hospitalized Covid-19 patients.
EnglishCOVID19, Incidence, Arterial, Venous, Thrombosis, ManagementIntroduction
Since its discovery in December 2019, SARS-CoV-2 has resulted in more than 23 million infected cases worldwide with more than 800.000 deaths with a mortality rate of about 3.47%.1 The respiratory system seems to be the primary target system for SARS-CoV-2, and infected patients may develop acute lung injury and Adult respiratory distress syndrome (ARDS). However, severe COVID-19 is a multi-systemic disease associated with a hypercoagulable state affecting the microvascular, venous, and arterial system.2,3
Hospitalized COVID-19 patients appear to have a high incidence of venous thromboembolism (VTE)compared to other critically ill patients with other severe medical conditions like sepsis or septic shock.4,5 COVID-19 associated coagulopathy affects arterial, venous, as well as microvascular systems. In a meta-analysis by Porfidiaa et al. in May 2020, the overall incidence of VTEwas around26%, including pulmonary embolism (PE)with or without deep venous thrombosis (DVT) in 12% and DVT alone in 14%. In another meta-analysis by Chi Zhang et al., the incidence of VTE was 25% (the incidence of PE 19%and the incidence of DVT 7%), with 35% in severe COVID-19 patients and 6% in non-severe cases.6
Klok et al. reported a cohort of 184 patients admitted to the ICU with proven COVID-19 pneumonia and assessed for the Netherlands' thrombotic events. He found a thrombotic incidence of 31%: 27%venousand 3, 7% arterial thrombotic events.PE was the most typical thrombotic complication occurring in 81% of the reported cases. Thrombotic complications were higher in older age and patients having prolonged prothrombin time > 3 s and or extended activated partial thromboplastin time > 5 s.17
There was a higher incidence of Arterialthrombosis among severe Covid-19 hospitalized patients, with an average acute ischemic stroke rate between 3%and 5%.7,8
Mechanism of Covid-19 induced coagulopathy
Angiotensin-converting enzyme 2 (ACE-2) receptors in alveolar cells arethe portal of entry of SARS-COV-2. Infected cells release inflammatory mediators as danger-associated molecular patterns (DAMPs), which signal the release of pro-inflammatory cytokines and chemokines.10 ACE-2 receptors were also found in endothelial cells. The activated damaged endothelium up-regulates the release of VWF (von Willebrand factor) and endothelin-1, which is a potent vasoconstrictor. The release of those inflammatory mediators with sub-endothelial collagen exposure leads to the recruitment of leukocytes and platelets and complement activation, leading to platelet aggregation and thrombus formation.11
The hypoxic environment aids in thrombus formation by releasing HIFs (hypoxia-inducible factors) that lead to up-regulation of endothelial TF expression. So far, D-dimer, which is a fibrin degradation product and a marker of coagulation activation, seems to be a strong prognostic marker of high mortality in patients with COVID-19.11 Laboratory findings in severe caseswithCovid-19 showed prolonged PT, high CRP, lymphocytopenia, leucopenia, mild thrombocytopenia, high D-dimers and low fibrinogen.2,3
Endothelial injury, low flow circulation and hypercoagulability are all present in COVID-19 infections. In terms of endothelial injury, there is clear evidence that the SARS-CoV-2 virus invades endothelial cells leading to cell injury.12 Other sources of endothelial injury may include intravascular catheters, acute phase reactants, and other inflammatory mediators.13 On the other hand, blood flow stasis due to immobilization during intensive care unit hospitalizations is an additional risk factor for VTE. In terms of hypercoagulability, many changes in circulating prothrombotic factors have been observed in COVID -19 patients that include loss of the protective endothelium with its glycocalyx layer, low levels of tPA, and the inhibition of the clot-lysing system that lead to a prothrombotic state; this can be augmented by platelet dysfunction, complement activation, and systemic immune reactions.13
Acute coronary syndrome and myocardial injury are common complications of severe COVID-19 clinical course and were found in up to 20% of COVID-19 hospitalizations with an adverse impact on mortality. 9 The underlying cause of myocardial injury is suggested to be due to direct damage of myocardial tissue by SARS-CoV-2. In addition to macrovascular complications, SARS-COV2 also causes microvascular thrombosis. Patients who died with severe ARDS were found to have pulmonary microvascular thrombosis in autopsies.14 Although this pulmonary thrombosis was found in MERS-COV infection and SARS-COV1 infection; this feature appears more prominent in severeSARS-CoV-2 infection. The lung histology from patients withCOVID-19 demonstrates a 9-fold increase in the prevalence of alveolar-capillary microthrombi compared with patients having other types of influenza. In this regard, autopsy findings have shown that, in addition to the usual features of diffuse alveolar damage found in ARDS, microthrombi was found in about 100% of cases in an autopsy.14 Indeed, considering other reported thrombotic events, the emerging microvascular thrombotic complications is a strong indication of interaction between the SARS-CoV-2 and coagulation.14
Management of thrombosis in Covid-19 patients
Evidence suggests that low molecular weight heparin treatment may be associated with lower mortality in COVID-19 patients with an elevated-dimer and/or elevated sepsis-induced coagulopathy score.15 In addition to its main action that inhibits coagulation by binding the antithrombin, which accelerates the inhibition of FXa or thrombin, heparin appears to have pleiotropic effects which provide special advantages in viral infection context, including antiinflammatory effects by their ability to bind to the inflammatory molecule, such as HMGB-1and pro-inflammatory cytokines.15
Based on currently limited evidence, there is a suggestion for the use of anticoagulants for a hospitalized patient with COVID-19 infection.16 Lin et al. investigate the role of therapeutic anticoagulant inpatient with raised inflammatory markers and D-dimer on day 7 and 14, Given the risk of sepsis-induced coagulopathy (SIC), the authors suggest anticoagulation for COVID-19 patients with increased D-Dimer levels four times above the normal limit, using a dose of 100 IU/kg of LMWH twice a day, for at least 3–5 days.18
Tang et al. investigated 449 patients with severe COVID-19; 99of them received LMWH for seven days or longer.15 D-dimer, prothrombin time, age, and low platelet count were positively correlated with 28-day mortality. But in a subgroup of patients with increased SIC score ≥4 or D-dimer >6-fold of the upper limit of normal, patients treated with heparin had lower 28-day mortality than those who were not treated with heparin (40.0% vs. 64.2%, P = .029), (32.8% vs. 52.4%, P = .017) respectively. The study suggested that anticoagulant therapy with low molecular weight heparin may be associated with a better prognosis in severe COVID-19 patients meeting criteria or with markedly elevated D-dimer.17 Based on previous studies Oxford University Press, on behalf of the European Society of Cardiology, published a recent algorithm/protocol for the management of coagulopathy in COVID-19 patients which recommended that
All hospitalized patients with covid-19 should receive anticoagulant according to the risk of thrombosis and D-dimer level.
If a patient is at a high risk of thrombosis or D-dimer level more than or equal to 3mcg/ml, the patient should receive enoxaparin 1mg/kg twice a day.
Low-risk patients with D-dimer between 0.5 -3 mcg/ml should receive enoxaparin 40 mg twice a day.
Low-risk patients with d dimer less than 0.5 mcg/ml should receive 40 mg once daily enoxaparin.
For high-risk patient admitted to ICU should receive heparin infusion with target aPTT 60-85 seconds.16
Discussion
Since the discovery of COVID19, there were many reports of venous, arterial and microcirculatory thromboembolic complications with higher incidence among critically ill patients.4,6 The incidence ranging from 20% to 31% in various reports.6,17 Pulmonary embolism is the most frequent reported thrombotic complications in critically ill patients with COVID 19 pneumonia.17 Elderly patients with prolonged prothrombin time >3s and activated partial thromboplastin time > 5 s were at higher risk of thromboembolic complications.17
Angiotensin-converting enzyme 2 (ACE-2) receptors in alveolar cells, proinflamtory cytokines and chemokines have a major role in the pathophysiology of thrombus formations.10 Indeed, the damaged endothelium increase the release VWF (von Willebrand factor) and endothelin-1 which augment vasoconstrictions, together with inflammatory mediators leads to platelet aggregation and clot formation.10,11 The triad of endothelial injury, low flow circulation and hypercoagulability are all present in COVID -19 infections.12 COVID 19 causes both macrovascular and microvascular thrombosis as shown in the pulmonary autopsies of severe ARDS cases. The lung histology from patients withCOVID-19 demonstrates a 9-fold increase in the prevalence of alveolar-capillary microthrombi compared with patients having other types of influenza.14
Treatment with low molecular weight heparin has been suggested to lower mortality in COVID-19 patients.15 Besides its major action of coagulation inhibition, it has anti-inflammatory action by its ability to bind to the inflammatory molecule, such as HMGB-1and pro-inflammatory cytokines.15
Based on current evidence, there is a suggestion for the use of anticoagulants for a hospitalized patient with COVID-19 infection.16
Conclusion
Severe COVID-19 disease is associated with features of disseminated intravascular coagulation (DIC) and hypercoagulable state, which can manifest as venous thromboembolism (VTE), arterial ischemia, and/or micro thrombosis. Data on anticoagulation at present based mainly on observational studies, but high-risk patients may benefit from an intensified prophylactic regimen.
Acknowledgement: The authors would like to acknowledge the dedicated work of Mohammed Kharabah, Mohammed Alyamiand RehamKharabah for their great effort and contribution in website review and language editing
Source of Funding: No Fund
Conflict of Interest: Authors declare no conflict of interest
Authors’ Contribution: All Authors contribute equally in writing and reviewing the review article
Englishhttp://ijcrr.com/abstract.php?article_id=3786http://ijcrr.com/article_html.php?did=3786
COVID Live Update. Worldmeters.info/coronavirus/ august 26 2020
Zhang Z, Ge W-H, Lin H-W. Incidence of Venous Thromboembolism in Hospitalized Coronavirus Disease 2019 Patients: A Systematic Review and Meta-Analysis. Front Cardiovasc Med. 2020;7:151.
3. Tang N, Li D, Wang X, Sun Z. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemost. 2020;18:844–847.
4. Zusman O, Paul M, Farbman L, Daitch V, Akayzen Y, Witberg G, et al. Venous thromboembolism prophylaxis with anticoagulation in septic patients: A prospective cohort study. QJM. 2015;108:197–204.
5. Lloyd NS, Douketis JD, Moinuddin I, Lim W, Crowther MA. Anticoagulant prophylaxis to prevent asymptomatic deep vein thrombosis in hospitalized medical patients: A systematic review and meta-analysis. J Thromb Haemost. 2008;6:405–414.
6. Porfidiaa A. Venous thromboembolism in patients with COVID-19: Systematic review and meta-analysis. Thromb Res 2020;196:67–74
7. Mao L, Jin H, Wang M, Hu Y, Chen S, He Q, et al. Neurologic manifestations of hospitalized patients with coronavirus disease 2019 in Wuhan, China. JAMA Neurol. 2020;77:1–9.
8. Oxley TJ, Mocco J, Majidi S, Kellner CP, Shoirah H, Singh IP, et al. Large-vessel stroke as a presenting feature of COVID-19 in the young. N Engl J Med. 2020;382:e60.
9. Shi S, Qin M, Shen B, Cai Y, Liu T, Yang F, et al. Association of cardiac injury with mortality in hospitalized patients with COVID-19 in Wuhan, China. JAMA Cardiol. 2020: e200950.
10. Walls AC, Park YJ, Tortorici MA, Wall A, McGuire AT, Veesler D, et al. Structure, function, and antigenicity of the SARS-CoV-2 spike glycoprotein. Cell. 2020;181:281–292.e6.
11. Jose RJ, Manuel A. COVID-19 cytokine storm: The interplay between inflammation and coagulation. Lancet Respir Med. 2020;8:e46–e47.
12. Varga Z, Flammer AJ, Steiger P. Endothelial cell infection and endothelins in COVID-19. Lancet. 2020;(20):30937–30935
13. Begbie M, Notley C, Tinlin S, Sawyer L, Lillicrap D. The Factor VIII acute phase response requires the participation of NF-kappa B and C/EBP. Thromb Haemost. 2020;84(2):216–222
14. Ding Y, Wang H, Shen H, Li Z, Geng J, Han H, et al. The clinical pathology of the severe acute respiratory syndrome(SARS): a report from China. J Pathol. 2003;200:282–289.
15. Tang N, Bai H, Chen X, Gong J, Li D, Sun Z, et al. Anticoagulant treatment is associated with decreased mortality in severe coronavirus disease 2019 patients with coagulopathy. J Thromb Haemost. 2020;18:1094–1099.
16. Atallah B, Mallah SI, AlMahmeed W. Anticoagulation in COVID-19. Eur Heart J Cardiovasc Pharmacother. 2020;6(4):260–261,
17. Klok FA, Kruip MJ, van der Meer NJ, ArbousMS, Gommers DA, et al. Incidence of thrombotic complications in critically ill ICU patients with COVID-19. Thromb Res. 2020;191:145-146.
18. Lin L, Lu L, Cao W, Li T. Hypothesis for potential pathogenesis offers-CoV-2 infection–a review of immune changes in patients with viral pneumonia. Emerg Microb Infect. 2020;9(1):727–32.
Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareLifestyle in Older Adults Who Attend a Primary Health Care Facility During the COVID-19 Pandemic in North Lima
English0611Rosa PSEnglish Hernan MSEnglish Eduardo MSEnglishBackground: The lifestyle of older adults due to the coronavirus pandemic has been highly affected because it restricts the daily activities they performed before the pandemic Objective: The objective is to determine the lifestyle of older adults who go to a primary health care facility during the COVID-19 pandemic in North Lima. It is a quantitative, descriptive, non-experimental cross-sectional study with a total population of 206 adults over 60 years old, who answered a questionnaire of sociodemographic data and the lifestyle instrument. Results: Their results show the lifestyle of older adults, where 82 (39.8%) of older adults have a suitable lifestyle, 41 (19.9%) have a fantastic lifestyle, 31 (15%) do a good job maintaining their lifestyle, 31 (15%) their lifestyle is in the danger zone and 21 (10.2%) have a low lifestyle although they can improve it. Conclusions: It is concluded strategies should be sought that allow the elderly to carry out their activities within the home to maintain their lifestyle.
EnglishLifestyle, Older adults, Care facility, COVID – 19, VulnerableIntroduction
Worldwide, since 2019, the coronavirus pandemic (COVID-19) has spread in all countries, expanding throughout the population, however, the pandemic has generated fatal outcomes in the adult population, the elderly and people with comorbidities.1 Also, it has been predicted that COVID-19 considerably affects adults over 60 years old since there is a high risk that it is contagious and can contract more serious diseases from COVID-19, although older adults tend to be vulnerable to any disease since they are in a stage where their defences are weakened.2
Likewise, COVID-19 has generated a negative impact on older adults since their lifestyle changes dramatically due to isolation and quarantine3, since changes in lifestyle due to COVID-19, such as decreased physical activity, increased anxiety and decreased communication, all of this exerts a vulnerability in the elderly people, generating fragility at a physical and mental level.4,5
There are many risk factors, whether modifiable or non-modifiable, that older adults present that further compromise their health,6 since it puts them highly at risk for contracting COVID-19, factors such as smoking, alcohol, BMI (body fat), obesity, sedentary lifestyle, arterial hypertension (HBP) and diabetes mellitus,7 all this compromises the health of the same, exposing it to being infected by COVID-19.8, 9 Also, it has been predicted that in older adults factors such as physical inactivity, poor diet and comorbidities tend to influence the immune system in response to viral diseases10, which is why it is interpreted that these factors make them vulnerable to older adults to COVID – 19.11
Not only this but also the lack of sleep in older adults tends to reduce their body's immunity and increase the risk of respiratory infection, mainly COVID-19,12,13 although habits Hygiene measures are essential to prevent COVID-19 since hand hygiene has been predicted to reduce the incidence of the virus since it cannot survive long in our body.14 In the study carried out in the Netherlands,15 results were observed in 1119 older adults, between 48.3% to 54.3% of the total population had a decrease in physical activity and exercise due to the pandemic due to the COVID-19, 20.3% to 32.4% of the total population had a nutritional behaviour that superposes overnutrition, although 6.9% to 15.1% of the total population had a behaviour that predisposes to malnutrition.
In the study carried out in Brazil,16 with 1197 participants, it was observed that in older female adults with depressive symptoms and multimorbidity (various diseases), it was more associated with physical inactivity, whereas inactivity in males physical was more associated with depressive symptoms and metabolic disease such as diabetes mellitus. In the study carried out in Spain,17 they stated that social distancing due to COVID-19 has generated negative consequences at a physical and mental level, such as anxiety, depression, poor quality of sleep and physical inactivity during isolation due to COVID-19.
Therefore, the objective of the study is to determine the lifestyle of older adults who attend a primary health care facility during the COVID-19 pandemic in North Lima.
Therefore, the hypothesis in the research work is that the COVID-19 pandemic considerably affects the lifestyle of older adults, making them more susceptible to contagion.
MATERIALS AND METHODS
Research Type
The present study, due to its characteristics, way of collecting data and measuring the variables involved, has a quantitative approach. Regarding the methodological design, it is a non-experimental, descriptive, cross-sectional study18.
Population
In the present study, the population is made up of 206 female and male adults older than 60 years old who attend a primary health care facility in North Lima.
Inclusion criteria
Older adults who come to the health facility.
Older adults who voluntarily agree to be present in the study by signing the informed consent ACTA N°040-2020-CE/UMA UNIVERSIDAD MARIA AUXILIADORA.
Technique and instrument
The technique used is the survey FANTASTICO questionnaire or data collection instrument whose purpose is to measure the lifestyle of older adults who attend the primary health care facility.
The FANTASTICO instrument is administered to assess how good its lifestyle is, consists of 30 items that are indicated in 10 dimensions, F: family and friends, A: associativity and physical activity, N: nutrition, T: toxicity, A: alcohol, S: sleep and stress, T: personality type and activities, I: inner image, C: control of health and sexuality, finally, O: order; which are evaluated with a Likert-type scale where “0 = Never”, “1 = Sometimes”, “2 = Always”. The final score is multiplied by 2, to obtain a final range from 0 to 120, where the ranges are quantitatively appreciated where “0 to 46 = is in the danger zone”, “47 to 72 = some low, you could improve”,“ 73 to 84 = adequate, you're fine”, “85 to 102 = good job, you're on the right track”, “103 to 120 = congratulations, you have a fantastic lifestyle”19.
Place and application of the instrument
The survey was applied to the elderly population of the San Martín de Porres district of the Ex-Fundo Naranjal Health Center, which is in North Lima.
First, the coordination with the Health Center was carried out so that they authorize us to carry out the surveys to the elderly at the time they come for care, and then the permission of the elderly, explaining about the survey and why the work is done the research so that they understand what is going to be done.
The survey was carried out during the mornings with a time of approximately 15 minutes for each older adult in the research work, concluding with satisfaction since it could be carried out with the support of the elderly for the research.
It is important to emphasize the presence of health workers at the time of filling in the survey, since they may benefit from the research since it will allow them to know what the lifestyle of the elderly adult at home is like and what strategies to use to maintain a healthy lifestyle of themselves.
Results
Below is a summary table of the surveys carried out following the guidelines corresponding to the research work:
In Figure 1, the lifestyle of the elderly is observed, where 82 (39.8%) of the elderly have a suitable lifestyle, 41 (19.9%) have a fantastic lifestyle, 31 (15%) do a good job maintaining their lifestyle, 31 (15%) their lifestyle is in the danger zone, and 21 (10.2%) have a low lifestyle although they can improve it.
In Figure 2, the lifestyle is observed concerning sex, where we can see that in the female sex 14 (45.2%) their lifestyle is in a danger zone and male sex 17 (54, 8%), in some low lifestyle, 15 (71.4%) are female and 6 (28.6%) are male, in an appropriate lifestyle, 47 (57.3%) are female and 35 (42.7%) male, those who do a good job in their lifestyle 7 (22.6%) are female and 24 (77.4%) are male and those who have a fantastic lifestyle, 33 (80.5%) are female and 8 (19.5%) are male.
In Table 1, the lifestyle is related to the level of education of the elderly who attend a primary health care facility during the COVID-19 pandemic in North Lima, which was determined with the test of Pearson's chi-square (X2). The level of significance of the test obtained a value of 0.51 (p> 0.05) (X2 = 85.479; d.f = 12). Therefore, a hypothesis of an association between variables is not rejected, for which there is statistical data that verify the relationship between the lifestyle and the educational level of the elderly. So, we interpret that the lifestyle that is in a danger zone, 19 (61.3%) of the elderly have a higher technical level of instruction, in some low lifestyle, 17 (81%) of the elderly have a primary level of education, inadequate lifestyle, 36 (43.9%) of older adults have a secondary level of education, in good lifestyle, 17 (54.8%) of older adults have a level of secondary education and a fantastic lifestyle 25 (61%) of older adults have a secondary level of education.
In Table II, the lifestyle is related to the occupation of the elderly who attend a primary health care facility during the COVID-19 pandemic in North Lima, which was determined with the chi-square test of Pearson (X2). The level of significance of the test obtained a value of 2.34 (p> 0.05) (X2 = 44.648; d.f = 12). Therefore, a hypothesis of an association between variables is not rejected, for which there is statistical data that verify the relationship between the lifestyle and occupation of the elderly. Therefore, we can interpret that in a good lifestyle 10 (32.3%) of the elderly have a stable occupation, in an adequate lifestyle, 27 (32.9%) of the elderly have a temporary occupation, in Adequate lifestyle 37 (45.1%) of the elderly do not have an occupation, in a lifestyle that is in a danger zone, 11 (3.5%) of the elderly are retired.
These results are important to be able to observe if the lifestyle of the elderly changes due to the COVID-19 pandemic, so that solution strategies are carried out to maintain the appropriate lifestyle.
Discussions
This study focused on the physical and mental health of older adults who are cared for in a primary care facility since elderly people who cannot perform their basic activities due to the COVID-19 pandemic have drastically changed their lifestyle since they cannot go outside the home since they are people with a high rate of contagion to COVID – 19.
In the results on the lifestyle in older adults, they demonstrated an adequate lifestyle, this is because at home older adults tend to spend more time with their families, also, they carry out the routine activities that they did before the pandemic, such as passive physical activities, adequate food, good quality of sleep, and social interaction with the family, all of this exerts emotional support since older adults, being alone, negatively affects them and therefore they are depressed or worried about interrupting routines they can do at home. The authors argue that older adults with physical inactivity, poor sleep quality and a poor diet increase their chances of contracting COVID-19 even though they are vulnerable people with age to any disease12. Similarly, the authors maintain that older adults who present some comorbidity and who do not perform any routine activity are potentially people at risk of contracting COVID-19, since comorbidities such as hypertension, overweight or obesity are both mainly make the elderly more susceptible to being able to have it quickly.11
In the results of lifestyle about sex, we have obtained that those of the female sex have an adequate lifestyle better than men, this is due to factors such as levels of education and that are involved in Domestic and care activities allow them to maintain their lifestyle, on the other hand, men have responsibilities at home, thus obstructing their routine activities. They argue that the female sex had less lifestyle since there were factors that made their participation in activities less, such as physical limitations, lack of confidence, they lived alone and had no company, therefore this made their style life was not adequate.16 They argued that women have a better lifestyle than men since they argue that they have a healthier life index due to habits and their state, whereas men present inappropriate habits and their state with the Age tends to be complicated since they can present some disease either due to high cholesterol.20 The limitation in the research work is access to each of the older adults since not all of them reported being participants in the research since they only wanted to be treated at the health center and access to the health center to be able to carry out the research work.
Conclusions
For older adults to have a good lifestyle during the COVID-19 pandemic, they need emotional and social support from their family, as it will allow them the confidence to carry out their daily activities. Strategies should be sought that allow the elderly to carry out their activities within the home to maintain their lifestyle. The COVID-19 pandemic has affected the nutritional behaviour of older adults since they are more susceptible to eating less and losing weight due to activities that they cannot perform. This research work will be beneficial for future work, since it details the lifestyle that older adults have followed during the COVID-19 pandemic and how it will be after the pandemic has ended.
Conflict of Interest: The authors declare no conflict of interest.
Funding Source: This research work doesn’t have Funding Source
Acknowledgement: The authors acknowledge the immense help received from the scholars whose articles are cited and included in references of this manuscript. The authors are also grateful to authors/editors/publishers of all those articles, journals, and books from where the literature for this article has been reviewed and discussed.
Author’s Contributions: Rosa PS: Conceived and designed the analysis, wrote the paper, contributed data and translation. Hernan MS: Collected the data, Performed the analysis.
Eduardo MS: Contact the people for the survey-taking.
Englishhttp://ijcrr.com/abstract.php?article_id=3787http://ijcrr.com/article_html.php?did=37871. Ferrante G, Camussi E, Piccinelli C, Senore C, Armaroli P, Ortale A, et al. Did social isolation during the SARS-CoV-2 epidemic have an impact on the lifestyles of citizens? Epidemiol Prev. 2020;44(5–6):353–362.
2. Yamada M, Kimura Y, Ishiyama D, Otobe Y, Suzuki M, Koyama S, et al. Effect of the COVID-19 Epidemic on Physical Activity in Community-Dwelling Older Adults in Japan: A Cross-Sectional Online Survey. J Nutr Heal Aging. 2020;24(9):948–950.
3. Sepúlveda W, Rodríguez I, Pérez P, Ganz F, Torralba R, Oliveira D, et al. Impact of Social Isolation Due to COVID-19 on Health in Older People: Mental and Physical Effects and Recommendations. J Nutr Heal Aging. 2020;24(9):938–947.
4. Somekawa Y, Miura T, Katsumata S, Nishida Y, Shimada M, Umeki H. The relationships among frailty index, various physical, psychological, lifestyle-related factors, and social connections in Japanese elderly women. Maturitas. 2019;124(2019):151–189.
5. Shinohara T, Saida K, Tanaka S, Murayama A. Association between frailty and changes in lifestyle and physical or psychological conditions among older adults affected by the coronavirus disease 2019 countermeasures in Japan. Geriatr Gerontol Int. 2021;21(1):39–42.
6. Türkmeno?lu C, Etaner A, Kiraz B. Recommending healthy meal plans by optimising nature-inspired many-objective diet problem. Health Informatics J. 2021;27(1):146045822097671.
7. Zhu H, Chen X, Zhang B, Yang W, Xing X. Family History of Diabetes and the Effectiveness of Lifestyle Intervention on Insulin Secretion and Insulin Resistance in Chinese Individuals with Metabolic Syndrome. J Diabetes Res. 2021;2021:1–9.
8. Ruissen M, Regier H, Landstra C, Schroijen M, Jazet I, Nijhoff M, et al. Increased stress, weight gain and less exercise in relation to glycemic control in people with type 1 and type 2 diabetes during the COVID-19 pandemic. BMJ Open Diabetes Res Care. 2021;9(1):1–7.
9. Ho F, Celis C, Gray S, Katikireddi S, Niedzwiedz C, Hastie C, et al. Modifiable and non-modifiable risk factors for COVID-19, and comparison to risk factors for influenza and pneumonia: Results from a UK Biobank prospective cohort study. BMJ Open. 2020;10(11):40402.
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14. Kilani H. Healthy lifestyle behaviours are major predictors of mental wellbeing during COVID-19 pandemic confinement: A study on adult Arabs in higher educational institutions. PLoS One. 2020;15(12):1–15.
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareA Study to Assess the Knowledge and Perception Regarding COVID 19 Among the Nursing Students of Different Nursing Colleges of Odisha
English1216Das NEnglish Rath KEnglish Mishra AEnglish Lenka AEnglishBackground: With the unprecedented outbreak of COVID19, there has been a significant rise in patients and deaths all over the world. Nursing professionals play an essential role in providing health services, promoting health and preventing diseases. Favourable knowledge and practice among them regarding the disease are essential to winning this battle of COVID 19. Objective: To assess knowledge, and perception of student nurses of different colleges of Odisha towards COVID19. Methods: An online survey was conducted among nursing students to collect information regarding COVID 19. It was a pre-designed, structured questionnaire prepared by the objectives. Ethical approval from IEC was obtained along with permission from the head of the institutions of the participating institutions. Results: More than 90% of participants had correct knowledge regarding mode of transmission, common symptoms and preventive strategy. They had limited knowledge (79.8%) on people with a high risk of complications or fatal disease whereas knowledge regarding correct incubation period, complications and method of dead bodies disposal was poor(EnglishCOVID 19, knowledge, Nurse, Perception, PandemicINTRODUCTION
Since December 2019, the COVID-19 outbreak has become the most challenging health emergency and it rapidly spread all over the world. On 30th January 2020, World Health Organization (WHO) declared COVID-19 as a public health emergency of international concern (PHEIC), and lastly, it was declared as a pandemic on 11th March 2020.1 Death tolls are high due to the unavailability of specific antiviral drugs for COVID-19and vaccines.2
COVID-19 is spread by human-to-human transmission through the droplet, feco-oral, and direct contact and has an incubation period of 2–14 days. To date, no antiviral treatment or vaccine has been explicitly recommended for COVID-19..3 Therefore, applying preventive measures to control COVID-19 infection is the most critical intervention.4COVID-19 infection is highly contagious and has affected a large population all across the globe, the total number of deaths caused due to this virus has increased sharply. There have been 14,668,105confirmed cases and 609,511confirmed deaths globally India death cases rise to42,585. India reported 3,28,903 new coronavirus cases in the first sixdaysofAugust.5 Indian’s infection growth rate of 3.1% was higher than the US and Brazil at the 2 million stages.6
Nurses are the primary healthcare providers in contact with patients and are an important source of exposure to infected cases in healthcare settings; thus, they are expected to be at high risk of infection.7 However, along with them, student nurses pursuing General nursing and Midwifery (GNM), B.Sc (N), and M.Sc (N) are also posted in various healthcare facilities and in a community setting as an additional workforce to combat the COVID-19; hence, they are also expected to be at high risk to get infected with the virus without adequate knowledge and poor perception about COVID-19. In many states of India, due to a shortage of workforce, student nurses are being utilized for different tasks, like in a community survey or as a helping hand in COVID-19 units. Therefore, the likelihood of acquiring the infection is higher among them. It is, therefore, of paramount importance that student nurses involved directly or indirectly caring for COVID-19 patients should equip with adequate knowledge about all aspects of the disease, that is, clinical manifestations, diagnosis, proposed treatment, and established preventive strategies.8 By the end of January, the WHO and the Centers for Disease Control and Prevention (CDC) had published recommendations for the prevention and control of COVID-19 for HCWs.9,10 The WHO also initiated several online training sessions and materials on COVID-19 in various languages to strengthen preventive strategies, including raising awareness and training HCWs in preparedness activities. In several instances, misunderstandings among HCWs have delayed controlling efforts to provide necessary treatment 11, led to the rapid spread of infection in hospitals 12,13 and put patients' lives at risk. COVID-19 knowledge helps encourage optimistic attitudes and maintain safe practice10 also, knowledge can influence the perceptions of student nurses due to their past experiences and beliefs. Indeed, poor knowledge and perception can delay the recognition and handling of potential COVID-19 patients during the pandemic period. However, the level of knowledge and perceptions of student nurses toward COVID-19 remains unclear as very limited studies are conducted on knowledge and perception. In this regard, the COVID-19 pandemic offers a unique opportunity to investigate the level of knowledge and perceptions of student nurses during this global health disaster. Therefore, this questionnaire-based online survey was planned to explore the knowledge and perception of student nurses in Odisha.
Previous studies on COVID-19 Knowledge and Perception among Budding Nurses: A Questionnaire-Based Survey study findings demonstrated that a significant number of participants was aware of the various aspects of COVID-19 disease with mean±SD 6.7 ± 0.87, that is, incubation period, causative factors, mode of transmission, and prevention and supportive treatment of disease, where a considerable number of responders were aware. A significant number of participants were aware of the etiological factor, incubation period, clinical symptoms, transmission, prevention, and treatments of COVID1914 The majority (52.89%) budding nurses had positive perceptions toward COVID-19. The effective vaccines and treatments of COVID 19 are still under processing. therefore, nurses face a potential risk of infection and work-related anxiety and mental health problems.15 It is important to update themselves with the latest knowledge to protect healthcare professionals and nursing staff who are caring for patients with COVID-19. They must be educated about COVID 19 and its prevention.16 Nurses being the first point of contact between the families and health care system, it is of utmost importance for them to be updated with the knowledge and good practices regarding COVID 19. Effective communication between the nurses and physicians can help in providing comprehensive care and addressing the common illness.17
MATERIALS AND METHODS
Study design: Web-based cross-sectional survey
Study area: Different nursing colleges of Odisha, state of India
Study subjects-: Nursing students of different colleges of Odisha
Sampling technique: A convenience sampling technique was used to collect information from participants.
Sample size: A total of 893 students completed the survey .5 out of them were removed from analysis due to missing answers in more than 50% of questions. 888 responses were taken for data analysis.
Sample criteria
Inclusion criteria
Nursing students of different colleges of Odisha (only those principals of different colleges who have given permission)
Those students were responded on google forms
Exclusion criteria
Those students were not responded on google forms
Students were excluded( only those principals of different colleges who have given permission)
Development of tool
The survey instrument was developed by having 28 closed-ended questions. It was divided into four sections: Participant demographic characteristics and related information (08 items), knowledge-based questionnaire (08 items), myths on COVID19 (05 items), and perception questionnaire (07 items). The online survey tool was emailed to the potential participants and they were asked to read, understand and answer all the questions if they were willing to participate in the study. Questions about Knowledge included etiological factor, incubation period, clinical symptoms, transmission, prevention, and treatments. Responses were scored from 1 to 8 with 1 for correct response and 0 for incorrect one. Perceptions toward COVID-19 were assessed using 07 items. Questions were adapted from the previous study18 and CDC and WHO guidelines. The tool was validated by five experts in the institute.
Method of data collection
Data were collected after approval from institutional ethic committee (IEC) wide letter no. (Ref.No.:KIIT/KIMS/IEC/322/2020). Written permission was obtained from principals of different colleges of Odisha through their mails and WhatsApp. Informed consents were taken from students who are designed on the google form and Individual willingness to participate consecutively enrolled in the study.
First of all, according to the INC and ONC statutory board the lists of recognized nursing colleges updated on the website as of May,2020 were included in the study. Approximately 32 private colleges and 3 government colleges were identified. Data were collected from 20 colleges where the principal allowed to conduct the study. The link of the questionnaire was sent through e-mails, WhatsApp, and other social media to the contacts of different colleges. The participants were encouraged to roll out the survey to as many students as possible. Thus, the link forwarded to students and they were asked to forward it to their friends and pass it forward. The link directed the participants to the consent form and on receiving consent the questionnaire opened. After completion of all four sections, the participants were instructed to submit the forms online. The data collection was initiated on 30 May 2020, at 6 PM IST and closed on May 6, 2020, at 6 PM IST.
Data entered into Excel sheets and Statistical Package for the Social Sciences (SPSS 21.0) was used for statistical analysis. Descriptive and inferential statistics have been used in the study to analyse the findings.
RESULTS
Total of 893 students completed the survey.5 out of them were removed from analysis due to missing answers in more than 50% of questions. Among 888 study participants, 801 (90.2%) were females and 781(88.0%) aged less than 25 years. Most of them 649 (73.1%) were pursuing BSC nursing education and 777(87.5%) were enrolled in private colleges of Odisha. All of the participants had heard about SARS Covid2 and 94% had attended a webinar or lecture on it (Figure 1).
When enquired about frequency of use of various resources for information regarding Covid19, most of them 377(42.5% )used news media most frequently as depicted in Figure 2 and Table 2. Social media(37.6%) was the 2nd most often used resource followed by a conversation with family and friends(29.1%) and govt or official websites (25.1%). Table 2. summarises the response of the study participants, 367 (41.3) participants less often used the official websites, which are the most reliable source of information.
Table 3 shows the knowledge about common myths of nursing students about Covid19. About 94% of participants were aware of the correct fact about Covid19 affecting all not only elderly. More than 80% knew the fact on prevention of disease with regards to drinking alcohol and eating non-vegetarian food items. But less than 80 % of students had correct knowledge about hand dryer and rinsing with saline to reduce the germ.
Table 4 describes the knowledge of study participants regarding the natural history of the Covid 19 disease. More than 90% of participants had correct knowledge regarding the mode of transmission, common symptoms and preventive strategy. They had limited knowledge(79.8%) of people with a high risk of complications or fatal disease whereas knowledge regarding the correct incubation period, complications and method of disposal of dead bodies were poor (Englishhttp://ijcrr.com/abstract.php?article_id=3788http://ijcrr.com/article_html.php?did=3788
WHO Director-General’s opening remarks at the media briefing on COVID-19 - 11 March 2020. [cited 2021 May 3]. Available from: https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020
Timeline: WHO’s COVID-19 response. [cited 2021 May 3]. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/interactive-timeline
Hussain A, Bhowmik B. COVID-19 and diabetes: Knowledge in progress. Diab Res Clin Pract. 2020;162:108-112.
Lai CC, Shih TP, Ko WC, Tang HJ, Hsueh PR. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges. Int J Antimicrob Agen. 2020;55:105924.
Coronavirus Disease (COVID-19) Situation Reports. [cited 2021 May 3]. Available from:https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports
World Health Organization. Infection prevention and control during health care when novel coronavirus (nCoV) infection is suspected: interim guidance, 25 January 2020. World Health Organization. https://apps.who.int/iris/handle/10665/330674.
HAN Archive - 00427 Health Alert Network (HAN). 2020 [cited 2021 May 3]. Available from: https://emergency.cdc.gov/han/han00427.asp
Zhong B-L, Luo W, Li H-M, Zhang Q-Q, Liu X-G, Li W-T, et al. Knowledge, attitudes, and practices towards COVID-19 among Chinese residents during the rapid rise period of the COVID-19 outbreak: a quick online cross-sectional survey. Int J Biol Sci. 2020;16(10):1745–1752.
Nemati M, Ebrahimi B, Nemati F. Assessment of Iranian Nurses’ Knowledge and Anxiety Toward COVID-19 During the Current Outbreak in Iran. Archives of Clinical Infectious Diseases. 2020 [cited 2021 May 3]. Available from: https://sites.kowsarpub.com/archcid/articles/102848.html#abstract
ePROTECT Respiratory Infections (EN) [Internet]. OpenWHO. [cited 2021 May 3]. Available from: https://openwho.org/courses/eprotect-acute-respiratory-infections
Hoffman SJ, Silverberg SL. Delays in Global Disease Outbreak Responses: Lessons from H1N1, Ebola, and Zika. Am J Pub Health. 2018;108(3):329–33.
Patidar K, Sharma M, Gautam A, Sharma D, Jain J. COVID-19 Knowledge and Perception among Budding Nurses: A Questionnaire-Based Survey. Int J Nurs. 2020;6:1–7.
Khalid I, Khalid TJ, Qabajah MR, Barnard AG, Qushmaq IA. Healthcare Workers Emotions, Perceived Stressors and Coping Strategies During a MERS-CoV Outbreak. Clin Med Res. 2016 Mar 1;14(1):7–14.
Aldohyan M, Al-Rawashdeh N, Sakr FM, Rahman S, Alfarhan AI, Salam M. The perceived effectiveness of MERS-CoV educational programs and knowledge transfer among primary healthcare workers: a cross-sectional survey. BMC Infect Dis. 2019;19(1):273.
Sharma RP, Pohekar SB, Ankar RS. Role of a Nurse in COVID-19 Pandemic. J Emer Med Surg. 2020;9(35):2550–5.
Selvaraj SA, Lee KE, Harrell M, Ivanov I, Allegranzi B. Infection Rates and Risk Factors for Infection Among Health Workers During Ebola and Marburg Virus Outbreaks: A Systematic Review. J Infect Dis. 2018;218(5):S679–89.
McCloskey B, Heymann DL. SARS to novel coronavirus – old lessons and new lessons. Epidem Infect. 2020;148:e22
Bhagavathula AS, Aldhaleei WA, Rahmani J, Mahabadi MA, Bandari DK. Knowledge and Perceptions of COVID-19 Among Health Care Workers: Cross-Sectional Study. J Med Res Pub Heal Surv. 2020;6(2):e19160.
Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareKnowledge and Attitude of First Aid Skills Among Medical, Dental and Nursing Students in the Time of COVID-19 Pandemic
English1722Gunjan KumarEnglish Suranjana Jonak HazarikaEnglish Avinash JnaneswarEnglish Diplina BarmanEnglish Priyanka BrahmaEnglish Rameswari AcharyaEnglishEnglish First aid skills, Dental, Medical, Nursing, TraumaINTRODUCTION
Approximately 1.35 million people succumb to road traffic accidents every year.1The2030 Agenda for Sustainable Development has set a target of reducing the number of deaths and injuries globally by 2020.1 In most countries, road traffic accidents incur a loss of 3% of the gross domestic product.2 In 2016, 4.94 lakh injures and 1.50 lakh deaths were recorded in India as a result of RTAs.3 The golden hour that refers to the first hour after trauma determines the fate of the RTA victims to a large extent.4 When a patient does not receive first aid immediately, it affects their condition to a large extent, mostly their quality of life that eventually results in death.3
According to the Red cross society, first aid is the first assistance or treatment given to a casualty or a sick person for any injury or sudden illness before the arrival of an ambulance, the arrival of a qualified paramedical or medical person or before arriving at a facility that can provide professional medical care.5 As a consequence of disasters, accidents or injuries the casualty requires urgent care and transportation to the nearest healthcare facility.5 Although medical students are taught how to handle emergencies in hospital settings, the knowledge required for handling a case is not adequate.6Studies have found that the knowledge of first aid among medical students has always been neglected as a topic of discussion. Hence, time and again it has come into notice that even resident doctors at certain healthcare setups cannot perform the first aid skills satisfactorily.7
Dental professionals also play an equally important role in providing first aid in their practice to a patient, relative or staff member.8 S Mathew et al. conducted a study to assess the awareness of first aid amongst undergraduate students in UAE where it was found that students belonging to the dental schools had the highest mean score as compared to other branches(8.3±2.4).9 Pei et al.carried out a study to evaluate nursing students knowledge and attitude of first aid behaviour as bystanders in a case of RTA.10 At the end of the study it was inferred that their knowledge was not satisfactory and hence it stressed conducting continuing education programmes to increase the efficiency in providing first aid skills.
The emergence of COVID-19 has raised questions among those who may need or choose to give care in an emergency. As the spread of the virus is very rapid with over more than 14 million cases in our country, the impact of first aid skills like providing other emergency casualty care is of utmost importance. While there is currently no specific data on COVID-19 transmission while performing CPR but it may be concluded that such first aid skills have the potential to generate respiratory droplets or aerosols and there may be a risk of transmission of the infection.
As future healthcare professionals, the ability to render first aid services is the need of the hour. With the increasing mortality of accidents and trauma, proper provision of first aid as the first line of treatment can help save lives. However, there is a dearth of literature related to a comparative assessment of first aid skills in students of the three specialities. Hence, this present study was taken up to assess the knowledge and attitude of first aid skills among medical, dental and nursing students.
MATERIALS AND METHODS
This was a cross-sectional questionnaire-based study conducted for a duration of three months i.e from July 2020 to September 2020. The study participants included 300 undergraduate medical, dental and nursing students in Bhubaneswar city, Odisha. Ethical approval was obtained from the Institutional Ethics Committee. Informed consent was taken from the students before the study.
A self-structured 21 item questionnaire regarding first aid skills was used to assess the knowledge and attitude of the students. For every correct answer for the knowledge questions, participants were given a score of 1 and for incorrect answers a score of 0. Questions assessing attitude were marked on a 5 point Likert scale. The validity of the questionnaire was checked by a panel of five subject experts and modifications were made accordingly before the start of the study.
The questionnaire was distributed among the study participants by the investigator and it was collected back on the same day. Training and calibration of the investigator were done in the Department of Public Health Dentistry, Kalinga Institute of Dental Sciences under the supervision of the guide. Data were entered in a Microsoft excel sheet and analysis was done using SPSS version 25. Inferential statistics were performed using Chi-square test. Categorical variables were described using frequency and percentages. Intergroup comparisons of various domain scores were done using Mann Whitney U test. The level of statistical significance was set at 0.05.
RESULTS
The Demographic characteristics such as the mean age group, the gender distribution, the year-wise distribution of the participants of various groups have been mentioned. The mean age of the participants has been mentioned in Table 1. The distribution of the gender in the groups has been graphically represented in Figure 1. The female population was higher. Figure 2 represents the year-wise distribution of the participants. It was seen that most of the participants from the dental and medical stream belonged to the third year and final year while the majority of the nursing participants belonged to the second year.
Statistically significant differences have been observed among the groups (Dental, Medical and Nursing) for the responses of the questionnaire. The correct responses have been mentioned in Table 3.
A moderate correlation was seen between the knowledge of first aid and individual knowledge levels. The findings have been mentioned in Table 4. A weak correlation was observed between the practice of an individual and the knowledge recommendation of first aid.
DISCUSSION
First aid skills play an important role among all healthcare professionals. With an increasing rate of accidents and trauma cases, the provision of first aid at the right time can result in a life-saving situation. The emergence of the COVID-19 pandemic, have put a halt to the application of these skills when managing a first aid incident. Medical, dental and nursing students can majorly contribute to improving the medical condition of a patient by acquiring adequate first aid skills and developing the right attitude towards it.
In the present study, it was found that 65.1% of the medical students were aware regarding first aid skills. Alsayali et al. in their study assessed the awareness regarding first aid among 500 participants which was found to be 56.6% good and 43.4% poor.11Medical students being more aware could be based on factors such as the acquaintance or association of them with the profession, which makes them realize the importance of first aid.11
It was observed that the majority of the participants disagreed with first aid training not being essential in the undergraduate curriculum. Similar findings were seen in a study conducted by Khan A et al. where 94.4% of medical students wanted first aid training to be part of their curriculum with 84% suggesting that it should be part of the pre-university curriculum.12 Educational institutions should make first aid and basic life support courses mandatory by incorporating them in academics through lectures coupled with hands-on practices to make it more effective.11Majority of the participants in the present study (98.2%) agreed that adequate first aid skills are necessary.
Only 45.5% of the dental undergraduate students knew about first aid skills. The findings were similar to a study conducted in Saudi Arabia where first aid courses were attended by 30.6% of the participants.11Another study done reported that 65.3% of female university students couldn’t provide first aid to the ones in need because of the lack of knowledge and other issues.13 The majority of the dental students(88%) in this study knew the management of victims with the hypoglycemic attack. In all three branches, students were not aware of the first aid skills required to arrest bleeding. Similar findings were seen in a study carried out in Mangalore where a correct response rate of 12.5% was found for both fracture and bleeding management.6
In this study, only 55.5% dental, 52.8% medical, 63.7% of nursing students knew regarding the steps of cardiopulmonary resuscitation (CPR) as a part of first aid management. In previous studies done in Salem, Tamil Nadu showed only 17.1% of medical students knowing about CPR.14 Similarly Tan EC et al. in their study observed that only 6% of the students knew and performed correct CPR.7 First aid courses and workshops should be routinely conducted and attendance should be supervised irrespective of the fields of study. This could help the students to confidently serve at the time of duty hours to serve during any life-threatening situation.
CONCLUSION
This study determined the need for introducing formal first aid training classes for medical, dental and nursing students so that students are confident enough to provide first aid in day to day situations. The study also evaluated first aid training as a felt need among the students in the academic curriculum. The study also highlighted the key areas in which first aid knowledge was lacking. More such studies should be conducted to evaluate the knowledge and attitude of first aid skills among students and healthcare workers. As healthcare professionals during the COVID-19 pandemic, we need to be aware of the risks to ourselves and others, remember our own needs and keep society informed and updated.
Financial support and sponsorship: Nil
Conflicts of interest: There are no conflicts of interest
Research quality and ethics statement: The authors of this manuscript declare that this scientific work complies with reporting quality, formatting, and reproducibility guidelines set forth by the EQUATOR Network. The authors also attest that this clinical investigation was determined to require Institutional Review Board/Ethics Committee review, and the corresponding protocol/approval number is KIDS/RES/004/2020.
Englishhttp://ijcrr.com/abstract.php?article_id=3789http://ijcrr.com/article_html.php?did=3789
Fact sheet. Road Traffic Injuries. WHO. https://www.who.int. Accessed March 2021.
Elvik R. How much do road accidents cost the National Economy. Accident Analysis & Prevention December 2000;32(6): 849-51.
Awasthi S, Pamela G, Solanki HK, Kaur A, Bhatt M. Knowledge, attitude, and practice of first aid among the commercial drivers in the Kumaon region of India. J Foren Med Res. 2019;8(6):1994-1998.
Advanced Trauma Life Support. Student Course Manual. American College of Surgeons. 2018. https://viaaerearcp.files.wordpress.com/2018/02/atls-2018.pdf.
Raje S, Patki M, Nizami Z, Oke S. Evaluation of Knowledge and Practices about First Aid among Medical Students. Res Med J. 2017;1(2):5-8.
Joseph N, Kumar G, Babu Y, Nelliyanil M, Bhaskaran U. Knowledge of first aid skills among students of a medical college in Mangalore city of South India. Ann Med Health Sci Res. 2014;4(2):162-166.
Tan EC, Severien I, Metz JC, Berden HJ, Biert J. First aid and basic life support of junior doctors: A prospective study in Nijmegen, the Netherlands. Med Teach. 2006;28:189–92
Jevon P. First aid in the dental practice. BDJ Team 2016;3:16155.
Mathew S. Awareness of first aid among undergraduate students in Ajman, UAE. IOSR J Den Med Sci. 2016; 15(6):30-38.
Pei L, Liang F, Sun S, Wang H, Dou H. Nursing students' knowledge, willingness, and attitudes toward the first aid behaviour as bystanders in traffic accident trauma: A cross-sectional survey. Int J Nurs Sci. 2018;6(1):65-69.
Alsayali R. Awareness, knowledge, attitude and practices of first aid skills among medical and non-medical students at Taif University. WFM 2019;17(11):34-43.
Khan A. Knowledge, attitude and practices of undergraduate students regarding first aid measures. J Park Med Assoc. 2010;60:68-72.
Halawani LM, Alghamdy SD, Alwazae MM, Alkhayal WA. Knowledge and attitude of Saudi female university students about first aid skills. J Family Community Med. 2019;26(2):103–07.
Chandrasekaran S, Kumar S, Bhat SA, Saravanakumar, Shabbir PM, Chandrasekaran V. Awareness of basic life support among medical, dental, nursing students and doctors. Indian J Anaesth. 2010;54:121–6.
Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareThe Effective Use of Full Online Learning to Replace Classroom Learning During the Covid-19 Pandemic
English2332HadiyantoEnglish Sovia WulandariEnglish Liza Septa WilyantiEnglish SupianEnglish Rengki AfriaEnglish NazarudinEnglishBackground: The emergence of the Covid-19 pandemic led to the global change of academic learning strategy from classroom to online mode. Before the emergence of the virus, online learning at a medium scale has been implemented to support the educational system in Indonesia. However, due to the pandemic and to reduce the spread of the virus, online learning became the main and sole place of learning. Objective: This study investigates the implementation and effectiveness of online learning at the faculty of humanities at a University in Jambi, Indonesia, since the Covid-19 pandemic. Method: This is quantitative research. Self-report questionnaires with 5-point Likert scales were used to obtain data from 441 respondents by using the Survey Monkey application. Result: The result showed that online learning was not implemented at the top level of 4.20, using an inadequate replacement process. Furthermore, all categories of online learning implementation, such as design, course communication, time management, and technical skills, were not optimally obtained, while the categories were inadequately effective in replacing classroom learning. Conclusion: Online learning was unable to replace classroom learning because its implementation and effectiveness failed to meet policymakers’ expectations at the university level. Therefore, there is a need for the increase in teachers’ competence and usage intensity regarding online learning in faculties and universities
EnglishInformation technology Skills, Online course design, Higher education, E-learning, Revolution Industry 4.0INTRODUCTION
In a bid to minimize the rapid spread of the Covid-19 pandemic, the government has issued various policies, such as isolation, use of face mask, online learning as well as social and physical distancing.1,2 The majority of universities in the world changed their learning mode from classroom to online, including those in Indonesia. However, this posed numerous challenges for educational institutions, especially those in universities. In mid-March 2020, the Indonesian Ministry of Education and Culture implemented a learn and work from home policy, which requires people to work, worship, and study from home. The implementation of this policy required all educational institutions to turn all learning processes from classroom to online mode. The Covid19 pandemic and the government's policy forced teachers to apply online learning as a significant classroom source. However, before then, a faculty in a University in Indonesia used online learning at a minimum scale to supplement classroom learning. Currently, online learning as a sole media is implemented at the top level of academic institutions.3
University teachers are not only required to be experts in delivering teaching materials in the classroom, instead, but they also need to be able to use the online learning systems to provide exercises, manage time, design appropriate learning strategies, operate supportive learning applications, etc. Online learning is not accessible due to the complexity associated with the planning, evaluation, and learning processes.4,5 In summary, it needs university teachers to apply their offline pedagogy, in teaching and learning online strategies and skills.
Several studies have discussed and reported the lack of facility and internet connection to carry out online learning, however, studies related to its usage is still lacking. There are three main areas in online learning that teachers need to anticipate, namely teaching materials, student interaction, and learning atmosphere.6 Teaching material plays a significant role in the learning process as a source of study, with interaction outlined as one of the factors used to help students achieve optimal learning outcomes. Teaching material and student interaction also plays an important role in achieving better learning outcomes.7
This research aims to investigate online learning usage and its effectiveness at a University’s faculty during the Covid-19 pandemic. Due to the frequent use of offline learning strategy by most lecturers before the pandemic, their ability to utilize the online learning process becomes questionable. Online learning is a substitute for conventional learning, this means that the interaction between teachers and students and all academic activities is conducted electronically. Finally, this research is expected to specifically contribute to university management, to quality assurance agencies, and prepare teachers for effective online learning strategies at the university.
LITERATURE REVIEW
Online Learning as a Substitution
Online or electronic learning (e-learning) is carried out via globally interconnected computer networks. This e-learning application formally and informally facilitates training and learning activities.6,8 Various applications such as computer-based learning, web-based learning, virtual classroom, virtual Schoology, Virtual Zoom, Edmodo, Moodle, Easy Class, etc can be used for online learning. Some universities have developed their template of e-learning that fit the needs of academic communities on their campus.6
E-learning is an online learning platform created to overcome limitations between educators and students, especially in time and space. Several online learning models can be applied (1) web Course, which is the act of conducting lectures through the internet, is a full online learning strategy with communication patterns between students and lecturers, dominated by remote systems via the web/internet. Furthermore, all teaching materials, assignments, consultations, examinations, and other learning activities are delivered online. (2) Web-Centric Course, which integrates distance learning with classroom using online lectures, with some of the learning processes carried out through face-to-face, and its functions are complementary. (3) Enhanced Web Course is a lecture improved through web/internet utilization. It is the learning reciprocity between lecturers and students as well as learning centred on the web/internet.9,10 There are at least three electronic learning functions (e-learning), namely supplement, complement, and substitution.8 Supplement (additional) is defined as the process where students are free to choose from online learning media. Therefore, there is no requirement for participating students to access the material electronic learning. Complement, means that the material Electronic learning is programmed to be reinforced or remedial for students in participating in conventional learning activities. Substitution, is the use of e-learning as a substitute for conventional lectures, conducted through the electronics media (without face-to-face mode directly in class).
The Indonesian Ministry of Education has implemented a policy for schools to turn physical classroom learning to online to anticipate students' mobility across provinces and regency.11 This is a substitute learning process used to provide students flexibility in managing their learning activities irrespective of their place and time. Teachers are assumed to be able to launch their lesson plan, and split learning topics according to time, and learning materials, with assignments, and quiz used to determine their abilities on the use of eLearning applications, irrespective of time and place.1 However, there is an increase in the optimized use of online learning as a substitute for classroom learning during the Covid-19 pandemics. Teachers need to understand how to use supportive applications such as zoom, Google, etc for adequate interaction with students, this is because having the right skills to operate e-learning is not enough skill for them.
Online learning becomes effective when a teacher can operate and manage learning activities based on internet pedagogical principles as understood and applied in physical classrooms.12,13 With this strategy, achieving optimal learning goals in classroom learning becomes possible because it is similar to the conventional classroom process. The facilities and features of e-learning are provided for teachers to engage students through group discussions, video presentations, providing links to other resources such as YouTube channel, giving assignments, scoring, providing e-library, etc. Teachers can combine the asynchronous and synchronous online learning process to obtain optimal learning effectiveness. Snow and Lakshmi & Chetan stated that teachers are expected to engage students through online learning and create interactive communication.5 Various learning strategies and activities can be organized based on online learning, thereby enabling students to indulge in active learning.14 Broadbent stated that one main characteristic of online learning is allowing students to learn by themselves, with teachers directing the process base on their needs and progress.15
Advantages of online learning
Several studies stated that classroom teaching and learning are still less effective in promoting students learning processes due to time constraints, difficulties, lack of flexibility, fear, etc. Conversely, numerous studies have proved lots of advantages associated with using online learning, such as an increase in the level of interaction between students and teachers, managing to learn anytime and place, wide-coverage, ability to invite students from other countries, reaching a global audience, and simplification of storage and learning material, easy updating of content and achievable capabilities.16 Furthermore, Vanslambroucket al. stated that online learning helps teachers and students to manage learning activities via android, computer, and laptop.17 Online learning acts as a virtual classroom, which allows interaction between students and lecturers and serves as a media that provides learning resources for efficient evaluation.18 The advantages of using online learning for students are high independence and interactivity, increased memory level of the course content, and the ability to provide more learning experience. Online learning allows teachers to combine text, audio, video, and animation while delivering content. It is easy to convey, update, and download. Students can send messages, comment on discussion forums, use chat rooms and a video conference link to communicate synchronously and asynchronously.4,6
Furthermore, the physical classroom learning activities and assignments where students work together can be conducted through electronic, online, or web-based learning. Online learning, such as an E-learning template, is an instructional process that involves electronic equipment in creating, fostering, delivering, assessing, and facilitating a learning process. These learning activities are normally conducted in a conventional classroom, and now they can be applied in online learning as well.16 The advantages are obtained when the teachers have knowledge and skills of online learning usage and the strategies needed for its preparation, management, and application.
Managing Online Learning and Teachers’ Role
A lecturer is a crucial factor in the successful organization of electronic learning activities and they play a very decisive role in motivating students. Therefore, lecturers need to be structural in conveying information on all aspects of learning activities to enable students to achieve adequate knowledge. They also need to possess higher skills in operating online learning applications to optimize the facilities and features. The techniques used by teachers to facilitate students understanding are different between classrooms and online. Teachers' are assigned the role of mentoring rather than giving lectures, and are less of disseminators and more facilitators. Before starting online courses, teachers need to search for related materials and ideas, design lesson plans that can be replicated and emulated, as well as exercise, quizzes, and tests for evaluation purposes. Furthermore, they need to design strategies and methods, to deliver the content by selecting tools and applications. The learning design needs to engage students in learning activities, interact, and acquire the necessary content. Finally, evaluations need to be regularly conducted to determine their progress.18
Teachers and students play respective roles in facilitating online learning activities. In addition, students play the role of knowledge constructor, skills practiser, independent learners, and problem solvers.2 It is a teachers' responsibility to ensure students interact among themselves while learning online to help them acquire more knowledge and achieve maximum objectives. The interaction between students and students need to be always built to improve communication and discussion about learning activities.7 For example, when students are unable to understand a question or an idea, they can ask others for explanations. However, when the answers are unable to solve the problem, then the teachers need to provide answers with detailed explanation. This interaction among the students and teachers need to be maintained for better learning outcomes.
Online Learning Principles and Criteria
Hadiyanto, Broadbent, and Cable et al. stated that adequate attention needs to be paid to the principles of online learning before the class starts.6,12,14 The first is learning materials, and exercises need to be evaluated to provide the right module accompanied by evaluation questions and courses at the end of the semester. These results can be used as a benchmark to score students. The second is a classroom or course community that enables students to develop online communities to interact, support, and share beneficial information with their communities or course members. Thirdly, teachers are allocated time to stay online to give students direction, answer questions, and help in discussions. Fourthly, students are opportune to work together online, with supportive applications set for online meeting, therefore, they can meet simultaneously in real-time without constraints due to distance. The fifth is associated with the use of technology audio and video delivery material to attract students' interest in learning. Furthermore, three main criteria need to be paid attention to when dealing with online learning. Firstly, online learning is a network that enables the ability of courses to be uploaded, saved, distributed, and shared quickly within a wide range. This criterion is very important in online learning and an absolute requirement. Secondly, online learning connects users via applications in the laptop, desktop, and android using standard internet technology. Thirdly, online learning is the most extensive learning method with solutions exceeding the traditional paradigm.20
MATERIALS AND METHODS
This is quantitative research with the questionnaire method used to obtain data from 1189 students of the Faculty of Humanities at a University in Indonesia. Out of the 1189 students, 441 participated in the online survey, with data collected using the SurveyMonkey application. Furthermore, self-report questionnaires with 5-point Likert scales were developed to measure online learning's usage and effectiveness. The questionnaire consisted of two parts, with the first used to gather students' demographic background, while the second was used to obtain their responses on the use of online learning. Furthermore, the use of online learning was classified into 5 constructs, namely, design, communication, resource authenticity, time management, and technical skills. The questionnaire was developed based on literature studies.4,10,12,13 The workshop was carried out twice to evaluate the questionnaire's constructs and elements until it was used for the data collection.
About reliability and validity testing, it is proposed that Cronbach alpha with a coefficient value of 0.60 consisting of 10 elements and below, while 0.70 was recommended for more than 10, despite accepting 0.30. In this study, consistency analysis based on the overall instrument of Online learning usage led to a Cronbach alpha coefficient value of 0.956.21,22 Further evaluation indicated that all sub-components in online learning had corrected item-total correlation at 0.30 and α level above 0.60. In simpler terms, the instruments were known to be reliable and valid for measuring online learning usage, as shown in Table 1.
The descriptive statistic and Pearson correlation were used to report the result. Furthermore, descriptive analysis, frequency, percentage, and mean score were used to report the level of online learning usage and its' effectiveness. The mean score of the respondents' level of instructional practices is descriptively calculated and interpreted in five levels, as shown in Table 2.
Table 2 shows that the mean score between 1.00 and 1.80, 1.81 and 2.60, 2. 61 and 3.40, 3.41 and 40 as well as 4.21 and 5.00 had very low, low, medium, high, and very high levels of core competencies.
Person correlation was applied to analyse the relationship between online learning users with the effectiveness of replacing face-to-face learning during the Covid-19 pandemic. A p-value of 0.001 was used to judge the significances of the Correlation coefficient (r) index. The relationship between two variables can be obtained from -1.00 through 0 to +1.00, inclusive. The greater the coefficient's absolute value, the stronger the relationship.21
Respondent Profiles
The respondents' profiles in terms of the gender indicated that 156 (35,4%) participants were male and 285 (64.6%) females. In terms of the program, 77 (17.5%) of the participants were at Indonesian Literature, 111 (25.2%) art, drama and music, 72 (16,3%) archaeology, 104 (23.6%) history, and 77 (17.5%) Arabic education. In terms of year of study, 153 (34.7%) were in the first year, 131 (29.7%) in the second year, 90 (20.4%) third year, 40 (9.1%) in the fourth year and 27 (6.1%) in the fifth year.
RESULTS
Table 2 provides an answer to the question, "When is online learning intensively applied to teaching and learning at your program? The findings showed that 420 (95.2%) out of 441 respondents used online learning intensively since the emergence of the Covid-19pandemic crises, and only 5% stated that they started using the process since their first semester. The finding implied that online learning intensively used for teaching and learning at the faculty was since the covid19 pandemic.
Figure 1 shows the overall usage and effectiveness of online learning for replacing classroom learning during the pandemic at the faculty of a University in Indonesia. Students' perception of online learning usage had an overall mean of 3.53 and at a high level. However, on the contrary, the usage effectiveness was rated at the average level of 3.15 and in the lower mean category. In terms of components, students rated implementation of the design of online learning was at a high level (3.51), while the effectiveness was rated at an average level (3.15), course communication was high (3.67), and its effectiveness was at an average level (3.25). Furthermore, their resources authenticity was at a high level (3.56), while its effectiveness was lower (3.42), time management (3.45) was at a high level, while its' effectiveness was average (3.21), technical skills were rated high (3.40), while the effectiveness was average (3.18). The finding indicates that despite the university's policy for full use of online learning during the pandemic, it did not obtain the maximum level and is not highly effective.
General Implementation of Online learning
Table 4 displays that online learning's general implementation was at a high level of the mean score (3,51). Students rated A1, A2, and A4 at the average level, while A3, A4, A6, A7, A8, and A9 were high. Students implied that the implementations of online learning based on the statement are at a high level. However, there is no mean score of general implementation statements rated at a very high level. Furthermore, they stated that all elements of online learning were not highly effective. The findings showed that online learning's general implementation was not optimally applied at the faculty, even during the pandemic.
Table 4 shows the result of online learning implementation in terms of course communication, which had a high overall course level of the mean score (4.01). Students also rated all statements regarding course communication high, while all elements' effectiveness was rated average. The findings indicated that online learning's course communication was not optimally applied and ineffective compared to students' and teachers' communication.???????
Resources authenticity is one of the components widely investigated in this study. The question "Are the authentic resources and academic standard applied in online learning activities?" needs to be determined. The findings show that high authentic resources (3.56) were applied in the teaching and learning process. However, two statements (C1 and C2) led to a high mean score of authenticity and one statement at an average mean score of 3.00. Furthermore, the effectiveness of authentic resource usage was also rated at a high level (3.42), while C1 and C2 produced effective high mean scores, and one statement (C3) was at an average level (3.30). The finding in Table 6 showed that resource authenticity was not very strongly and effectively emphasized during teaching and learning and carrying out students' assignments.
Table 7 shows that time management of online learning was at the bottom of the high level of the mean score (3.45). Students rated four statements of time management, namely D1, D2, D3 and D4 at the average level. Three other time management statements, namely D2, D3, and D5 were rated at a high level, while none were very high. Therefore, the effectiveness of applying time management application and its elements were rated at the average level. The study indicated that teachers' time management needs to be repaired to positively impact students' learning activities since the effectiveness level was average for the overall elements of effectiveness.
Table 8 shows that the research findings in terms of technical skills in online learning implementation have an average level of the mean score (3.40). Furthermore, students' rated 8 out of 10 statements with the use of technical skills in the implementation of online learning (E2, E4, E5, E6, E7, E8, E9, and E10) at an average level. Two other technical skills implementation, namely E1 and E3, were rated very high, with no mean score, and time management statements rated very high. Therefore, the effectiveness of overall technical skills implementation and its elements were rated averagely. The findings implied that online learning's technical implementation needs to be optimally applied and expected to boost the students' learning effectiveness online.
Correlation of usage and effectiveness of online learning
Person correlation was used to investigate the relationship between online learning usages and its effectiveness in replacing classroom learning. The result shows that the use of overall online learning has a significant relationship with effectiveness (r= 0.605, sig. (p).= 0.000< 0.05). There was also a significant relationship between the elements of the design of online learning and effectiveness (r= 0.605, sig. (p).= 0.000< 0.05), course communication and effectiveness (r= 0.605, sig. (p).= 0.000< 0.05), resources authenticity, and effectiveness (r= 0.605, sig. (p).= 0.000< 0.05), time management and effectiveness (r= 0.605, sig. (p).= 0.000< 0.05) and technical skills implementation and effectiveness (r= 0.605, sig. (p).= 0.000< 0.05). These findings implied that the higher the online learning usage, the greater the effectiveness of online learning for replacing classroom class.
DISCUSSION
The research shows that the overall teachers' online learning was at a high level, while the use in replacing classroom learning satisfied students. In more specific ways, the design of online learning, course communication, resource authenticity, and time management was not fully applied by teachers. Furthermore, technical skills were considered an important factor used to facilitate and encourage students to learn. Moreover, students perceive online learning as ineffective in learning and delivering course content. Each implemented components were not significant enough to help students fully learning online. Therefore, online learning is unable to replace conventional learning.
Online learning or e-learning assists students in improving quality learning, however, its usage cannot replace classroom learning.10 Other evidence thereby causes unsatisfied online learning usage at the faculty, which indicates that not all teachers' apply online learning. Teachers' competence using online learning features, which are supported by the application, was also low, therefore, the implementation result of online learning at the faculty was not maximal and ineffective. This is in line with Rapanta et al. research, which stated that some teachers effectively failed to implement the e-learning process.15 This also confirmed by other findings, which stated that lack of teachers' competence in online learning is one factor responsible for the poor utilization of online learning during the Covid-19 pandemic. Therefore, the full replacement of classroom learning with online learning is less possible.16
Teachers need to present an online syllabus to let students know the description, objectives, topic, and lecturing schedule of an online assessment. The use of video as a media means to convey messages is also lacking and needs to be inserted into the online design. This is because the use of video helps improve students' understanding and comprehension of the topic. It also allows them to interact and discuss with friends e-modules and use different online teaching methods to brainstorm collaborative activities and give online quizzes, tests, and assignments. However, due to the need to produce online learning, teachers need to optimize these issues in design.
In terms of course communication, all elements were not very highly applied by the teacher because they used both email and WA as alternative tools for announcements, reminders, and communication. Therefore, to obtain a level of commitment, there needs to be high effectiveness of course communication.7 Teachers also need to be more intense in creating and moderating the discussion, responding to students' questions promptly, providing feedback on assignments, using synchronous web-conferencing tools, and communicating their expectations on student behaviour.
The authenticity results of online learning places more control on original resources and the application of academic standards. The use of authentic material, resources, and online references with copyright is needed to provide attention. This provides emphasis on the use of authentic resources, which is important to ensure students use the academic standard of their assignment resources.13 Time management needs to be properly organized and scheduled for the punctual provision of the online syllabus, for students and teachers to be more ready with topics and activities. To improve the effectiveness of online learning, it is necessary timing for online live discussion to be optimally carried out to produce space for students to interact and communicate with the topic directly.13 Scheduling and timing feedback, assessing assignments, and asynchronous and synchronous discussion are essentially organized better.
Good implementations of technical skills are indicated by clear direction, optimally used for the provision of collaborative tools, updating resources, and enabling students to communicate and discuss with lecturers and friends. Other important factors that support students' effective learning process are creating and editing videos for assignments, online video presentations, optimizing e-learning features, and online meeting application (zoom, google meeting, etc.). E-learning based on online learning needs to be used optimally designed with features and other supporting applications for students' collaboration, cooperation, interaction, communication, discussion, and integration.23 The maximal effort of teachers leads to optimal online learning usage, thereby obtaining the effectiveness of online learning.
General Implication
Identifying lecturers' level of online usage in course communication, resource authenticity, time management, and technical skills is a pervasive step towards full online learning usage in universities' teaching and learning process. As the human resources, the lectures directly utilize the technology in a teaching and learning process. Therefore, their inability to understand and possess adequate skills to operate online learning leads to its unsuccessful implementation.17 The long-term success of implementing a policy is largely dependent on the abilities to support staff on the use of the online application in the teaching process, thereby making the environment stable and reliable.
The crucial problem associated with online learning usage in the higher education sector is lecturers' poor skills in operating technological devices. The low level of lecturers' knowledge and skills in online learning usage is also an obstacle in implementing and impacting learning goal achievement. When the lecturers are not willing, unmotivated, lack awareness, knowledge, and skills to use the online learning application for teaching and learning, the learning process becomes ineffective and inefficient. The outcome is not optimally obtained.
As higher education institutions strive to meet the Covid-19 pandemic situation's needs, they face challenges associated with the full application of online teaching-learning. However, more higher education institutions are required to adopt and adapt to the delivery of courses to students online. The lecturers' awareness, knowledge, and skills assessment need to be evaluated toward online learning implementation, which needs to be conducted on tight and regular schedules. The online learning usage is implemented, and human resources are vital for its preparation and change. Consequently, lecturers as human resources that are directly in contact with the implementation need to possess good level of online learning knowledge and skills in order to contribute to effective implementation. Bao (2020) stated that most teachers’ were unprepared to use online learning during the pandemic.20
CONCLUSION
Due to the inception of the Covid-19 pandemic, universities have changed from classroom learning mode to full online learning in a bid to minimize the spread of the virus. However, the study concludes that online learning usage associated with the general implementation, course communication, resource authenticity, and time management, particularly for technical skills, are not optimally implemented at the faculty level in Universities. Therefore, lecturers need to pay attention, apply, and evaluate the necessary indicators required to obtain online teaching and learning goals. Furthermore, policymaker needs to take into account various solutions and steps as future projections. Obstacles, solutions, and projections of brave learning to prospective teachers are important to learn because the online learning system is presently by lecturers in institutions due to the outbreak of Covid-19.
ACKNOWLEDGMENT
This research was funded by the Research centre of the Universitas Jambi, Indonesia.
Conflicts of interest: The study had no conflicts of interest.
Source of Funding: DIPA of Universitas Jambi, Fiscal Year/No. 2020SP DIPA-O23.77 .2.67756512020
Authors contribution: The first author was responsible for the whole process of writing this paper. Co-authors had contributed to the process of writing this paper, data collection, data analysis, checking references, and formatting of this paper.
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareAdverse Events Following COVID 19 Vaccine Shot
English3338Lathamangeswari CEnglish Pandurangan HEnglish Ramaiah PEnglish Muthukrishnan AEnglishBackground: Coronavirus disease 2019 (COVID-19) is a rampant disease caused by severe acute respiratory syndrome coronavirus 2. The initial case was diagnosed in China, in late 2019. Since then, the disease had spread globally, leading to the current pandemic situation. Signs and symptoms of coronavirus disease are unpredictable, but includes mild to moderate fever, coughing, weakness, dyspnoea, and decreased sensation of smell and taste. Swift development of an efficient vaccine is needed to control and prevent the coronavirus disease 2019. Objective: To assess the incidence of adverse events following the Covid 19 vaccine shot. Methods: It is a descriptive study conducted among the health care students (nursing and paramedical) who took the first dose of the Covishield vaccine. This study aimed to assess the incidence of adverse events following the Covid 19 vaccine shot. A total of 150 students were selected by using the convenience sampling technique. The adverse events checklist was prepared to collect data from health care students by structured interview technique. Results: The findings of the study show that 57 % had very common adverse events such as pain, itching, tiredness, headache, nausea, muscle ache 16% had common adverse events such as joint pain, lump at the injection site, vomiting, flu-like symptoms, 4% had uncommon adverse events loss of appetite, abdominal pain, excessive sweating. Conclusion: Even though few newly developed vaccines demonstrate the efficacy up to 95%, multi-disciplinary level rigorous studies are required to determine the safety of vaccine which includes minimizing the adverse events following the vaccination shot.
EnglishAdverse events, Coronavirus, Health care students, Pandemic, VaccineINTRODUCTION
Coronavirus disease 2019 (COVID-19) is a rampant disease caused by severe acute respiratory syndrome coronavirus 2. The initial case was diagnosed in China, in late 2019. Since then, the disease had spread globally, leading to the current pandemic situation. Signs and symptoms of coronavirus disease are unpredictable, but includes mild to moderate fever, coughing, weakness, dyspnoea, and decreased sensation of smell and taste.1-4 Mostly the person infected with coronavirus develops mild to moderate symptoms such fever and cough, and less than 15% develop severe symptoms such as severe dyspnoea, Hypoxemia pneumonia, and less than 5% has critical symptoms such as respiratory failure and multiple organ dysfunction. The disease signs and symptoms commence from 1 to 14 days after exposure to the deadly virus.5-8 Currently, there is no specific antiviral drug therapy available to treat ever spreading coronavirus disease, so we must use the existing treatment modalities for this pandemic disease. On the other hand, a preventive measure which includes physical distancing (6 feet), quarantining, aeration of indoor spaces, covering coughs and sneezes with the handkerchief, washing hands frequently, wearing a face mask can reduce transmission of infection. The vaccines also play a vital role in the prevention of Coronavirus disease.9-12
A COVID?19 vaccine is expected to provide protection against severe acute respiratory syndrome coronavirus 2, the virus causing coronavirus disease 2019. Before the COVID?19 pandemic, there was an established body of knowledge about the structure and function of coronaviruses causing diseases which enabled accelerated development of various vaccine technologies during early 2020.13-16 For rapid development of an efficient vaccine against the severe acute respiratory syndrome coronavirus-2, the cause of the coronavirus disease 2019 (COVID-19) pandemic, and multi-disciplinary level rigorous studies are required to determine the safety of vaccine which includes minimising the adverse events following the vaccination shot. In Phase III trials, several COVID?19 vaccines have shown efficacy as high as 95% in the prevention of symptomatic COVID?19 infections.17-20
Even though this newly developed vaccine has an efficacy of up to 95% in the prevention of coronavirus virus disease, many adverse events are reported globally. Logunov et al. conducted a study on the safety and efficacy of vector-based heterologous prime-boost COVID-19 vaccine by non-randomised trial consist of 76 participants between the age group 18 to 60 years. In this study, the most common adverse events reported were pain at the injection site 58%, fever 50%, headache 42%, and muscle and joint pain 24%. Most adverse events were mild to moderate and no serious adverse events were detected.21 Ella et al. conducted a study on safety and efficacy of inactivated COVID-19 vaccine by randomised trial consist of 375 participants between the age group 18 to 55 years and reported that most common adverse events were pain at the injection site 5%, fever 50%, headache 3 %, nausea and vomiting 2% muscle and joint pain 24%. Most adverse events were mild 69 %, moderate 31% and no serious adverse events were detected.22
MATERIALS AND METHODS
A descriptive study was conducted among the health care students (nursing and paramedical) who took the first dose of the Covishield vaccine between January – February 2021. The study aimed to assess the incidence of adverse events following the Covid 19 vaccine shot. A total of 150 students were selected by using the convenience sampling technique. The tool has five sections which include demographic variables and an adverse events checklist. The demographic variables consist of Age, Gender, History of Allergy, History of respiratory problems, symptoms before vaccination, vaccination has taken in the past 6 months, participants ever tested for positive COVID 19, Family members ever tested positive for COVID 19, Motivation for vaccination. The adverse events Checklist consists of 3 sections which include very common adverse events, common adverse events, uncommon adverse events. Each section is further subdivided into two subparts namely the onset of symptoms of adverse events and duration of symptoms. The visual analogue pain scale was used to assess the level of pain at the injection site, a Modified 5D itch scale was used to assess the severity of itching at the injection site, the modified verbal rating scale was used to assess the level of swelling at the injection site, the infrared thermometer was used to assess the body temperature. The tool was validated by 5 medical experts in the field of pharmacology. The content validity index was found to be S-CVI/Ave = 0.9 and the reliability of the tool was calculated using Cronbach’s alpha it was found to be 0.8. Before data collection, informed consent was obtained from the participants and the purpose of the study was explained to them. In the 30 minutes structured interview, the adverse events checklist was used to collect data and it is rated as Yes for the presence of adverse events and No for the absence of adverse events. Data analysis was done by using descriptive and inferential statistics using SPSS 20.
RESULTS
Table 1 shows the distribution of demographic variables among health care students. Out of 150 students, 90 (60%) were in the age group of 18-20 years. Regarding gender, 105 (70%) were female. Regarding allergy 142 (95 %) had no history of allergy. Regarding respiratory problems, 135 (90%) had no history of respiratory problems. In symptoms before 127 (85) had no symptoms before vaccination. Regarding motivation for vaccination 38 (26%) were motivated by vaccine awareness program. No students tested positive for COVID-19 before vaccination.
Table 2 shows the frequency and percentage distribution of very common adverse events after vaccination among health care students. Out of 150 students, 98 (65%) had pain, redness, warmth, swelling, itching at the injection site, 102 (68%) felt unwell after vaccination, 90 (60%) had the symptoms of tiredness after vaccination, 87 (58%) had a headache after vaccination. 72 (48%) had muscle pain after vaccination.
Table 3 shows the frequency and percentage distribution of common adverse events after vaccination among health students. Out of 150 students, 30 (20%) had joint pain, 18 (12%) had a lump at the injection site, 21 (14%) had the symptoms of vomiting, 27 (18%) had flu-like symptoms.
Table 3: Frequency and percentage distribution of common adverse events after the first dose of vaccination among health care students (N=150)
Table 4 shows frequency and percentage distribution of uncommon adverse events after vaccination among health care students. Out of 150 students, 5 (3%) had a feeling of dizziness, 3 (2%) had a loss of appetite, 6 (4%) had abdominal pain, 2 (1%) had excessive sweating.
Table 4: Frequency and percentage distribution of uncommon adverse events after the first dose of vaccination among health care Students (N=150)
Table 5 shows that there was no significant association of gender, history of allergy and history of respiratory problems with the body temperature among health care students.
Table 5: Association between selected demographic variables with body temperature among health care students (N=150)
DISCUSSION
This study was conducted to assess the incidence of adverse events following the Covid 19 vaccine shot among health care students. The findings of the study show that 57 % had very common adverse events, 16% had common adverse events, 4 % had uncommon adverse events. This was supported by Polack et al. in the study on the safety and efficacy of the BNT162b2 mRNA Covid-19 Vaccine. It is a randomised clinical trial with a sample of 43,548. Participants are randomly assigned in a ratio of 1:1 to the experimental group and the control group. The experimental group and control group received two doses of BNT162b2 vaccine and placebo 21 days apart respectively. The results the indicated that BNT162b2 vaccine was 95% effective in preventing Covid-19 (95% credible interval, 90.3 to 97.6). The participants were developed adverse events such as short-term, mild-to-moderate pain at the injection site, fatigue, and headache. The incidence of serious adverse events was low.23 The study concluded that a two-dose regimen of BNT162b2 gave 95% protection against Covid-19. The present study is also consistent with a study conducted by Zhu et al. aimed to assess the immunogenicity property of recombinant adenovirus type 5 vectored COVID-19 vaccine.
It is a randomised clinical trial consist of 195 samples. Adults aged between 18 and 60 years were registered and allotted to any one of three dose groups (Low, Medium, High) divided based on the concentration of viral particles to receive an intramuscular injection of vaccine. Adverse events were studied one-week post-vaccination. The results of the study indicated that at least one adverse reaction within the first 7 days of post-vaccination was reported in 83% of participants in the low dose group, 83% participants in the medium-dose group, and 75% participants in the higher dose group. The commonly reported adverse reaction was injection site pain in 54% of recipients, and the most reported systematic adverse reactions were fever 46%, fatigue 44%, headache 39%, and muscle pain 17%. Most adverse reactions that were reported in all groups were mild or moderate level of severity. The study concluded that the adenovirus type 5 vector vaccine needs further investigation.24
CONCLUSION
Even though few newly developed vaccines demonstrate efficacy up to 95%, multi-disciplinary level rigorous studies are required to determine the safety of the vaccine which includes minimizing the adverse events following the vaccination shot.
ACKNOWLEDGEMENT: The investigator expresses to deep gratitude towards the authors cited in this study which was not limited to Dr.Fernando P. Polack, M.D, Fundacion INFANT (F.P.P.) and trials-Hospital Militar Central (G.P.M.), Prof Feng-Cai Zhu, NHC Key Laboratory of Enteric Pathogenic Microbiology, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China, Dr Denis Y Logunov, N F Gamaleya National Research Centre for Epidemiology and Microbiology, Moscow, Russia, Dr. Richa Ellas, Bharat Biotech, Hyderabad, India.
ETHICAL CLEARANCE: The principal investigator obtained informed written consent from the samples before commencing the study. The study was conducted according to the World Medical Association Declaration of Helsinki ethical principles for Medical Research Involving Human Subjects.25
FUNDING: None
AUTHORS' CONTRIBUTIONS
Lathamangeswari C - Conception and drafting of the work, analysis and interpretation of data, critical revision, Manuscript writing and accountability for the work.
Pandurangan H – Collection and interpretation of data, critical revision, finalizing the Manuscript and accountability for the work.
Ramaiah P- Literature review, interpretation of data, critical revision, finalizing the Manuscript and accountability for the work.
Muthukrishnan A -Design of the work, analysis and interpretation of data, critical revision, Manuscript writing and accountability for the work.
CONFLICT OF INTEREST: The authors declare no conflict of interest
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Association world medical. WMA Declaration of Helsinki -Ethical principles for medical research involving human subjects. JAMA. 2013;310(20):2191-2194
Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11Healthcare
Women’s Role in Raising Awareness of Rumours during the COVID-19 Crisis in Saudi Society
English3947Loulouh Brikan AlbrkanEnglish Hend Faye AL-shahraniEnglishEnglishWomen’s Role, Awareness, Rumours, Coronavirus Disease (COVID-19), Saudi Society
INTRODUCTION
In late 2019, a new coronary virus, COVID-19, emerged in China, and within three months, the virus spread across the world rapidly, as it is highly contagious and there is no known vaccine or specific treatment.1,2 Therefore, the World Health Organization (WHO) had to declare it a global pandemic, as well as many precautionary and extraordinary measures have been taken by the Governments of the world to address the epidemic and curb its spread within the States.3 On January 30, 2020, WHO declared health emergency status. As a result of the rapid spread of the Coronavirus epidemic, COVID-19 and, on March 11, 2020, announced the new Coronavirus, COVID-19, as a global epidemic. As a result of the high incidence and the prevalence worldwide.4,5 All people worldwide panic in different behaviours, which has had a significant impact on the social, economic, and psychological aspects of the world.6 Notwithstanding, whether a person is infected or has direct contact with an infected person or is distant from infection locations.7
Saudi Arabia is considered one of the first countries to seek bold and early precautionary measures to prevent the spread of the Coronavirus among society members and support the efforts of States and international organizations to confront this pandemic.8 Further, the new Coronavirus outbreak, COVID- 19, has led to a massive global crisis, with fear and anxiety among people caused by uncertainty and rapid spread of the disease with no effective treatment. Additionally, States such as closures and quarantine are taken precautionary measures, creating fertile ground for spreading rumours across all societies.9 The lack of appropriate messages and information in a new outbreak also leads to panic and anxiety, with more devastating severe effects on livelihoods, societies' social fabric, the economic landscape, and political stability. Besides, There were significantly high levels of panic, which was accompanied by an expeditious rise in people's fear and anxiety.7 Then it could lead to many social problems like isolation, moving away from family members, socializing with friends, changing lifestyle.10 Moreover, the spread of misinformation through social media aims to spread terror and fear among people; as well, some disseminate rumours to disturb security at home and perplex social.11
The rumour has a profound impact on the individual and society, as it hampers the process of understanding by communities of the conditions they are going through also, it leaves them unable to absorb the imperatives that affect the direction of their movement growth on the ground. Likewise, rumours are breaking communities, deepens and expands the crisis promptly.12 Rumours are considered anonymous, rapidly spreading the news, containing part of the truth, which forms the core of its construction and spread.13 Rumours of challenges facing humanity, such as the Coronavirus pandemic 2019, swell within weeks of the virus's emergence in China.14 More specifically, deceptive rumours and conspiracy theories have globally spread about its origin, combined with tension, leading to increased storage and purchases of goods and face masks. The abundance of information, including misinformation, has been closely linked to the twenty-first-century communication system and social media.15 Hence, The oversight role of the security and government agencies must be activated in the follow-up on social media; to reduce the spread of rumours among members of Saudi society, and to raise awareness of the dangers of rumours that pose a new security threat to Saudi society.7 Misleading information via social media leads to many mental health problems, such as social isolation, bad individual relationships.16 family problems, post-traumatic stress disorder, and panic.17 depression, social isolation, and anxiety.18 Also, behavioural disorders and strain.3 Therefore, public health measures must reduce the spread of the COVID-19 pandemic and clarify misinformation spread through social media. We need to respond promptly to minimize the adverse effects on individuals and society.19 Additionally, expose rumours and public perceptions, attitudes, and behaviours about COVID-19 discussed through social media.20,21 Therefore, it is essential to start with the family through women in society as an educator and an influential factor in the socialization process, to protect her family members from rumours.
Women are responsible for their duties to husband and children in the family during the socialization process. As a result, they have multiple roles in society, such as inculcating behaviours, religious and social values, and guiding children.22-24 They also contribute to raising the family's awareness of society's values, norms, and culture, distinguishing between right or wrong, whether it is behaviours, information, or rumours.25 Besides, they contribute to raising the awareness of her family members about the gravity of negative behaviour and misinformation about the positive effects that some of her children's peers may have on some alcohol or drugs.26 Further, they contribute to raising awareness about the risks and downsides of certain information that is generally shared through social media sites.27
This study is based on role theory, whose Founders are Ralph Linton in sociology and George Herbert Mead in social psychology; it refers to cultural norms relating to the psychological and interactive aspects of society members, such as mothers, fathers, sons, daughters, and grandparents.28 Additionally, the role refers to family roles' expectations and social texts - how roles are shaped by society's cultural traditions and collective ideologies. More specifically, one aspect of role theory studies how to learn roles during the process of social interaction, i.e., people interact with others, see themselves and others as occupants of particular situations, and learn clues to work. In other words, specific social texts or expectations are linked to certain roles.29 Women play several roles at the family and community levels. For example, they may be wives, mothers, relatives, employees, friends of different individuals, and members of a social institution.30,31 To raise awareness for the people of society.
This study's importance has been highlighted because it focuses on women's role, who plays a significant part in the upbringing process in the family and other parts of society. Also, through her work, mainly if she contributes to the educational process through her career as a teacher for the younger generations to prevent danger and her contribution to avoiding misleading information that adversely affects family members and society. Thus, their contribution to raising awareness of the dangers of rumours during the Corona crisis. Therefore, women need to play their part in raising awareness to reduce rumours during the problem of COVID-19pandemic.
Research Questions
What is the role of women with family in raising awareness to reduce rumours during the COVID- 19 crisis?
What is women's role in raising awareness to reduce rumours during COVID- 19crisis?
What are the proposals and mechanisms to activate women's role in raising awareness to reduce rumours during COVID- 19 crisis?
Materials and Methods
Study design and participants
The quantitative research methodology was used for this study. This study was applied after the closure in the Kingdom of Saudi Arabia in December 2020. The institutional ethical committee clearance was obtained from the Deanship of Scientific Research at Princess Noura Bint Abdul Rahman University in Riyadh, Saudi Arabia. The authors expected to receive 460 responses within six days, but only 436 questionnaires were returned. Twenty of them were excluded for not following the survey instructions. Thus, the study sample was 416 women, aged 40-60 (M ± SD 46.12 ± 1.52). The sample was selected through the snowball sampling technique to provide an equal and independent opportunity for selection for the sample. All of the participants live in the city of Riyadh, the sample consisted of mothers of students at Princess Noura Bint Abdul Rahman University. All participants completed the voluntary consent section of the questionnaire and confidentiality was confirmed. Complete the survey on social media sites such as WhatsApp.
Data collection tool and technique
To collect data, a questionnaire was developed by researchers through a review of previous literature and studies on the role of women in raising awareness of rumours during crises. The questionnaire consisted of two parts. Part I includes demographic data for the sample study. Part II covers the three dimensions of the study. The first dimension is women's role in raising awareness to reduce rumours during the Coronavirus pandemic crisis and consists of 16 items. The second dimension is women's role in raising awareness to minimize rumours during the new Coronavirus pandemic, COVID- 19, and consists of (9) items. The third dimension is proposals and mechanisms to activate women's role in raising awareness to reduce rumours during the crisis of the new Coronavirus pandemic, COVID- 19, and consists of 12 items. The three-point Likert scale was used (yes, somewhat, no). Content Validity Ratio (CVR) and Content Validity Index (CVI) measurements were also used in the quantitative method. The Cronbach Alpha values were calculated for the three dimensions and were 0.811, 0.787, and 0.897, and the scale as a whole was 0.899.
We applied descriptive statistics to analyze the data, including average, standard deviation and percentage. Participants' responses to the dimensions of the questionnaire were analyzed by frequency, percentage, average, and standard deviation. It was analyzed using SPSS 21.
RESULTS
To answer the first question, what is the role of women with the family in raising awareness to reduce rumours during the crisis of COVID-19pandemic? Iterations, percentages, means, standard deviations, and ranks of sample responses were calculated on the first dimension items: the role of women with families in raising awareness to reduce rumours during the crisis of COVID-19, table 1 shows these results:
According to the above table, it appears that the sample of female students' mothers at Princess Noura University approved the roles offered to women with society in raising awareness to reduce rumours during the crisis of the Coronavirus pandemic, with an average of (2.81 out of 3), i.e., yes. More specifically, this axis comprises nine items representing women's roles in society. All came with means indicating Yes, i.e., approval, with averages ranging from 2.66 to 2.96 of 3 degrees. "Pride and prestige in the efforts of the State in what it offers citizens in the Corona crisis" ranked first, with an average of 2.96, i.e., yes. With a very low standard deviation of 0.23, it showed no difference in the study sample's views on this role, and 96.6% of the study sample supported it, only 0.7% rejected it, and 2.6% indicated somewhat. However, item No. 1, "Explain the importance of the blessings of security and food during the Corona crisis," ranked second, with an average of 2.95, i.e., yes, and the rest of this axis has a variety of items, all of which refer to yes." Reducing the transmission of any rumours of the Corona crisis without checking the source," ranked 9th and last in axis items with an average of (2.66), i.e., yes, but with a standard deviation of 0.505; to show differing views of the study sample on this item, as well as 67.1% of the study sample, supported it and only 1.4% rejected it, while 31.5% indicated somewhat.
DISCUSSION
This study aimed to identify women's role in raising awareness to reduce rumours during the COVID-19 crisis and present some proposals and mechanisms to activate their role. According to the first question results, participants demonstrated their strong agreement on women's role with the family in raising awareness to reduce rumours during the new coronavirus crisis, COVID-19. More specifically, its role in developing national pride and prestige and attitudes in the Corona crisis has been approved and alert children not to accept unreasonable or logical information about the Corona crisis. In addition to instil and promote my sons' patriotism and repel any rumour of the Corona crisis. Further, educate their children about the choice of social media sites and appropriate content in the Corona crisis or discuss the rumour's seriousness and its psychological impact during the Corona crisis. Moreover, they are concerned with educating and committing family members to precautionary measures and preventing Coronavirus or providing correct Coronavirus information. Thus, discuss the State's efforts that provide citizens in the Corona crisis with their children. Select websites that provide information about the Corona crisis, and not mention or convey rumours with their children in the Corona crisis. Additionally, ensure that leisure time is occupied for the benefit of her children and keep them away from the Corona crisis and make her family members aware of the danger of rumours on the individual and society and the purpose of its dissemination at the time of the Corona crisis. The results also confirmed that one of the women's roles was to discuss their children's goals behind the dissemination of rumours and misinformation about the Corona crisis, make them aware of the danger of transmitting information and the need to verify its origin and motivate them to take part in volunteer work and serve their society during the crisis. Those findings are consistent with many previous studies, such as.27,32,33
According to the second question results, participants demonstrated their strong agreement on women's role with the society in raising awareness to reduce rumours during the new coronavirus crisis. More specifically, her role in the development of pride and prestige in the State's efforts in what it offers citizens in the Corona crisis, explain the importance of the blessings of security and food during the Corona crisis and promote a culture of confidence and trust in the State's efforts in the Corona crisis. She also clarifies and discusses the seriousness of rumours and their psychological impact on society in the Corona crisis. Furthermore, her contribution to discussing the severity of rumours on the individual and society in the Corona crisis, clarifying citizens' role to face rumours, and strengthening the spirit of belonging and the rumour's gravity in the Corona crisis during the Corona crisis. Results also confirmed her contribution to raising awareness about the goals behind spreading rumours and misinformation about the Corona crisis. In addition to reducing the transmission of any rumours of the Corona crisis without checking the source. Those findings are consistent with many previous studies, such as.34-36 Ultimately, the study results showed participants' agreement on a set of proposals and mechanisms to activate women's role in developing awareness to reduce rumours during the new Coronavirus crisis, COVID-19. More specifically, legal understanding of women through the statement of penalties for disseminating and circulating rumours. In addition to activating the role of women in the education sector to raise awareness against the threat of rumour, especially in crises, and promote their role in the health sector to participate in the statement of rumours and their psychological and health impact on people, especially in times of crisis. The results also confirmed that it was essential to include crisis theories in school curricula, learn how to apply them at the educational level, and disseminate awareness through the various media that illustrate the danger of rumour during the crisis.
It also emphasizes the importance of raising women's awareness through the various media about their role in their families and society; to reduce the spread of rumours, and clarifying their role in the media and social media platforms; to contribute to the psychological balance and reassurance of their family and community and reducing rumours in the Corona crisis. In addition to organizing training courses for women in family and community crisis management through E-learning. Thus, urging women through various media to develop their knowledge capacities in crisis management, and publish seminars and conferences explaining the threat of rumour and how to prevent it through e-learning. Moreover, hold ongoing interactive sessions to sensitize women to the dangers of the transmission, circulation of rumours, and their psychological impact on the individual and society.
CONCLUSION
The paper reviewed the results of women's role in raising awareness of rumours during the COVID-19 crisis. These measurements were applied to a sample of community members in Riyadh, Saudi Arabia. This research's main contribution is that it provides a deep understanding of Saudi women's role in raising awareness of rumours during the COVID- 9 crisis. Besides, some proposals have been made to activate Saudi women's role in raising awareness of rumours in crises, particularly the COVID-19 crisis. In particular, this issue has not received sufficient attention in Arab society, especially in Saudi Arabia. Therefore, this study attempted to shed light on women's role in raising awareness of rumours during the COVID-19 crisis. In addition to the need for the Saudi Government to look at this vital issue with greater depth and attention and give full support to Saudi women to help them fulfil their role in raising awareness of rumours in crises. One of the most severe crises we are now experiencing, the COVID-19 crisis. That's through: Expanding the organization of lectures, seminars, and meetings at universities, schools, and awareness centres for debate to demonstrate the danger of rumour and its spread. Study and improve family conditions by national authorities to create a risk-conscious environment such as rumours. Moreover, through socialization processes, all society institutions - family, school, and community - should raise awareness against rumours. In addition, launch a national initiative to raise awareness of rumours in society and at all levels, and intensify all institutions of social forces through conferences, seminars, and courses. Further, establish an integrated information Center overseen by a government body to provide information, correct rumours, and reduce it. School curricula should also be taken into account, and the dangers of rumours and their impact on the individual and society should be incorporated. Eventually, legislate and apply penalties for rumours on the individual, community, and the State.
Funding: We are thankful for funding from the Center for Promising Research in Social Research and Women's in Princess Nourah bint Abdulrahman University in the Kingdom of Saudi Arabia in 2020.
Acknowledgements: We acknowledgements the Deanship of Scientific Research and Center for Promising Research in Social Research and Women's Princess Noura bint Abdulrahman University in the Kingdom of Saudi Arabia for its support and facilitation of the procedures for implementing the study. We also acknowledgements the mothers for whom the study tools were applied, and help them achieve the goals of the study.
Conflicts of Interest: No conflicts of interest.
Informed consent: Verbal informed consent was obtained before the collectionof data.
Authors’ contributions:
Loulouh Albrkan; Designed the study, funding acquisition, protocol writing and manuscript preparation.
Hend Al-Shahrani; Protocol writing, manuscript review, formal analysis and guided research work.
Englishhttp://ijcrr.com/abstract.php?article_id=3792http://ijcrr.com/article_html.php?did=3792
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcarePublic Opinion and Practices Regarding Social Distancing During COVID-19 Pandemic: A Cross Sectional Study in a Major City of India
English4854Kumar VEnglish Rizvi JZEnglish Saini MEnglish Mishra PEnglishIntroduction: Following the outbreak of the COVID-19 pandemic, the Indian government appealed to the citizens to adhere to preventive health behaviours including social distancing, wearing face masks, maintaining hand hygiene and others. Social distancing at public places is an important measure to control the spread of disease. Objective: Current study was carried out with the objectives to evaluate public opinion and their practices regarding social distancing in a major city of India. Methods: A cross-sectional survey was conducted using an online questionnaire in the form of Google Forms and the link to the survey was distributed through WhatsApp and also via e-mail. All the eligible participants were requested to forward the questionnaire to as many contacts as possible. A total of 560 participants were approached out of which 452 responded. Data were entered into Microsoft Excel and analyzed using SPSS version 15. Results: Among the total study participants, 92.9% were practising social distancing outside the home. Although most of the participants (79.2%) believed that it provides self-protection from COVID-19, only 43.8% of them were aware of the minimum recommended distance for social distancing. Only 12.6% of participants attended cultural gatherings and 20.4% had visited gym, restaurants or bars. Conclusion: Awareness regarding social distancing was higher among males than females. Only about one-third of the study participants were satisfied regarding social distancing followed in their area. Approximately three-fourths of the participants felt stress and anxiety to be the impact of social distancing. Future prospective studies need to be conducted with a larger sample size and among all classes of society to generalize the study findings.
EnglishNovel coronavirus, COVID-19, Awareness, Opinion, Practice, Social distancingIntroduction
There is a lot to discover about the novel coronavirus (SARS-CoV-2) that has led to a pandemic of coronavirus disease 2019 (COVID-19). Recent studies revealed that not only people with symptoms but also those without any symptoms of coronavirus disease were likely to play role in the spread of COVID-19. The disease transmission is well known to occur more commonly through respiratory droplets than through objects and surfaces including doorknobs, countertops, keyboards and toys. Current evidence has suggested that SARS-CoV-2 might stay viable for the variable period on surfaces made from a variety of materials.1 A consistent feature of every national response to the COVID-19 pandemic was issuing the guidelines to the public regarding social distancing. Different countries had different minimum distances which they advised their citizens to maintain.2 Since SARS-CoV-2 emerged as a new virus, currently, pharmaceutical interventions like vaccines are not available. Therefore, non-pharmaceutical interventions (NPIs) have been the mainstay for the prevention and control of COVID-19. In the United Kingdom as well as other developed nations, the key NPIs being used in relation to the COVID-19 pandemic were social distancing and social isolation besides maintenance of personal hygiene including regular and thorough hand washing.3 A systematic review and meta-analysis including 172 observational studies across 16 countries and six continents were done to investigate the optimum distance required to prevent person-to-person virus transmission. The findings supported physical distancing of 1 m or more.4 In India, an advisory on social distancing was proposed on 31st March 2020 to avoid contact between people.5,6 However; public response to social distancing norms in India was yet to be assessed. This study was conducted with the objectives to evaluate public opinion and practices regarding social distancing in a major city of India.
MATERIALS AND METHODS
A cross-sectional study was conducted in Lucknow city of India between August-September 2020. The target participants were those above the age of 18 years, who were able to understand the English language and more specifically among those having some digital equipment with internet access, setting a non-probabilistic sample with convenience bias.7 All information regarding the study, participant’s rights and investigator’s contact details were provided on the first page of the questionnaire. Participants were required to give mandatory consent before proceeding to the next page. Permission to conduct the study was obtained from the institutional ethics committee vide letter Ref. no. MIMS/EX/2020/209.
Data Collection tools
An online questionnaire (survey) was developed using Google Forms and the link to the survey was distributed through WhatsApp contacts of the authors of this study and also via e-mails between 26th August to 10th September 2020. All the eligible participants were requested to forward the questionnaire to as many contacts as possible. Respondents that followed the link were first provided with a clear declaration of their rights as participants, including voluntary non-obligated participation or the right to refuse. Further, the confidentiality of the data and strict anonymity of participant’s identity was assured. Non-responders were sent a reminder and the link to the questionnaire at an interval of 15 days. At the end of one month, acceptance to any further response was stopped. The questionnaire was structured into three parts: a) Questions regarding the respondents’ socioeconomic profile including age, gender, religion, education, occupation, marital status, type of family and income; b) Questions on opinion regarding social distancing and c) Questions about their practices to maintain social distancing.
The questionnaire mainly contained closed-ended questions with tick-box options, 5 points Likert scale and yes/no responses. A 5-point scale was used to measure the opinion of the participants about social distancing being followed in their respective areas, in which 1 referred to “highly satisfied”, 2 to “just satisfied”, 3 to “neutral”, 4 to “not satisfied” and 5 to “highly dissatisfied”. The questionnaire also contained few open-ended questions.
Data analysis
Data from the completed surveys were entered into Microsoft Excel and analyzed using SPSS version 15. The data was analysed descriptively by calculating frequency and percentage. Pearson’s Chi-square test, which calculates the value of the chi-square variable and the p-value of that sample, was applied for each relationship between categorical variables.
Results
A total of 560 individuals fulfilling the eligibility criteria were approached out of which 452 individuals participated in the study. Hence, the non-response rate was 19.3%. Approximately two-thirds of the participants were aged up to 25 years (64.4%) and more than three-fourth belonged to the Hindu religion (79.0%). Based on educational status, the majority of the participants were from college and above level (94.0%) and the rest have completed secondary level of education (6.0%). Further, out of the total 244 (54.0%) males and 208 (46.0%) females, 234 (95.9%) and 191 (91.8%) were having college and above level education respectively. In terms of marital status, approximately three-fourths of the participants were unmarried/single (74.1%) and about their occupation, 62.2% of the participants were students and 19.5% had a private job. Nuclear family was the common type of family among the participants (64.1%) especially among females (71.2%) than males (58.2%). The socio-demographic details of the participants are shown in Table 1.
As depicted in Table 2, awareness about minimum recommended distance for social distancing was higher in males (48.4%) as compared to females (38.5%) and this was found to be statistically significant (p=0.034). Regarding the impact of social distancing in human life, the majority of the participants believed that it protects COVID-19 (79.2%). Although, other responses regarding the impact of social distancing were stress (35.4%), anxiety (36.3%), drop-in day to day activities (42.9%) and no impact (9.9%). More males (86.5%) than females (81.7%) responded that COVID-19 can be prevented by maintaining required social distancing but it was not statistically significant. Similarly, more males (63.6%) than females (56.7%) had the opinion that eating outside food can spread COVID-19 disease. Most of the participants (83.4%) responded that meetings, conferences, seminars or workshops can lead to the spreading of disease while few participants (10.4%) were not sure about it. Statistically, a significant difference was found between males and females regarding their opinion whether working outside can also pose the risk of COVID-19 to their family members (p=0.017). The majority (90.9%) considered work from home to be a better idea to avoid the COVID-19 spread.
As shown in Table 3, males went out of home for work, business or essentials more than their female counterparts and this difference was found to be statistically significant (pEnglishhttp://ijcrr.com/abstract.php?article_id=3793http://ijcrr.com/article_html.php?did=3793
Cleaning and Disinfection for Households Detailed Disinfection Guidance; 2020. Cdc.gov website https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/cleaning-disinfection.html. Accessed 15 September 2021.
Guo ZD, Wang ZY, Zhang SF, Li X, Li L, Li C, et al. Aerosol and surface distribution of severe acute respiratory syndrome coronavirus 2 in hospital wards, Wuhan, China, 2020. Emerg Infect Dis. 2020;26(7):1583-91.
Williams SN, Armitage CJ, Tampe T, Dienes K. Public perceptions and experiences of social distancing and social isolation during the COVID-19 pandemic: a UK-based focus group study. BMJ Open. 2020;10(7):1-8.
Chu DK, Akl EA, Duda S, Solo K, Yaacoub S, Schünemann HJ, et al. Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis. Lancet. 2020;395:1973-87.
Advisory on Social Distancing Measure because of the spread of COVID-19 disease [Internet]. Mohfw.gov.in. website https://www.mohfw.gov.in/pdf/SocialDistancingAdvisorybyMOHFW.pdf. Accessed September 2021
Social Distancing. Centres for Disease Control and Prevention. 2020 [cited 17 September 2020]. Available from: https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/social-distancing.html
Bezerra AC, Silva CE, Soares FR, Silva JA. Factors associated with people's behavior in social isolation during the COVID-19 pandemic. Ciência & Saúde Coletiva 2020;25(1):2411-21.
Ahmed H, Ahmed A, Saeed MA. Knowledge, attitude and practices (KAP) regarding the prevention against COVID–19 infection at the outset of the outbreak in Pakistan amongst smartphone users. Biomedica 2020;36:267-73.
Tomar BS, Singh P, Nathiya D, Suman S, Raj P, Tripathi S, et al. Indian community’s Knowledge, Attitude & Practice towards COVID-19. medRxiv 20092122 [Preprint]. 2020. https://www.medrxiv.org/content/10.1101/2020.05.05.20092122v1.full.pdf
Maheshwari S, Gupta PK, Sinha R, Rawat P. Knowledge, attitude, and practice towards coronavirus disease 2019 (COVID-19) among medical students: A cross-sectional study. J Acute Dis. 2020;9(3):100-4.
Zhang X, Sun Y, Ye D, Sun Z, Su H, Ni J, et al. Analysis on the mental health status of community residents in Hefei during SARS spread. Chin J Dis Contr Prev. 2003;7:280-2.
Jiao J, Tang X, Li H, Chen J, Xiao Y, Li A, et al. Survey of knowledge of villagers in prevention and control of SARS in Hainan Province. Chin Trop Med. 2005;5:703-5.
Seale H, Heywood AE, Leask J, Steel M, Thomas S, Durrheim DN, et al. COVID-19 is rapidly changing: Examining public perceptions and behaviours in response to this evolving pandemic. PLoS ONE 2020;15(6):1-13.
Zhong BL, Luo W, Li HM, Zhang QQ, Liu XG, Li WT, et al. Knowledge, attitudes, and practices towards COVID-19 among Chinese residents during the rapid rise period of the COVID-19 outbreak: a quick online cross-sectional survey. Int J Biol Sci. 2020;16(10):1745-52.
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Singh S, Singh RK. Awareness, Attitude and Practices towards COVID-19 among People of Bihar during Lockdown 1.0: A Cross-Sectional Study. Int J Sci Healthcare Res. 2020;5(2):432-43.
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Aldarhami A, Bazaid AS, Althomali OW, Binsaleh NK. Public Perceptions and Commitment to Social Distancing “Staying-at-Home” During COVID-19 Pandemic: A National Survey in Saudi Arabia. Int J Gen Med. 2020;13:677-86.
Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcarePrediction of COVID-19 with Supervised Regression Algorithm Through Minimum Variance Unbiased Estimator
English5562Manikandan AEnglish Shriram SEnglish Sarathchandran CEnglish Palaniappan SEnglish Rohith NDEnglishIntroduction: COVID-19 is found as an irresistible sickness pandemic that has carried uncommon difficulties to worldwide networks across open and private sectors. Data processing and creating awareness is the important tool that implements the powerful actions to mitigate the spread of covid-19. Objective: To develop a supervised regression algorithm with minimum variance unbiased estimator which could the data on daily basis to assure the safety movement in post-covid-19. Based on the number of infected cases, the data will be trained for better prediction to create awareness for the public in the safety movement. Methods: The proposed supervised regression algorithm was able to model the relationship between the number of cases registered and a continuous target variable. By optimizing the error rate, the training algorithm was fine-tuned and the prediction was able to closely approximate the actual values. The proposed method was compared with other methods like Linear Regression, Logistic Regression and Supporting Vector machine (SVM). Results: Simulation results proved that the proposed supervised regression with minimum variance unbiased estimation provides better prediction when compared to the other methods. Conclusion: An attempt was made to predict the number of cases by a suitable regression algorithm and the prediction was compared with other regression algorithms. The algorithm was able to predict the infections rates and death count with the least error when provided with training data. This data purely depends on the lockdown implementations, movement of people without restrictions and the lack of awareness to face this pandemic situation. In future, the aspects can also be incorporated into the model for a better and accurate prediction.
EnglishCovid-19, Machine Learning, Prediction, Linear Regression, Logistic Regression, Supervised RegressionIntroduction
The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) in China has been declared by World Health Organization as a Pandemic.1 Virus found to be transmitted through respiratory droplets from an infected person and can stay on different surfaces ranging from hours to 10 days, which makes it a highly transmissible disease. The virus has spread to more than 200 countries with the number of people infected as 56,563,840 and has taken 1,354,806 human lives (as of 19/11/2020). Vaccinating the entire human population is estimated to be completed by 2024 according to WHO, which renders human need to employ other methods to prevent the virus infection. Identification and isolation of infected people become a critical parameter in controlling a pandemic. Symptoms in an infected person surface after 3 days and in some cases it could 14-28 days, which adds another dimension to the pandemic control.2 The infected person could transmit the virus to multiple people before identification of the virus and isolation.
Government agencies across the world employed methods like the closure of schools, colleges, offices, factories and restricting the movement of people, have yielded positive response to contain the virus spread. However, a prolonged closure of community has hit the economy of the world by bringing down the global growth at -4.9% in 2020. It has hit millions of people under poverty and hunger. At this moment, there is no specific treatment and the therapeutic methods to deal with the infection are only supportive, and prevention aimed at reducing community transmission. This situation has brought intense research into traditional systems of medicine.3 An approach of social distancing and restricted movement has emerged as the way for economic recovery and to control the spread of the virus.4,5 Such approach was found to delay the peak of the virus spread and also reduce the load on the health industry like hospitals, testing centre etc. A simulated study estimated a reduction of 23% in the transmission of H1N1 when workplace social distancing was followed and reduced the peak of the virus. Physical distancing measures were found to be effective to reduce the infection rate by 92% if the working pattern was staggered across the employees. A study on social contact matrices highlighted that social contact patterns has not been developed or understood at a large scale across the world.6 Such a study would help in understanding the behaviour of people movement and predict the transmission of the virus. A model with a capability to understand the contact pattern and restrict the movement of infected person would bring down the level of transmission of the virus and help to isolate the infected people. Machine learning has proved its potential in recent days of research by solving complex real-time problems with suitable algorithms.
Machine learning uses Artificial Intelligence (AI) that gives frameworks the capacity to consequently take in and improve for a fact without being unequivocally modified. Supervised learning involves autonomous training to obtain information and predicts the outcome of the input data. It begins with observation of data from different population and derives the pattern to make better decisions further. However, semi-supervised learning methods were found to be employed in the prediction of many real-world applications like medicine, manufacturing industry due to lack of time and cost involved in data collection.7-12 Reviews indicate that machine learning methods have been successfully used in the epidemiological study to predict the number of infections and provide a forecast to government agencies to take adequate measure and methods to control the spread of infections.13-15 Machine learning techniques like Support Vector Machine, Long-Short term memory networks, artificial neural networks have been used in various applications for better prediction.16-18 As the information on Covid-19 data are widely available which makes the supervised regression algorithm a potential machine learning tool to predict the infection numbers accurately henceforth. This paper proposes a safety movement algorithm derived from the supervised regression method based on the historic data on the number of infections recorded in the region. The algorithm would predict the number of infections at the required time intervals which can be used as a tool to employ methods to contain the virus or take adequate actions to restrict/allow the movement of people in the region. Section 2 would describe the data collection process (data from India) for the training and prediction of the supervised regression algorithm. Section 3 elaborates the methodology of the supervised regression algorithm and Section 4 demonstrates the efficiency of the developed algorithm and also compares the predicted data with Linear Regression, Logistic Regression and SVM. Section 5 concludes the paper and also provides future directions in the research area.
Data Collection
The main objective of this work is to study the future forecasting of the virus spreading based on the infected case, recovered cases and the number of deaths. The approximate data has been acquired from
https://www.who.int/dg/speeches/detail/who- director-general-s-opening-remarks-at-the-mission-briefing-on- covid-19—12-march-2020. for the period over January 2020 to December 2020. In the proposed work, the dataset for India has been taken to analyze with a supervised algorithm from January 2020 to December 2020.
Linear Regression
Linear regression is the basic form of regression in which the dependent variable is continuous and the dependent variable depends on the independent variable is linear.19 A simple linear regression model with the regression coefficient b can be expressed by
y=βo+β1x+ε (1)
Where βo and β1 are the unknown constants that define the intercept and slope respectively. Here the error ε is derived based on the assumption with zero mean and variance of σ2. Here one error is uncorrelated with another error in nature. To train any machine learning model with this linear regression model y is represented with the current data set. The main objective of this linear regression model is to find the unknown regression coefficients βo and β1 that will make the error to minimum or zero. In this paper, all the models have been compared through the Mean Square Error (MSE) which is described in section 6.
Logistic Regression
The logistic regression model for machine learning is another famous regression model which is most widely used in biological sciences. The linear regression model may not be suitable for some of the applications since it requires some threshold value to classify the data. The logistic function z can be defined as,20
Here α and β are unknown parameters and X is the independent variable of interest. This model defined logistic regression since the probability of developing the disease can be defined with X as
Here α and β are to be estimated for the given group of objects. Once the values α and β found then X will be determined easily for the virus spread through this logistic model.
SVM
SVM is another important machine learning algorithm that can be used for both regression and classification. This algorithm search for the optimal separating surface through its kernel functions. The main objective of this vector machine is to find the optimum function in a suitable multidimensional space that will be able to classify the training data into known classification criterion. Due to the control over the error and stability in multidimensional data, this SVM is preferred in most of the pieces of training and classifications. Here the optimum function will be obtained with a minimum cost function of,21
Where ωT, xi∈R2 and b∈R1, ω2= ωTω, C is the fixing parameter between the margin error and the training data. Here the parameter C is common for all the kernels. The lowest value of C will give the smooth decision surface and the high value of C will concentrate the classification of the training samples accurately.
Supervised Regression
Linear regression explained in section 3 is very much prone to outliers. Before applying the linear regression, it is mandatory to remove the anomalies from the data set. But it is not possible for the cases like virus prediction since it will possess more uncertainties. Even though logistic regression is very simple with its low dimensional data sets, on high dimensional data sets this model will compromise inaccuracy in the test. Also, this model will face some issues due to its non-linearity optimization in the decision surface. SVM depicted in section 5 is relatively fair with clear margin specification during the classification. But when the data sets are large, SVM performs poor due to the overlapping in the data sets. Among these issues, since non-linearity will be treated as an important issue, in this work it is identified that the linear regression depicted in section 3 can be modified with a suitable estimator to address the above-discussed issues. In this work, a supervised regression model with minimum variance unbiased estimation has been proposed to predict the virus spread with unknown input data. In supervised regression, it defines an algorithm that learns the pattern mapping from the input to the output. The objective of the regression is to approximate the mapping function that can predict the output for the new input with the help of trained data.
This supervised regression will train the data with well-known input in training mode and predict the optimized values as shown in Figure 3. For example, a classification algorithm will train to identify the alphabets or numbers after being trained on a particular data set of images with alphabets and numbers through some identifying characteristics. The output of the supervised regression can be linearly expressed as,22
Where x=x1,x2…..xkT, f1, f2…..fn are known parameters and y1, y2……yn are the unknown parameters to be estimated. These parameters to be estimated are called regression parameters. Here the known parameters can be represented in a matrix form as (6)
Also,
z=F.y (7)
To derive the genuine value of y,
y=F-1. z (8)
For better optimization, the error will be calculated as the difference between the actual values and the predicted values with mean square formula as,
Where are the predicted values and yi are actual values.
Based on the Mean Square Error (MSE) it is must find the dependent parameters with an optimized fit line. To minimize the error and to find the optimized fit line, Mean square estimators are used. These estimators minimize the distance between the fit line and actual outputs. Evaluate the statistical qualities of MSE equation (7) has to be investigated in a statistical framework.
The error mentioned in equation (9) will be minimized when =yi with the condition,
If the term FTF in the left side of the equation (6), is non singular, unbiased and minimum variance then
Now the Accuracy of the estimated value through this MSE can be done by
Also,
Then
From these equations, it is evident that the estimator depends on two components bias {}and variance of{} Here the tradeoff between the variance and bias will decide the rate of change of MSE. When the estimator is purely unbiased, MSE ()=Var (). Also while increasing the bias, the variance will decrease and when decreasing the bias the variance will increase. To obtain the minimum MSE through this minimum variance unbiased estimator, the known parameters in x can be obtained by gradient descent optimization as,23
Equation (15) helps us to find the optimized updates of known parameters proposed in the regression model as equation (5). The weights are initialized with zeros and ones with truncated normal distribution. The prediction has been accomplished by using supervised machine learning approaches. The data sets utilized here include the number of daily cases and deaths from December 2019 to December 2020.
Figure 3 depicts the process of the data classification and prediction with supervised regression. In the first step, the data will be collected from the authorized sites and it will be processed and classified based on the geographical area. In this paper, the prediction will be done for India based on the data from January 2020 to December 2020. Here, the prediction was done based on the relationship between the dependent variable and some independent variables.
Results and discussion
Once the data for the geographical area has been preprocessed, the data will be divided into two sets, one is a training set and another one is a tracking set. The window size of the training set is fixed as 236 days and the tracking set is 100 days. The prediction will be based on the training set with the supervised external target from tracking a set of 25 days. As mentioned in the previous section, the weights are initialized with truncated normal distribution and the batch size as 10 sets. After each batch has been processed, the bias and variance will be calculated to update the weights further which will minimize the MSE further. The pattern for infected cases and death was captured using the supervised regression model to minimize the MSE between the actual and the fit line. Based on the pattern, the MSE will be evaluated and optimized under unbiased and minimum variance conditions. Figure 4 depicts the prediction of the linear regression through curve fitting for infected cases. The ideal way to fit curves with the data using linear regression is to track the polynomial coefficients as predictors. Each transition in the data will make more bends in the line. The optimum fit line will be found with the help of MSE. When the MSE is high, the fitted line will underpredict the data points. This linear model with reciprocal coefficients will provide the best curve fitting line for optimized prediction. From the figure, it is visible that the linear regression model fails to find the optimum fit line for a huge volume of data. Figure 5 elucidates the prediction of infected cases through curve fitting optimization for logistic regression. Logistic regression can provide a better prediction when the range of uncertainties is wide. Figure 6 depicts the prediction of infected cases with SVM. From the figure, it is visible that even though SVM is more efficient with high dimensional cases but with the ambiguity in the margin separation, SVM fails to predict the cases in certain situations. Figure 7 projects the predicted cases with supervised regression for infected cases. Even though it is a modified version of linear regression, the curve fitting can be optimized through equation (10) to equation 15. To determine the optimized polynomial, the number of bends will be counted in the curve fitting and the MSE will be updated by Eqn(9). Based on the number of bends, the variance and the biasing of the estimation will be done to update the weight coefficients further. The range of error and the MSE for all the models projected in Table- 1. From Table- 1 it is evident that the proposed supervised regression with minimum variance unbiased estimator can predict the infected cases better than other models with margin MSE. Also, it is observed that SVM attempts to find the best margin that will make the crisp boundary between classes of data and risk towards the error. But due to the large volume of data set, SVM was not able to predict well when compared to the supervised regression algorithm. Figure 8,9,10 and 11 projects the prediction for the deaths with linear regression, logistic regression, SVM and supervised regression respectively. The newly confirmed cases for day by day also projected for different models. Figure 8,9,10 and 11 projects the prediction of deaths with Linear regression, Logistic Regression, SVM and Supervised regression respectively. Here the models have been trained with the available data from 30.1.2020 to 21.09.2020. The predicted death rate has been compared with the actual deaths that happened during the next 100 days. From the figures, it is visible that the supervised regression can predict reasonably when compared to the remaining algorithms. With more clarity, the range of error and the MSE for different algorithms are depicted in Table II. The number of deaths was predicted well in the supervised regression algorithm when compared to the former algorithms. MSE also marginally low in supervised regression when compared to the remaining algorithms.
Conclusion
Data availability and awareness on the number of infections in the localized region may hinder the movement of people and/or follow the safety requirement to protect them from infections. Government agencies need the information to foresee the infection rates and take suitable measures to contain the virus spread. This paper attempted to compare the predictions made by linear regression, logistic regression, support vector machine and supervised regression algorithm based on the historic data available. The algorithm was able to predict the infections rates and death count with the least error when provided with training data. The proposed supervised regression model proved that the prediction can be done for any geographical area with a suitable window, batch size and weights. Simulation results also proved that the proposed algorithm performed better when compared to the other models of machine learning. Our model has been proposed to predict the data based on the historic data alone. However, in a pandemic the infection rates and death rates can vary depending on various factors like imposing lockdown for a brief period, restricting movement of people, increased rates of testing and isolation etc in the particular region. In future, the aspects can also be incorporated into the model for a better and accurate prediction.
ACKNOWLEDGEMENT: The authors are grateful to faculty members of Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Chennai.
Conflict of Interest: The authors declared that there is no conflict of interest.
Funding: No funding
Author Contributions
Conceptualization, Manikandan Arunachalam; Methodology and Validation, Manikandan Arunchalam, Palaniappan S, Neeruvai Devaiah Rohith; Writing – Manikandan Arunchalam and Shriram S; Review and editing, Sarathchandran.
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareRed Cell Distribution Width in Hospitalized Covid Patients - A Study in a Tertiary Care Covid Centre in Eastern India
English6366Dasgupta SenjutiEnglish Osta ManishEnglish Talukdar ManasEnglishIntroduction: The global pandemic caused by coronavirus disease 2019 (COVID-19) is associated with a high rate of hospitalisation and mortality in severe cases. This emphasizes the requirement of parameters that can predict the progression of the severity of the disease among COVID-19 infected patients. Red cells distribution width (RDW) is a cheap and easily available parameter included in Complete Haemogram and is associated with an increased risk of mortality with various diseases. Objective: The present study aimed to find out if there is any correlation between RDW and the severity of COVID-19 infection. Methods: The study was conducted on 111 admitted patients of COVID 19 diagnosed by RT-PCR, among those who were 18 years or older and not requiring treatment for anaemia. Eighty-seven of the patients were having moderate and 24 severe diseases. Analysis of the EDTA blood samples was done by Sysmex XT-4000i automated haematology analyser. RDW-CV and RDW-SD along with haemoglobin and haematocrit values were recorded. Statistical analyses were performed using GraphPad QuickCalcs. Results: Mean value for haemoglobin, haematocrit, RDW-SD and RDW-CV were 11.48 ± 2.22 g/dl, 35.18 ± 6.07 %, 47.26 ± 6.50 fl and 15.69 ± 2.12% respectively. Values for haemoglobin, RDW-CV and RDW-SD were statistically significant when compared between moderate and severe COVID-19 infected patients. Conclusion: Patients with raised RDW values are at a significantly higher risk of developing severe COVID-19 disease which can be fatal.
EnglishRDW-CV, RDW-SD, Covid-19, Correlation, Prognostic significance, PandemicIntroduction
The global pandemic of the acute respiratory disease called coronavirus disease 2019 (COVID-19) has been wreaking havoc since December 2019. It is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease has been associated with a high rate of hospitalisation, the requirement of critical care and mortality.1 It has been reported that 26.1 to 32% of confirmed cases become critical and of these severe cases, the fatality rate is as high as 61.5%.2 These figures emphasize the requirement of parameters that predict the progression of the severity of the disease among COVID-19 patients. In third world countries like India, which has a huge population at risk and limited resources in hospitals, it is especially necessary to define simple and feasible predictors of severity.
The complete hemogram is a baseline investigation undertaken in hospitalized patients and one of the common parameters included in the report is red cell distribution width (RDW). An increase in RDW has been reported to be associated with increased risk of mortality in various diseases ranging from heart diseases, pulmonary diseases, sepsis and carcinomas. This observation led to the assumption that RDW may be a potential marker of risk stratification in COVID-19 as well.3
There is a dearth of studies from India regarding the role of RDW in predicting the prognosis of COVID-19. The present study aimed to find out if there is any correlation between RDW and the severity of COVID-19 infection. Even with the advent of vaccines, it is invariably a matter of substantial time before the entire population is protected from the deadly disease. So, the importance of exploration of the role of cost-effective parameters like RDW which has the potential to guide proper triaging of patients cannot be overemphasized.
Materials and methods
A hospital-based study was conducted in a dedicated tertiary care COVID centre of Eastern India for one month. The study had been approved by the Institutional Ethics Committee (Ref no. MC/KOL/IEC/NON-SPON/855/12/2020, dated 22nd December 2020). The study subjects were those adult patients who had been admitted in the COVID ward and COVID ICU (intensive care unit) during this period and had tested positive for COVID-19 by RT-PCR (reverse transcriptase-polymerase chain reaction) test. Those patients who were below 18 years of age were excluded. Patients who were already receiving treatment for anaemia were also excluded.
The patients included in the study were then categorized into “moderate disease” and severe disease” based on criteria defined by the Ministry of Health and Family Welfare and Director General of Health Services, Govt. of India.4 A patient was classified as “severe” when there was the presence of clinical signs of pneumonia along with any one of the following: respiratory rate >30 breaths/min, severe respiratory distress, SpO2 Englishhttp://ijcrr.com/abstract.php?article_id=3795http://ijcrr.com/article_html.php?did=37951. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel corona virus-infected pneumonia in Wuhan, China. JAMA. 2020;323(11):1061-9.
2. Yang X, Yu Y, Xu J, Shu H, Xia J, Liu H, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centre, retrospective, observational study. Lancet Respir Med. 2020;8(5):475-81.
3. Foy BH, Carlson JCT, Reinertsen E, Valls RPI, Lopez RP, Palanques-Tost E, et al. Association of Red Blood Cell Distribution Width With Mortality Risk in Hospitalized Adults With SARS-CoV-2 Infection. JAMA Netw Open 2020;3(9):e2022058.
4. Clinical Management Protocol for COVID19. https://www.mohfw.gov.in/pdf/ClinicalManagementProtocolforCOVID19.pdf.
5. Gralinski LE, Menachery VD. Return of the Coronavirus: 2019-nCoV. Viruses 2020;12(2):135.
6. Gates B. Responding to Covid-19-A Once-in-a-Century Pandemic? N Engl J Med 2020;382(18):1677-9.
7. Feng Z, Yu Q, Yao S, Luo L, Duan J, Yan Z, et al. Early Prediction of Disease Progression in 2019 Novel Coronavirus Pneumonia Patients Outside Wuhan with CT and Clinical Characteristics. medRxiv 2020: 2020.02.19.20025296.
8. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 2020;395(20): 507-13.
9. Gong J, Ou J, Qiu X, Jie Y, Chen Y, Yuan L, et al. A tool to early prediction of severe coronavirus disease 2019 (COVID-19): a multicenter study using the risk nomogram in Wuhan and Guangdong, China. Clin Infect Dis. 2020;71(15):833-40.
10. Wang B, Gong Y, Ying B, Cheng B. Relation between Red Cell Distribution Width and Mortality in Critically Ill Patients with Acute Respiratory Distress Syndrome. BioMed Res Int. 2019;2019:1942078.
11. Havens JM, Seshadri AJ, Salim A, Christopher KB. Red cell distribution width predicts out of hospital outcomes in critically ill emergency general surgery patients. Trauma Surg Acute Care Open. 2018;3:e000147.
12. Mahmood NA, Mathew J, Kang B, DeBari VA, Khan MA. Broadening of the red blood cell distribution width is associated with increased severity of illness in patients with sepsis. Int J Crit Illn Inj Sci. 2014;4(4):278-82.
13. Karsten E, Breen E, Herbert BR. Red blood cells are dynamic reservoirs of cytokines. Sci Rep. 2018; 8:3101.
14. Janz DR, Ware LB. The role of red blood cells and cell-free haemoglobin in the pathogenesis of ARDS. J Intens Care 2015;3:20.
15. Sarkar M, Rajta PN, Khatana J. Anemia in Chronic obstructive pulmonary disease: Prevalence pathogenesis, and potential impact. Lung India 2015;32(2):142-51.
16. Wang C, Deng R, Gou L, Fu Z, Zhang X, Shao F, et al. Preliminary study to identify severe from moderate cases of COVID-19 using combined haematology parameters. Ann Transl Med. 2020;8(9):593.
17. Lu G, Wang J. Dynamic changes in routine blood parameters of a severe COVID-19 case. Clin Chim Acta. 2020; 508:98-102.
18. Vaid A, Somani S, Russak AJ, Freitas JDK, Chaudhry FF, Paranjpe I, et al. Machine learning to predict mortality and critical events in COVID-19 positive New York City: Model Development and Validation. J Med Internet Res. 2020;6;22(11):e24018.
Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareEarly Pulmonary Hypertension in COVID 19 Patient: A Case Report
English6770Nimkar SVEnglish Dubey AEnglish Talwar DEnglish Kumar SEnglishIntroduction: This COVID 19 pandemic has altered the routine practices and the acute management of PAH patients. Patients with pre-existing PAH are very sensitive to the changes in their cardiopulmonary status. Objective: To study a case of early pulmonary hypertension in COVID 19. Case Report: A 45-year-old male presented with complaints of breathlessness at rest for 15 days which was gradually progressive. PCR was done and tested positive. The patient was treated for the same with negative PCR repeated report after 14 days and then again patient presented with the above complaints after 14 days of discharge. COVID-19 infection poses a bizarre threat to the vulnerable population with PAH. The inpatient needs have been increased during the pandemic. Observations: PAH patients had great difficulty in surviving the interventions which pose a significant cost during a pandemic that is stretching the health care system. So far, we have not seen many PAH patients with COVID-19 infections, and most of them have had favorable outcomes. This reflects the ability of these patients to isolate and quarantine themselves properly. Also, we are approaching having a successful treatment option or a vaccination for COVID-19 shortly. Conclusion: In the current report, PH was associated with clinical, imaging and laboratory findings of more severe post-COVID-19 and with no morbid outcomes. A final decision is yet to be made. We believe it is important to screen for COVID-19 infection in all patients.
EnglishCOVID 19, Pulmonary hypertension, 2d echo, Mortality, PandemicIntroduction
Pulmonary hypertension (PH) is primary (idiopathic) or secondary to a variety of underlying conditions like pulmonary, cardiovascular or systemic conditions.1,2 A prolonged elevation of pulmonary arterial pressure to more than 25 mm Hg at rest or more than 30 mm Hg with exercise, with mean pulmonary-capillary wedge pressure and left ventricular end-diastolic pressure of less than 15 mm Hg, is known as pulmonary arterial hypertension.3 Histologically, the appearance of lung tissue is similar in each condition: intimal fibrosis, increased medial thickness, pulmonary arterial occlusion, and plexiform lesions predominate.4
The COVID-19 pandemic has exhibited many peculiar challenges when caring for patients with pulmonary hypertension (PH), particularly the patients with pre-existing pulmonary arterial hypertension (PAH), and chronic thromboembolic pulmonary hypertension (CTEPH). The presence of whether pre-existing PH or PH as a direct result of the lung injury that occurs post COVID-19 infection, or cardiomyopathy resulted from COVID-19 infection, or other comorbidity is likely to be a major contributor to the morbidity and mortality associated with COVID-19 infection.5,6
We report a case of a 45-year-old male with Bilateral Pleural Effusion with Mild ascites and Grade III Renal parenchymal disease (RPD) who had an asymptomatic course of COVID-19 infection who tested positive for infection by screening.
Case Presentation
A 45-year-old male presented with complaints of pedal oedema and distension of the abdomen for 6 days. He also had complaints of breathlessness at rest for 15 days which was gradually progressive. 14 days back On the previous admission, due to the current COVID-19 pandemic and as per our local strategy to eliminate COVID-19 infection among the population nasopharyngeal swab to screen for COVID-19, PCR was done and tested positive. Patient treated for same and PCR report repeated after 14 days it was negative (JNMC/COV/10175/2020) patient was discharged and then again patient presented with above complaints after 14 days discharge. On general examination: Pulse: 90/min, BP: 100/70mmHg, Oxygen saturation on room air was 95%, respiratory rate: 24/min. On abdominal examination: Distended abdomen with free fluid present in the abdomen, shifting dullness present. On respiratory examination: Dull percussion note, reduced breath sounds in both lung bases with bronchial breathing over the left infra-scapular area.
Blood investigations revealed-CBC investigations on cell count with PS showed 9.4 g/dL Hb, 31.2 g/dl MCHC, 72.5 p MCV, and 22.6 pg MCH. Total RBC count was 4.16 million/mm3, 7800/ microlitre, and 2.49/microlitre of blood. Prothrombin Time of 12.5 seconds, APTT 30.20 seconds, INR: 1.28.CK-MB value 29 IU/L. random blood sugar 151 mg/dL, Calcium: 6.g/dl, KFT showed Urea: 45 mg/dl, Creatinine: 1.2 mg/dl, Serum sodium: 146 mmol/L, Serum potassium: 4.7 mmol/L, LFT showed Serum albumin: 3.9 g/dl, alkaline phosphatase level was 55 IU/L. Albumin(3.9g/dL), Bilirubin conjugated(0.2 mg/dL), bilirubin unconjugated (0.7 mg/dL), globulin (3.8 g/dL), SGPT (50 U/L), SGOT (34 U/L), total protein (7.7 g/dL), and total bilirubin (0.9 mg/dL) Lipid profile showed total cholesterol of 170 mg/dL, triglycerides value was 102 mg/dL dHDL, LDL, and VLDL values of 57mg/dL, 93mg/dL, and 20 mg/dL.Arterial blood gas (ABG) test showed 7.4 pH, 36.3 mmHg PCO2, and 110 mmHg PO2. Hepatitis B/Hepatitis C/ HIV status of this patient was negative (Figure 1).
2D Echo showed dilated right atrium, tricuspid regurgitant velocity 3.2 m/sec, systolic pulmonary artery pressure 44mmhg, left ventricle (LV), hypokinetic Inferior wall(IW) and Anterior wall (AW). Motion mode (M.mode) of 2D echo showed LVIDD value of 5.5cm, LVIDS value of 4.5cm, IVs value is 10, posterior wall is 10, EF value is 30%, LA is 4.0cm. Dilated LV, severe pulmonary hypertension, mild AR, severe TR, Grade 3 diastolic dysfunction. IVC congested and non-collpsing (Figure 2).
A coronary angiography report showed mild coronary artery disease (CAD). HRCT scan of the thorax showed moderate pleural effusion on both sides with basal lung atelectasis with subsegmental collapse consolidation on the left basal lung. The severity score was 0/25 indicating no obvious abnormality in bilateral lung fields.
The treatment regime followed for the patient during his hospital stay was as given:
Injection Heparin 2500 IU
Injection NTG 100mcg
Injection Dilzem 5mg
The patient was discharged after treatment and waiting for follow up.
Discussion
Patients with pre-existing PAH are very sensitive to the changes in their cardiopulmonary status. Any interruption of care or the emergence of new cardiac or pulmonary pathology may result in death. In PAH, hospitalization is a risk factor for disease development. A newly available case series suggested that 86% of critically ill COVID-19-infected patients had heart failure and chronic kidney disease as the most common underlying medical conditions.7 PAH patients have shown to have worse outcomes with all-cause hospitalizations.5 One should always keep in mind that COVID-19 infection may lead to hypoxemia leading to hypoxic pulmonary vasoconstriction, which increases pulmonary vascular resistance (PVR) resulting in decompensation of a high-risk PAH patient.7 Keep oxygen saturation above 92%, continuing oral and parenteral therapies like phosphodiesterase-5 inhibitors (PDE5i), soluble guanylate cyclase stimulator (sGC), endothelin receptor antagonist (ERA), oral prostacyclin, oral prostaglandin I2 receptor agonist (PGI2) and intravenous or subcutaneous prostacyclins while treating with concurrent therapies for COVID-19 infection are recommended. As per institutional guidelines, continuing treatment with investigational therapies for COVID-19 infection along with the PAH treatment is also recommended.
COVID-19 infection poses a bizarre threat to the vulnerable population with PAH. PAH patients had great difficulty in surviving the interventions which posses a significant cost during a pandemic that is stretching the health care system. There is a shortage of ventilators during the COVID-19 pandemic, which poses even further concern for their utility in this population. A study done by Pagnesi et al. in 2020 stated that the prevalence of PH was 12.0%. Patients with PH were older and had a higher burden of pre-existing cardiac comorbidities and signs of more severe acute respiratory syndrome in COVID-19 infection.8
Lee and colleagues in their study found that the cumulative incidence of recognized COVID-19 was similar to the general population. However, outcomes were worse, with a 50% rate of hospitalization and a 12% mortality rate in patients with PAH and CTEPH affected by COVID-19. The fact that incidence was higher in higher prevalence states seems to validate the conclusion that the rate is probably similar to the general population.9 A small survey and other COVID-19 PAH/CTEPH series reported to date have suggested that COVID-19 has a relatively benign course in the setting of PAH and surprisingly favourable clinical outcomes.10-12
So far, we have not seen many PAH patients with COVID-19 infections, and most of them have had favourable outcomes. This reflects the ability of these patients to isolate and quarantine themselves properly. Also, we are approaching having a successful treatment option or a vaccination for COVID-19 shortly.
Conclusion
To our best knowledge, it is the first report of pulmonary hypertension in post-COVID 19 infections. We reported a smooth clinical course for COVID-19 infection in a male with Bilateral Pleural Effusion with Mild ascites and Grade III Renal parenchymal disease (RPD). In the current report, PH was associated with clinical, imaging and laboratory findings of more severe post-COVID-19 and with no morbid outcomes. One of the worthiest questions to ask nowadays is whether those patients should be considered high risk for COVID 19 infection and its complications during this pandemic or not. A final decision is yet to be made. We believe it is important to screen for COVID-19 infection in all patients.
Acknowledgement: None
Conflict of Interest and Source of Funding: None
Author Contribution:
Nimkar SV: Manuscript Preparation, Data Collection
Dubey A: Manuscript editing
Talwar D: Pliagrism Removal
Kumar S: Final editing and preparation according to journal guidelines
Englishhttp://ijcrr.com/abstract.php?article_id=3796http://ijcrr.com/article_html.php?did=3796
Farber HW, Loscalzo J. Pulmonary arterial hypertension. N Engl J Med. 2004;351(16):1655-65.
Wiltshire E, Peña AS, MacKenzie K, Shaw G, Couper J. High dose folic acid is a potential treatment for pulmonary hypertension, including when associated with COVID-19 pneumonia. Med Hypoth. 2020;143:110142.
Simonneau G, Montani D, Celermajer DS, Denton CP, Gatzoulis MA, Krowka M, et al. Haemodynamic definitions and updated clinical classification of pulmonary hypertension. Eur Respiratory J. 2019;53(1):1801913.
Rubin LJ. Primary pulmonary hypertension. N Engl J Med 1997;336:111-7.
Ryan JJ, Melendres-Groves L, Zamanian RT, Oudiz RJ, Chakinala M, Rosenzweig EB, et al. Care of patients with pulmonary arterial hypertension during the coronavirus (COVID-19) pandemic. Pulm Circ. 2020;10(2):2045894020920153.
Jain A, DhruvTalwar SK. Spectrum of Respiratory Involvement in COVID 19 Era: An Overview. Indian J Forensic Med Toxicol. 2020;14(4):6593.
Sahay S, Farber HW. Management of hospitalized patients with pulmonary arterial hypertension and COVID-19 infection. Pulm Circ. 2020;10(3)1-5.
Pagnesi M, Baldetti L, Beneduce A, Calvo F, Gramegna M, Pazzanese V, et al. Pulmonary hypertension and right ventricular involvement in hospitalised patients with COVID-19. Heart. 2020;106(17):1324-31.
Lee JD, Burger CD, Delossantos GB, Grinnan D, Ralph DD, Rayner SG, et al. A survey-based estimate of COVID-19 incidence and outcomes among patients with pulmonary arterial hypertension or chronic thromboembolic pulmonary hypertension and impact on the process of care. Ann Am Thorac Soc 2020;17:1576–82.
Horn EM, Chakinala M, Oudiz R, Joseloff E, Rosenzweig EB. Could pulmonary arterial hypertension patients be at a lower risk from severe COVID-19? Pulm. Circ. 2020;10(2):1-2.
Scuri P, Iacovoni A, Abete R, Cereda A, Grosu A, Senni M. An unexpected recovery of patients with pulmonary arterial hypertension and SARS-CoV-2 pneumonia: A case series. Pulm. Circ. 2020;10:204589402095658.
Nuche J, Pérez-Olivares C, De La Cal TS, López-Guarch CJ, Ynsaurriaga FA, Escribano-Subías P. Clinical course of COVID-19 in pulmonary arterial hypertension patients. Rev Esp Cardiol. 2020;73:775–778.
Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareTo Assess the Attitude and COVID-19 Preparedness Among Postgraduate Medical Trainees in a Tertiary Care Hospital in Central India
English7176Patidar ShashikantEnglish Chakma ChayanEnglish Suresh SnehaEnglish Bajaj NareshEnglishEnglish Attitude, Central India, COVID-19, Medical students, Post-graduate trainees, PreparednessINTRODUCTION
In December 2019, multiple cases of pneumonia of unknown aetiology were identified in China Wuhan city. World Health Organization (WHO) named it COVID-19 and declared it as a pandemic on 11th March 2020.1-5 COVID-19 has so far affected the general population including a large number of health professionals around the world.6 Since the postgraduate trainees are one of the first hands of contact in the health care system, they are prone to greater risk of acquiring the infection during their duty hours. The emergence of COVID-19 made everyone reach out to Health care facilities and get prepared with their own best measures for the impact of the pandemic. Even medical fraternity around the world was changing and updating their protocols day today. As COVID-19 is a highly infectious disease spreading through asymptomatic contact, it has been diagnosed in every age-groups worldwide.7With time passing by, the attitude towards the disease has been changed among people. The preparedness for the infection has also varied from person to person. Even with maximum available knowledge and precautions are followed as per protocols, till September 2020, 2238 doctors were infected with the COVID-19 disease and of them, 382 lost their lives in India. Whereas in our college, 67 healthcare workers including 29 postgraduate medical students were diagnosed COVID-19positive with no mortality till the date of the present study. Hence this survey was warranted to assess the attitude and COVID-19 preparedness among postgraduate medical students in our tertiary care hospital in central India.
MATERIALS AND METHODS
This prospective, cross-sectional study was carried out in September 2020 among postgraduate trainees in Shyam Shah Medical College and associated hospitals at Rewa, Madhyapradesh in India. A self-administered, anonymous, questionnaire comprising of 30 close-ended questions was circulated to gather the relevant information. A total of 200 PG students submitted a response, out of which 111 complete responses were included in the statistical analysis. The study proposal was addressed to the institutional ethical committee and ethical clearance was obtained. During the process, all the information related to participants were kept confidential. All the data was collected from various departments of our institute.
At the end of the study, the data was analysed statistically by using SPSS ver 22.0. A p-value of Englishhttp://ijcrr.com/abstract.php?article_id=3797http://ijcrr.com/article_html.php?did=3797
1. Huang X, Wei F, Hu L, Wen L, Chen K. Epidemiology and clinical characteristics of corvid-19. Arch Iran Med. 2020;23(4):268-271.
2. Chen Y, Liu O, Guo D. Emerging coronaviruses: genome structure, replication, and pathogenesis. J Med Virol. 2020;92(4):418-423.
3. Backer JA, Klinkenberg D, Wallinga J. Incubation period of 2019 novel coronavirus (2019-Nov) infections among travellers from Wuhan, China. Euro Surveill. 2020;25(6):20–8.
4. Guan WJ, Ni ZY, Hu Y. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382:1708–20.
5. Chan JF, Yuan S, Kok KH. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet. 2020;395(10223):514–23.
6. Zhang Z, Liu S, Xiang M. Protecting healthcare personnel from 2019-nCov infection risks: lessons and suggestions. Front Med. 2020;14(2):229-231.
7. To KK, Tsang OT, Chik-Yand Yip C. Consistent detection of 2019 novel coronavirus in saliva. Clin Infect Dis. 2020;71(15):841-843.
8. Meng L, Hua F, Bia Z. Coronavirus disease 2019. COVID-19 Emerging future challenges for dental and oral medicine. J Dent Res. 2020;99(5):481–7.
9. Arora S, Abullais SS, Attar N, Pimple S, Saifullah ZK, Saluja P, et al. Evaluation of knowledge and preparedness among Indian dentists during the current COVID-19 pandemic: a cross-sectional study. J Multidiscip Healthcare. 2020;13:841-54.
10. Putrino A, Raso M, Magazzino C. Coronavirus (COVID-19) in Italy: knowledge, management of patients and clinical experience of Italian dentists during the spread of contagion. BMC Oral Health. 2020;20:200.
11. De Stefani A, Bruno G, Martinelli S, Gracco A. COVID-19 outbreak perception in Italian dentists. Int J Environ Res Public Health. 2020;17(11): 3867.
12. Cagetti MG, Cairoli JL, Senna A, Campus G. COVID-19 outbreak in North Italy: an overview on dentistry. A questionnaire survey. Int J Environ Res Public Health. 2020 Jun; 17(11): 3835.
13. Khader Y, Al Nsour M, Al-Batayneh OB Dentists’ awareness, perception, and attitude regarding COVID-19 and infection control: A cross-sectional study among Jordanian dentists. JMIR Public Health Surveill. 2020;6(2):e18798
Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareData Analysis and Visualization of the Coronavirus Pandemic [Covid-19] in Major Countries Using Python
English7780Kulshreshtha V.English Garg NKEnglish Maherchandani JKEnglishIntroduction: Human being is facing with an invisible enemy; the novel COVID-19 coronavirus. It was at the start found in the Wuhan province of China. Now it is spreading around the globe. Objective: The paper aims to explain total cases, new cases, total deaths, and new deaths caused by coronavirus pandemic [Covid-19] of three major countries viz. USA, Brazil, and India during this pandemic. Methods: This paper explains the data analysis and visualization of the coronavirus pandemic. The data is analyzed and visualized by using Python programming language. Case Study: Three case studies with the original dataset are shown in this paper, which is useful for the researcher to analyze the COVID-19 pandemic further. Conclusion: This data analysis result proves the lower Standard Deviation in India and the USA, which shows that data is aggregated close to mean value, which shows that data is reliable and uses further research
EnglishCovid-19, Data analysis, Data visualization, Pandemic, Standard Deviation, PythonINTRODUCTION
Human being faced various pandemics during the past wherever several were very disastrous. During this time, we are facing a very challenging and tough time and struggling with an invisible enemy globally.1The first case of coronavirus emerged in Wuhan city, China, in December 2019 with the clinical characteristic of the severe acute respiratory syndrome.2 The number of COVID-19 cases has increased exponentially across China and has become a global pandemic.3 There were 79,515,525 confirmed cases and 1,757,947 death globally as of 28 December 2020.4Covid-19 becomes a global threat for public health.5 Clinical features, findings, management and preventions for Covid-19 were discussed and analyzed.6 This paper presents data analysis and visualization of the USA, Brazil and India. Further sections will explain the used methodology, case studies and discussion.
MATERIALS AND METHODS
The Covid-19 data has been analyzed and visualized using the Python programming language of the major affected countries from the pandemic coronavirus, including the United States of America, Brazil, and India. Python plays a very important role in the data analysis and data visualization of the above-mentioned countries.7,8
The data sets of twelve months have been taken i.e. from January 2020 to December 2020. The dataset contains the whole world's data for those countries that are affected till today around 79.5 million people are confirmed cases. This data keeps on increasing. The dataset has been taken from 01.01.2020 to 28.12.2020. The four major aspects have been considered for their data analysis and visualization. These are as follows:
Total cases: This field tells us about the total cases reported monthly of the countries as mentioned earlier in the above time frame
New cases: This data set tells us about the total new case reported monthly of the countries as mentioned earlier in the given time frame
Total deaths: It tells us about the total deaths recorded in the given time frame
New deaths: It tells us about the new deaths reported of the given countries in the given time frame.
CASE STUDY
The United States of America is a highly affected country in the world. Presently the maximum numbers of active cases are in the USA. Table 1 shows data analysis of the USA. The total active cases till 28.12.2020 were 18.03 million; it is very threatening data. The total number of new cases reported in the USA until the date was 9570514, which is the highest data of new cases reported globally. The total number of deaths recorded in the USA till 28.12.2020 was 319364, which is disturbing data. This data shows that the USA is in an alarming stage. The total number of new death reported in the USA till 28.12.2020 was 187097, and it is kept on increasing.4-6 Table 1 shows no deaths recorded in January and February, but there was a burst in March, which reached 60966 in April. In May, the figure was 103781, and new death data was decreased, which were 45955. In June, it was again increased with 80185 and decreased to71885 in July. It was again increased and reached up to 187097 in December.7
Brazil is the second-largest country affected by a coronavirus. Till 28.12.2020, the total active cases were 7263619, which put the country at high risk. Table 2 shows the data of Brazil month wise. Table 3 shows the data of India. In India, the situation was under control in the first three months, January, February, and March, but there was a burst in April until July. Total new cases recorded were 33468 and 1267648, respectively. The death rate has also increased in these months. The figures of new deaths recorded were 1042 and 20970, respectively. It keeps on increasing up to December.8
Standard Deviation is a measure of how spreads out the numbers are. In other words, it is a mathematical tool that helps us to access how far the values are spread above and below the mean (Table 3, Figures 1 and2). A low standard deviation indicates that the data points tend to be very close to the mean, which means that data is more reliable; a high standard deviation indicates that the data points are spread out over a large range of values, which means data is less reliable. Population Standard Deviation is calculated using Equation 1.
Where σ is standard Deviation, Xi is the individual values, N is total cases, and μ is the mean of all the values8.
As it is clear from the above Figures 1, 2, and Table 1, there were very few active cases in the USA in January and February month. But in March 2020, there was an outbreak in the active cases in the USA, and Table 2 shows that active cases were reached at 164620, which was very disturbing. In March 2020, 164560 new cases were reported; this is the highest figure of this month. From February to December, there was an exponential growth in the total active cases. This trend kept on increasing in April 2020 and the cases reached 1039909, and the total number of new cases reported was 875349. In May 2020, these cases reached 1770384 and total new cases 895035, which was lesser than the previous month. In December, this figure reached 18035209. Total death till 23.12.2020 reported was 319364, which was a very threatening figure.
In Brazil, Table 2 shows that there was a burst of cases in December, and the data reached 7263619, and new cases recorded were3894120, which is the highest in all the months. Similarly, in December, the total number of death was 187291, and the total number of new death recorded was 94748. It is an alarming stage for Brazil.
Table 3 shows that in India, the situation was under control in the first three months: January, February, and March, but a declination happened in November and December. Total new cases recorded were 5167793 and 4907323, respectively. The death rate has also decreased in these months. The figures of new deaths recorded were 137621 and 146111, respectively. Table 4 and Table 5 show the mean and standard deviation values of the USA, Brazil, and India. It is clear that the USA has the highest Deviation for total cases, and India has the minimum value.
CONCLUSION
This paper presents the data analysis and visualization of the coronavirus pandemic [COVID-19] of the USA, Brazil, and India using Python. In the case of new cases, the standard Deviation is very less in the USA and India, which shows that data is clustered close to the mean, which means that data is more reliable. Data analysis shows that the USA and Brazil are in a critical position. These countries need to follow the guidelines issued by WHO and other global organizations, which includes wearing a mask, social distancing, especially in public places. Data shows that India is in a better position due to continuous lockdowns and follows the guidelines issued by the Government of India. India has become successful in controlling the spread of coronavirus until December 2020 because of many factors. These factors mainly include following social distancing, wearing masks, washing hands, and doing proper sanitization. It is shown in the medical reports that this virus is only be suppressed when we break the chain of its spread. This data is beneficial for further research for Covid-19.
ACKNOWLEDGEMENTS: The authors are extremely thankful to the reviewers for their valuable suggestion for the improvement of the paper.
Conflict of Interest: The authors declare no conflicts of interest.
Financial support: We don't have any financial assistance from anywhere.
Ethical Issue: The data used in this study is taken fromwww.ourworldindata.org
and freely available for research use thus there is no need for ethical clearance.
Englishhttp://ijcrr.com/abstract.php?article_id=3798http://ijcrr.com/article_html.php?did=3798
Said N. Coronavirus covid-19: available free literature provided by various companies, journals and organizations around the world. J Org Chem Res. 2020,5: 7-13.
Lai CC, Wang CY, Wang YH, Hsueh PR. Global coronavirus disease 2019: What has daily cumulative index taught us. Int J Anti Asgn. 2020;55(6): 238.
Wang L, Wang Y, Ye D, Liu Q. Review of the 2019 novel coronavirus (SARS-CoV-2) based on current evidence. Int J Anti Agen 2020,55(6):324-327.
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Li H, Liu SM, Yu XH, Tang SL,Tang CK. Coronavirus disease 2019 (COVID-19): current status and future perspectives. Int J Anti Agen. 2020; 15(5): 231-236.
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Villar M, Ballinas Y, Gutierrez C, Abgulo Y. Análisis de la Confiabilidad del Test Fantástico para medir Estilos de Vida saludables en trabajadores evaluados por el programa “Reforma de Vida” del Seguro Social de Salud (Essalud). Rev Peru Med Integr. 2016;1(2):17–26.
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareEffet of COVID-19 Pandemic on the Diagnosis of Breast Lesions Compared with Breast Lesions in the Previous Five Years
English8185Rohi WaniEnglish Sheema SheikhEnglish Josepheen ShahmiriEnglish Abdul Maajed JehangeerEnglish Misbah RashidEnglish Salma GullEnglishBackground: Covid-19 pandemic caused havoc in both people, the health care system and more so in patients with malignancies. Breast malignancies being one of the most common and relatively curable malignancies got hit a lot due to the impact of the Covid-19 pandemic. The multistep impact of pandemic delayed the diagnosis as also the treatment of this multidisciplinary approach disease. Objective: To determine the effect of the Covid-19 pandemic on the diagnosis of breast lesions. Methods: This is a descriptive type of histopathological study in which we have collected and studied the data of Breast lesions over the Five years (Jan 2015-Dec 2019) and compared it with the data of Breast lesions in the Covid-19 year (the year 2020). Results: The number of cases of breast specimens received for histopathology per year declined from an average of 224/year to 124/year in the Covid-19 year of 2020. Conclusion: The decrease in the number of cases alludes to the downfall in the number of patients coming for diagnosis and in turn getting treatment. This study also highlights the importance of anticipation of various cases which will cluster shortly.
EnglishBreast Carcinoma, Covid-19, PandemicINTRODUCTION
As the Covid-19 started in Wuhan in early January 2020, significant disruptions occurred in the health care system. This pandemic has hit all the aspects of our life and among the malignancies, it has hit Breast carcinoma badly, not only by hampering both its primary and secondary prevention but also its treatment.1 Breast Carcinoma is the most common and feared malignancy of women globally, with 1.7 million women getting diagnosed every year with this disease and it kills one patient out of the three.1 It stands second in the list of frequently occurring newly diagnosed cancers worldwide. It is estimated that nearly 70% of malignant tumours are caused by environmental factors, whereas in breast cancer this percentage reaches 90–95%.2
The breast although being an accessory organ is quite vital in terms of its varied pathologies and more importantly its accessibility to diagnostic procedures and potential of cure to its various life-threatening diseases. Most of the breast lesions turn out to be benign, however, the malignancies are nonetheless quite common. Diagnosis of breast lesions occurs usually by palpation in younger age groups and by screening mammographic procedures in the elderly. Covid 19 Pandemic has affected every aspect of this deadly disease with a potential cure, whether it's self-examination, an appointment with a doctor, clinical facility accessible to the patients and ultimately the treatment also. 3-6
Breast Carcinoma is the most common malignancy of women worldwide (except for non-melanoma skin carcinoma) and causes the majority of cancer deaths in women. Breast stands out most of the organs of the body in terms of its commonality of lesions, both benign and malignant, the convenience of self-examination, accessibility to diagnostic workup, better prognosis, and better cure rates.3,4
This article summarises the comparative data-driven impact on breast surgeries in the Covid-19 year 2020 and thus the diagnosis of breast lesions. We are presenting the data of breast lesions over the last five years in addition to the data of the last year of covid 19 the pandemic year 2020.
MATERIAL AND METHODS
All the breast specimens submitted for histopathological examination over six years were studied which is from Jan 2015-Dec 2020 including the specimens of the Covid-19 year 2020. It is a descriptive and retrospective type of study. All the breast cases which included Lumpectomy, Excision biopsies, Tru cut biopsies, Mastectomy specimens and Blocks for Review were included in the study irrespective of age and sex. There were no exclusion criteria. Only the specimens which had the pure diagnosis of dermal pathology were excluded. Specimens were received in 10% formalin and were subjected to routine Hematoxylin and Eosin stains.
All the sections were studied in detail, clinical history was noted down and the morphological diagnosis was made. Cases were clustered as those of the previous five years (2015-2020) of the non-Covid period and those of the Covid era which is the year 2020. The data was tabulated as benign and malignant lesions. The average number of cases from the previous Five year period was calculated and compared to Covid 2020 year. The total number of cases received in the departments every year for all Six years was calculated. Necessary details and variations in histopathology were noted down.
RESULTS
The total number of breast specimens received in our department in the last six years was 1223. Out of these total of 1099 breast specimens were received in the last five years from Jan 2015 to Dec 2019 with an average of 220 specimens in a year, whereas in the last year of the Covid 19 pandemic a total of only 124 specimens were received in our department. Among the total of 311 malignant specimens received in the last six years, 259 were received in the previous five years making an average of 52 (259/5) malignancies of breast reported per year. Interestingly we have reported a total of exactly 52 malignancies of the breast in the Covid 19 pandemic year (the year 2020). Among the benign breast cases were the total of 912 reports in all the six years, of which 840 were reported in the previous 5 years with an average of 168 (840/5) benign reports per year and an alarming number of only 72 benign reports in the Covid Pandemic year-2020year (Table 1).
When we compare the data of all the Six years individually (Table 2), we can make out that we received a total of 191 Breast specimens in 2015, 193 in the year 2016, 241 in the year 2017, 238 in the year 2018, 238 in the year 2019 and only 124 in the year 2020. As our data suggest that the most significant impact of the Covid-19 pandemic was on the benign breast lesions, which dumped down from an average of 168 cases per year to less than its half, and turned out to be only 72 cases in Covid-19 year, directly alluding to the impact of Covid -19 on the decreased number of surgeries performed on breast and in turn to the exponential decrease in the number of patients amenable to the medical amenities in the menace of Covid-19 (Table 1). Such an impact of the pandemic on this organ is quite distressing for such a disease which is accessible, amenable and even curable.
The total number of cases received in our department decreased to just 124 cases in the year 2020 from 238 cases of the year 2019, decreasing to almost half (Table 2). The decrease in the number of specimens occurred even though in addition to the routine breast specimens operated in our tertiary care, most of the surgeries done in the local surgical centres who used to sent these cases previously to the distant pathology labs landed in our department for histopathology, due travel restrictions.
DISCUSSION
There was a block at multiple levels for the diagnosis of this disease. It was not only multidisciplinary care algorithms that forced patients into delaying care but patients also self-selected to delay care. Almost 4 out of 10 patients revealed that the economic changes from the pandemic affected their ability to pay for medical care.3 Another survey by ACEP showed that almost one-third of patients (29%) delayed or avoided going to the medical facilities just to avoid COVID-19 exposures. And out of every Five patients Four were fearful of contracting the virus from a patient or health care worker if they did go. More than 81% of participants acknowledged practising social distancing.4,5 An Italian study, during the height of the outbreak, showed a significant increase in patients refusing to undergo diagnostic appointments and breast biopsies at a major cancer centre.5 In another study of 600 patients, almost four-fifth stated they had routine and follow up appointments delayed, two-thirds had reconstruction surgery delayed, and almost two-third had delayed diagnostic imaging. Therapies that require in-person visits to the hospital (radiation, chemotherapy infusion, and surgical lumpectomies) were more likely to be delayed than those that could be obtained through telehealth appointments or a prescription pick-up. On average, about 30% of patients experienced delays in the mainstays of breast cancer treatment including lumpectomies, radiation therapy, and chemotherapy.6
A survey conducted by Radiology reported that 97.4% of 228 radiology practices (urban, academic, and rural) experienced declines in imaging volume in March/April 2020, with a drop of greater than 90% of elective procedures and 60% of urgent procedures.7
A multidisciplinary group survey done by the European Breast Cancer Research Association of Surgical Trialists was distributed by breast cancer societies to 377 breast cancers in 41 countries. It was seen that the estimated time interval between the diagnosis and treatment initiation increased to almost 20% of institutions. There was a modification in 56% of cases for primary systemic therapy, with upfront surgery increasing from 33.7% to 42.2% and 39.8% to 50.7% in ER-negative/HER2-positive and T1cN0 triple-negative cases, respectively. Chemotherapy was considered as an increased risk for developing COVID-19 complications by 67% of responders and 51% reported modification in chemotherapy protocols. A large majority (68%) recommended endocrine treatment to postpone surgery in patients with luminal-A tumours. Even postoperative radiation therapy was postponed in 20% of cases. Thus breast cancer management was considerably modified during the COVID-19 pandemic.8,9
The full understanding of the implications of the delay in diagnosis and access to treatment of breast cancer cannot be done unless it is contextualised to the biology of this cancer itself and the patterns of clinical presentation, for example, the stage of the disease and the setting of care. Every patient presenting with a new breast lump that is highly suspicious of malignancy or the one who has already undergone a screening procedure with image findings highly suspicious for malignancy should be promptly referred for tissue diagnosis and/or imaging and should be designated as a high priority. So based on clinical and pathological criteria, priority must be given to the undeferrable cases in a multidisciplinary assessment. Histopathology diagnosis can have an immediate impact. For some, as with patients of symptomatic metastatic relapse, whenever the provision of treatment can be simply life-saving and/or can significantly modify the quality of life, a histopathology diagnosis should be simply included in a set of undeferrable health services.10
Pacino et al. stated very vividly that It is extremely important to maintain breast disease awareness and guarantee a safe health infrastructure of awareness for both the patients as well as the health workers.11 Assessing the data-driven delay in diagnosis we can make out approximately the burden of disease in the community which could not communicate to the medical centres. As of now when the vaccination has already started and we have been able to control the Covid-19 to some extent, we have to anticipate the number of cases that will increase to show up.12
This pandemic has led to a bid and sudden disruption in routine medical care, as also the treatment of cancer patient which is an especially vulnerable group, as here the outcome is strictly dependent on timely and high-quality multidisciplinary interventions. To add insult to the injury are the travel restriction which has made it difficult for some cancer patients to reach the hospital.13
Citgez et al. in their review in a Turkish hospital explained some changes in the approach to breast cancer and emphasized the tailored therapy must be our first goal individually. Breast cancer treatment requires a multidisciplinary approach. Dynamic changing of local conditions may affect the priority levels and treatment options. Patient treatment decisions should be made by thinking about both during and after the pandemic management of healthcare systems. Social distancing and precautionary measures are still in effect. More recently, regions with low rates of infection are planning to gradually reopen their economies and begin lifting some of these measures. Unfortunately, remaining at home may be difficult for patients among us with severe medical conditions that require urgent attention, but COVID-19 impacts everything, including how we manage breast cancer.14
As the experts in Italy rightly said that in the coming future we will see and assist in a shift toward a clinical presentation of more advanced breast cancer which could impair the oncological outcome, worsen the quality of life due to more invasive surgery, chemotherapy radiotherapy, and also increase the relative cost for the Public Health System. To organize our departments and hospitals as efficient as possible and to be ready to restart, epidemiological studies could be useful tools to evaluate the impact of screening programs to prevent a setback to where we were 20 years ago when a 2 cm lesion was considered an early diagnosis of breast cancer.15
One modelling study of 6281 new stage 1 to 3 cancer cases in the United Kingdom who were delayed multidisciplinary workup during the covid-19 pandemic suggested that an additional 181 lives and 3316 life years would be lost with a conservative estimate of only 25% of cases backlogged for 2 months.16 Just in the early phases of the pandemic, the number of new cancers diagnosed decreased.17,18 This drop was likely secondary to patients not presenting for care and not a true drop-in incidence. Thus, these cancers will come to the radar eventually at greater size or stage than they would have with earlier detection, which may affect prognosis. A model that assumed only a 6-month disruption of care during the pandemic estimated the potential excess deaths from breast and colorectal cancer secondary to the Covid-19 pandemic disruptions in care simply demonstrates an excess of more than 10,000 deaths in the next decade, peaking in the first few years.19
Analysis from a study in the UK suggests that the number of breast cancers diagnosed during the first half of 2020 is not as low as initially feared, and a substantial proportion of the shortfall can be explained by the suspension of routine screening in March 2020, thus underscoring the importance of the very first step of routine screening in this organ.20
CONCLUSION
We all need to gear up for the extra burden of this disease which is unaccounted for but highly anticipated for in this very common but curable disease. We all should have consensus guidelines to guide fair decision-making and developing empathic communication about these issues. Also, the effects of limiting care during this pandemic, and potentially in future crises, on both cancer specialists and patients should be carefully managed.
ACKNOWLEDGEMENTS: All the authors of this study have contributed to the scientific content and/or providing technical support. Further authors acknowledge the immense help received from the scholars whose articles are cited and included in references of this manuscript. The authors are also grateful to authors/editors/publishers of all those articles, journals and books from where the literature for this article has been reviewed and discussed.
Source of Funding: Nil
Conflict of Interest: Nil
Englishhttp://ijcrr.com/abstract.php?article_id=3799http://ijcrr.com/article_html.php?did=3799
Kumar V, Abbas AK, Aster JC, Turner JR. Robbins & Cotran Pathologic basis of disease. 10th ed. Elsevier: Elsevier Health Science; 2021.
Kolak A, Kami?ska M, Sygit K, Budny A, Surdyka D, Kukie?ka-Budny B, et al. Primary and secondary prevention of breast cancer. Ann Agric Environ Med. 2017;24(4): 549–553.
Printz C. When a global pandemic complicates cancer care: Although oncologists and their patients are accustomed to fighting tough battles against a lethal disease, Coronavirus Disease 2019 (COVID-19) has posed an unprecedented challenge. Cancer. 2020;126(14):3171–3173.
American College of Emergency Physicians COVID-19. 2020. Available at: https://www. emergencyphysicians.org/globalassets/emphysicia ns/all-pdfs/acep-mc-COVID19-april-poll-analysis.pdf. Accessed September 8, 2020.
Vanni G, Materazzo M, Pellicciaro M. Breast Cancer and COVID-19: The Effect of Fear on Patients’ Decision-making Process. Ann Agric Environ Med. 2020;34(3 Suppl):1651–1659.
Papautsky EL, Hamlish T. Patient-reported treatment delays in breast cancer care during the COVID-19 pandemic. Breast Cancer Res Treat. 2020:1-6.
Malhotra A, Wu X, Fleishon HB, DuszakJr R, Silvia E, Bender C, et al. Initial Impact of Coronavirus Disease 2019 (COVID-19) on Radiology Practices: An ACR/RBMA Survey. J Am Coll Radiol. 2020;17:1525-1531.
Gasparri ML, Gentilini OD, Lueftner D, Thorsten Kuehn, Kaidar-Person O, et al. Changes in breast cancer management during the Corona Virus Disease 19 pandemic: An international survey of the European Breast Cancer Research Association of Surgical Trialists (EUBREAST). The Breast. 2020;52:110-115.
Tsang-Wright F, Tasoulis MK, Roche N, MacNeill F. Breast cancer surgery after the COVID-19 pandemic. Future Oncol. 2020;16(33):2687-2690.
de Azambuja E, Trapani D, Loibl S, Delaloge S, Senkus E, et al. Management and treatment adapted recommendations in the COVID-19 era. Breast Cancer. ESMO Open 2020;5:e000793.
Pacino FAC, Ruiz CA, Sorpreso ICE, Costa AMM, Soares-Junior JM, et al. Management of benign and suspicious breast lesions during the coronavirus disease pandemic: recommendations for triage and treatment. Clinics. 2020;75:e2097.
Editorial. COVID-19: global consequences for oncology. Lancet Oncol. 2020;21(4):467.
Curigliano G, Cardoso MJ, Poortmans P, Gentilini O, Pravettoni G, et al. Recommendations for triage, prioritization and treatment of breast cancer patients during the COVID-19 pandemic. Breast. 2020;52:8-16.
Citgez B, Yigit B, Capkinoglu E, Yetkin GS. Management of Breast Cancer during the COVID-19 Pandemic. Med Bull Sisli Etfal Hosp. 2020;54(2):132–135.
Vanni G, Pellicciaro M, Materazzo M, Palombi L and Buonomo OC. Breast Cancer Diagnosis in Coronavirus-Era: Alert From Italy. Front Oncol 2020;10: 938.
Sud A, Torr B, Jones M, Broggio J, Scott S, Loveday C, et al. Effect of delays in the 2-week-wait cancer referral pathway during theCOVID-19 pandemic on cancer survival in the UK: a modelling study. Lancet Oncol. 2020;21(8):1035-1044.
IJzerman M, Emery J. Is a delayed cancer diagnosis a consequence of COVID-19?. 2020? Available at https://pursuit.unimelb.edu.au/articles/is-a-delayedcancer- diagnosis-a-consequence-of-covid-19. Accessed September 9, 2020
Kaufman HW, Chen Z, Niles J and Fesko Y. Changes in the Number of US Patients With Newly Identified Cancer Before and During the Coronavirus Disease 2019 (COVID-19) Pandemic. J Ame Med Ass 2020; 3(8)e2017267.
Sharpless NE. COVID-19 and cancer. Science 2020;368(6497):1290.
Gathani T, Clayton G, Macinnes E and Horgan K. The COVID-19 pandemic and impact on breast cancer diagnoses: what happened in England in the first half of 2020. Brit J Cancer. 2021;124:710–712;
Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareA Real-Time Deep Transfer Learning-Based Classification and Social Distance Alert Framework Based on Covid-19
English8692Anurag SinghEnglish Naresh KumarEnglish Tapas KumarEnglishIntroduction: Covid-19 is a novel virus that has exponentially increased the number of infected persons and the death of human beings into millions within a few months. This virus spreads when a person comes into contact with another person, coughing, sneezing and droplets. Objective: To avoid loss of lives, human direct assistance and early precaution, automated systems required for reducing the number of cases. Deep Learning can facilitate human life much better way by automating human visual intelligence into machine intelligence. Methods: In this novel research work, we are implementing transfer learning methodology to improve the learning of a related objective task on top of base deep learning model in developing a mask/non-mask detection model along with changing the hyperparameters and data augmentation technique by using less input dataset for smart healthcare, smart home in reducing and detecting corona cases. Results: We used object detection model Single Shot Multi-box Detector and classification model mobile net, which achieved significant accuracy and much faster for both training and inference with prediction accuracy of 87% with IOU=.75 on our own created trained dataset comparable with other real-time object detection model such as Faster Regional Convolutional Neural Network by tuning the hyperparameters. Conclusion: The automated system not only reduces the false alarm but also enhanced the performance accuracy by detecting the mask and non-mask due to which the number of covid-19 cases can be reduced at an early stage.
English
INTRODUCTION
World at this stage facing a huge pandemic due to the novel coronavirus Covid-19. Millions of peoples are suffering and millions of people have lost their lives due to this virus. The main guidelines by various health agencies are to keep social distancing, wearing a mask and remain in isolation. But still, people are not following the guidelines and no proper precautions, due to which the number of cases is increasing exponentially.1
Governments using surveillance drones to capture crowd and tracking of human in public places to avoid mass gathering which are initial steps to fight against covid-19. Currently, these devices require human assistance to take decisions. For immediate action and accuracy in detection, technologies will play a vital role in the smart healthcare area to avoid the spreading of covid-19 in the community.2-5
For the last many years, many researchers have worked on machine learning and computer vision-based algorithms for multiple applications such as driverless cars, healthcare systems, agriculture domain, medical imaging, surveillance and various automation systems. For such applications, Deep Learning (DL) and Machine Learning (ML) are the key domains, which are inspired by the human brain for classifying and solving complex problems.6-8 For all vision-based applications, computer vision plays a vital role in bridging a semantic gap between human understandings with the help of tagging and labelling the images in identifying the objects in real-time scenario.9
One of the major areas which require more attention and deployment is the advanced surveillance system for smart healthcare systems, smart home security and defence systems but still, the surveillance field is facing some accuracy issues in identifying potential mask, classification and requires more training time to train the machine learning models along with huge datasets.
These topics are closely related to Human-Computer Interaction (HCI), which has developed more interaction between human and computer using Computer Vision (CV) framework and increased its popularity in both academics, healthcare and surveillance systems.10 Human being perceives information from sense organs and its perception varies from person to person in terms of features such as shape, colour and size. Based on the learning and experience, the human being classifies the object and annotates it either in the form of memories or it makes a dataset using hands and verbal responses for tagging the image frames.11,12
All the features in traditional object detection methods are handcrafted and shallow trainable, due to which it has restrictions for a few application and detections while testing. Whereas in deep learning the features and classification are done automatically by the neural network but it required more training with a huge amount of data along with more computational time for training the machines.13,14
Literature Survey
Deep Learning Model in Object Detection
Image annotations and tagging are tedious and time taking task required in computer vision related applications by researchers. The majority of tagging is used in large scale retrieval system, managing and organizing multimedia databases which are done manually. Classification of video is more focused on labelling of video clips dependent on their semantic substance such as human activities or complex events. Detection and recognition of objects are carried out by researchers in multiple ways.15
Feature-Based Object Detection.
Viola-Jones Object Detection.
Support Vector Machine (SVM) Classifications with Histogram Orientation Gradient (HOG) & HAAR like features.
Object Detection using deep learning.
But still, the performance of computer vision algorithm lags in many key domains, due to variations in viewpoints, different postures, occlusions and lighting conditions, which is difficult in accomplishing the object detection within the localization process (objects positions are located in a given image). Hence, researchers have more focused on this field in developing various applications by optimizing the algorithms and hyperparameters for better accuracy with less computational process and training.
In this exploration work, the objective is to simulate the human patrolling and decisions system into smart surveillance in detecting potential mask who have not to wear the mask in crowd area and mass gathering with the help of deep learning model and computer vision to replace man operated patrolling with advance automated surveillance system that can be deployed in smart healthcare, smart home for automated mask detection and defence applications, that can be installed at railway stations, Hospitals, Airports, Malls etc to avoid the spread of this virus. Our novel contribution in the research work is the use of very little dataset and optimization of hyperparameters for training the deep learning model using data augmentation technique and detecting the mask/non-mask person, bounding box, captions generation and human pose detection with high accuracy which can be done using transfer learning techniques, which is still not carried out by any researcher in this domain.
In 2012, Convolutional Neural Network (CNN) came into the picture which can represent high-level features and robustness. In deep learning, model object detection grouped into two-stage detection and one-stage detection. Abnormal behaviour detection in a smart surveillance system that majorly discussed in three sections. 16
Human detection and discrimination for a subject
Module-based on posture classification
Module-based on abnormal behaviour detection
And the models used for the above three sections are as follows
You only look once (YOLO) network
VGG-16 Net
Long short-term memory (LSTM)
Single Shot multi-box Detection (SSD)
Researchers have discussed more intelligent video surveillance using deep learning techniques for crowd analysis.17 Deep Learning techniques provides two major components; training the model and testing the model, which required a huge amount of data for better accuracy. Researchers have discussed in brief about recognition of the person and object detection automatically and deduction of complex events in two ways; low level and high level. People and object detection done under low level whereas the detection from low to high used for event detection.18
Event modelling
Action modelling
Detection of action
Modelling of complex events and detection
The above four are the major architecture used in modelling and detection. Whereas recognizing a system involves:
Pre- Processing
Feature Extraction
Object Tracking
Understanding of behaviour
The researcher has used a similar method in avoiding suicide attempts in prison using an RGB-D camera and analyzing the body joints which represents the suicidal behaviour. It was suggested that CNN is a better improvement than traditional Neural Network. In the paper computational resources were reduced using dimensionality reduction which happens in reducing computation of 1x1 Convolutional before 5x5 Convolutional.19-22
The selection of the best model was carried out based on the high mean Average Precision (MAP) used for the evaluation of the test dataset. As a result, the use of Faster R-CNN along with VGG-16 shows better performance in detecting drones but requires more computational power.23 Most of the common object detection system follows the following pipeline:
Potential Object Detection
Bounding Box
Feature Extraction
Classify using good Classifier
Following are the datasets for object detection used by many researchers for object detection in deep learning.
PASCAL Visual Object Class (PASCAL VOC)
ImageNet Large Scale Visual Recognition (ILSVR)
Microsoft Common Object in Context (MS COCO)
In the current scenario, two famous object detection algorithms are the centre of attraction among the researchers for applications based on real-time object detection, which was achieved by You Only Look Once (YOLO) and Single Shot MultiBox Detector. YOLO object detector completely works on region proposal and sliding window-based approach which has divided the images into a grid of cells and each cell predicts the class and bounding box of the object which provides final accuracy for the object class.
Whereas Single Shot MultiBox Detector is completely followed on feed-forward CNN that generates fixed size bounding box along with confidence scores of every class and produces final detection results. Regional based Convolution Neural Network (R-CNN) model is not able to achieve real-time object detection because of its time taking training process and inefficiency of region proposition. Whereas YOLO was developed for object detection and classification which requires single-step process. After 1 input image, it starts evaluating and predicting the class along with the bounding box. YOLO architecture is capable of achieving 45 FPS and YOLOv2 can achieve 244 FPS on CUDA GPU. The simultaneous process of bounding box prediction and class prediction makes YOLO different from other traditional systems.
YOLO and single-shot multi-box detector takes input images and divides them into the grid of S x S and defines a bounding box at each grid cell with a confidence score which is a probability of an object existing in each bounding box discussed in the equation. (1) where IOU is intersection over union and represent the fraction between 0 and 1.Its is an overlapping area between the predicted bounding box and ground truth and it should be close to 1.
Similarly, Class probability C also gets predicted for each grid cell simultaneously for the bounding box and class-specific probability for each grid cell is defined as (2):
Transfer learning enables us to utilize knowledge from previously learned task and applying it to the new model as per research requirement for better performance. As most of the traditional machine learning algorithms are performs a specific task and requires a lot amount of data for training the model which requires more computation and training from scratch. By use of transfer learning the feature, weights can be used for other applications with less computation and data.
There are two principal ways to deal with executing move learning; they are:
Weight Initialization.
Feature Extraction.
The weights in re-utilized layers might be utilized as the beginning stage for the preparation procedure and adjusted in light of the new issue. This use treats transfer learning as a kind of weight initialization scheme. This might be helpful when the principal related issue has significantly more marked information than the issue of intrigue and the likeness in the structure of the issue might be valuable in the two settings.
The task t1 from the pre-trained model (weights, features) used for task t2 with fewer data.
Instead of training the CNN model from scratch for our mask warning system, we are using a pre-trained model initially which helps us in detecting mask and mask-based object for our domain and task.
A framework of transfer learning:
D is the domain that defines two elements; X a sample data point and P(X) marginal data point.
For a given domain D, a task is defined by:
Label space: y
A predictive function: η
Learned from feature vector/label pairs xi,yi where
For each feature vector in the domain, η predicts its label η(xi)= yi
MATERIALS AND METHODS
In this research work, our novel contribution is to train the deep learning model with a one-shot training technique on images generated from real-time video cameras which are comprised of mask and non-mask faces along with detection of crowds and mass gathering, The trained model will play a vital role in avoiding corona covid-19 type epidemic in society at an early stage and currently have not used by any researcher. We have also optimized and fine-tune the hyperparameters for the pipeline created for this covid-19 application with the help of an inductive transfer learning technique that reduces the overfitting problem on the pre-trained model as a base task over our trained model displayed in Figure 1.
We have optimized the basic hyperparameter that is the dropout value which is also known as the regularization technique in optimizing the model. Initially, we have used TensorFlow and OpenCV for training a pre-trained model and detecting objects in a real-time surveillance system with the help of object detection algorithm single shot multi-box detector and Mobilenet as classification model over MSCOCO dataset with common features from 90 classes and can identify and performs multiple bounding boxes around the objects and compared with Faster Regional Convolutional Neural Network, which is more advance than a single-shot multi-box detector in terms of the number of classes and categories. But the major issue while training a Faster Regional Convolutional Neural Network model was more computational time taking than that of a single-shot multi-box detector model.
In the system, the number of classes/labels was changed to 2(Mask/Non-Mask) and the number of CNN layer was changed to 6 with RELU as activation function with minimum depth 16, batch size 128 and learning rate of 0.004 and 0.04, RMS prop for gradient descent on own dataset with 400 training examples for a person with face covered with cloth pretend to be a mask and not mask. For better performance of machine learning algorithms, hyperparameters play a key role. The followings are the hyperparameters, which are used in enhancing the accuracy of the deep learning-based object detection model.
Table 1 Discuss the hyperparameters and their values are set before the training process in optimizing the performance of the model. Apart from the above parameters number of epochs, dropouts, hidden layers and units, Activation functions are also part of hyperparameters in building accuracy of the model. Optimization of the learning rate is done because it controls the weights after each batch size which helps our model to learn fast and accurate with minimization of the loss function. Optimization of batch size requires less memory because we are training our model on fewer sample data, So a large amount of data cannot be fit into our memory. So small batch size plays an important hyperparameter for the deep learning model so that weights can be updated after each propagation.
Along with the use of the transfer learning technique, we have used the data augmentation technique which helps our model to overcome the use of a large dataset as a basic need of the deep learning model required. We have used fewer data and this technique fulfils our requirement in converting it to a large dataset with different viewpoint such as Rotating images, Flipping, padding, cropping and transformation with change in colours like brightness, saturation, hue and contrast for training our deep learning model. For data augmentation, we have changed the code of our deep learning model and incorporated the data augmentation codes.
RESULTS
In the figure 2, graph shows the relationship between loss and number of steps. The hyperparameter which controls the values of weights in deep CNN are adjusted in our network concerning loss gradient are known as learning rate in terms of localization loss discussed in figure 2. In table 2, it shows the accuracy of the model varies for different learning rate and value of learning rate determines the travel rate along the slope of the function i.e. if the value is low we move slowly along the slop and high value of learning rate results in a faster movement along the slop. So, while deciding the learning rate value we need to be careful so that we do not miss any local minima. To ensure the coverage of local minima the low learning rate is preferable but at the same time, it would take a longer time to converge. So to keep both aspects in mind, we start training with a relatively large learning rate. The reason behind selecting large learning is that the initial random weight assigned to the network is far away from the actual optimal value. During the Figure 3 discuss the comparison results between two object detection model single shot multi-box detector and Faster Regional Convolutional Neural Network, which shows the model is trained properly as compared to loss functions.
The training loss is done with the help of the loss function which a method to describe how a particular algorithm earns using a loss function (5) (6). It’s a method of evaluating how well specific algorithm models the training data. In figure.4, we have observed that the initial loss value is quite high and it gradually decreases and approaches to 1 after a certain step. It means that a decrease in loss leads to an increase and accuracy in classification.
Initially, the predicted value deviated too much from the actual value that is why the initial loss value is too high. We used the cross entropy loss function which gives a better convergence rate as compared to other loss function hinge. In classification, it is tried to predict output from a set of finite categorical values that are given large data set of images of mask and non-mask, categorizing them into mask class and non-mask class.
DISCUSSION
The implemented transfer learning technique and experimental results in figure.5., shows the success of the unmanned threat warning system and detection of the mask to avoid covid-19 with accuracy mAP 87% on testing validation using own tuned single shot multi-box detector model on top of the pre-trained model. Whereas Faster RCNN is taking too much time in training and reaching the accuracy and takes a lot of time while detecting the mask. Overall, the proposed single shot multi-box detector system shows improved accuracy, learning rate, classification loss at the time of training of a model and takes less time in training than that of training the model from scratch and compared to the Faster RCNN model which takes more time and makes it computationally efficient. Therefore, any type of threat can be annotated by the model and can perform automatic mask detection and performs better with IOU=.75 on our dataset, which shows the successful implementation of transfer learning techniques and optimization/tuning of hyperparameters on the single-shot multi-box detector_mobilenet_v1 model. Our adopted training strategies lead to improved performance in choosing appropriate bounding box, sampling of various location, scaling and aspect ratio than that of existing methods.
The future work will be more focused on improvising the model with different techniques such as loss function and fine-tuning the optimizer on the different pre-trained model for enhancing the performance along with IoT devices and sensors.
ACKNOWLEDGEMENT
I would sincerely thank my Guide, Supervisor and research mates for their motivation and guidance.
Conflict of interest: There is no conflict of interest
Source of Funding: Nil
Englishhttp://ijcrr.com/abstract.php?article_id=3800http://ijcrr.com/article_html.php?did=3800
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareStress in Nursing Professionals Who Work in Three Hospital Institutions That Care for Patients with COVID-19 In Lima, Peru
English9397Rosa PSEnglish Hernan MSEnglish Eduardo MSEnglish Anika RAEnglishBackground: Stress in nursing professionals is one of the predictors generated by the care patients with COVID-19 and their ability to cope is important to provide good care. Objective: To determine stress nursing professionals who work in three Hospital Institutions that care for patients with COVID - 19 in Lima. Methods: It is a quantitative, non-experimental, descriptive, cross-sectional study with a total population of 329 nursing professionals, who answered a survey with sociodemographic data and the Nursing Stress Scale. Results: We observe that 97 (29.5%) of nursing professionals present low stress, 173 (52.6%) medium stress and 59 (17.9%) high stress. Conclusion: It is concluded, the detection of mental and emotional problems will allow the development of prevention strategies to protect the psychological well-being of nursing professionals.
EnglishStress, Nursing professional, Pandemic, Coronavirus, Work stressIntroduction
The new coronavirus disease (COVID - 19) that has affected the entire world has become one of the large-scale health challenges during these times for health professionals who are in the first line of care for patients with COVID–19,1 and as a result, many of the health professionals, due to the high demand for patients, the excessive hours of work and the environment in which they are found, have affected their mental health.2,3
Because of the high transmissibility and expansion of COVID – 19, it is considered one of the high occupational risks in health professionals, especially in nursing professionals,4,5 since they are professionals vulnerable to experiencing acute stress disorder because they are in the first line of care for COVID-19,6 where they are in direct contact 24 hours a day for their care, and therefore stress, exhaustion and overload work negatively influence their care.7,8 For this reason, the mental health of nursing professionals is becoming more vulnerable,9 since the cases of COVID - 19 today continue increasing, where the supply found in hospitals is no longer sufficient,10 where the high demand for care causes nursing professionals to increase their stress levels, bringing consequences that affect both personal and occupational levels.11
Therefore, to counteract stress in nursing professionals in the first line of care of patients with COVID-19, it is important to seek support strategies so that they can cope with it and be able to reduce the stress and work exhaustion that occurs in them,10 not only to improve on a personal level but also to improve their care and in their work environment.12 In a study carried out in Egypt,13 it was observed in 374 nursing professionals, in their results of stress due to COVID - 19, 13.4% presented mild stress, 26.2% severe stress and 52.1% moderate stress, interpreting that factors such as fear of infection, the work environment, fear of infecting the family, are predictors of increased stress in nursing professionals. In a study carried out in Spain,14 with 605 professional nursing participants, they stated about stress, that exhaustion and emotional overload in younger and less experienced professionals were witnessed higher levels of stress compared to professionals who already had more than 5 years of experience. In a study carried out in Jordan,15 with 448 professional nursing participants, in its results it was observed that 64% presented an acute stress disorder (ASD) and 41% presented psychological suffering, where the younger nurses were more vulnerable to perceive psychological anguish and due to this, while the perception of psychological anguish increases, the levels of ASD becomes higher.
Therefore, the objective of the study is to determine stress in nursing professionals who work in three Hospital Institutions that care for patients with COVID - 19 in Lima. Research hypothesis is that stress compromises the mental health of the nursing profession, making them more susceptible to being infected by COVID - 19.
MATERIALS AND METHODS
Type of Research
The research for its properties is quantitative, its methodology is descriptive, not experimental, cross-sectional. 16
Population
The population is made up of a total of 329 nursing professionals.
Inclusion criteria
Nursing professionals who have been working for 1 to more years
Nursing professionals who work only in hospital centres
Nursing professionals who have voluntarily agreed to be present in the study and who signed the consent informed ACTA N°048-2020-CE/UMA UNIVERSIDAD MARIA AUXILIADORA.
Technique and Instrument
A questionnaire was carried out, in which the data instrument The Nursing Stress Scale (NSS) is written. It has been structured as follows:
In the first block are the sociodemographic data such as age, sex, marital status, type of family and years working and in the second block is the NSS instrument that comprises 34 items divided into 3 dimensions, in the Physical Environment dimension it consists of 6 items, Psychological Environment with 18 items and Social Environment with 10 items, which is assessed with a Likert-type scale with 4 response options: “0 = Never”, “1 = Sometimes”, “2 = Frequently” and “3 = Very frequently ”, in which a total score of 0 to 102 points is obtained, where“ 0 to 34 ”is a low-stress level,“ 35 to 68 ”is a medium stress level and“ 69 to 102 ”is a high-stress level, the higher the score, the higher the level of stress in nursing professionals.
The validity of the instrument to measure stress was determined based on the exploratory factor analysis technique. The Kaiser-Mayer-Olkin sample adequacy measure obtained a coefficient of 0.956 (KMO> 0.5), while the Bartlett sphericity test obtained significant results (X2 approx. = 7783.760; gl = 561; p = 0.000).
The reliability of the instrument was determined based on the Cronbach's Alpha statistical test, for all the items (i = 34), resulting in a coefficient of 0.965 (α> 0.8).
The data collection processing was carried out in a data matrix designed in the statistical program IBM SPSS Statistics Base 26.0, its corresponding analysis was carried out, in which it will allow us a better data processing for the realization of tabulations and figures that will be described and interpreted in results and discussions, respectively.
Instrument location and application
The questionnaire was carried out to measure stress in nursing professionals who work in three hospital institutions in Lima in the Agustino district. First, we coordinate with each of the nursing professionals so that they voluntarily agree to be participants in the research work, in addition to being detailed about the study. After that, the questionnaires were carried out, where the support of each of the nursing professionals was seen since it was satisfactory at the time of data collection.
Results
In Figure 1, we can see that 97 (29.5%) of nursing professionals present low stress, 173 (52.6%) medium stress and 59 (17.9%) high stress.
In Figure 2, we observe that in the nursing professionals in the physical environment dimension, 87 (26.4%) present low stress, 178 (54.1%) medium stress and 64 (19.5%) high stress.
In Figure 3, it is observed that in the nursing professionals in the psychological environment dimension, 102 (31%) present low stress, 167 (50.8%) medium stress and 60 (18.2%) high stress.
In Figure 4, it is observed that nursing professionals in the social environment dimension, 143 (43.5%) present low stress, 159 (48.3%) medium stress and 27 (8.2%) high stress.
In Figure 5, it is observed in nursing professionals concerning sex, where 14 (21.9%) of the male sex present low stress and in the female sex 83 (31.3%), in medium stress 44 (68.8%) are male and 129 (48.7%) female and in high stress 6 (9.4%) male and 53 (20%) females.
Figure 6 shows the relationship of stress with the work condition of nursing professionals, where 62 (23.9%) of the hired professionals present low stress and 35 (50%) in appointed professionals, 139 (53, 7%) of the professionals hired present medium stress and 34 (48.6%) are appointed professionals; and 58 (22.4%) of the hired professionals present high stress and 1 (1.4%) in appointed professionals.
In Figure 7, the relationship of stress with the years of service of nursing professionals is observed, where it is observed that in nursing professionals with 1 to 5 years of service, they are those who present the most stress, 40 (29.2 %) high stress, 61 (44.5%) medium stress and 36 (26.3%) low stress.
Discussion
This study is carried out from the mental health perspective in the prevention of stress in nursing professionals who are in the first line of care in the face of COVID-19, were coping with stressful situations is becoming higher. The development of strategies that reduce stress will be of great help at a personal and work level in nursing professionals.
In the results regarding stress in nursing professionals, we can observe in the results that most nursing professionals present medium stress, we can interpret that the nurse is being mentally affected by COVID - 19, not only due to the fact of the attention and the increase in infections that are becoming more noticeable, even factors such as the work environment, the excessive demand of patients, the excessive workload due to overtime, makes nursing professionals, the level of stress tends to increase dramatically and, over time, stress contributes to serious health problems, either at a cardiac or mental level in professionals. The authors argue that the increase in stress in health professionals, especially in nurses, is due to the excessive burden of patients for a single nurse, where care is not done effectively, being done every time more noticeable, since the fear of being infected by treating COVID-19 patients causes the nurse to become mentally overloaded, generating stress.13
Concerning its dimensions, we can observe that in its physical, psychological and social environment dimension, nursing professionals show a medium level of stress, this is interpreted that in nursing professionals factors such as general well-being, work environment, the presence of any mental or physical illness, lack of concentration and the alteration of relating to others, causes stress levels to increase, therefore, the mental pressure present in nursing professionals brings consequences since their ability to cope is seen hindered, and therefore it will be difficult for them to relate to others. They argue that factors such as exhaustion and emotional overload in professionals make their environment where they work become increasingly difficult, where the relationship of nurse-patient, communication and quality care is interrupted due to stress.14
About sex, we can observe that the female sex presents medium stress, where we can interpret it that the way of responding to stress is different in both sexes, in the female sex that implies being more emotional and wearing out more with emotional stress, due to the difference in roles in the workplace, where the role of care and empathy, makes them more prone to stress. They argue that women are vulnerable to stress because their mental health is more emotional and sentimental with the patient.15
Concerning the years of service, we can observe that professionals who have experienced no more than 5 years present medium stress, this is because the incoming professionals, due to the little experience they present in caring for COVID-19 patients, makes them more susceptible to presenting symptoms of stress, because the management of equipment and advanced care in patients infected by COVID - 19 cannot handle it or they do not understand it, since the care of COVID - 19 patients and the high demand of themselves makes it increasingly susceptible to symptoms of stress. The authors argue that newly admitted nursing professionals, being young and lack experience makes it more related to increased stress since the management of COVID-19 patients is something new for them, and who feel the fear of providing inadequate care, where also, the fear of catching it makes them more susceptible to stress.10
Conclusions
Mental health problems in nursing professionals on the first line of care for COVID - 19 need care that seeks to strengthen at the psychosocial level to improve their mental resilience. The detection of mental and emotional problems will allow the development of prevention strategies to protect the psychological well-being of nursing professionals. An intervention program for stress management should be carried out in nursing professionals who care for COVID-19 patients. The research work will be beneficial both for nursing professionals and for other studies in our country since it will allow observing in other regions how nursing professionals are at a mental level during the care of COVID-19 patients.
Conflict of Interest: The authors declare no conflict of interest.
Funding Source: This research work doesn’t have Funding Sources
Acknowledgement: The authors acknowledge the immense help received from the scholars whose articles are cited and included in references of this manuscript. The authors are also grateful to authors/editors/publishers of all those articles, journals, and books from where the literature for this article has been reviewed and discussed.
Author’s Contributions
Rosa PS: Conceived and designed the analysis, wrote the paper and translation.
Hernan MS: Collected the data, Performed the analysis.
Eduardo MS: Contact the people for the survey-taking.
Anika RA: Contributed data and analysis tools.
Figure 7: Stress in relation to years of service in Nursing Professionals who work in three Hospital Institutions that care for patients with COVID-19 in Lima
Englishhttp://ijcrr.com/abstract.php?article_id=3801http://ijcrr.com/article_html.php?did=38011. Hou T, Zhang R, Song X, Zhang F, Cai W, Liu Y, et al. Self-efficacy and fatigue among non-frontline health care workers during COVID-19 outbreak: A moderated mediation model of posttraumatic stress disorder symptoms and negative coping. PLoS One. 2020;15:1–16.
2. Aslan H, Pekince H. Nursing students’ views on the COVID-19 pandemic and their perceived stress levels. Perspect Psychiatr Care. 2020;1(7):128.
3. Walton M, Murray E, Christian M. Mental health care for medical staff and affiliated healthcare workers during the COVID-19 pandemic. Eur Hear J Acute Cardiovasc Care. 2020;9(3):241–247.
4. Arnetz JE, Goetz CM, Arnetz BB, Arble E. Nurse reports of stressful situations during the COVID-19 pandemic: Qualitative analysis of survey responses. Int J Environ Res Public Health. 2020;17(21):1–12.
5. Ferreira V, Yuri T, Pereira A. Dificultades y temores de las enfermeras que enfrentan la pandemia de COVID-19 en Brasil Difficulties and fears of nurses facing the COVID-19 pandemic in Brazil. Humanidades Médicas 2020;20(2):312–333.
6. Yan S, Xu R, Stratton T, Kavcic V, Luo D, Hou F, et al. Sex differences and psychological stress: responses to the COVID-19 pandemic in China. BMC Public Health. 2021;21(1):1–8.
7. Carrasco O, Castillo E, Salas R, Reyes C. Estresores laborales y satisfacción en enfermeras peruanas durante la pandemia de COVID–19. SciELO Prepr. 2020;1(1):1–14.
8. Dos Santos L. Stress, Burnout, and Low Self-Efficacy of Nursing Professionals: A Qualitative Inquiry. Healthcare. 2020;8(4):424.
9. Salari N, Khazaie H, Hosseinian A, Khaledi B, Kazeminia M, Mohammadi M, et al. The prevalence of stress, anxiety and depression within front-line healthcare workers caring for COVID-19 patients: a systematic review and meta-regression. Human Resour Health. 2020;18(1):1–14.
10. Zhang Y, Wang C, Pan W, Zheng J, Gao J, Huang X, et al. Stress, Burnout, and Coping Strategies of Frontline Nurses During the COVID-19 Epidemic in Wuhan and Shanghai, China. Front Psychiatry. 2020;11:1–9.
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareAssociation of CRP Levels as an Inflammatory Marker in Prognosis of COVID-19 Cases
English98100Sharanya KEnglish Lakshmi KEnglish Vinod KEnglish Chitralekha SEnglishIntroduction: The pandemic COVID-19 is an important global threat because of its high infectivity and case fatality rate in high-risk patients. The studies related to physiological mechanisms, pathogenesis, effective laboratory diagnostic methods are still in investigative stages. Several studies on COVID-19 report that inflammatory reactions play a vital role in disease progression. Objective: In this study, we evaluated the association between the C-Reactive Protein (CRP) and the severity of COVID-19 pneumonia. This study would help the clinicians to monitor the prognosis and severity of the disease. Methods: A Retrospective study conducted in the Department of Microbiology over 3 months with 336 laboratories confirmed Corona positive patients. Blood samples were collected from these patients and CRP was estimated by the Latex Agglutination test. Results: Of the 336 patients, 142 (42.2) showed positive results for CRP. Among CRP positive cases, 67 showed high CRP(>24mg/L) and 75 showed low CRP(≤24mg/L). High CRP was found to be associated with increased severity of the illness. Conclusion: CRP may be a marker of disease severity and may be a valuable indicator for determining the severity of patients with COVID-19. Continuous CRP monitoring may also predict the prognosis of cases. Further investigations are required to demonstrate the mechanisms by which increased CRP is seen in patients suffering from SARS- CoV-2.
EnglishCRP, Covid-19, Inflammatory marker, Corona Virus, PandemiINTRODUCTION
Coronavirus belongs to the family Coronaviridae and order Nidoviridales. An unknown viral outbreak has been reported in December 2019, which was later diagnosed to be SARS – CoV2. The pandemic COVID-19 is an important global threat because of its higher infectivity and case fatality rate in high-risk patients.1The studies related to physiological mechanisms, pathogenesis, effective laboratory diagnostic methods are still in investigative stages. Appropriate monitoring and proper follow up of treatment strategies are important in the clinical improvement of the cases. Computerized Tomography (CT) scan plays an important role in the assessment of the disease. However, some patients do not show any hypoxemia or respiratory distress which further indicates that the disease is multifaceted.
Several studies on COVID-19 report that inflammatory reactions play a vital role in disease progression.2,3 Several inflammatory markers have been studied to detect the severity and fatality of COVID-19.4 Some of the inflammatory markers such as Procalcitonin, serum ferritin, C reactive protein (CRP), Erythrocyte Sedimentation Rate (ESR) are associated with the severity of COVID-19 disease.5,6 Many studies in the recent past have reported that CRP is positively associated with dengue infection indicating that CRP monitoring is one of the reliable biomarkers in predicting the severity of viral infections.7,8
We assume that CRP can be used in monitoring the prognosis of the COVID-19 patients. CRP levels help in the early diagnosis of the cases. Patients with severe infection are often associated with high levels of CRP. In this study, we evaluated the association between the CRP and the severity of COVID-19 pneumonia. This study would help the clinicians to monitor the prognosis and severity of the disease.
MATERIALS AND METHODS
Study design and participants
This is a retrospective study of 336 adult patients (males and females) admitted with laboratory-confirmed COVID-19. The study was conducted in the Department of Microbiology, Sree Balaji Medical College and Hospital (Bharath Institute of Higher education & Research), Chennai, Tamil Nadu, India over 3 months (June 2020 to August 2020). Institutional ethical committee approval was obtained.
Procedure
On admission, the patients with complaints of cough, chest pain and other respiratory or digestive symptoms with or without fever were screened by RT PCR for SARS- CoV-2. Only those who were RT PCR positive for SARS-CoV-2 were included in the study. 2-3ml of Venous blood samples were collected from all these patients in a sterile tube without anticoagulant for estimation of CRP. The blood was allowed to clot at room temperature. Serum was separated after centrifugation at 3500 rpm for 10 min. The test was performed immediately using the Qualitative and Semi-Quantitative Rapid Latex slide agglutination test.
Qualitative slide test
The test serum was placed within the circled area on the special slide provided in the kit to which a drop of CRP latex agent was added. Both were mixed well and the slide was gently rocked for 2 minutes and noted for macroscopic agglutination.
Interpretation
Coarse agglutination - Strongly positive
Finer agglutination – Weakly positive
Smooth suspension without any noticeable change – Negative
Semi-Quantitative Slide test
A series of dilutions of a test serum in normal saline (eg:1:2,1:4,1:8 etc) was prepared. One drop of CRP latex reagent was added to them and observed for agglutination for 2 minutes. The highest dilution which shows agglutination is taken as the CRP titre of the test serum.
Interpretation
The highest dilution that gives agglutination was multiplied with a factor of 6 (sensitivity of antigen 6 microgram/ml)
Once CRP was positive, they were categorized into High (>24mg/L) and low CRP (Englishhttp://ijcrr.com/abstract.php?article_id=3802http://ijcrr.com/article_html.php?did=3802
Chen N, Zhou M, Deng X, Qu J, GongF, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan. China: a descriptive study. Lancet. 2020; 10223(395): 507–513.
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Gao Y, Li T, Han M, Li X, WuD, Xu Y, et al. Diagnostic utility of clinical laboratory data determinations for patients with the severe COVID-19. J Med Virol. 2020;92(7):791-796.
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcarePrevalence of Psychological Distress Among Undergraduate Medical Students During the Covid 19 Pandemic and Their Perception of the Influence of the Pandemic on Their Academics
English101104Chandra SubhashEnglish KP LakshmiEnglishBackground: Undergraduate medical students have been affected by the COVID 19 pandemic and are under psychological distress. Objective: To assess the prevalence of psychological distress among MBBS students using the K10 questionnaire and to assess the influence of the COVID 19 pandemic on the perception of the students on how it has affected their academic activities and skills Methods: The study design was a cross-sectional observational study. A two-section questionnaire was sent to MBBS students of all five years studying currently in the institution. The first section: K10 Questionnaire to assess psychological distress and the second: assess the perception of the MBBS students on how the pandemic has influenced their skills and academics. Participation in the study was voluntary. Results: Among 123 students who participated prevalence of psychological distress was 67.5 % (n=83). The mean K10 score was 24.46±7.91. 33.3 % of the students had severe psychological distress. 82 % of the students had a perception of reduced clinical skills due to the lockdown and 66 % felt that the pandemic would lead to an extension of their courses. Conclusion: There is a high prevalence of psychological distress among medical undergraduate students during the pandemic. There is an increased perception of reduced clinical skills and possible course extension among undergraduate students.
EnglishCOVID19, K10 score, Psychological distress, Clinical skills, Undergraduate studentsIntroduction
An outbreak of pneumonia of unknown aetiology, which was identified first at Wuhan city in Hubei province of China in December 2019, was caused by a new strain of coronavirus named as 2019 novel coronavirus (2019-nCoV) or SARS-CoV 2.1 This virus rapidly spread across the globe, and subsequently a pandemic was declared on March 11, 2020.2 Since its global recognition, the novel coronavirus disease (COVID-19) pandemic has spread to over 200 countries in less than five months.3 In India, the first case of COVID-19 was reported on January 30, 2020.4 As of February 7, 2021, 10,827,314 laboratory-confirmed cases and 1,55,032 deaths were reported from India. The case reporting is based on the testing of individuals by real-time reverse transcription-polymerase chain reaction (RT-qPCR).5 It is performed by taking nasopharyngeal swabs or throat swab or saliva. A range of RNA target gene is used for detection by different manufacturers, mostly targeting the envelope (E), nucleo-capsid (N), spike (S), RNA-dependent RNA polymerase (RdRp), and ORF1 (Open reading frame)genes.6
In COVID-19 infected symptomatic individuals, viral load can be detected early from day one of symptoms and it peaks within the first seven days of onset of symptoms. This viral load can be measured by the cycle threshold (Ct) value, which is the number of replication cycles required to produce a fluorescent signal. Thus lower Ct values representing higher viral RNA loads. The positivity of the samples starts to decline by 3rd week and subsequently becomes undetectable.6 If only one SARS CoV-2 target gene is detected in the test (multiplex SARS CoV-2 RT PCR test) with valid internal control, the result should be interpreted as Inconclusive and repeated the test.7 Inconclusive SARS CoV-2 reverse transcription-polymerase chain reaction (RT-PCR) reports for the detection of infection in symptomatic patients or during the screening of asymptomatic contacts can cause clinical, diagnostic and infection control uncertainty.8
This study aims to analyze inconclusive results of samples tested at our Centre based on Ct value, duration of symptoms at the time of testing and the results of repeat testing.
Materials and Methods
The study was carried out over 94,443 suspected symptomatic COVID 19 patients as well as asymptomatic groups, such as high-risk contacts or high-risk healthcare workers, as per ICMR guideline9 from June 2020 to December 2020 at COVID laboratory in the Department of Microbiology of Nil Ratan Sircar Medical College & Hospital, Kolkata.
Sample collection & transport
Nasopharyngeal swab and throat swab were collected in Viral transport media from suspected cases in different wards and designated COVID wards of NRS Medical College & Hospital as per ICMR guidelines10 and sent to our Laboratory.
Samples from other district and rural hospitals were also sent to our laboratory as stated by the West Bengal Department of Health and Family Welfare updated from time to time.
Testing and collection of Data
All the samples were processed in our Laboratory as per standard protocol and tested by quantitative reverse transcriptase PCR test with the kits supplied by ICMR and the state Health Department. Results were analyzed thereafter and all data were collected.
Results
A total of 94,443 samples were tested in the COVID laboratory of Nil Ratan Sircar Medical College & Hospital, Kolkata for a duration of seven months (June 2020 to December 2020). 8,455 samples were found to be inconclusive (Table 1).
All inconclusive samples were subjected to re-testing (from repeat samples) after 3 days. Out of which, 1287 (15.22%) became positive and 3858 (45.63%) became negative [Table 2]. 1770 (20.94%) samples were found to be inconclusive again. In 1540 (18.21%) cases, they didn’t send their samples again to our Laboratory for re-testing (lost to follow up).
Ct value of these inconclusive results was analyzed and found that among positive samples, 64.96% had Ct value 36 [Figure 1].
When all the cases were distributed according to the duration of disease, among positive cases, maximum patients (28.36%) had symptoms of less than 2 days’ duration, whereas, among negative cases, 36.86% cases had symptoms of more than 12 days’ duration (Figure 2).
Discussion
During COVID 19 pandemic in India, there had been a spurt in ‘inconclusive’ reports that are leaving patients and clinicians baffled and postponing treatment. There may be various reasons for being only “one target gene” positive starting from sampling error to a technical error in consideration of the dynamics of target genes like N and ORF1ab gene.
We found that 8.95% of COVID RT PCR reports became ‘inconclusive’ in our study. Although It has been noted that up to 5% of COVID RT-PCR reports may be inconclusive [8]. It may be due to low viral load, faulty sample collection and transport and technical issues related to RNA extraction.8 When all inconclusive samples were subjected to re-collection, RNA extraction and re-testing by RT PCR after 3 days, 15.22% cases became positive whereas 45.63% cases turned negative. When these inconclusive reports were analyzed according to Ct value, 64.96% positive cases after retesting had Ct value 36. Again, after comparing these cases with the duration of disease, it was found that among positive cases, maximum patients (28.36%) had symptoms of less than 2 days’ duration, whereas, 36.86% cases had symptoms of more than 12 days’ duration among negative cases.
This can be explained in this way that during the pre-symptomatic phase of infection, when the virus started to replicate and the disease curve started to ascend, the viral load is too low to detect or may detect one target gene, which when tested later after 3 days turned to be positive. Being asymptomatic or pre-symptomatic cases of less than 2 days’ duration, most of the inconclusive reports with Ct value Englishhttp://ijcrr.com/abstract.php?article_id=3803http://ijcrr.com/article_html.php?did=38031. Doherty DT, Moran R, Kartalova-O’Doherty Y. Psychological distress, mental health problems and use of health services in Ireland. Dublin: Health Research Board. Health Res Board Res Ser 2008;5:14.
2. Kessler RC, Andrews G, Colpe LJ, Hiripi E, Mroczek DK et al. Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol Med. 2002;32(6):959-976.
3. Koochaki GM, Charkazi A, Hasanzadeh A, Saedani M, Qorbani M et al. Prevalence of stress among Iranian medical students: a questionnaire survey. East Mediterr Health J. 2011;17(7):593-598.
4. Lyons Z, Wilcox H, Leung L, Dearsley O. COVID-19 and the mental well-being of Australian medical students: impact, concerns and coping strategies used. Aus Psychiatry. 2020;28(6):649-652.
Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareInconclusive in COVID-19 Reverse Transcriptase-Polymerase Chain Reaction Test: A Diagnostic Dilemma
English105108Sikdar SubhenduEnglish Bhunia SomnathEnglish Majumdar Amit KumarEnglish Sarkar SomaEnglish Ganguly Bhattacharjee SwagataEnglishIntroduction: Cases of COVID 19 pandemic in India has been reported since 30th January 2020. Cases are detected by RTqPCR targeting one or more gene (E/S/N/RdRp/ORF1) based on ICMR guidelines. Results are to be reported as ‘Inconclusive’ if only one target gene is detected in multiplex qPCR. Objective: The present study is aimed to analyze the “Inconclusive” results based on the Ct value of the detected target gene, duration of symptoms of cases and by repeat testing with another fresh sample after 3 days. Methods: This cross-sectional observational study was conducted from June 2020 to December 2020 in a tertiary care hospital in Kolkata. Nasopharyngeal and throat swab from suspected cases were collected and sent to our laboratory for RT-qPCR. Results: A total of 94,443 samples were tested in our laboratory for seven months. 8,455 (8.95%) samples were found to be inconclusive. Out of them, 1287 (15.22%) cases became positive and 3858 (45.63%) became negative after retesting with another fresh sample after 3 days. 1540 (18.21%) cases were lost for follow up. All inconclusive results were correlated with Ct value and duration of the disease symptoms. Maximum cases (64.96%) with Ct value 36 became negative on retesting. Most of the cases (28.36%) had symptoms of less than 2 days duration, among positive cases, whereas, 36.86% cases had symptoms of more than 12 days duration among negative cases. 1770 (20.94%) samples were found to be inconclusive again on retesting after 3 days. Most of these cases (46.55%) had a Ct value within 34- 36 on the first test. Conclusion: All inconclusive samples should always be subjected to further testing after 3 days. There is a chance to get positive results from inconclusive cases whose Ct value is less than 34. Quality control and quality assurance of all processes should be done to check any pre-analytical or analytical fallacies. Clinicians and patients both are to be educated about the probable reasons of inconclusive also
EnglishCOVID 19, Ct value, Inconclusive, RT-PCR, Target gene, RetestingIntroduction
An outbreak of pneumonia of unknown aetiology, which was identified first at Wuhan city in Hubei province of China in December 2019, was caused by a new strain of coronavirus named as 2019 novel coronavirus (2019-nCoV) or SARS-CoV 2.1 This virus rapidly spread across the globe, and subsequently a pandemic was declared on March 11, 2020.2 Since its global recognition, the novel coronavirus disease (COVID-19) pandemic has spread to over 200 countries in less than five months.3 In India, the first case of COVID-19 was reported on January 30, 2020.4 As of February 7, 2021, 10,827,314 laboratory-confirmed cases and 1,55,032 deaths were reported from India. The case reporting is based on the testing of individuals by real-time reverse transcription-polymerase chain reaction (RT-qPCR).5 It is performed by taking nasopharyngeal swabs or throat swab or saliva. A range of RNA target gene is used for detection by different manufacturers, mostly targeting the envelope (E), nucleo-capsid (N), spike (S), RNA-dependent RNA polymerase (RdRp), and ORF1 (Open reading frame)genes.6
In COVID-19 infected symptomatic individuals, viral load can be detected early from day one of symptoms and it peaks within the first seven days of onset of symptoms. This viral load can be measured by the cycle threshold (Ct) value, which is the number of replication cycles required to produce a fluorescent signal. Thus lower Ct values representing higher viral RNA loads. The positivity of the samples starts to decline by 3rd week and subsequently becomes undetectable.6 If only one SARS CoV-2 target gene is detected in the test (multiplex SARS CoV-2 RT PCR test) with valid internal control, the result should be interpreted as Inconclusive and repeated the test.7 Inconclusive SARS CoV-2 reverse transcription-polymerase chain reaction (RT-PCR) reports for the detection of infection in symptomatic patients or during the screening of asymptomatic contacts can cause clinical, diagnostic and infection control uncertainty.8
This study aims to analyze inconclusive results of samples tested at our Centre based on Ct value, duration of symptoms at the time of testing and the results of repeat testing.
Materials and Methods
The study was carried out over 94,443 suspected symptomatic COVID 19 patients as well as asymptomatic groups, such as high-risk contacts or high-risk healthcare workers, as per ICMR guideline9 from June 2020 to December 2020 at COVID laboratory in the Department of Microbiology of Nil Ratan Sircar Medical College & Hospital, Kolkata.
Sample collection & transport
Nasopharyngeal swab and throat swab were collected in Viral transport media from suspected cases in different wards and designated COVID wards of NRS Medical College & Hospital as per ICMR guidelines10 and sent to our Laboratory.
Samples from other district and rural hospitals were also sent to our laboratory as stated by the West Bengal Department of Health and Family Welfare updated from time to time.
Testing and collection of Data
All the samples were processed in our Laboratory as per standard protocol and tested by quantitative reverse transcriptase PCR test with the kits supplied by ICMR and the state Health Department. Results were analyzed thereafter and all data were collected.
Results
A total of 94,443 samples were tested in the COVID laboratory of Nil Ratan Sircar Medical College & Hospital, Kolkata for a duration of seven months (June 2020 to December 2020). 8,455 samples were found to be inconclusive (Table 1).
All inconclusive samples were subjected to re-testing (from repeat samples) after 3 days. Out of which, 1287 (15.22%) became positive and 3858 (45.63%) became negative [Table 2]. 1770 (20.94%) samples were found to be inconclusive again. In 1540 (18.21%) cases, they didn’t send their samples again to our Laboratory for re-testing (lost to follow up).
Ct value of these inconclusive results was analyzed and found that among positive samples, 64.96% had Ct value 36 [Figure 1].
When all the cases were distributed according to the duration of disease, among positive cases, maximum patients (28.36%) had symptoms of less than 2 days’ duration, whereas, among negative cases, 36.86% cases had symptoms of more than 12 days’ duration (Figure 2).
Figure 2: Showing the distribution of cases according to the duration of disease (n= 94,443)
Discussion
During COVID 19 pandemic in India, there had been a spurt in ‘inconclusive’ reports that are leaving patients and clinicians baffled and postponing treatment. There may be various reasons for being only “one target gene” positive starting from sampling error to a technical error in consideration of the dynamics of target genes like N and ORF1ab gene.
We found that 8.95% of COVID RT PCR reports became ‘inconclusive’ in our study. Although It has been noted that up to 5% of COVID RT-PCR reports may be inconclusive [8]. It may be due to low viral load, faulty sample collection and transport and technical issues related to RNA extraction.8 When all inconclusive samples were subjected to re-collection, RNA extraction and re-testing by RT PCR after 3 days, 15.22% cases became positive whereas 45.63% cases turned negative. When these inconclusive reports were analyzed according to Ct value, 64.96% positive cases after retesting had Ct value 36. Again, after comparing these cases with the duration of disease, it was found that among positive cases, maximum patients (28.36%) had symptoms of less than 2 days’ duration, whereas, 36.86% cases had symptoms of more than 12 days’ duration among negative cases.
This can be explained in this way that during the pre-symptomatic phase of infection, when the virus started to replicate and the disease curve started to ascend, the viral load is too low to detect or may detect one target gene, which when tested later after 3 days turned to be positive. Being asymptomatic or pre-symptomatic cases of less than 2 days’ duration, most of the inconclusive reports with Ct value Englishhttp://ijcrr.com/abstract.php?article_id=3804http://ijcrr.com/article_html.php?did=3804
Zhu N, Zhang D, Wang W, Li X, Yang B, Song J. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med. 2020; 382:727-33.
World Health Organization. WHO Director-General’s opening remarks at the media briefing on COVID-19. Available from: https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-oncovid-19---11-march-2020. Accessed on June 1, 2020.
Chatterjee P, Anand T, Singh JK, Rasail R, Singh R, Das S, et al. Healthcare workers & SARS-CoV-2 infection in India: A case-control investigation in the time of COVID-19. Indian J Med Res. 2020;151:459-467.
Andrews MA, Areekal B, Rajesh KR, Krishnan J, Suryakala R, Krishnan B, et al. First confirmed case of COVID-19 infection in India: A case report. Indian J Med Res 2020;151:490-2.
Murhekar MV, Bhatnagar T, Selvaraju S, Rade K, Saravanakumar V, Vivian W, et al. Prevalence of SARS-CoV-2 infection in India: Findings from the national sero-survey, May-June 2020. Ind J Med Res. 2020;152:48-60.
Sethuraman N, Jeremiah SS, Ryo A. Interpreting Diagnostic Tests for SARS-CoV-2. JAMA. 2020:323(22):2249–2251.
TaqPath™ COVID-19 Combo Kit and TaqPath™ COVID-19 Combo Kit Advanced Instructions for Use. Interpretation of the results; Analysis and results, Chapter 11: 105-106
Bhattacharya S, Vidyadharan A, Joy VM. Inconclusive SARS-COV2 reverse transcription-polymerase chain reaction test reports: Interpretation, clinical and infection control implications. J Acad Clin Microbiol. 2020:22:59-61.
Indian Council of Medical Research. Strategy for COVID19 testing in India (Version 4). New Delhi: ICMR; 9 April, 2020.Available from: https://www.icmr.gov.in/ pdf/covid/strategy/ Strategey_for_COVID19_Test_v4_09042020.pdf. Accessed April 30, 2020.
Indian Council of Medical Research. Specimen referral form for COVID-19 (SARS-CoV2). New Delhi: ICMR; 2020.Available from: https://www.icmr.gov.in/ pdf/covid/update/SRF_v9.pdf. Accessed April 30, 2020.
COVID 19 antigen testing. RAMS testing 2021. Available from: https://ramstesting.co.uk/covid-19-antigen-testing/
Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareAarogyaSetu: Behavior Analysis and Its Efficacy
English109114Umang KumarEnglish Vivek ChaudhariEnglish Devvret VermaEnglish Anshika SinghEnglishBackground: To be victorious over COVID-19 and to fight half a million cases daily, an individual’s cognitive and behavioural attitude towards the Aarogyasetu application is very vital. Objective: To interpret behavioural aspects and approach of people along with assessing their views and opinions through a survey. Methods: A cross-sectional situational based survey was circulated through different digital platforms among Indian residents aged above 15 years. A descriptive statistical approach was used to analyze the data. Results: A total of 220 people have completed the survey. The majority of them expressed that they have always forgotten to use the application, whereas most individuals accepted that they have generally provided incorrect information in the application due to the fear of quarantine procedures. It has been recorded that the Participatory disease surveillance (PDS) based contact tracing application has several limitations, as they do not cater to asymptomatic people in addition to it disabling Bluetooth and GPS services. Conclusion: Geospatial technology used in contact tracing application had been very beneficial for disease surveillance and to curb plus control the spread of COVID-19 disease. The data recorded through the survey was irregular and showed many variations, as a result true potential of the technology was not liberated. The doubts of people should be acknowledged to increase efficacy of the Aarogyasetu application.
EnglishCOVID-19, AarogyaSetu, Behavioral Analysis, PDS System, Corona, SARS-COV-2
Introduction
The beginning of 2020 was marked by the catastrophic spread of the COVID-19 Pandemic. The epicentre of SARS-COV-2 was later identified to be in a wet market of Wuhan, China. The first cluster of cases of pneumonia were recorded on 31st of December, 2019 in Wuhan, which was later recognised as “The Novel Corona Virus.1After that the Cases started multiplying at a rapid pace with total death of 4,634 in China only because of contact tracing was difficult. The first case in India was recorded on the 30th of January 2020 in the province of Kerala. The theCOVID-19 virus spreads primarily through droplets of saliva or discharge from the nose when an infected person coughs or sneezes.2
The transmission of outbreaks like COVID-19 is very difficult to monitor. To control the spread of such disease, it is important to detect early symptoms with immediate responses. Disease surveillance forms a basic component for understanding disease, the study of disease transmission and gives a sound premise to start control measures. Surveillance is the persistent, precise assortment, resemblance, investigation, and translation of wellbeing related information required for the arranging, usage, and assessment of general wellbeing rehearses.3Participatory disease surveillance (PDS)is a tool that is developed for disease surveillance. It uses Participatory appraisal approaches and methods, combines them with local veterinary knowledge with convection methods to establish the presence and absence of any particular disease. The model of the PDS system has been established for Influenzas like illness(ILI) and foodborne diseases. Many European countries have made PDS models to collect data about diseases. Thailand also launched the Doctor my mobile application to collect data for ILI in 2014. Sri Lanka has also launched a mobile application in 2016, to control the spread of dengue by continuing monitoring dengue mosquito breeding sites.4
India launched its first PDS Model for COVID-19i.e.Aaogyasetu which is an initiative by the Indian government. It is a surveillance application forCOVID-19 that performs two basic functions for data collection and analysis: “Syndromic Surveillance” and “Contact Tracing”. It was designed in such a way that one can know the status of people at risk with the help of the phone’s Bluetooth and GPS capabilities. The Aarogyasetu application is designed to detect other devices with the application when brought within the Bluetooth proximity. When brought close both the phones change digital signatures between them including time proximity duration and location. Data is then securely recorded by both the application. If a person comes in contact with a COVID positive person from the past 14 days, the Application calculates the danger of contamination depends on the number of connections, the vicinity of collaboration and suggests reasonable activity. The refreshed danger of disease is breaking down by the Government of India, to encourage appropriate clinical mediations, as and when required.5
Aarogyasetu was made mandatory in containment areas as of 1stof May 2020 and voluntary in others. With cases rising at such an alarming rate and shortages of healthcare workers, hospital beds and likely community transmission; it’s very tough to curb in the upcoming scenario. Participatory disease surveillance app Aarogya Setuuses real-time data and analytics to detect COVID positive patients nearby but the efficacy of Aarogya Setuand user acceptance towards this app is still questionable. It is because the reporting is optional to the government, a non-transparent process of contact tracing and voluntary participation in the application prevents using it for movement permits. Technology-based contact tracing application can encourage and mechanize the cycle, empowering contact tracers to educate clients who had contact with a COVID-19 casualty. This can be empowered by utilizing a global positioning system (GPS), Wireless Fidelity (Wi-Fi), Bluetooth Technology, Social diagram, network-based API, mobile tracking data, and card transaction.6
The current research was aimed at interpretation and evaluation of behavioural aspects like inconsistency among people and their approach towards this application through contemplating their views and opinions by a survey. Situation based questions were included to analyse and evaluate the viewpoint of individuals in a different scenario. Description statistical methodology was used to undertake this research.
Material and Methods
Study Design
A questionnaire survey was made, and primary research was conducted with a sample size of 220 people belonging to more than 15 years of age groups from various locations over India. The Face and content validity of the survey was performed by taking the opinions of the experts.
Survey Tool
The tool used for conducting the survey was survey monkey. It is an online platform that Gathers opinions and transforms them into People Powered Data. With the help of Microsoft office, excel tool data analysis and documentation were performed.
Data Collection
The questionnaire was circulated through different digital platforms like social media and direct messages. Active participation was done by an individual with reliable data. Data collection was also done by the fellow website survey monkey and the data was exported to excel with correct information and data charts. The database is original and no counterfeit responses were recorded.
Data Type
The data was primary and the survey was filled without any bias by known and unknown individuals. Mostly the age group >15, which is most technically advance and diverse, participated in data collection. The questionnaire was made with the most obvious and behavioural type questions that talks about the rational and cerebral nature of the individual and the most basic reason behind not using this application in this emerging situation. A descriptive statistical approach was used for analysis.
Results
A total of 220 individuals completed the survey which shows that the highest respondents were aged between 14-34, the most technologically advanced and active age group. The first question was targeted to know the usage of the application where 178 people claimed to utilize the application whereas 41 people were still not using it with the ratio of 81:19 (Figure 1).
Another question was focused on recognizing the actual utilization of the application as it emphasises how many people use the application at the time of requisite. Only 29 individuals check the application regularly i.e. 90% above before going outdoor. 34 individuals usually i.e. 60% above go through the app,99 people usually or rarely i.e. 20-50% above check the application and 57 individuals never use the application as suggested (Figure 2).
The next question was on knowing the basic requirement of Aarogyasetu application to locate COVID-19 patients. The survey was performed on the people whether they should always keep the GPS and Bluetooth proximity technology ON and only 105 people i.e.45% people keep it ON rest 114 i.e. 52% people keeps it off (Figure 3).
This question specifies the feasible reason of individuals for not using the application. 42% do not use the application because they often forget it, 32% people do not use the application because they have to keep Bluetooth and GPS always on as it is battery consuming, 22% people think that it lacks accuracy and the application is not useful whereas 16% people say that they have data security issues as every detail of their location goes to the government last but not least 8% people have their views about not using Aarogyasetu (Figure 4).
This question emphasizes the reason that why people might input false or inaccurate self-declaration on the application. 61% of the people were afraid that they will be sent to quarantine centres and will be separated from their families, 40% people are afraid of social acceptance, 15% people didn’t fill the form accurately, 23% people thought that it might cause panic in the locality and 3% people have their reasons (Figure 5).
The motive of this question was to know the cognitive behaviour of individuals by putting themselves into the imaginary scenario of taking the self-reassessment test and the results suggest that 73% of people agreed to self-assessment and 27% of people will not take self-reassessment as it is not necessary for the application (Figure 6).
In this question the cognitive behavioural assessment of individuals was performed by putting them into imaginary scenarios and asked them the reason why they were not willing to update their symptoms on the Aarogyasetu application by self-reassessment. The results were astonishing as 36% of people thought that they did not find it useful, 13% thought that the process is time-consuming,14% thought that it would be troublesome, 40% thought that they might be asymptomatic i.e., they won’t have any symptoms of COVID-19, and 11% people specified other reasons(figure 7).
The motive of this question was to directly ask about their thoughts on the reliability of the application and 48% people replied yes, 32% people replied withNo whereas 21% people were confused (figure 8).
This question aims at the efficacy of Aarogyasetu application if the patient is asymptomatic i.e., they do not show any symptom of the disease but still might have it.7Total 29% agreed, 46% disagreed whereas 24% of people were not sure about this question (Figure 9).
Discussion
A right perception towards myths and facts about COVID-19 can encourage good practices among the public. The survey was conducted to measure the perception of individuals towards the Aarogyasetu application and thereupon its efficacy. The Aarogyasetu application must reach its maximum potential mark but as seen within the results there are various factors by which the standard of knowledge is often hampered and therefore the tracking might get difficult. Privacy-preserving technology and public education are both necessary conditions for effectiveness but not sufficient. Many of the populations that are most susceptible to COVID-19, like those that are elderly, low-income, homeless, and even have lower telephone ownership, therefore, won't use the Aarogya Setu.8In the survey, a total of 220 individuals participated from a different region of India with minimum age group range from 14 and maximum to 89. It was observed that 81% of users were aware of the application and were using that application, whereas 19% of people have never used this application. With the survey going further next important question was that how many people used this application before going outside i.e. refer to this application for COVID patients nearby. 26% of people did not bother to check the application before going outdoor whereas 27% of people rarely go through the application and only 13% checks the application on regular basis. A basic requirement for using the application is keeping Bluetooth and GPS proximity “ON” always in which only 48% kept it on rest 52% do not and 32% of people don’t use this application because of this reason alone, other probable causes which people think are it lacks accuracy (22%), do not offer data security(16%), the application is futile(17%) and some people also specified that application does not cater to the needs of COVID-19 patient after giving every detail about the patient in the application. But majority of people i.e. 42%, often forget using the application because there is no notification reminders or SMS’s from the application.
Other than the usage of application the behavioural aspects of people also differs a lot and that is one of the causes why the efficacy of the Aarogyasetu application not met as pre forecasted by the application developers and it is the first healthcare PDS model launched in India to cater the pandemic.9 It came to our notice that some people also input false or inaccurate mandatory self-declaration after downloading the application software from the web. With the result astonishing 61% of the people still fear quarantine centres, 40% people fear social acceptance, 23% thought that it might cause panic in nearby areas and 15% people did not fill the form attentively. With this varied response individuals’ diverse cognitive needs came into light such as fear, trepidation of quarantine centres, agitation of social acceptance and rejections, panics by the adjoining areas and carelessness of individuals towards this considerable disease.
Another behavioural aspect that was tested included honest evaluation with a situational based question that if a person might have COVID like symptoms will they report in the application by taking the self-reassessment test again and the ‘yes’ 73% people may report themselves but 27% might not. The reason for not reporting to the application can be 40% the perspective of the people as they think that they can be asymptomatic, 36% thinks that the app is not useful and the rest thinks that it is time-consuming and troublesome. This also leads up to another significant question, do people rely on this application of which 48% of people said yes, 32% said direct no and 21% of people were still in doubt. This derives that people think useless to the application because it also cannot oblige asymptomatic people as 46% agreed to this, 24% were not sure.
After the pandemic outbreak, many countries took protective measures of digital surveillance application to regulate the outbreak. In Taiwan, the government took strict action for people that breaks their home quarantine by sending GPS based messages levies fines. The Hong Kong government also introduced wristbands that are linked through cloud technology to a database that alert authorities if quarantine is breached.10 Iceland also launched a mobile solution to watch COVID-19. European government authorities ensured that individuals data should be retained for 14 days only, and European countries also deployed an opt-in smartphone tracking application with anonymised data, no central database, and no GPS information.11Like India all the countries are having concerns regarding digital surveillance platform and each day comes with a replacement problem because the concept of this technology is contemporary to everyone.12
Seattle-based Swedish Health Services accompanied Microsoft in creating a digital surveillance application to track and report real-time data like patient volumes, personal protective equipment, and other critical information. This application was the replica of the COVID-19 Emergency Response Application whose function was to take the information of Swedish Health staff reports and inputs it into dashboards for hospital staffs. The health system used the dashboards to trace the status of its five hospitals, two freestanding emergency departments and important areas in response to the COVID-19 pandemic.13 The Chinese government also made a virus tracking system that needed personal information of people like recent travels and health. The software uses the data to assign a code green, yellow or red which tells the person is COVID positive or negative. Security guards outside shops, malls and offices were not allowing anyone inside without a valid green code. The country’s leaders have long sought to harness vast troves of digital information to regulate their sprawling, sometimes unruly nation more efficiently. With computer system having full authority over individuals lives minor software bugs and inaccurate data can have. It is also not completely sure that the citizens of any country are comfortable with the government with knowing every detail about their personal lives such as location even with favourable intentions.14
Conclusion
After this big initiative i.e. participatory disease surveillance model Aarogyasetu was launched by the government, many individuals approved it whereas many relucted. Diversity in people’s perception of this application was noticed. This study also focused on the behavioural and cognitive needs of many people; some were met but some were not. Life of many people is in danger because of carelessness, fear, trepidation, acceptance and many psychological factors affecting one’s brain. Human errors and negative cognizance towards this application affects society in a very lethal way. Many variations and irregularity were noticed. Data recorded were in fragments and was not in pattern as expected showing discrete understanding and interpretation of this application. Understanding public perception of these contact tracing application is important because broad uptake within the public is key to the app’s public health success. A study suggests that the PDA type application can stop the epidemic with 60% uptake in the population. Providing the right information and awareness about this application might improve people’s conception and beliefs about the myths of security over Bluetooth and battery drainage. Myths and questions of the public should be acknowledged for the wholesome success of the application.
Acknowledgement: The authors express a deep sense of gratitude to the Symbiosis Institute of Health Science, Pune and Graphic Era University for all the support, assistance, and constant encouragements to carry out this work.
Source of Funding: No source of funding
Conflict of Interest: All authors have none to declare
Authors’ Contribution: Umang Kumar and Devvret Verma initiated the idea of the study and was involved in the writing, Vivek Chaudhary and Anshika Singh were involved in manuscript refinement.
Englishhttp://ijcrr.com/abstract.php?article_id=3805http://ijcrr.com/article_html.php?did=3805
Chan JF, Yuan S, Kok KH, To KK, Chu H, Yang J, Xing F, Liu J, Yip CC, Poon RW, Tsoi HW. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet. 2020;395(10223):514-23.
Xu R, Cui B, Duan X, Zhang P, Zhou X, Yuan Q. Saliva: potential diagnostic value and transmission of 2019-nCoV. Int J Oral Sci. 2020;12(1):1-6.
Qazi S, Ahmad S, Raza K. Using Computational Intelligence for Tracking COVID-19 Outbreak in Online Social Networks. In Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis. Int J Oral Sci. 2021; 10(3):47-59.
Garg S, Bhatnagar N, Gangadharan N. A Case for Participatory Disease Surveillance of the COVID-19 Pandemic in India. J Med Int Res Public. 2020 6(2):e18795.
Mankar MV, Mankar MV, Narayane MM, Chakole S, Mankar MV. The Rise And Impact of COVID-19 In India: Aarogyasetu App. Eur J Mol Clin Med. 2021;8(1):294-300.
Zeng W, Gautam A, Huson DH. On the Application of Advanced Machine Learning Methods to Analyze Enhanced, Multimodal Data from Persons Infected with COVID-19. J Comput. 2021;9(1):4.
Shankar EM, Che KF, Yong YK, Girija AS, Velu V, Ansari AW, Larsson M. Asymptomatic SARS-CoV-2 infection: is it all about being refractile to innate immune sensing of viral spare-parts : Clues from exotic animal reservoirs. Pathog Dis. 2021; 79(1):ftaa076
Landau S, Lopez CE, Moy L. The Importance of Equity in Contact Tracing. https://www.lawfareblog.com/importance-equity-contact-tracing (Accessed 06 August 2020)
Garg S, Bhatnagar N, Gangadharan N. A Case for Participatory Disease Surveillance of the COVID-19 Pandemic in India. J Med Int Res. 2020;6(2):e18795
The Washington Post. www.washingtonpost.com/technology/2020/04/17/governments-aroundworld-are-trying-new-weapon-against-coronavirus-yoursmartphone/ (Accessed 15 Sep 2020).
Just Security. www.justsecurity.org/69549/can-governmentstrack-the-pandemic-and-still-protect-privacy/ (Accessed 06 August 2020)
Whitelaw S, Mamas MA, Topol E, Van Spall HG. Applications of digital technology in COVID-19 pandemic. Lancet Dig Heal. 2020;2(8):e435-e440.
Becker's Health Information Technology. Swedish health services tap Microsoft to build app that tracks COVID-19 patients and hospital capacity. https://www.beckershospitalreview.com/healthcare-information-technology/swedish-health-services-taps-microsoft-to-build-app-that-tracks-covid-19-patients-hospital-capacity.html. (Accessed 12 Apr 2020).
Zhong R. China’s virus apps may outlast the outbreak, stirring privacy fears. The New York Times. 2020 May 26.
Big Data Institute, University of Oxford. Digital contact tracing can slow or even stop coronavirus transmission and ease us out of lockdown. https://www.research.ox.ac.uk/Article/2020-04-16-digital-contact-tracing-can-slow-or-even-stop-coronavirus-transmission-and-ease-us-out-of-lockdown. (Acessed 06 Aug 2020).
Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareAI-based COVID-19 Airport Preventive Measures (AI-CAPM)
English115122Vergin RSEnglish Anbarasi LJEnglish Rukmani PEnglish Sruti VEnglish Ram GEnglish Shaumaya OEnglish Nithesh GurudaasEnglishIntroduction: Corona has affected everyone’s lives. Most companies and services have found new ways to approach their work while at the same time preventing the further spread of the virus. The mode of travel has also significantly changed. Governments are trying their best to maintain all possible safety norms at airports and railway stations. The idea is to ensure that people are maintaining social distancing and wearing a mask. Objectives: The main aim of the proposed work (AI-CAPM) is to reduce the contact between staff and the passengers. To ensure the genuineness of the Aadhar card and other IDs, the system uses face detection algorithms that will detect the faces and only allow those faces who have their tickets booked for that particular day. Methods: The proposed work uses Local Binary Patterns Histograms (LBPH) Face Recognizer for face recognition, Social Distancing Monitor Using DNN and Mask Detection with CNN. Results: This research work builds a social distancing monitor model using Single Shot Detector (SSD). SSD takes one single shot to detect multiple objects within the image. SSD covers all computation in a single network by eliminating subsequent pixel or feature resampling stages and object proposal generation. This makes SSD easy to train and simple to integrate into systems that require detection component. SSD is faster and has much better accuracy compared to other algorithms. Conclusion: The software developed in this work proposes an autonomous verification of a passenger as it displays the passenger's train/flight details and boarding area by recognizing them from the passenger’s database. This removes the need for manual checking of the passenger before boarding. Overall, the software developed automates tasks and reduces human involvement as it is the need in this pandemic struck world to contain the spread of the virus.
EnglishCOVID-19, Safety measures at the airport, Face detection/authentication, Mask detection, Social distancingIntroduction
The entire world has come to a standstill because of the Covid-19 pandemic. The spread of the disease is increasing day by day and has shaken the stability of the entire world. The whole pharmaceutical industry is striving hard to produce a vaccine on one side, and the medical experts are trying their best to bring down the death rate on the other. Prevention is always a better solution for any situation, so social distancing and wearing masks have become an absolute priority. The spreading of the covid-19 virus is mainly because of the droplets that a carrier expels and this flies through the air until gravity pulls them down. The maximum distance those droplets can fly is about six feet. Masks work as an effective barrier against the droplets and protect the wearer as well as the people around.1,2
The irresponsible members of the community are those who don’t wear the mask and roam outside thus increasing the risk of transmission. Humans expel aerosol particles, which are about 1/100th the size of human hair, and are more difficult to defend against. Social distancing and staying outdoors, where there is more airflow will be the remedial solution for this kind of transmission. About 30% of infections are caused by asymptotic people who don’t have any symptoms of COVID-19 but if they are irresponsible and don’t follow the social distancing norms and don’t wear masks then they would be the main reason behind spreading the disease. It has been found out that the risk of transmission of the virus is reduced to about 65% of people wear masks and 90% of people follow the social distancing norms.
The research work focuses on reducing the contact between the staff members working at the airport & the passengers and ensuring that all the passengers follow social distancing norms and wear masks. The installed cameras at the airport will be used to detect people not obeying the social distancing norms or wearing masks and the installed speakers will be used to announce.3,4 The staff members working at the entry gate of the airport who see the passengers tickets and other documents and then allow them to enter the airport can be replaced by the face detection/authentication model where the passengers face images and their details which were registered at the time of booking the tickets will be stored in the airport database and this data would be utilized when for the authentication of the passengers through the installed cameras at the entry gate of the airport. Application of artificial intelligence and computational intelligence techniques is diversified in various area such as e-healthcare smart city and smart grid9, data processing10, predictive maintenance.11 Likewise, the application of AI-based techniques plays a vital role in this research work. The proposed research work uses LBPH which labels the pixels of an image by thresholding the neighbourhood of each pixel and considers the result as a binary number.5,6
In today’s world which has been widely affected by the COVID-19 people must wear masks to prevent the spread of the novel-corona virus. It is a necessity to monitor people in public places and ensure that they are wearing masks and following social distancing norms. The research work is based on a deep learning model developed using Keras that helps to classify people without masks from live camera feed and instruct them to wear masks as soon as possible. This model can be deployed in the CCTV cameras of the airports and railway stations to continuously monitor people remotely without manual checking and hence reducing human involvement also. Then the speakers can be used to call out the people violating the norms and ask them to wear masks.7,8
This research work also intends to build a social distancing monitor model using which the security officials can just look at the screen displaying the video footage of the airport instead of being physically present everywhere, whether people are maintaining social distancing norms or not, and for those who are not following the safety norms, the speaker automatically announces “Please Maintain Social Distancing” and based on facial recognition, it can extract information about the concerned person and store it as a violation under their ID and then later they can be fined during boarding of flight. To develop this Single Shot Detection Algorithm has been used for Object Detection, MobileNet for Image Classification and MobileNet pre-trained-model files that were trained in the Caffe-SSD framework.
The Social Distancing Monitor Model uses Single Shot Detector for Object Detection, SSD takes one single shot to detect multiple objects within the image. SSD covers all computation in a single network by eliminating subsequent pixel or feature resampling stages and object proposal generation. This makes SSD easy to train and simple to integrate into systems that require detection component. SSD is faster and has much better accuracy compared to other algorithms. Other algorithms which could have been used for object detection are Faster RCNN, Yolo.9,10
Literature Survey
The research work explains in detail the MobileNetV2 SSD layer,12 its architecture, the image it has been trained on and how it can be applied in deep learning models using Transfer Learning. The research work explains the different architecture of CNN and how to implement them to solve a deep learning problem. This research paper has been used to know what MobileNet is and how it is used in embedded vision applications, and how it work’s and what is its structure, it has also been used to know how MobileNet is better than the other models for ImageNet classification, and how it is effective across a wide range of applications like object detection.13,14
The authors explained the scope of facial recognition based on depth learning. It highlights the immense usage of facial recognition in the field of biometrics. Researchers in16 explains a system that performs facial recognition/authentication from a live video feed. Challenges of detecting and authenticating people with similar facial characteristics or identifying people who are identical twins are addressed. The work in17 proposes an algorithm for face detection and recognition based upon CNN where student’s attendance is taken using facial recognition. Using an IoT and edge-based computing approach, the generated data of the facial recognition of the students are computed and then transmitted. This system gives an accuracy of about 97.8%.15
The research work has been used to know how SSD can be used to detect objects in an image, and how it works by generating scores for the presence of an object and drawing a box around the shape of the object, this paper has also been used to know how SSD is easy to train and integrate into systems, and how it is much faster and has greater accuracy than the other models. The work has been used to learn about the MS-COCO dataset, what all images it comprises of, how it is more accurate than the other datasets for object detection and classification, and how it surpasses other datasets like PASCAL, SUN by giving a detailed mathematical analysis of the database and analysis of the basic performance of the box that combines the results of component discovery.16-19
Proposed Work
Face detection
LBPH is used for face detection and it uses parameters such as Grid X which are the number of cells in the horizontal direction, Grid Y which are the number of cells in the vertical direction and radius which is used to make the circular local binary pattern and represents the radius around the central pixel and is usually set to 1. Sample images of the passengers are taken while they are booking their flight tickets. Histograms of the sample images are stored in the airport database along with an id number which can be the aadhar card number. When these passengers go to the airport, the camera takes an input image. This input image’s histogram is compared with the sample images histogram from the airport database and the output displayed are the details such as the passenger’s name, age, gender, flight details from the image with the closest histogram. If the passenger’s details aren’t there in the airport database their face would be identified as UNKOWN and they wouldn’t be allowed to go inside the airport. Id number or the aadhar card number is used to uniquely identify the samples images and the details of each passenger. Euclidean distance20 is used to compare any two histograms (hist1 and hist2) are given in equation 1. The overall flow of this work is given in figure 1.7,8
Algorithm
(While booking the flight tickets)
Step 1: Enter aadhar card number, name, age, gender, flight details etc.
Step 2: Dataset consisting of 200 samples of face images are taken.
Step 3: The images are converted to grayscale format and represented as 3x3 matrices.
Step 4: Central value of the matrix is used as the threshold value.
Step 5: For each neighbour of the central value of matrix, if values are =>than the threshold, set 1 else set 0.
Step 6: Each binary value from each position from the matrix are taken line by line and concatenated to form a new binary value.
Step 7: Binary value is converted to a decimal value and the central value of the matrix is replaced by this decimal value.
Step 8: The image is divided into multiple grids by using Grid X and Grid Y.
Step 9: From each grid, there is a histogram that contains occurrences of each pixel intensity i.e. occurrences of the intensity value from 0-255.
Step 10: Each histogram is concatenated to form a bigger histogram.
Step 11: Repeat Step 3,4,5,6,7,8,9 & 10 for all the 200 samples.
Step 12: The obtained images along with details are stored in the airport database.
(At the airport)
Step 13: Show the face to the camera at the airport entrance.
Step 14: Algorithm trained creates histogram for the input image by doing step 3 to 10.
Step 15: The above face is matched with the histograms in the airport database.
Step 16: If the input image has a histogram matching with any of the histogram of the images of the airport database, display details of the passenger. Else display Unkown.
Dataset
The passengers have to face the camera of their mobile phones or laptop while booking the tickets and around 200 image samples will be collected. All the details of the passengers along with their face images will be stored in the airport database. The passengers entering the details (figure 2) while booking the ticket. (Here id is entered as 1 by the passenger but in real time this id would be the aadhar card number which uniquely identifies each citizen of India). The database collected, stored and trained are shown in figure 3(a), 3(b) and 4. 9,10,11
Mask Detection
The proposed work tries to identify all the people in the image or live feed and distinguishes them based on the differences in the Regions of Interests on their face with a CNN(Convolutional Neural Network) Model and classifies them as wearing masks or not. The regions of interest are the Mouth and Nose region as they differ for people with masks and without masks. By training a Deep Learning Model, the weight scores can be estimated for the network architecture to classify people as wearing Masks or not by identifying the difference in the regions on the face. The overall architecture of the proposed flow is shown in figure 5 and the database created with and without the mask is given in figure 6(a) and 6(b).
Algorithm
Step 1: Load the training dataset images
Step 2: Train the developed neural network architecture over the training dataset with masked and non-masked inputs and obtain the accuracy metrics
Step 3: The neural network locates the regions of interest in the face – nose, mouth which would differ for masked and non-masked images and adjusts the weights of each neuron to maximize the accuracy.
Step 4: Deploy the model in a camera to monitor the live feed.
Step 5: The camera captures each frame of the feed and passes it to the model.
Step 6: The model first recognizes the faces in each frame with the pre-trained res10 Caffe architecture and passes the coordinates of the face to the mask detector.
Step 7: The mask detector model receives the coordinates of the faces and calculates the probability of whether the person is wearing a mask or not and labels him as safe or unsafe
Step 8: Then the camera feed passes the next frame and checks the same repeatedly.
Dataset
The dataset consists of 700 people wearing masks and 700 people without masks for training the model.
Deep Learning Model
The input image is passed to Google’s MobileNetV2 CNN Architecture. Transfer Learning is a deep learning algorithm where the weights of a model trained for a similar purpose is used in another model. Here the pre-trained weights of the MobileNet V2 Model are used. Mobile Net V2 is a pre-trained Neural Network architecture released by Google. It is a CNN Model that has 53 layers and trained on over million images on ImageNet. The MobileNet V2 takes a 224*224* dimension image as input gives the tensor of the last convolutional layer block of the model, which represents the scores for the input image. The MobileNet layer’s output is passed on to a set of custom neural network layers using Keras - Flatten, Dense, Dropout and finally to the output layer with 2 units - one indicating the probability of wearing the mask and another for not wearing the mask) with softmax activation. Other Libraries that can be used instead are: Pytorch and Densenet.12-16
Social Distancing Algorithm
The social distancing detection phase is explained in Figure 7. It is further described in the following steps.
Step 1: Camera records video/get live video.
Step 2. The Social Distancing Monitor Model gets each frame from the video and processes it.
Step 3. It then detects all objects present in the frame.
Step 4. It will then check which of the objects qualify as human beings, and sketch a bounding box around all human beings.
Step 5. Now, it will calculate the distance between the centroid of all the bounding boxes and determine which of the boxes violate the distancing norms.
Step 6. Whenever the camera notices red bounding boxes i.e they are not maintaining social distancing norms, it automatically plays the audio of “Please Maintain Social Distancing”
and reminds all to maintain distance or even the officer can speak on the mike as the mike gets switched on whenever there is a violation for the officer to speak.
Step 7. Furthermore, based on facial recognition, it can extract information about the concerned person and store it as a violation under their ID and then fine him or her during boarding.
Dataset
A pre-trained MobileNet model has been used in the Social Distancing Monitor Model that has been trained in the Caffe-SSD framework and can accommodate up to 20 classes. This model has been first trained using the MS-COCO dataset and then optimized in VOC0712. Except for MS-COCO recreation, it can only get mAP = 0.68. After pre-receiving training mAP = 0.727. There are 2 key points in this model - Here the ReLU6 layer is replaced by ReLU & [(0.2, 1.0), (0.2, 2.0), (0.2, 0.5)] are set as the anchors for the conv11_mbox_prior layer. MS-COCO DATASET - This database contains images that can be easily detected by a 4-year-old, with images of 91 objects. These Objects are labelled using the division of each model which assists in the construction of an accurate object. VOCO712 DATASET - This database includes a total of 11540 images, each of which contains a set of objects, out of 20 different categories, making a total of 27450 objects defined.15-17
Deep Learning Model for Detecting People
Caffe is a Deep Learning library that is well suited for visual and predictive applications. Caffe can create a net with sophisticated configuration options and can access pre-networked networks in the online community. Caffe allows the user to set hyper net limits. Computational costs for various services are optimized. Caffe also supports the use of GPUs as it is built using CUDA.
Detecting people in a video file and calculating distance between people
The Social Distancing Monitor Model uses OpenCV in Python and have used Caffe model files to detect a person. The Caffe model being used is a very generic model which can detect almost all objects in a frame. So, it takes a for loop to check each object detected by the detector, it first checks the confidence value of the object detected which is the probability that an anchor box contains an object, if it is above the threshold value, it takes the index of the object and then check if that index of the class of objects is a “person” or not if it is a person it takes the coordinates of the object and then makes a rectangle around the object.
The distance can be calculated using the coordinates of the bounding box of the detected person. If the distance is less than a predefined threshold value, then it shows red and starts playing the audio “Please Maintain Social Distancing” which means persons are close to each other. If the distance is greater than the threshold value, then it shows green. So previously it had stored all the persons detected along with their ID and bounding box in an array then it will create another array of centroid_dict where it would store the object ID along with its centroid of the bounding box, then it makes use of combinations header file in python to create combination among all the value of centroid_dict so for each combination it will calculate distance between them using their centroid value and see if it is within the threshold value or not.18,19
Results and Discussion
Figure 8 shows how the website grants control to the staff members to check for Mask detection, Face detection & Authentication and ensure Social Distancing.
Face Detection
The face is available in the airport dataset, so all details are displayed and this passenger would be allowed to enter the airport and a sample is shown in Figure 9A. Figure 9B detects the face that is not available in the airport dataset, so shows Unknown and this passenger won’t be allowed to enter the airport.
Mask Detection
The model is trained for 20 epochs with a batch size of 32 for testing its performance. The accuracy obtained was 98.4% for 20 epochs. The result is two probability values – One for With Mask and one for without Mask. The model predicts the output based on which value is higher. Results of wearing the mask and the No mask classification is shown in Figure 10.
Social Distancing
People are identified and rectangular boxes are drawn around them. As given in Fig 4.3.1, if they are at safe distance, they are displayed in green boxes and if they are not at a safe distance i.e., they are beyond a threshold, they will be displayed in red boxes and the audio says “MAINTAIN SOCIAL DISTANCING” starts. In a practical situation, this can be done by speakers installed at the airport system. Even the officer can himself speak on the mic to alert people to maintain a distance because the mike gets automatically switched on whenever someone violates the norm or are under red boxes. Here the threshold distance is 75 which is the Euclidean distance. Setting the threshold depends on the resolution, angled view, height of a person, etc. similarly social distancing identified using the proposed models is shown in Figure 11.
Conclusion
Deep Learning has been a massive influence in today’s computer vision-based applications and is a constantly growing field. In this paper, Deep Learning Models are used to help automate tasks in public places – Railway Station and Airports and reduce manual human work. With the COVID-19 lockdown ending and the world ready to move back to normal without a proper vaccine people must follow the safety protocols – Wearing a Mask and Maintaining Social Distancing to reduce the spread of the virus. The Deep Learning model developed – To detect mask and monitor social distancing when deployed can be used for remote monitoring of people in airports and railway station and ensure they follow the safety protocols. Facial Recognition is also another growing field in today’s authentication methods. The software developed proposes an autonomous verification of a passenger as it displays the passenger’s train/flight details and boarding area by recognizing them from the passenger’s database. This removes the need for manual checking of the passenger before boarding. Overall, the software developed automates tasks and reduces human involvement as it is the need in this pandemic struck world to contain the spread of the virus.
Acknowledgement: Authors acknowledge the immense help received from the scholars whose articles are cited and included in references of this manuscript. The authors are also grateful to authors/editors/publishers of all those articles, journals and books from where the literature for this article has been reviewed and discussed.
Conflict of Interest:
The Author(s) declare(s) that there is no conflict of interest.
Source of Funding: None
Englishhttp://ijcrr.com/abstract.php?article_id=3806http://ijcrr.com/article_html.php?did=3806
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareBreastfeeding During COVID-19 Pandemic in India: Challenges and Prospects
English123130Patnaik SEnglish Jena DEnglish Subudhi RNEnglish Behera MREnglishBreastfeeding during infancy is important for good health and wellbeing. In India, rapid response to the COVID-19 pandemic resulted in lockdowns and limited or no mobility. As a result, Anganwadies (institutions supporting infant and young child feeding practices and supplementary nutrition) were closed for long durations. Frontline grassroots health workers such as ASHA (Accredited Social Health Activists), who also is responsible for promoting breastfeeding, were stretched with additional allocated work on COVID-19 rapid response. In India, myths around the transfer of COVID-19 from mothers to infants during breastfeeding did round. It is expected that the nutrition status of newborns and infants may have further worsened during COVID-19. Researchers have established that limited or no breastfeeding impacts the growth and development of infants during the critical first 1000 days also resulting in stunting. In this review we aimed to ascertain the status of breastfeeding practices during COVID-19 pandemic times and the impact it may have on infants. The objective of this article is to review the challenges and prospects of breastfeeding in India during the COVID-19 pandemic period. Through a review of literature, case studies, experience from on-ground rapid response to the COVID-19 pandemic, the current status of breastfeeding practices in India was reviewed. It may be concluded that there are many challenges as well as prospects for future pandemic preparedness and planning and to reduce risks addressing under-nutrition conditions of children such as stunting. A practical way forward maybe by using the suggested prioritization of States and interventions thereof based on stunting and breastfeeding status and efforts towards doing away with myths around breastfeeding.
EnglishCOVID-19, Breastfeeding, Stunting, Under-nutrition, Frontline Workers, Food Security
INTRODUCTION
Breast milk is an elixir for newborn babies and infants. It contains all important nutrients and antioxidants helping infants survive, grow and develop.1 UNICEF and WHO, recommend breast milk to be fed to a child within an hour of birth and exclusively for six months.2,3 The World Health Assembly’s 55th round declared that there is none next to breastfeeding as an ideal food for development and growth of infants, breastfeeding has implications on the reproductive health of mothers also, and that infant should be exclusively breastfed for the first six months for their optimal growth, development and health, this would be the global public health recommendation to be followed by all.4
Global Nutrition Report (GNR), 2020 indicates, India made limited progress towards achieving exclusive breastfeeding targets (58.0% infant aged 0-5 months breastfed). India is 'on course' to meet the target for stunting with 34.7% of children under 5 years stunted (higher than average for the Asia region: 21.8%).5 There is a need to address these gaps. If stunting is addressed effectively and given that it is “on course”, India can achieve being a nation free of at least one of the “nutrition burdens” i.e. “stunting”.6 In this scenario, development practitioners and frontline health workers shifted attention from nutrition towards rapid response to the COVID-19 outbreak.
Myths around the transmission of the disease while breastfeeding need to be dismissed as there is no evidence of transmission of disease from mother to baby or from breast milk consumption by a baby.7-11 UNICEF (2020), in its brief on Infant & Young Child Feeding (IYCF) during COVID-19, highlights that where episodes of diarrhoea, respiratory infections and infectious morbidity are common in babies, the possibility of risk of transmission of COVID-19 while breastfeeding is yet to be reported and that known risks associated with replacement feeding are far more devastating.12
At the same time, the production, marketing and sale of breast milk substitutes were found to be steady globally during the pandemic (Fortune Business Insights, 2020). Such products have been distributed to the needy by promoting them as instant food for infants and food security to vulnerable people impacted by COVID-19. A decline in breastfeeding practices is expected, unless, strong measures are taken to practice and promote it. Achieving SDGs for equitable health and nutrition will be a challenge, in this light.
The article aims to ascertain the status of breastfeeding practices during COVID-19 pandemic times and the impact it may have on infants. The objective of this article is to review the challenges and prospects of breastfeeding in India during the COVID-19 pandemic period. Information from the secondary review of literature, case studies, and experience from on-ground rapid response to the COVID-19 pandemic was used to ascertain the current status of breastfeeding practices in India during the ongoing pandemic period.
BREAST MILK: A NATURAL PREVENTIVE AND COMPLETE BABY FOOD
An irreplaceable food for a baby, breast milk helps in gaining muscular weight, mental agility, physiological functions, capacity to fight pathogenic infections and diseases and is a rich source of antibodies and antioxidants.13,14,15 Exclusive breastfeeding for the first six months of infants and breastfeeding within the first hour of birth is a preventive measure for numerous diseases, infections and unwanted health outcomes of infants/ children.1 The likelihood of infants dying is 14 times more if they have not been breastfed, as breastmilk consumption protects from sudden infant death syndrome, it also catalyses childhood development and higher intelligence, and lowers the risk of getting leukaemia, obesity or type-II diabetes.16 Breast milk during the first 1000 days of an infant provides a unique opportunity for its future holistic growth and development of physical and mental health.1
STATUS OF BREASTFEEDING IN INDIA
Worrisome to note that percentage change of children under the age of 5 years who are stunted shows a minimal decrease of 0.3% between the 4th and 5th National Family Health Survey (NFHS), despite budget allocation, initiatives by the Government and other development partners under “Poshan Abhiyan” (a Govt. of India nutrition scheme). Further between NFHS-4 & 5 (Figure 1), the % change in exclusive breastfeeding for children under age 6 months is in the negative (-2.1%). It is expected, that in 2020-21 during the ongoing COVID-19 pandemic, with decreased access to supplementary nutrition, mid-day meals and take-home rations, as well as limited counselling on nutrition and breastfeeding practice by frontline health workers as a result of lockdowns, norms of social and physical distancing, the status of stunting and exclusive breastfeeding practices would further decelerate. This calls for a special study specifically to ascertain the status of stunting and breastfeeding practices during the COVID-19 pandemic and a speedup of initiatives by prioritizing on States.17,18
Figure 1. Status of change in percentage of stunting, breastfeeding within the first hour of birth and exclusive breastfeeding between NFHS 3 (2004-05) and NFHS-4 (2015-16), and NFHS 4 (2015-16) & NFHS-5 (2019-20), Source: NFHS-3, 4, & 517-19
The NFHS-5 survey (2019-20) reports 99% institutional births, yet it is reported that only 46.9% of the children were breastfed within the first hour of birth. Additionally, 46.9% of the children under age 3 years that were breastfed within the first hour of birth as reported by the NFHS-5 (2019-20), was a marginal increase from NFHS-4 (2015-16) where the exclusive breastfeeding status was 41.9%.17,18
Ogbo et al.20 undertook a study in India to evaluate the prevalence of Exclusive Breastfeeding Practices (EBF) and found differing EBF practices from region to region. EBF practices were as high as 79.2% in Southern India, while it was 68% in North-Eastern India. EBF prevalence declined with infant age, declining faster in the South (about 44% at 5 months) as compared to that in the North-East region (54% at 5 months). Authors established, the association between higher maternal education with EBF in the Southern region, and an opposite association of same in the Central region of India, additionally, they found mothers from more wealthy households were less likely to engage in EBF in comparison with poorer households of Central India. A path-breaking study established that regional and local solutions are the need of the day and not an overall strategy for improving breastfeeding practices and overall nutrition outcomes of children.
Kumar et al.21 observed that under-fives were at a significantly high risk of under-nutrition whenever there were delays in initiating breastfeeding, colostrum deprivation and improper weaning, thus, optimal infant feeding practices must be promoted ad protected in this context.
The percentage change (decrease) in stunting between NFHS 4 & 5 (2015-16 and 2019-20) is found to be 0.9%. GNR (2020) indicates that India will miss the bus to achieving local nutrition targets by 2025 as India stands among those countries which have the highest rates of social and domestic inequalities and malnutrition.5,6,17,18
COVID-19 AND BREASTFEEDING CHALLENGES IN INDIA
There is no direct evidence as yet of transmission of the SARS-Cov-2 virus which causes COVID-19 disease from lactating mothers to breastfeeding infants7-11 although there are myths doing rounds. However, the indirect impact of COVID-19 could be manifold. On separation from a mother infected by the disease or a mother’s death because of the disease or any myths around breastfeeding during the pandemic, infants can be indirectly impacted. Food security issues during the lockdown period leading to poor health & nutrition of the lactating mother may result in the inability to produce enough milk. This needs further exploration.
While, ASHA workers have been appointed as the first point of contact for any health-related demands of deprived women and children, who find it difficult to access health services22, including the important role of creating awareness on determinants of health including nutrition and counselling women on prenatal and anti-natal care, safe delivery, breastfeeding, immunization etc. yet, during the lockdown period in 2020, ASHA workers could not function optimally because of social and physical distancing, lockdowns and limited access to protective gear. Although the Ministry of Home Affairs (MHA) informed that all essential services would be functional across the country, additional responsibility was given to them for rapid response to COVID-19 by tracking COVID-19 transmission cases, thus diverting those from their core work. Thus, impacting nutrition and counselling on breastfeeding practices.23
Stakeholder consultations during rapid response to COVID-19 in Jagatsinghpur & Mayurbhanj district (Odisha) by Arupa Mission Research Foundation (AMR, 2020) to address food security and safety of the community, frontline workers (ASHA, Anganwadi) and elected representatives during the peak pandemic period in 2020 and learning from the case study from Angul district of Odisha by Saigal (2020) indicate the following important points24: a) Absence of Take Home Rations (THR) for pregnant & lactating women for initial two-three months; b) Messaging and counseling on breastfeeding or nutrition by ASHA and Anganwadi workers took a backstage as focus was on COVID-19; c) Clarification on myths around transmission of COVID-19 disease from lactating mother to breastfeeding infant in both urban and rural areas was not done clearly; d) Families feared to receive THR as they felt it may lead to transmission of disease; e) Lockdown and fear of disease left some children of reversed migrants and migrants nutritionally vulnerable; and f) Focus of elected representatives, local governance bodies and CSOs was more towards food security for other vulnerable groups including migrants, and elderly, and contact tracing of super spreaders of COVID-19 disease; not on nutrition of infants.
Kumar et al.21 surveyed 1292 mothers who delivered in rural Mysore Block of South India between 2008 and March 2011. They found the following: firstly, 23.7% of mothers felt that they did not have enough breastmilk and this was the most common reason cited by them for non-exclusive breastfeeding; secondly, 42.6% of non-exclusively breastfed infants were fed formula/animal milk and 18.4% were fed oil/ghee.
Food security was an issue during the COVID-19 pandemic and may have impacted the nutrition intake of lactating mothers. There were also myths around the transmission of COVID-19 from animal milk and dairy products. Both these factors may have led to poor breastfeeding practices and the onset of stunting. This needs to be explored. Bhatt26 reports that in Delhi, West Bengal and Jharkhand poor mothers and babies were donated milk substitutes during the pandemic, following which, Arun Gupta of Breastfeeding Promotion Network (BPNI), the Government’s appointee to monitor implementation of the ‘Infant Milk Substitute Act of 1992’25 which prohibits distribution and promotion of infant milk substitutes to children under two years of age, filed an action alert. The action alert was in the form of a public notification, “issued on the BPNI website and to the media, urging the central government to tell state authorities to stop acceptance and distribution of infant formula in pandemic relief. He also sent letters to the Ministry of Health & Family Welfare and the National Disaster Management Authority, highlighting the separation of mothers and babies in suspected and confirmed covid-19 cases in hospitals and asking for a committee to investigate formula companies that exploit the pandemic for commercial gain.”27
CAGR (Compound Annual Growth Rate) forecast for 2019 to 2025 baby food market size by-products is 13.5% with value projection being 33 billion USD in 22 geographies including India.29 Although India adopted ‘The International Code of Marketing of Breast milk Substitutes (the Code) 1981’28 and ‘The Infant Milk Substitutes, Feeding Bottles and Infant Foods (Regulation of Production, Supply and Distribution) Act, 1992’25 further amended in 200327, regulating production, supply, and distribution of infant milk substitutes, feeding bottles and infant food to protect/ enhance breastfeeding practices and regulated use of infant food, there is an evident spurt in baby food production worldwide. Thus the core objective of limiting the negatives of milk substitutes, its promotion and enhancing breastfeeding practices including exclusive breastfeeding is unmet. The aggressive baby food market in India, together with myths on breastfeeding during COVID-19 and the diverted role of frontline health workers to covid-19 rapid response, distribution of infant milk products to poor mothers instead of provisioning with a nutritious diet will certainly impact the breastfeeding narrative outcomes in India.29
WHO and UNICEF encourage women to continue to promote breastfeeding during the COVID-19 pandemic, even if mothers have confirmed or suspected being infected with COVID-19 and that the multiple benefits of breastfeeding prevail over the potential risks of illness associated with COVID-19, infant formula milk not being a safer or only option.30
PROSPECTS OF IMPROVING BREASTFEEDING DURING PANDEMIC
COVID-19 pandemic ushered in a new way of living. New strategies to improve breastfeeding practices and nutrition outcomes of infants are the need of the hour. No “one shoe fits all” formula for improving determinants of nutrition (including breastfeeding within an hour of birth and exclusive breastfeeding for six months) will be effective given the diverse Indian culture and geography. However, recommendations from nutrition experts have bright prospects to address concerns around breastfeeding and stunting. Direct and indirect impacts of COVID-19 on breastfeeding practices and infant nutrition needs to be unfolded.
Menon et al.,31 on their district?focused analysis of stunting considered breastfeeding within an hour of birth and EBF as immediate determinants of stunting in India and recommended that there was a need for nationwide prevention of stunting as well as resolving district level variations of critical determinants of nutrition as well as inequalities and childhood stunting highlighted that in the Indian context if breastfeeding education is imparted at every anti-natal checkup, then even mothers with less than 10 checkups can learn about the benefits of breastfeeding and its methods. In the context of the COVID-19 pandemic, myths and fears doing rounds during the pandemic need to be studied and a campaign to address these via breastfeeding education implemented.32
Myths and fears doing rounds during the pandemic need to be studied and a campaign to address these via breastfeeding education is needed. Ogbo et al.,20 stated that multidimensional efforts at national and sub-national level, dedicated financial allocation, appropriate policies need to be in place to address the regional variations in breastfeeding practices in India were much needed. Concerning the COVID-19 pandemic, regional variations in EBF practices during the ongoing pandemic needs to be studied and addressed.
American Association of Pediatrics11 emphasized that infants should be exclusively breastfed for the first six months of life and only fed infant formula which is fortified with iron in case human milk is not available at all. Thus in the context of COVID-19, when it is established that the disease is not transmitted through breastmilk, then myths on it should be dismissed and breastfeeding promoted.
NFHS 5 (2019-20) data has been released for 22 States and Union Territories (UTs) of India. Based on findings from the NFHS 5 for two indicators: stunting among children aged 0 to 5 years and 0 to 3 aged children having been breastfed within an hour of birth, States and UTs can be divided into low risk, medium risk and high risk. High-Risk States and UTs must have a robust programme to ensure breastfeeding practices are improved for better nutrition outcomes of infants, especially for reducing future stunting. The objective of this categorization is to consider stunting and limited or lack of breastfeeding practices as risks to infant and child nutrition which is detrimental to their growth and, which when coupled with the challenges that COVID-19 has posed with regards to access, services and myths, may be disastrous for the nutrition outcomes of children. It may be so that in the event of natural calamities or epidemics and pandemics infants and children under three years of age would be doubly vulnerable not because of the disease as much as because of access, consumption and practice of nutrition. The strategy also lays the ground for focusing initiatives based on State needs rather than a one shoe fits all strategy as given below:
Category 1: High-Risk States
Those States which have high stunting levels (36% to 66% or more, Figure 2) and the percentage of children under the age of 3 years breastfed within the first hour of birth varies between 0 to 35%. The Graph below indicates the same:
Figure 2. States with a high risk of stunted children 0 to 5 years and high-risk status of children in 0 to 3 years breastfed within an hour of birth (NFHS 5, 2019-20) in percentage.
According to the NFHS 5 data, Bihar has 42.9% of children in the age group of 0-5 years who are stunted, breastfeeding practice within an hour of birth is as low as 31.1% for children in the age group of 0 to 3 years and breastfed children exclusively breastfed for 6 months stands at 58.9 %. Similarly, two other critical States that need attention on priority are the Union Territories of Dadra & Nagar Haveli and Daman & Diu and the States of Gujarat, and Meghalaya. Meghalaya is an outlier, with a High proportion of stunting of children aged 0 to 5 years (46.5%) despite having a proportion of 0 to 3 year aged children breastfed within 1 hour of birth as high as 78.8%. The probability is that exclusive breastfeeding which is as low as 42.7% in Meghalaya, may be contributing to the stunting along with other factors. Meghalaya needs to be studied from the perspective of the impact
Category 2: Medium Risk States
Those States have the proportion of stunting varying from16% to 35% and the percentage of children under age 3 years breastfed within the first hour of birth varying from 36% to 66% (Figure 3). The States coming under the Medium Risk category include Andaman & Nicobar Islands, Kerala, Manipur, Goa, Jammu and Kashmir, Mizoram, Ladakh, Himachal Pradesh, Andhra Pradesh, Lakshadweep, Tripura, Nagaland, Telangana, West Bengal, Maharashtra, Assam and Karnataka. Sikkim is an outlier with a medium risk of stunting and a high risk of poor breastfeeding practices and would require special attention. The graph below indicates the same:
Figure 3. States with medium risk of stunted children in 0 to 5 years and medium risk of children in 0 to 3 years breastfed within an hour of birth (NFHS 5, (2019-20) in %
Category 3: Low-Risk States
Those States which have low stunting (0 to 15%) levels and the percentage of children under age 3 years breastfed within the first hour of birth varies from 67% to 100%. It may be noted that none of the States come under the low stunting category. However, the data for breastfeeding within an hour of birth is as high as 76.3% in Lakshadweep and 78.8% in Meghalaya. Given that Lakshadweep has 32% stunting, it has been included in the Medium Risk category and Meghalaya having 46.5% stunting has been included in the High-risk category.
A robust plan by prioritization of States and UTs with separate strategies for high and medium risk States is the need of the hour especially in the light of the COVID-19 pandemic and its indirect impact on infants and children (Table 1). If we map the on the ground experience of the lockdown from 25th May 2020 onwards in India with the categorization of States based on stunting & breastfeeding as given in Table 1 below, we can correlate the risks to infants in the context of stunting and breastfeeding practices, as well as the level of risks.
As part of the prioritization strategy mentioned earlier, serious thought needs to be given to the risk to infant’s access to breast milk and nutrition because of limited counselling of mothers by frontline health workers during the pandemic and limited access to take-home rations. The fears and myths around COVID-19 and breastfeeding can be minimized through a focused campaign and behavioural change methods. Further, the need of the hour is also to conduct a nationwide study on the impact of the indirect impact of COVID-19 on breastfeeding practices and infants thereof. “Capacity Building and Training of Frontline workers on managing nutrition concerns and innovative ways of delivering roles and responsibilities during natural calamities, epidemics and pandemics.”23
Additionally, display of nutrition status at the Anganwadi and Panchayat Office. Mapping of nutritionally vulnerable children to be done, especially children of reverse migrants and migrants already present in the local area.23 Other to do’s include, provision on personal protection equipment to frontline workers, busting the myths around the transmission of COVID-19 disease through breast milk since research is ongoing and no evidence has been found as yet. Promotion of WHO and UNICEF norms for breastfeeding during the COVID-19 times, Participatory and interactive ICT based strategies for motivating working women and educated women to practice exclusive breastfeeding., Greater role to be played by Panchayats/elected leaders and civil society for addressing issues around the promotion of breastfeeding, stunting and COVID-19 preparedness and prevention in general and Effective implementation of the Food Security Act, especially for Take-Home Rations for pregnant and lactating mothers and the monitoring of the same.23
Importantly, the Government needs to put strict directives for regulation of the distribution of manufactured baby food during such episodes of disease outbreaks unless it is truly a need where natural breastfeeding is not an option. The messaging by manufacturers of baby food products needs to be monitored so that they don’t take advantage of such situations for their profits. Advocacy for stricter norms for the reduction in production, marketing, advertising and push selling of artificial infant formulas, baby food and milk supplements.23 The importance of having social and domestic support for mothers for sharing breastfeeding experience, voicing concerns and initiating mothers support groups at the community level (both rural and urban) as part of the strategy. For future studies, clear subject selection criteria and definition of “exclusive breastfeeding”, reliable collection of feeding data, controlling for important confounders including child-specific factors, and blinded assessment of the outcome measures will help.33 Empirical studies on breastfeeding practices during the COVID-19 pandemic will inform on minimizing risks to nutrition because of pandemics, relevant information for all stakeholders to design and implement effective strategies for better nutrition outcomes for India. Addressing the burden of stunting effectively, given that it is on course, India must at least work towards achieving being a nation free of at least one of the nutrition burdens which is “stunting” with political will, right strategies and proper implementation.
CONCLUSION
India is at a critical juncture about nutrition outcomes. It is expected that India will miss achieving nutrition goals unless need-based measures are taken. It is found that there can be no one she fits all solution to address the nutrition gaps in a country with diverse cultures ad ways of living. Further, with the COVID-19 pandemic, access to nutrition and nutrition-based services had become restricted. Also, there were many myths around breastfeeding practices and the spread of the disease. Baby food manufacturing companies are also taking advantage of the situation. Given these challenges, India still has prospects to be a nation free of stunted children if the right strategies and prioritization of initiatives for improving breastfeeding practices and reducing stunting are adopted. There is scope for further empirical studies on how and to what extent breastfeeding practices and nutrition were impacted in the country during the ongoing pandemic. This would also be important for improved future risk management and preparedness in such situations of pandemics. Recommendations for national, regional, district-level need-based interventions based on variations and prioritization of strategies for high, medium and low-risk States is critical.
CONFLICT OF INTEREST: None
SOURCE OF FUNDING: None
ETHICAL APPROVAL: Not applicable for this manuscript
ACKNOWLEDGEMENTS: The authors thank Nibal Dibiat for his sincere comment on the draft and formatting of the manuscript. Further, we appreciate the body of work by various scholars and acknowledge that their articles cited & referenced in this manuscript has been of immense help. We are also grateful to authors/editors/publishers of all those articles, journals and books from where the literature for this article has been reviewed and discussed.
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8. Davanzo R, Moro G, Sandri F, Agosti M, Moretti C, Mosca F. Breastfeeding and coronavirus disease?2019: Ad interim indications of the Italian Society of Neonatology endorsed by the Union of European Neonatal & Perinatal Societies. Matern Child Nutr. 2020;16(3):e13010.
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11. Breastfeeding guidance post-hospital discharge for mothers or infants with suspected or confirmed SARS-Co V-2 infection. Dostupno na https//services aap org/en/pages/2019-novel coronavirus-covid-19-infections/breastfeeding-guidance-post-hospital discharge/(Pristupljeno 2504 2020). 2020;
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareImpact of the COVID-19 Crisis on the Working of Saudi Women, and her Role in Confronting Them
English140145Haifa Abdulrahman Bin ShalhoubEnglish Mohammad Ahmed HammadEnglishBackground: The COVID-19 epidemic has undoubtedly affected the working conditions of large segments of society. More specifically, a growing body of studies has raised the possibility that precautionary measures and closures, as a result of, the COVID-19 crisis could affect women and men working in different ways, mostly due to the traditional division of domestic work between the genders in Saudi society. Objective: In this study, we are trying to explore how the impact of the closure epidemic on domestic responsibilities and the work from home on men and women. Methods: The researchers developed a questionnaire to identify the impact of the closure on childcare, domestic chores, and the work environment within the home, and applied it to 370 faculty members and teachers, with an average age of (38.5±9.6). Results: The results indicated that there were statistically significant differences between men and women in childcare and domestic chores, which affected in favour of women. Additionally, the results indicated that the sample of students with children was significantly affected during the lockdown compared to peers without children. However, there were no differences between the faculty staff and the teachers on the dimensions of the questionnaire. In addition, there were no differences in the level of age over the questionnaire dimensions between them. Conclusion: Based on these results, the study recommended the importance of urging university officials and the Department of Education to provide a range of rescue and stimulus packages, including support to faculty members and female teachers by providing flexible working hours after the epidemic, part-time work arrangements, telecommuting, support during pregnancy, and parenting. In addition, they should take into account the disparity between women and men in domestic responsibilities when evaluating for scientific promotion or managerial positions.
English Saudi women, Crisis confronting, working conditions, COVID-19 pandemicINTRODUCTION
By late December 2019, the COVID-19 epidemic, which is so lethal, plagued the entire world in Wuhan city and rapidly worsened around the world in the first three months of 2020.1,2 Therefore, The Kingdom of Saudi Arabia government has imposed a policy of strict quarantine and physical separation between its people through towns and cities. To, control the infection source, and reduce the spread of the epidemic in society. Likewise, implemented various emergency measures, including government agencies with employees working from home via the Internet. On March 8, 2020, The Ministry of Education announced that, under preventive and precautionary measures to control COVID-19, it was decided to suspend the study in all regions and governorates of the Kingdom and to activate the distance learning system during the suspension period.3
MacIntyre, Gregersen state that online teaching is the dominant method of teaching now, as it replaced the traditional method of teaching.4 As well as, the lack of external activities for children to vent all suppressed energy has increased the competition between siblings. The fact that the house was quarantined escalated the situation. As a result, children get out of their tension through tantrums and violent outbursts., marking an escalation of the crisis among parents.5 In line with, Boretti despite unprecedented national measures to combat the spread of the disease which have contributed to reducing the increase in the rate of infection and fatality, as well as to reducing the prevalence of the epidemic within the Kingdom.6 These precautionary decisions have had a significant impact on changing the daily lifestyle both within the family and the work within Saudi society. Furthermore, there was a significant negative impact on the level of job performance from home during the closure period.7 Many recent studies have shown that the boundaries of work and family are becoming blurred, and the gender distribution of responsibilities within the family is becoming clearer.8-10 In the same place, Zamarro, Perez-Arce point out that gender inequality has worsened during closure.11 More specifically, strike a balance between personal professional roles is a challenge for many women, especially working women, who had children who needed their attention. In particular, the global epidemic, COVID-19, has caused a lot of difficulties for women: health concerns for self and loved ones, social and material divergence, travel restrictions, closed borders, lack of daily necessities, work pressures, and demands.4,12 Accordingly, Increase women's responsibilities as primary caregivers and as employees who need to work from home. This was previously described as the double burden or second shift, increasing demand for both family and work.13
Once women have children and take care of their responsibilities, gender inequality is further strengthened.10 This double burden is one of the obstacles to work-life balance where the negative impact between work and domestic duties has a significant effect on women.14 Moreover, some studies have indicated that the boundaries of work and family are becoming blurred, and the gender distribution of responsibilities within the family is becoming clearer.9,10 Besides, some recent studies point out that gender inequality has worsened during the quarantine period.15,16 Adisa et al. suggest that if state governments do not undertake proactive interventions to reduce these consequences, the COVID-19 crisis and beyond will have many negative consequences for women and families for many years.17 Likewise, Zhou indicates that many families need to raise and educate their children without the support of educational and educational institutions, which will put more pressure on mothers than men inside the house.18 However, if parents do not increase their household contributions, the epidemic may exacerbate gender gaps in childcare and the burden of domestic work at the expense of women's work obligations. Furthermore, Thébaud et al. imply that women and men in some countries may assert that the domestic tasks, which should be performed will be equal for each, but men are likely to ignore these responsibilities, leaving them to the wife.19 From this perspective, greater clarity in the distribution of childcare and domestic work responsibilities may not be a motivation for men to fulfill their homework responsibilities. Instead, the loss of childcare support through educational institutions may increase women's unpaid domestic as well as job work, causing further disruption to their jobs and working lives.20
This study assumes that actions resulting from the COVID-19 epidemic have increased the couple's time at home with family and children, while reducing time in paid work for many people. However, the main question is whether moving to work at home, home education and self-isolation hurt women more than men. For instance, Jessen and Waights report that working mothers combine their paid work with the care and education of their children during the COVID-19 crisis by working long hours in the evening.21 In the same context, Andersen et al. express that the spread of COVID- 19 has led women to devote more time to caring for and educating their children, while men remain relatively less affected.22 In addition, Collins et al. point out that when examining a sample of couples from February to April 2020 in the United States of America, mothers with young children reduced their working hours from four to five times more than fathers. As a result, the gender gap in working hours had widened by 20-50 percent, which had a negative impact on women.20
Likewise, while women were already doing most of the unpaid care work in the world before the emergence of the COVID- 19 epidemic, emerging research suggests that the crisis and its post-closure response have significantly increased the burden on women.8 In particular, women suffer a greater reduction in well-being than men during the crisis.23-25 According to other results, Andrew Set up that women bore the majority of overtime (childcare and domestic work) in Italy and the United Kingdom.26 As well, Adams-Prassl state that women were more likely to lose jobs than men.27
The authors argue that the epidemic had a clear impact on the parents’ work and that women were more affected in their careers than men during the COVID-19 crisis. Therefore, we join this growing body of research in trying to illustrate gender differences in employment during the COVID-19 crisis. As far as we know, there are no published studies showing gender differences in job performance during the COVID-19 epidemic in Saudi Arabia. Thereby, this study aims to measure the degree of gender differences in the level of job performance during the COVID- 19 epidemics in Saudi Arabia. Accordingly, the problem of the study could be formulated in the following main question: Are there differences between males and females in the level of job performance during the COVID- 19 pandemic in Saudi Arabia?
MATERIALS AND METHODS
Study Design and Sample
This study uses data from a CT survey conducted in Saudi Arabia, following the end of curfew and closure. More specifically, the authors used an online questionnaire distributed through social media apps, and participants were encouraged to distribute the questionnaire. Participants received the request for a survey through WhatsApp groups of colleagues, family or friends, faculty, and teachers in Riyadh and Najran, Saudi Arabia. Informed approvals were obtained via the Internet before questions were followed up. In this case, informed consent offered two options of "yes," for those who volunteered to participate in the study, and "no," for those who did not want to participate. Only those who chose the positive answer were taken to the questionnaire page to complete the questionnaire.
Respondents were clearly informed of the purpose and objectives of the study and they were free to withdraw at any time, without giving reasons, and all information and opinions provided would be anonymous and confidential. The study protocol was approved by the Board of Institutional Audit of Princess Nourabint Abdurrahman University in Riyadh. Surveys were completed by 380 responding parents. A total of 10 cases were excluded because the response was contrary to the attached instructions with the questionnaire, of the remaining 370 respondents, 244 (76%) were women, and 126 (34%) were men, with an average age of (38.5±9.6). as well as, 67% of faculty at Princess Noura and Najran Universities, 32% of teachers in Riyadh and Najran education. 86% have children, 89% work in the government sector.
Questionnaire
A questionnaire has been built to collect data by researchers after reviewing relevant literature.7,8,17,22,25,26 The questionnaire consists of two main sections: Section I, collected information on the socio-demographic characteristics of respondents, including age, gender, marital status, level of education, and employment status. Section II, collected information on significant changes in domestic work and working conditions after closure, consisting of three dimensions: the first dimension, measures the impact of the work from home on performance during the COVID- 19 crisis, it has 7 items. For example, I have to complete my job work at night when the boys go to sleep. The second dimension measures the usual role in doing the domestic work and caring for children, and it contains 5 items. For example, the COVID- 19 crisis has greatly affected my habits in caring for my children. The third dimension measures the contribution to domestic work and childcare after the COVID-19 crisis and contains 6 items. For example, my contribution to domestic work takes more time after the COVID-19 crisis.
The Likert 3-point scale was used (agree - neutral - disagree), the scores were distributed from 3 to 1, 1 to "disagree," 3 to "agree." The questionnaire was tested in terms of face, content, and constructiveness by an arbitration panel of 3 specialists in sociology and psychology. Instrument reliability was done using Cronbach’s Alpha coefficient test, indicating high reliability of three dimensions (0.88, 0.92, 0.89, 0.91), total questionnaire (R = 0.90)
Data analysis
We applied descriptive and inferential statistics to analyze the data. The descriptive statistics included frequency, percentage, average, and standard deviation; these were analyzed using SPSS 21 (IBM., 2012). To address the research question, we conducted a univariate analysis to compare the differences between participants’ characteristics based on the impact of the COVID-19 pandemic on their professional work.
RESULTS
Table 1 shows the descriptive statistics of the main variables. A total of 370 parents from Riyadh and Najran participated in this study. The number of men was lower than the number of women, which was 126, with an average of (33.06%), and the number of women was 244, with an average of (66.94%). All of the sample members were employed in the field of education, both the 250 faculty members in universities, with an average of (66.94%), and the rest of the sample of teachers in general education schools, the age of the sample was divided into four levels, with the highest number in the sample aged 40-49, reaching 34.5% of the total sample. As well, the number of parents with children was more than the number of parents without children, parents with children accounting for 86.4% of the total sample.
Table 2 shows how the closure period has affected the level of work performance of faculty staff and teachers differently for both men and women, as demonstrated by the responses to the three questionnaire dimensions. The first dimension, the performance has been affected by your work from home. The second dimension, your routine in doing your domestic work and caring for children. The third dimension, your contribution to domestic work. The gender variable in the three dimensions of the questionnaire is statistically significant, indicating that the COVID- 19 crisis disproportionately affected the working conditions of female faculty members and teachers, compared to their male counterparts. In addition, faculty staff and teachers with children report that they were significantly affected during the closure period compared to peers without children. However, there were no differences between the faculty staff and the teachers on the dimensions of the questionnaire and no differences in the level of life of faculty staff and teachers.
DISCUSSION
The COVID- 19 crisis caused radical changes in the working life of most parents within and outside the family. Therefore, many measures have been taken; its impact on Saudi society has been significant, such as closure, social exclusion, and self-isolation. According to the evidence, the impact of the epidemic on families with children in education was more severe, especially when educational institutions and childcare places were closed down. The impact on parents within the family working as teachers or faculty members may reasonably be expected to be uneven.
Based on a survey of faculty staff and teachers in schools in Riyadh and Najran, Saudi Arabia, the gender gap in the impact of the COVID-19 crisis on the working conditions of academics has been notable and statistically significant. As well, the gap was worrying between teachers and faculty members with children compared to those without children. More specifically, the daily routine of female teachers and faculty with children has been disproportionately affected by the closure associated with the epidemic, as the burden on women has increased. Hence, these results largely correspond to the results of several studies indicating that mothers with young children have reduced their working hours from four to five times more than fathers work. As a result, the gender gap in working hours has widened by 20-50%.8,10,21,26
These findings point to another negative effect of the COVID-19 epidemic, highlighting the challenges that pose to women's working hours and employment 20. Furthermore, these results are consistent with the results of other studies, which indicate that work in universities, where career advancement, depends on the number and quality of a person's scientific publications, is not essentially compatible with childcare.10 In the same vein, Lutter and Schröder indicate that having children leads to a decrease in women's academic productivity compared to men's.27 In this case, closing schools and caring for children means that children are at home, and need care, for at least six more hours a day. Mothers do less paid work two hours a day than fathers, but they do childcare work and domestic work within two more hours each. Accordingly, mothers combine paid work and other activities (almost childcare) in 47% of their working hours, compared to 30% of fathers' working hours.22 Likewise, women had significantly reduced working time than fathers, especially those with primary school-age children or younger children at home, whose care and home education requirements are severe.20
Our results provide strong support for recent research that has found similar gender gaps.20 Our findings indicate that the traditional gender distribution of work within the family disproportionately affects men and women working as teachers and faculty members. Despite the results of our studies may not allow us to explain the causal mechanism at work, one of the most acceptable possibilities, consistent with the culture of our Arab societies, is that closure may have forced women teachers and faculty members to give priority to home care and child care responsibilities, to promote traditional gender roles in the home.20 We believe that, under the current circumstances of the absence of a specific date for the normalization of life, through the normal return of students to their schools and universities, the gender gap in the perceived challenges of family care and work requirements is unlikely to fade soon, if there is a disruption of educational institutions as a result of the worsening of the epidemic in the coming months. Moreover, there were no differences between the faculty staff and the teachers, due to the similar working conditions of both faculty and teacher, as students are taught online, each with a school schedule and required teaching hours, and they have the same household tasks and responsibilities.
Certainly, the increasing importance of distance learning by the Saudi Ministry of Education will require many faculty staff and teachers in educational institutions to reorganize their teaching strategy for Internet connectivity, which may come at the expense of faculty research activities or teacher promotion requirements. Thus, while it is too early to know the long-term consequences of this trend for faculty research activities or promotion requirements for teachers, the gender gap in perceived disorders in daily routines may translate into gender disparities in meeting fully occupational requirements. Future research that goes deeper into these possibilities may help us better understand how the COVId-19 epidemic affects families around the world.
CONCLUSION
Our paper adds to previous literature on gender equality, an important topic in the social sciences, and the COVID- 19 crisis has highlighted a long-standing problem. More specifically, our Arab societies, as a result of, the culture of masculine society; the inequality faced by women, who often do more childcare and domestic work. We contribute to the literature by providing new studies illustrating the impact of the epidemic crisis on gender inequality in academia and education. According to the study results, female faculty members and teachers are unable to prepare promotion research or to promote as teachers to higher positions in a position of vulnerability compared to male faculty peers and teachers, as it is a justice issue that may expose women to higher unemployment or occupational risk in the future.
We hope that our findings will increase awareness of this problem. Some measures can be taken to ensure that domestic responsibilities are balanced between spouses. As a result, universities and education departments could provide additional support, such as childcare support, to female faculty members and teachers whose research productivity or promotion may be disproportionately affected. Universities and education departments should take this disparity into account when evaluating for scientific promotion or managerial positions. Despite the advantages offered by remote work of the opportunity for parents to take care of their children while completing their professional tasks, on the other hand, remote work may have unintended consequences for gender inequality. Thus, educational institutions should take gender equality into account when designing and implementing telecommuting policies. We hope that the results of this research will encourage officials to view this vital issue in greater depth, and to provide full support to female faculty and teachers by providing more flexible working hours after the epidemic ends, part-time work arrangements, telecommuting, support during pregnancy, and parenting. Thereby, supporting work-life balance and the quality of its practices are crucial factors in facilitating women's quality work. However, the study has a few limitations. Firstly, since the study is CT, the results may not be generalizable to other professions. Secondly, the small sample size means that the results cannot be disseminated to all female faculty and teachers in Saudi Arabia and Arab communities. However, if we want to circulate it within Saudi Arabia and other Arab communities, it will be cautiously.
Funding: We are thankful for funding from the Center for Promising Research in Social Research and Women's in Princess NourahbintAbdulrahman University in the Kingdom of Saudi Arabia in 2020.
Acknowledgements: We acknowledgements the Deanship of Scientific Research and Center for Promising Research in Social Research and Women's in Princess Nourabint Abdulrahman University in the Kingdom of Saudi Arabia for its support and facilitation of the procedures for implementing the study. In addition, we grateful to the faculty members and teachers for their participation and cooperation with us in implementing the study tools.
Conflicts of Interest: The author declares no conflict of interest
Author’s contribution:
Haifa Abdulrahman Bin shalhoub: Project development, data collection and management, data analysis and manuscript writing.
Mohammad Ahmed Hammad: Data analysis, manuscript editing and Statistical analysis.
Both uthors have read and approved the manuscript.
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4. MacIntyre PD, Gregersen T, Mercer S. Language teachers’ coping strategies during the Covid-19 conversion to online teaching: Correlations with stress, wellbeing and negative emotions. System. 2020;94:1-14.
5. Al-Balushi B, Essa MM. The impact of COVID-19 on children− parent’s perspective. Int J Nutr Pharmacol Neurol Dis. 2020;10(3):164-165.
6. Boretti A. COVID-19 fatality rate for Saudi Arabia. J Glob Antimicrob Resist. 2020;22:845-846.
7. Almaghaslah D, Alsayari A. The effects of the 2019 Novel Coronavirus Disease (COVID-19) outbreak on academic staff members: a case study of a pharmacy school in Saudi Arabia. Risk Manag Healthcare Policy. 2020;13:795-802.
8. Alon TM, Doepke M, Olmstead-Rumsey J, Tertilt M. The impact of COVID-19 on gender equality. NBER. 2020;2898-2937.
9. Cui R, Ding H, Zhu F. Gender inequality in research productivity during the COVID-19 pandemic. arXiv preprint arXiv:200610194.2020;1-30.
10. Yildirim TM, Eslen?Ziya H. The differential impact of COVID?19 on the work conditions of women and men academics during the lockdown. Gend Work Organ. 2021;28:243-249.
11. Zamarro G, Prados MJ. Gender differences in couples’ division of childcare, work and mental health during COVID-19. Rev Econ Househ. 2021;19(1):11-40.
12. Czymara CS, Langenkamp A, Cano T. Cause for concerns: gender inequality in experiencing the COVID-19 lockdown in Germany. Eur Sociat. 2021;23(sup1): S68-S81.
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14. Mushfiqur R, Mordi C, Oruh ES, Nwagbara U, Mordi T, Turner IM. The impacts of work-life balance (WLB) challenges on social sustainability. Empl Relat. 2018; 40(5):868-888.
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18. Zhou X. Managing psychological distress in children and adolescents following the COVID-19 epidemic: A cooperative approach. Psychol Trauma. 2020; 12(s1): 576-578.
19. Thébaud S, Kornreich S, Ruppanner L. Good housekeeping, great expectations: Gender and housework norms. Social Methods Res. 2019; 1-29.
20. Collins C, Landivar LC, Ruppanner L, Scarborough WJ. COVID?19 and the gender gap in work hours. Gend. Work Organ. 2021;28:101-12.
21. Jessen, J., & Waights, S. (2020). Effects of COVID-19 daycare centre closures on parental time use: Evidence from Germany. Retrieved from https://voxeu.org/article/covid-19-day-care-centre-closures-and-parental-time-use.
22. Andersen JP, Nielsen MW, Simone NL, Lewiss RE, Jagsi R. Meta-Research: COVID-19 medical papers have fewer women first authors than expected. Elife. 2020;9:1-7.
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareAvailability of Healthcare Resources on Equitable Bases in the United States During Covid-19: A Review on Health Economic Aspects
English146151Narasimhan SEnglish Zubair SMEnglish Narasimhan MSEnglishBackground: COVID-19 is a respiratory ailment spreading from human to human unprecedently bringing about a condition of a pandemic. Rehearsing individual cleanliness is carefully suggested by the U.S. as a prudent step. In any case, to control the ailment spread at the network level there is the need to guarantee an even-handed conveyance of the medicinal services offices among the favoured and oppressed populace of the U.S. Objective: To understand the resource allocation of the healthcare sector in the U.S. and the challenges faced while mitigating the economic impact on the U.S. during COVID-19. Methods: The literature review was performed using databases such as PubMed, Scopus, EBSCO and Google Scholar for the government reports released. Results: Although the U.S. has taken several measures to control the impact of COVID-19 on the population as well as the economy. The underprivileged and some minority groups in the country have been affected by this pandemic. Health insurance plan has been modified in favour of the people so, they could have access to healthcare services. However, in the entire effort of mitigation, the country has faced certain challenges. Compare to the whites, Native Americans, African American and LatinX are facing consequences of the lockdown of the country at a greater intensity. Due to unemployment access to basic necessity was not possible for poor people. The downfall of the economy created a divide in the rural and urban hospitals on the grounds of revenue, availability of health resources and specialists. Conclusion: It is essential to contain and get ready to alleviate further episodes, especially in nations with battling or under-resourced health system framework. As a consequence, marginal propensity to consume and marginal propensity to save comes down to per capita expenditure of individuals on health and medical care hikes.
English COVID-19, Economics, Healthcare sector, Health resources, Health insurance, United StatesINTRODUCTION
Coronavirus is an emerging zoonotic disease that is causing public health threats all over the world. The history of human coronavirus. started in 1965.1 It is a disease of animal origin as the natural reservoir of coronavirus is found in cats, bats, cattle and camels.2 This new group of viruses was named coronavirus (corona denoting the crown-like appearance of the surface projections).1 The only method to address this highly fatal and contagious disease is to provide prompt symptomatic treatment.3 Center for Disease Control (CDC) updated on 4th June the total number of cases in the U.S. is 1,842,101 and total deaths are 107,029.4 The mortality rate ranges between 0.25% and 3.0%.11.5 The vulnerable population have elevated fatality rates such as person aged 80 years and the person with comorbidities like cardiovascular disease and diabetes.6
COVID-19 is a respiratory disease transmitted by aerosols and fomite is spreading from human to human unprecedently resulting in coronavirus infection worldwide leading to a pandemic state. Practising personal hygiene is strictly recommended by the nation as a precautionary measure. However, to control the disease spread at the community level there is the need to ensure there is equitable distribution of the healthcare facilities among the privileged and underprivileged population of the U.S. The population of the U.S. is 329748944 and the demand for healthcare services is high as the COVID-19 cases continue to grow at a speedy rate.7 As the vaccination for COVID-19 is yet to innovative, early diagnosis and symptomatic treatment is the only way to stop the disease from worsening. Hence it becomes important that all people receive early and prompt treatment at the initial stage of the disease.
The paper focused on the country’s preparedness in terms of budgeting and allocation of healthcare resources and funds on an equitable basis. The steps lay down by the U.S. to combat the disease through health insurance schemes. Also, the challenges faced by the country to mitigate the economic impact on health sectors in this pandemic. The review paper will be contemplating the two principal of health economics explicitly efficiency impacts and the equity impacts during the COVID-19 outbreak in the U.S. Both the outcome of the concept is to measure the quality-adjusted life years (QALYs). Efficiency in health economics is used to get the finest cost for money for the health care resource.8 It is the relation between resource inputs such as costs, in the terms of labour or equipment and intermediate outputs or final health outcomes i.e. QUALYs.
MATERIALS AND METHODS
Databases and government official websites providing information regarding health economics and insurance of the U.S. in the state of COVID-19 pandemic were considered. Databases such as PubMed, Medline, EBSCO host, dimensions and web of science were searched for the impact on the healthcare sector due to this pandemic from 2019. The language of included articles was restricted to English only. Data extracted to meet the objectives of the paper are costs to the health system, allocation of health care resources, health insurance companies, and health system funding and fiscal implications. Accessibility to healthcare facilities, out-of-pocket expenditure, impoverishing, health status., vulnerable groups (Existing challenges) and mitigation strategies to reduce the impact of the pandemic.
RESULTS
Status of healthcare resources as per the American Hospital Association (AHA)
As per the report of AHA of 2018, the U.S. maintains 5198 community hospitals and 209 federal hospitals. The availability of hospital beds in community hospital are 792,417 and ICU beds counting is 96,500 which includes beds for neonatal and paediatric. The ventilators specifically required in the treatment of the COVID-19 are approximately 62,000 in the entire country. The buffer stock according to Strategic National Stockpile is estimated to be around 10,000 to 20,000. The ventilators with basic functions which can be of great help during the shortage of resources are 98,000 because there exist certain norms for the amount of supply and manufacturing the ventilators.9
The present scenario in the country states there is an urgent need for trained health care professionals specializing in the respiratory disease speciality such as respiratory therapists and trained critical care staff who are capable of managing the ventilators. Availability of the doctors as well as other healthcare providers in the hospital at all shift is one of the necessities to be met. The nation possesses 76,000 full-time respiratory therapists and 512,000 critical care nurses only from Community hospitals.9 As per the law in California, for every four ventilated patients one respiratory therapist should be allotted. If followed then with the available respiratory therapist only 100,000 patients can care per day.10
Healthcare professionals and providers are facing a shortage of essential PPE like N95 masks, regular masks, protection gowns and diagnostic kits. The letter requesting the grant of funds worth 100 billion U.S.$ has been issued to the speaker of the house Congress. The appeal is done by AHA President and CEO, Executive Vice President and CEO of the American Medical Association, nevertheless CEO of American Nurses Association Enterprise too.11
Health insurance and out of pocket expenditure
COVID-19 is a public health emergency to sensitize the disease concern and to encourage people to take a righteous decision in terms of testing and treatment. Americans have been allowed to avoid governmental or monetary interruption to seek medical help regarding COVID-19 and all the medical care services are provided by a health plan will be without deductibles. An individual paying a high deductible health plan (HDHP) under the Internal Revenue Service (IRS) as a part of their health insurance can avail the health grant to test and treat COVID-19 without deductibles. If the deductibles paid towards HDHP will be with minimum deductibles for self or family.
The HDHP fulfils to deliver health benefits for testing and treatment of COVID-19. without claim to deductible or cost-sharing. This health plan satiates constraint with certain requirements to minimum deductibles and maximum out of the pocket expenditure. A certain amount is paid by the policyholder before the insurance provider starts bearing the expenses. Under this plan as preventive care, vaccination is pondered with HDHP.9
Efficiency impacts
The treatment of COVID-19 is pricy and variable, this requires a good trace of finances. Which will help to support the financial crisis to make up for the COVID-19 outbreak to contain several hospitals in the U.S. who are trying to protect the capital instead of using them in elective procedures and store buffer stock to treat the COVID-19 patients in a state of emergency.11
The current situation of rising coronavirus cases in the country has led to the paucity of basic healthcare resources like beds and ventilators besides, the scarcity of specialised doctors and nurses. Those on duty are either infected or being quarantined due to exposure. The impact of this pandemic has left with a scarcity of healthcare providers in the big cities as well. It has been observed, in the rural hospital's space is less, health care supply is inadequate to meet the increasing cases of COVID-19. Now, the U.S. priority is to make it cost-effective by reducing the gap between therapeutic needs and the supply chains productions of the resources and allocation of scarce resources efficiently among the population in serious.10
To keep the hospital financially stable, manage the limited resources and reduce the spread of the disease, many hospitals closed outpatient departments. Some of them started postponing or cancelling elective visits and procedures. This step affected the financial viability of a few hospitals as the revenue from the outpatient department (OPD) and elective visits were hampered. According to the 2014 reports of Healthcare Research and Quality, out of total inpatient revenue, 30% is contributed from elective admissions. Which accounts for more than $700 from elective admissions if compared to the emergency department.12
Hospitals in certain districts will encounter both more prominent income due to COVID-19 hospitalizations and more noteworthy costs identified with extra staff and assets, while different medical clinics will encounter generally less income because of state or government direction to limit unnecessary admissions.12
This pandemic situation has brought a divide between rural and urban hospitals (bigger or smaller hospitals) in terms of revenue content. As per the 2019-year analysis 1 in 5 rural hospitals has a probability of shutting because of financial difficulties. The concern is, according to the March 28, 2020 report almost 10 million people have claimed unemployment insurance. Bearing all the consequences of the pandemic COVID-19, the U.S. economy will be hit by 10 to 25% in the second quarter and will face a recession.13
Equity impacts
Equity in treatment
COVID-19 pandemic has uncovered numerous deficiencies in U.S. medical care, especially the ability to deal with a general wellbeing crisis. Insufficiencies in the foundation, underreporting, distribution and admittance to COVID-19 screening tests, logical inconsistencies in the transmission of factual real-time information, and deficient arrangement of resources such as PPE to overburdened hospitals and health care workers are a couple of the issues intensifying the antagonistic impacts of this pandemic on the wellbeing and government assistance of the country. In any case, perhaps the most serious issue the COVID-19. has lit up is the wide scope of disparities in our country's way to deal with medical services. These have just gotten more straightforward all through the COVID-19 emergency.14
The public health measure of social distancing has given rise to discrimination in the society, racism and differences between the vulnerable populations of the U.S. The extent of discrimination has gone based on gender where women are found to be affected more and increased discrimination among Asian Americans in society, especially in the workplace.15 For people from poor or underestimated foundations, instructive frameworks, labour groups, and work environment conditions regularly propagate frameworks of persecution, force, and benefit, bringing about them encountering minimization and segregation inside these frameworks and getting less fortunate and professional results. This period has generated fear among the population of losing their jobs due to a major economic crisis. COVID-19 is having an unemployment impact on the workers of colour, low-income groups, uneducated and people with less liquid assets if compared to the white-collared professionals and people of higher middle-class society. Compare to the whites, African American/Black, Native Americans and LatinX are facing consequences of the lockdown of the country at a greater intensity.29
Considering the vulnerable population of the U.S.
The U.S. government supports the vulnerable population by giving them financial aid/ relief for sustainability during the COVID-19 outbreak. Under Coronavirus Aid, Relief, and Economic Security (CARES) act Americans have been benefitted from Economic Impact Payments (Payments). The Internal Revenue Service (IRS) endures to self-direct the payments to the authorized individual; however, some may have to supply additional information to the IRS to receive the rewards.16
Paid sick leave for 10 days will be given for an employee who is not capable to work due to quarantine or self-quarantine or has coronavirus symptoms and is seeking medical help. The wage on the worker’s ordinary rate of pay, or, if higher, the Federal minimal salary or any applicable State or local minimum salary, up to $511 in line with day, overall, no more than $5,110.17
If someone is not capable to work since to care for someone who is affected by COVID-19 or to take care of the child as a result of the closure of the child’s school or paid caretaker is not available due to COVID-19. The employee is eligible for two weeks of paid sick leave at two-thirds the employee’s regular rate of pay or, if better, the Federal minimum salary or any relevant State or local minimum wage, up to $200 in step with day, however, no extra than $2,000 in general.17
If an employee is not able to work so has to look after the child since the closure of the school, place of care, if the caretaker is not available because of COVID-19. He is eligible for paid family and medical leave of 10 weeks. The employee will be remunerated equal to two-thirds of the employee’s normal pay, up to $200 per day and $10,000 in total.17
Challenges faced in mitigating strategies
Congress in the U.S.A permitted the CARES Act and the Paycheck Protection Program and Health Care Enhancement Act, in which $175 billion is provided as emergency funding for hospitals and other health care organizations. However, the disbursement of this fund is challenging as it has to be provided to the neediest in this pandemic situation. The big hospitals are producing revenue from outpatients and elective services. Whereas, the problem is faced by the smaller rural hospitals who are unable to get the revenue. If these budgetary lacunas continue to persist then it might lead to the shutting down of the rural hospitals.12
Deferred shipments and manufacturing plans make monetary issues for organizations with substantial obligations in the U.S. The effect on the worldwide equity value markets and departure from financial specialists selling resources, for example, high return securities and unpredictable stocks. Concerns about budget risks may evaporate liquidity in the financial economy. National banks of the U.S. are working hard to deal with a V-shaped recession to cope up with the downturn after the pandemic is over.18
Some of the challenges faced in the process of implementing preventive measures by the U.S. government were depicted on the vulnerable population. Women found it difficult to continue working as the daycares, schools, and external resources are closed down.19 Social distancing protocols have led to the shutting of restaurants/bars, travel and transportation, entertainment, personal services, and certain types of retail and production factories.20 Thus, causing a major impact on the employment status of the U.S. population.
Workers who work in basic administrations, for example, markets, may not be furnished with the essential PPE gear to shield them safe from getting the infection. A few of them need to pick between their wellbeing and the need to procure wages to pay for fundamental necessities. The instability of the circumstance is additionally exacerbated by the way that numerous. market labourers are utilized in the lowest pay permitted by law occupations with little access to benefits, for example, medical expenses and paid sick leave.19
Due to the current outbreak, the dentist worldwide is sufferers since dentistry is a profession where dentist work in close contact of the patient mouth was more likely to spread COVID-19 is at greater risk. Due to which many regulatory bodies of dental practitioners are performing only emergency procedures. In the U.S. due to monetary dread to pay salaries to employees and economical losses some practices are shut down and some continue to work.21
DISCUSSION
Novel coronavirus first case was identified and later found a cluster of pneumonia cases in Wuhan Municipal Health Commission, China, which reported a cluster of cases of pneumonia in Wuhan, Hubei Province China. On 30th January WHO Director-General declared novel coronavirus 2019 a public health emergency of international concern (PHEIC). WHO released a strategic preparedness and response plan to help and protect the health system of the weaker State.2 Every country has taken up an appropriate measure to control and contain the disease spread. In the U.S., the Centres for Disease Control (CDC) has taken initiative to respond to support COVID-19 along with the National Healthcare Safety Network to track the disease. The CDC along with multiple surveillance system, epidemiology network and in alliance with the state, local, and academic partners scrutinise the evolution of the disease and its impact in the U.S. 22
Financial aid and management
The CDC emphasis on laboratory test to both symptomatic and asymptomatic in detect and report the case timely for public health action. To encourage people to test, the U.S. government supports the vulnerable population by giving them financial aid/ relief for sustainability during the COVID-19 outbreak.22 Under CARES act Americans have been benefitted from Economic Impact Payments (Payments). The IRS endures to self-direct the payments to the authorized individual; however, some may have to supply additional information to the IRS to receive the rewards.16 In Vietnam detection of cases, isolation, tracing cases and the surveillance of the suspected cases with the support of the Emergency Public Health Operations Centre was set in the General Department of Preventive Medicine to guide the provincial CDCs.23 In the UK National Health Service (NHS) monitors the testing and treatment of COVID-19 cases for British citizens and visitors as well.24
Financial aid is provided by the UK government for COVID-19 cases through NHS, which comprises sick pay leave, COVID-19 testing, remote management of patients, support for stay home models.25 In the U.S.A due to monetary dread to pay salaries to employees and economical losses some of the dental practices are shut down and some continue to work.21 In the UK, The British dental association has also stopped routine dental practice hence dental doctors are in financial losses.26
Efficiency impacts
The current rise in the number of cases as the U.S. stands with a greater number of COVID-19 cases across the world, to keep the hospital financially stable, manage the limited resources and reduce the spread of the disease wherein poses the shortage of health care workers, respiratory therapist and PPE. Community health workers in Brazil are trained for 4- 6 weeks on health promotion and public health surveillance along with that online course are conducted to train them.27 Vietnam is confronting with a shortage of medical equipment, PPE for the medical staffs.23 In U.K. new NHS guidelines to release bed capacity, free up the maximum possible IPD and critical care, increasing respiratory support. The U.K is also providing aftercare support for the patient recovered from the COVID-19 with rehabilitation.25 Availability of intensive care and intermediate care beds is also deficient in Europe, Germany ranging from 29.2 to 4.2 beds per 100,000 populations in Portugal, the study conducted in 2010-11.28 Most the countries are less prepared for the outbreak as there is a greater number of cases and disease spread is faster in rate.
Equity impacts
A remarkable disparity among the ethics groups across the U.S. is seen from the past even with the medical system for their timely care about the health. The equitable impact which is seen in treating the COVID-19 is also with the socio-economic backgrounds. Due to structural racism among the black and brown families, they have denied access to quality health care, affordable housing and financial security.29 A person from a low socio-economic group has factors connected to his lifestyle of not taking nutritive food, spending enough money to take care of his health. The risk factor of COVID-19 is identified as comorbidity. People from the low-income group cannot offer to maintain the social norms to prevent disease since their livelihood is dependent on their work; hence a greater number of cases are seen among the ethnic groups which are giving rise to inequity. Unemployment is also seen as a major setback to the pandemic crisis. Many Americans during this crisis lack decent work to protect them from the risk of being infected.19
Long term and short-term effect of COVID-19
The short-term effect of the pandemic is a risk among the population being infected by the disease. Economic instability for a freelancer working on the field, daily wage worker, employment insecurity in the short course until the epidemic slows down. Temporary suspension of school and colleges to control the outbreak put parents in a situation to stay back to take care of their kids leading to job instability. E-Commerce and internet-based jobs will be more secured and safer. An uninsured person due to coronavirus infection has to bear the catastrophic health expenditure. Shortage of PPE leads to a rise in demand and production of the goods and downfall in the other products will be stagnant. In the healthcare industry due to the cancellation of elective surgery and procedure, the income of the hospital will be condensed. Impediment in travel and tourism decrease the income of the individual, the corporate sector as well as the public sector. It leads to too much pressure on the government to ensure the health care and social security of the mass.
If the pandemic persists for a long time the rate of growth of Gross Domestic Product (GDP) declines. Disequilibrium between the aggregate supply and aggregate demand occurs; effective demand will be inelastic due to unemployment. As a consequence, marginal propensity to consume and marginal propensity to save comes down to per capita expenditure of individuals on health and medical care hikes. Instantly it leads to continuous raise in public expenditure in general, health, medical care, research and development investment in the pharmaceutical industry, social security measures, relief measures in particular. Interruptive circular flow of factors, product and wealth strains international trade. Trade cycles, market failure and disequilibrium balance of payments curtail the entire global economy.
CONCLUSION
It is essential to contain and get ready to alleviate further episodes, especially in nations with battling or under-resourced health system framework. The COVID-19 pandemic speaks to an exceptional clinical and financial test for the U.S. medicinal services framework. Without strong and continued administrative help, practically all emergency rural hospitals will encounter finance-related challenges. Be that as it May, small hospitals, autonomous, provincial, and have basic access status. are especially in danger. Policymakers ought to offer committed help to these emergency clinics to get to CARES Act reserves and consider distributing extra subsidizing to them during the COVID-19 pandemic. There is a need to support the countries which are struggling to find the vaccine to stop the spread of the disease. Enlightenment of self-interest in preventing the infection is an essential nevertheless equitable approach to global health is of foremost importance.
Authors contribution:
Concept and design: Dr Sabah. Mohd. Zubair and Dr Sphoorti. Narasimhan
Acquisition, analysis, or interpretation of data: Dr Sphoorti. Narasimhan
Drafting of the manuscript: Dr Sphoorti. Narasimhan and Dr Sabah. Mohd. Zubair
Critical revision of the manuscript for important intellectual content: DR. Narasimhan M. S and Dr Sabah. Mohd. Zubair
Source(s) of support: None
Acknowledgement: Authors acknowledge the immense help received from the scholars whose articles are cited and included in references of this manuscript. The authors are also grateful to authors/editors/publishers of all those articles, journals and books from where the literature for this article has been reviewed and discussed.
Conflicts of Interest: All the authors declare that there is no conflict of interests. This study did not receive any financial support.
Englishhttp://ijcrr.com/abstract.php?article_id=3809http://ijcrr.com/article_html.php?did=3809
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareAwareness Regarding COVID-19 Among Health Care Workers in a Tribal District of Chhattisgarh, India
English152158Nag SEnglish Noor SEnglish Tiwari AEnglish Gavel SEnglishBackground: Covid-19 pandemic has over 79 million cases and 1.7 million deaths globally till the last week of December 2020. Its high burden is a great concern for the entire world. The pandemic has placed extraordinary levels of physical risk & psycho-logical stress on health care (HCW) workers. Addressing HCW knowledge, attitude & practice is very crucial in managing the covid-19 outbreak. Objective: Explore knowledge, attitude & practice about Covid-19 among HCW of a tribal district in central India. Methods: This is hospital-based cross-sectional study. 125 participants were included following convenient sampling and a self-developed questionnaire was used to collect data regarding knowledge, attitude & practice of newly appointed HCW in a tertiary care hospital in Raigarh which is a tribal district of Chhattisgarh. Knowledge, Attitude and practice score calculated and tested with socio-demographic variables. Results: Proportion of adequate knowledge, positive attitude and good practice among participants were 41%,75% & 84% respectively. We found higher knowledge score for age (9+3.49 & 8+3.06 ), family type (8.63±3.47 & 7.82±2.57), work sector (8.55±3.41 & 7.68±2.24), education (9.09+3.61 & 6.33+2.06 ), profession (9.04+3.32 & 7.35+ 3.38 ) and specific work (9.33+3.28 & 7.86+2.93) in covid-19. 71% Doctors & nurses have positive attitude comparing to 29% among nurses which is significant (p=0.04). Conclusion: HCW must be motivated by authorities to acquire adequate knowledge, a positive attitude and good practices. Knowledge attitude and practice of the health care workers is crucial in fighting the Covid-19 outbreak and authorities can empower health care workers with updated knowledge which in turn will improve their attitude and practice
EnglishINTRODUCTION
Covid-19 is currently the most challenging pandemic that has over 79 million reported cases and over 1.7 million deaths globally till the last week of December 2020.1 After the experience of the H1N1 Swine flu pandemic in 2009 that had affected more than 214 countries including over 18398 deaths.2 It has once again shifted the focus of the entire world towards the importance of communicable disease and Public health. The causative agent of Covid-19 is an enveloped, positive-sense, single-stranded Ribonucleic Acid virus that belongs to the B lineage of the beta coronavirus.3 This novel strain called SARS-Cov2 has less pathogenicity but higher transmission competence leading to the rapid increase of cases globally.4 During December 2019 almost a year back world health organization (WHO) first learned of this new virus following a series of case reports from Wuhan city, China.5 With a continuous explosive rise of cases WHO declared this outbreak as Public health emergency of international concern (PHEIC) on 30th January 2020 and a global pandemic on March 11, 2020.6,7 Its high burden is a great concern for the entire world. As of 9th January, 2021 total cumulative cases was 100, 566, 51 in India and 27,5042 in Chhattisgarh state.8
WHO in its press release on 17th September 2020 emphasized the persistent threat to the health and safety of health workers in the fight against Covid -19 disease. Infection among Health workers is 14%-35% of total covid-19 infection reported to WHO from various countries. In addition to physical risks, the pandemic has placed extraordinary levels of psychological stress on health workers. This makes addressing health workers knowledge, attitude & practice very crucial in managing the covid-19 outbreak.9
MATERIALS AND METHODS
Study Design
A cross-sectional study was carried out among health care workers, working in a recently inaugurated tertiary care hospital dedicated to catering to the health care needs of the Tribal population of the area.
Study subject
Our study population comprises newly joined Nurses, Doctors & Faculties of this institute. We collected a staff list from the establishment section of our institute.
Sampling technique
Following convenient sampling, we contacted 150 persons.
Patient selection
All staffs who gave consent to participate. Patients those not using a smartphone with internet connectivity were excluded. Out of 150 twenty-five persons were excluded based on criteria and the final sample size was 125. We have used a self-designed questionnaire to estimate study objectives.
Study tool
The questionnaire was prepared by reviewing previous articles on a similar topic and visiting websites of the Ministry of Health and Family Welfare, GOI and Indian Council of Medical Research.10-13 The questionnaire is divided into four sections: thirteen questions in section one on socio-demographic variables, ten questions in the second section for knowledge, eight questions in the third section for attitude and nine questions in the last section addressing the practice of participants. Each correct response was given a score of 1 for attitude as well as practice and in the knowledge section, each correct response assigned a score of 2. All incorrect responses were assigned zero scores each. Total possible scores are 20, 9 and 5 for knowledge, practice & attitude respectively. Cut off score was kept to categorize each domain as 10 for ≥ 10 for adequate knowledge, ≥3 for the positive attitude and ≥ 5 as good practice. We prepared this questionnaire as a survey form using Google docs and sent it to all participants directly in online modes like Email and WhatsApp.14 Sufficient care was taken to maintain confidentiality for participants, one participant could submit responses once only. After receipt of all 125 forms, we changed the Google docs setting to “stop receiving responses” and planned data analysis. Data was downloaded from Google docs, entered in Excel format, coding is done for variables to maintain anonymity.
Data Analysis
Descriptive statistics used to describe study variables as proportion, frequency, mean and standard deviation. Appropriate statistical tests like the Chi-square test for categorical value in 2x2 tables, student t-test for comparing means of two groups, one way ANOVA for comparing means of more than two groups, Pearson correlation test for continuous quantitative data are used for data analysis.
RESULTS
Socio-demographic characteristics of participants
More than half of the participants were younger than 30 years age in the current study. The proportion of female participants is more, the male-female ratio being 1:1.19 in our study. Participants staying in the urban locality and unmarried at the time of study makes around two-thirds of all. Again it was noted that most of them resides in the nuclear family and working in government facilities. Nearly half of the participants have completed MBBS & around one-fourth comprised of recently graduated BSc nurses. About 50% of participants were frontline worker which is defined as health care workers involved directly in inpatient treatment, handling of patient’s sample or dealing with biomedical wastes of the patient.15 Around 60% were involved in special tasks of Covid-19 management that include the screening of Covid-19 suspected, working in RTPCR or treat laboratory, treatment of admitted Covid patients at Covid care centre or Dedicated Covid hospital of our institute. Almost 70% at present didn’t have a risk factor for corona infection such as diabetes mellitus, hypertension, smoking, under-nutrition, obesity, immune-compromised state.16A high level i.e. 83% use of the private vehicle for transportation by participants could be due to the effect of lockdown and awareness about social distancing. Around one-third of participants reported covid-19 incidence in their locality in the past fourteen days as can be seen in Table 1. We asked for the local outbreak in the previous fourteen days because it is recommended duration of quarantine as recommended by Centres for disease control & prevention, Atlanta, USA.17 The proportion of adequate knowledge, positive attitude and good practices found in this study was 41%, 75% & 84% respectively.
Knowledge & associated variables
Knowledge was associated with other variables. We found a higher knowledge score of 9±3.49 among older participants compared to younger participants with a score of 8±3.06. knowledge level of participants was not found to vary much in regards to factors like gender, marital status, frontline worker, rural or urban residence, mode of daily transport and participants awareness about local outbreak. On the other hand variables like nuclear family, working in government facilities, higher education, doctor profession and specific area of work in covid-19 resulted in higher mean knowledge score as can be seen in Table 2. However, these differences are not significant statistically when appropriate statistical tests like student t-test and one-way ANOVA was applied.
Attitude and associated variables
The proportion of positive attitude and good practice was 78% & 88% among those above 30 years age whereas this proportion was only 73% & 80% among participants aged less than 30 years. Around 71% of Doctors & nurses have a positive attitude in comparison to only 29% proportion of positive attitude among nursing student and teaching faculties. It was found statistically significant at p=0.04 in the chi-square test. The difference in attitude and practice was also seen due to variables like the specific task, government institutes, doctor designation, but these differences are not found significant statistically. Appropriate questions were asked to access Knowledge on prevention, discharge policy and clinical management as shown in figure 1. Maximum number of participants correctly answered the question on filtration size of N-95 filter where a poor correct rate was seen for the question on RTPCR testing of moderate Covid-19 patient before discharge.18,19 Pearson correlation test was applied on participants knowledge, attitude and practice and a weak positive correlation were found as seen in Table 3.
DISCUSSION
The study was conducted from July to October 2020 among health care providers who had then recently joined a tertiary care hospital in Chhattisgarh. In our study, correct knowledge regarding Covid-19 was 41% which was lower than other studies where 70% -80% proportion of participants reported correct knowledge.20-22 The reason for the present study finding was the inclusion of junior doctors and nursing trainees in our current study. Furthermore, more focus was laid on clinical components of Covid care in the questionnaire, which may have resulted in a lower result. Another study among HCW using a 23 item self-designed questionnaire reported correct knowledge among sixty-one per cent of study subjects.23
A study was done at a cardiology hospital in Nepal also reported correct knowledge of 57% among physician and 54% among nurses.24 Another study among Chinese residents using Wenjuanxil electronic platform for data collection reported a 61% score in this domain which was higher than our study.25
So far as the attitude domain is concerned we found 76% proportion of participants having a positive attitude about the Covid-19 pandemic. Our finding was higher than a study on tertiary care hospital staffs of Nepal that reported only 53% positive attitude.26 A comparable result of 68% was reported from an online survey conducted in Vietnam.27 However another study on Chinese residents reported a 90% positive attitude which was more than our findings. Such difference is attributed to the inclusion of generalized questions like successful control of Covid-19, confidence of winning the Covid-19 battle in their questionnaire.28 Another study among adolescent of Bangladesh also reported 62% which was lower than the current study.29
The findings of 84% good practice in the current study are comparable to a study among residents of China (89%) where they used a self-administered questionnaire to estimate knowledge, attitude and practice among participants.10 Another study from Pakistan conducted during July 2020, reported a similar result of 88% about the correct use of the medical mask.11 Among medical students of Uganda only 57% of good practice was reported in a previous study.30,31 Regarding the source of information for Covid-19 nearly 84% of participants have used authentic sources like the ministry of Health and Family Welfare guidelines and didn’t rely only on social media messages. The lower findings in the knowledge component are unfortunate among HCW and higher health authorities need to encourage HCW by providing useful and concise guidelines and limiting the practice of using multiple guidelines from multiple sources.
A lower knowledge level was also seen in a study by Hadil & co done in Riyadh, Saudi Arabia.31During Middle East Respiratory Synicitial (MERS) outbreak similar results were seen in a study from Riyadh, UAE, Vietnam and Uganda.32-35 The present study found that persons with higher education scored more in the knowledge domain comparing others. Similar findings can also be seen in previous studies.28,36-39 Certain questions in our survey could be answered correctly by few participants only. For example, only 29% correctly answered about mask use & isolation criteria for Covid-19 suspects. A study from India reported 40% knowledge among health staffs regarding the use of the medical mask.40 Updated guidelines on medical masks can be seen in the WHO guideline which was updated on 1st December 2020.41 Another study done in Mumbai, India also reported 79% correct habit for mask use and disposal.42
About 90% of study participants shown adequate knowledge regarding the discharge policy of corona patient. This matches to successful completion of prior training by 90% of our participants. More than 80% practised four times daily handwashing with soap and water. One study among adolescent of Poland has found a similar frequency of handwashing.43More than 90% of participants reported minimum five-time daily use of hand sanitiser is on par with findings from Saudi Arabia.43-45 Prophylaxis from Covid-19 by using Hydroxychloroquine (HCQ) was found among 35% of participants. A previous study among physicians in Romania reported similar findings of 48%.45Only 50% usage of HCQ was seen in an Indian study. The lower proportion of HCQ prophylaxis may be due to its unproven efficacy and potential side effects.
CONCLUSION
Health care workers irrespective of their designation should be provided with Covid 19 related information frequently by using the available mode of education like Online classes, what's app group etc. Authorities should plan to collect feedback from HCWs about Covid knowledge and those HCWs with deficient knowledge must be given focus for knowledge acquisition by different participatory approach. A high standard of attitude and practices has to be maintained by motivating workers using various approaches like financial incentive, compensatory leave, early diagnosis and treatment in case of any illness, health insurance, occupational safety and welfare etc.
CONFLICT OF INTEREST: No conflict of interest was observed or likely to be observed between Authors and other stakeholders in content, method or any other area of work related to this research.
SOURCE OF FUNDING: The research work did not involve any source of funding.
AUTHOR CONTRIBUTION:
Nag S: conceptualization, methodology, data collection, software use, data analysis, writing original draft, writing review and editing. Noor S: methodology, data collection, drafting, editing. Tiwari A: data collection, writing the original draft.Gavel S: Data collection, statistical analysis
ETHICAL CLEARANCE: The study was cleared by Institutional Ethics Committee bearing letter-number sno/Med/Ethics commit./2021/48 dated 16/02/2021.
ACKNOWLEDGEMENT: Authors acknowledge the immense help received from the scholars whose articles are cited and included in references of this manuscript. The authors are also grateful to authors/editors/publishers of all those articles, journals and books from where the literature for this article has been reviewed and discussed.
Englishhttp://ijcrr.com/abstract.php?article_id=3810http://ijcrr.com/article_html.php?did=3810
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareAnxiety, Depression, Stress and Post-Traumatic Stress Disorder Among the General Population in Assam During the Early Phase of the COVID 19 Pandemic
English159165Dhrubajyoti BhuyanEnglish Seujee GoswamiEnglish Mustakim AhmedEnglish Hiranya SaikiaEnglishEnglish COVID 19, Anxiety, depression, Stress, Post-traumatic stress disorderINTRODUCTION
The 2019–20 coronavirus pandemic caused by the novel coronavirus (COVID-19) has led to extensive suffering and death all over the world.1 As of April 2020, more than 896 000 cases of COVID-19 have been reported with over 45525 deaths in at least 170 countries and territories, with major outbreaks in China, Iran and the European Union.2 Its management consists of providing symptom relief and supportive therapy to the patient. To date, no vaccine or specific antiviral medication against this illness is available to mankind. An outbreak of such magnitude and severity can naturally be expected to have a deep and widespread impact on the mental well-being of people as well as instil terror into the hearts of millions globally. The measures implemented to stop the further transmission of this virus comprise limitations on travel, quarantines, lockdowns of wide areas, closure of offices, business establishments and educational institutions that have greatly affected the day to day life of populations all over the world.3
Similar epidemics in the past such as the Severe Acute Respiratory Syndrome (SARS) outbreak of 2003 and the Ebola virus disease outbreak of 2009 have shown a higher incidence of several psychiatric manifestations including those of post-traumatic stress disorder (PTSD), depression, stress, insomnia, grief and emotional exhaustion among the affected populations.4-9 In the light of this grave pandemic, initial studies have revealed symptoms of anxiety depression increased stress levels PTSD and insomnia among the general public as well as the health care workers in the affected nations.10-14
Thus, more studies focusing on the psychological consequences of the COVID 19 pandemic as well as planning interventions for their alleviation appear to be the need of the hour. Since very few such studies are currently available from the northeastern states of India, we conducted a study to evaluate the psychological consequences of the ongoing COVID 19 pandemic on health care professionals as well as the general public of Assam by assessing the occurrence of anxiety, depression and post-traumatic stress disorder among them. As only a few cases of COVID 19 had been detected in the study population till the phase of our data collection, the information thus obtained could be of great relevance in the preparedness phase of any future outbreak of this nature.12,13
MATERIALS AND METHODS
After obtaining due permission from the Institutional Ethics Committee via letter no. AMC/EC/1064 dated 06/04/2020, the sample for the study was drawn from the health care workers as well as the general public of Assam by quota sampling. Persons of 18 years and above with a valid email address were identified and subdivided into five groups based on feasibility. The first of these groups consisted of health care professionals including doctors and paramedical workers. The second, third and fourth groups included faculty of higher educational institutions, sales and marketing professionals and employees of the judicial system respectively. The fifth group of students of various fields of study was incorporated, as was a sixth group consisting of business persons and other persons working in commercial enterprises. From each of these six groups, 50 subjects were emailed the questionnaires. The questionnaires included an initial section for obtaining informed consent from the subjects which was necessary for the data collection in the subsequent sections via the various tools.
The tools used for the study included the 21 item version of the self-reporting Depression Anxiety Stress Scales (DASS 21) developed by Lovibond et al. which was used for evaluating the symptoms of depression, anxiety and stress.15 Moreover, the revised version of the Impact of Events Scale, developed by Weiss and Marmar in 1997 for measuring the subjective distress due to traumatic events was used to assess the risk for Post Traumatic Stress Disorder(PTSD) in the subjects.16 The obtained data were kept strictly confidential. Statistical analysis of the data was carried out via the SPSS version 24, and the calculated results were presented in terms of frequencies, percentages and mean ? standard deviation. The statistical significance was tested using the chi-square test or Fisher's exact test, and a p-value of less than 0.05 was considered statistically significant.
RESULTS
A total of 300 subjects were identified and emailed the study questionnaires, and 249 responses were obtained. Two responses with incomplete data were discovered that were rejected by the researchers. Thus, data from 247subjects were available for the final statistical analysis. It was observed that the mean age of the respondents was 35.59 ± 9.42 years, and 97.7 per cent of the respondents consisted of young adults and middle-aged persons between 20 and 49 years of age. Females constituted 43.7 per cent and males made up 56.3 per cent of the respondents. 61.9 per cent of the respondents were married and a majority of them were found to hail from nuclear families (57.1 per cent). For the sake of feasibility, the data from three groups of faculty of higher educational institutions, sales and marketing professionals and employees of the judicial system were analysed together as a single group of non-medical professionals. 23.1 percentages of the responses were from the group of health care professionals, while the groups of non-medical professionals, students and the others made up 51.0, 8.9 and 17 per cent of the responses respectively. The responses from the medical students were shifted to the group of health care professionals for the final analysis.
Tables 1-3 show the results of evaluation with the DASS 21. Here, mild depressive features were revealed in 18.9 percent respondents, while severe depression was noted in 10.6 percent of the respondents. A significantly higher rate of these symptoms was seen in the group of students (p value = 0.002).18.5 percent of the respondents were found to have features of mild to moderate anxiety, whereas 11.2 percent reported symptoms of severe anxiety. The overall anxiety levels were significantly higher in the group of non-medical professionals (p=0.043). However, the occurrence of severe anxiety (3.6%) and extremely severe anxiety (14.3%) were observed more frequently in the group of health care professionals as compared to the non-medical professionals. Also, a mild to moderate increase in stress levels was seen in 11.1 per cent of respondents with 8.6 per cent of them being found to be severely stressed as a consequence of the ongoing pandemic. Interestingly, a significantly greater level of stress was noted in the group of students (p=0.001) and the unmarried individuals (p=0.001) among all the groups under study.
Table 4 shows the results on assessment with the revised version of the Impact Of Events Scale Here, 43% of the respondents had a score ≥24, indicating clinical concern for Post Traumatic Stress Disorder (PTSD). These scores were found to be significantly greater in the group of students (p=0.006). On the other hand, 26.6% of the respondents had a score ≥33, which showed a probable diagnosis of PTSD, with significantly higher scores being observed in the group of students under study (p=0.02). 20.1% of the respondents showed a score of 37 or more, indicating a high risk of developing PTSD.
DISCUSSION
During the ongoing coronavirus pandemic, the various global health organisations including the World Health Organisation and the CDC are increasingly laying stress on measures for the prevention and treatment of the infection. These include early detection and segregation of affected individuals, identification of contacts, establishing reliable diagnostic criteria as well as effective interventional strategies for combating this serious illness. The grave impact of the pandemic as well as the ensuing quarantine on the mental health of millions across the globe stands sadly neglected.17,18
On evaluation with the DASS 21, features of depression, anxiety and increased stress levels ranging from mild to severe were noted in a significant fraction of the respondents. Similar findings have been reported in several other studies carried out during this ongoing COVID 19 pandemic. Such a study by Wang et al on the general public in 194 cities of China has revealed a moderate to severe psychological impact of the COVID 19 pandemic in 53.8% of respondents; while moderate to severe levels of depression, anxiety and stress were observed in 16.5%, 28.8% and 8.1% respondents respectively.10
The evaluation with the revised version of the Impact of Events Scale helped to measure the impact of the pandemic on the minds of the subjects, focusing on the presence of symptoms of post-traumatic stress in them. Our study detected the presence of certain symptoms of post-traumatic stress in 43% of the respondents indicating clinical concern for PTSD in them. Meanwhile, 26.6% of the respondents were detected with a probable diagnosis of PTSD, with a need for adequate monitoring and follow up to rule out PTSD. Furthermore, 20.1% of respondents were found with a high risk of having PTSD as an aftermath of the COVID 19 pandemic and required prompt and detailed evaluation and treatment for their condition. In another similar longitudinal study by Wang et al. including the general public from 190 cities of China, the initial mean scores on IES-R revealed PTSD symptoms that persisted in the second survey done four weeks later. Using the DASS, moderate and severe levels of stress, anxiety and depression were observed in 8.1%, 28.8% and 16.5%, of the respondents respectively without any significant changes in their levels longitudinally. (p>0.05).11
Furthermore, a study on 470 healthcare workers in Singapore by Tan et al. has revealed anxiety, depression, stress and clinical concern of PTSD in 14.5 %, 8.9%, 6.6% and 7.7% respondents respectively.13 A multinational, multicentre study by Chew et al. on healthcare workers during the COVID 19 pandemic included 906 respondents.12 Among them, 5.3%, 8.7% and 2.2% reported varying levels of depression, anxiety and stress respectively. Also, 7.4% of respondents had shown a clinical concern of PTSD, among who 34 exhibited moderate to severe levels of psychological distress. In another similar study by Tan et al on 673 working people in China, 10.8% were diagnosed with PTSD following their rejoining the workforce during the COVID 19 pandemic.14 This is consistent with the finding of a study at the time of the Ebola outbreak in Nigeria in 2014 that showed a high level of psychological distress in survivors as well as the persons closely in contact with them. The participants reported various symptoms including difficulties in concentration, insomnia, feeling of unhappiness, feeling constantly under strain as well as the inability to enjoy the activities of their daily life.19 Moreover, several other studies during epidemics like SARS in 2003 and Ebola viral disease in 2014 have revealed greater levels of emotional stress among health care professionals battling these epidemics.20-22 Symptoms of anxiety, depression and stress are persistent in them long after the resolution of the epidemic.22 The occurrence of depression and post-traumatic stress disorder (PTSD) has also been found to be increased among the persons who recovered from SARS.23-25 A similar study on patients under quarantine and undergoing haemodialysis as well as the medical professionals treating the people infected with Middle East Respiratory Syndrome (MERS) detected higher levels of psychological distress and PTSD in the early stage of the epidemic.26 Persons who had been hospitalised with the MERS have also been shown to have a low quality of life after one year of recovery.27
It is pertinent to mention here that in our study, the symptoms of depression and PTSD were found to be significantly greater in the group of students .This is similar to the study by Wang et al, that showed a higher level of anxiety, PTSD, depression and stress levels in the student group during the COVID 19 pandemic.10 This is similar to the study by Wang et al during the COVID 19 pandemic, wherein a higher occurrence of PTSD was noted in the age group with a majority of students.11 This finding could be explained by their isolation from peers and friends ,their academic and career related concerns coupled with the uncertainty caused by the postponement of their classes and exams as an aftermath of the pandemic .
In our study, the non-medical professionals were found to have a significantly higher overall anxiety level. This could stem from the relative lack of adequate knowledge regarding the pandemic and measures to contain it compounded by the major uncertainty and sudden upheaval brought into their lives by this devastating pandemic. The medical professionals were seen to have a higher frequency of experiencing severe and extremely severe levels of anxiety. Interestingly, in a recent study by Tan et al during the COVID 19 pandemic, the scores for anxiety, stress and PTSD were greater in health care workers excluding the physicians and nurses, after adjusting for the various confounders.13 This could be a consequence of being exposed to greater workloads and burnout, increased exposure to suffering and death as well as concerns related to their health and wellbeing while battling this terrible pandemic. It is interesting to note here that the stress levels were found to be significantly greater in the group of students and the unmarried individuals among all the groups under study.
Therefore, the designing of further studies on the psychological effects of this pandemic on these aforementioned high-risk groups and planning strategies targeted at their alleviation appears to be the need of the day. The limitations of our study include a relatively small sample size covering the population of a single state, Assam in India, due to which, the generalizability of the findings might not be as desired. Moreover, the findings of the study are limited to the early phase of the outbreak in the covered geographical area, as only a few cases had been diagnosed in the population here till the phase of data collection. Follow-up studies in this direction are being planned by the researchers for information on the later phases of the pandemic.
Conclusion
Thus we found that the on-going 2019 novel coronavirus pandemic is leaving a deep psychological impact on the health care professionals as well as the general public. These include features of anxiety, depression and post-traumatic stress disorder. Students, as well as non-medical professionals, was found to be affected to a greater degree by the pandemic, while health care professionals were found to be exhibiting more severe levels of anxiety.
Future studies designed in this area of research with a larger sample size and focused on the high-risk groups could be beneficial in planning interventions to mitigate the psychological impact of the COVID19 pandemic.
Acknowledgements: Authors acknowledge the immense help received from the scholars whose articles are cited and included in references of this manuscript. The authors are also grateful to authors/editors/publishers of all those articles, journals and books from where the literature for this article has been reviewed and discussed.
Financial support: There are no funding sources or source of financial support to be declared by any of the authors.
Conflict of interest disclosures: There is no conflict of interest to be declared by any of the authors.
Author contribution: Concept and design: Bhuyan, Goswami and Saikia
Acquisition, analysis, interpretation of data: All authors
Drafting of the manuscript: Bhuyan, Goswami, Ahmed
Critical revision of the manuscript for important intellectual content: All authors
Statistical analysis: Saikia
Supervision: Bhuyan, Goswami and Saikia
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareCOVID-19 Vaccine - Public Sentiment Analysis Using Python’s Textblob Approach
English166172P. SivalakshmiEnglish P. Udhaya KumarEnglish M. VasanthEnglish R. SrinathEnglish M. YokeshEnglishBackground: In the present scenario, weeks over social media creates a high impact on the verdict of individuals& organizations. Opinions in the form of tweets reflect one's attitude and emotions towards a specific person or an event. Also, Companies can benefit from this massive platform by collecting data related to opinions on them. Objective: To infer the public opinion towards the tag Covid-19 Vaccine. Which is one of the natural language processing. Methods: The TextBlob approach is used to extract emotions and visualize them from the raw data collected from Twitter. Initially, tweets were collected on Covid -19 Vaccine after preprocessing the collected data set, the TextBlob approach classifies polarity of textual data in positive, strongly positive, weakly positive, neutral, negative, strongly negative & weakly negative categories. Results: The sentiment scores for collected tweets is calculated and shown under the results section. Which projects the emotions of all the people using social media towards covid-19 vaccination. Conclusion: There are surplus opportunities in future for exploring trend sentiments over some time. Also analysis over the different location of the world. Based on which necessary measures could be taken by the government or any organizations to create positivity among the public.
EnglishEmotion analysis, Natural Language Processing, Social media, TexBlobINTRODUCTION
People started sharing different varieties of emotions very first in the year 1970.1,2 Thereafter, people started analyzing that information in bits and pieces, has various application in turns like predicting the election results, realizing the society’s attitudes towards an event or a specific person. It has been shown that how social media expresses collective wisdom which, when properly used, can yield and accurate predictions over any issues.3 Meanwhile, the advent of technologies these obtained more popularity due to their high accessibility.1 Now a days, people use social media sites to share opinion. Twitter is one such platform in which users send, read post known as tweets and interact with different community people. Users exchange their opinion on daily lives, brands and places. Sentiment analysis, which is called opinion mining, is a field of study that analyses the opinions, evaluations, attitudes, and feelings of individuals according to entities such as products, services, organizations, individuals, events, issues, and their characteristics.4 Beliefs are important almost in every human activity. At present, any small scale or large scale businesses and organizations want to find out consumer’s viewpoints of their products and services. On the ballot, voters want to know other’s opinions about the candidates before they vote. In the past, individuals used to ask their family and friends about their viewpoints. Small & large scale businesses and organizations used to post a questionnaire when they want to know about the individual’s opinion.4Business marketing, public relations, and political advertising companies for a long time with an increase in usage of the social networks, individuals and organizations increasingly use the content on these networks and make decisions.4 The EAS system had been designed to extract emotions in social network data. To check the performance of the designed system, the data of Twitter gathered in the range of the Iran presidential election in 2017.5,6 Amongst the earlier social networks studies, we chose Twitter because of its importance.7 For fetching tweets data from Twitter. Initially, an API request was made to Twitter which was later approved. Afterwards, as explained by Shah et al. python’s tweepy library which is specifically developed for retrieving tweets data from Twitter is used along with Twitter streaming API and authentication keys (Consumer key, consumer token key, access token and access token secret.) provided by twitter.8,9 For authentication purpose, the Tweepy library used the AuthHandler function for verification of authentication keys. Once, the authentication request is approved it starts fetching the tweets. Then panda’s library was used to fetch all the collected tweets in a data frame. Next, pre-processing of a tweet using a regular expression library performed through which unnecessary data removed from tweets. In the last stage of pre-processing the textual data changed into numerical form named count vectorization. It is a type of encoding that helps in performing tokenization. Which involves crucial steps performed as a part of natural language processing (NLP). Through which sentences were tokenized into tokens of each word to form a feature set. Once the dataset pre-processed, in the next stage sentiment scores were calculated using Python’s TextBlob Library, the same discussed in detail in further sections.
RELATED WORK
A system designed for real-time visualization of Twitter microblogs and their analysis. For offering enhanced semantic insights, a weighted tag network has been designed.8 There is a system to find semantic patterns through heterogeneous data and without any social network structure like Instagram to detect events.9 Not only Instagram users’ posts, but also the combination of Instagram and Twitter users’ posts employed to improve event detection quality.10 The numbers of four comment categories of Trump and Clinton, supporters and opponents have been studied.11 Usually classification accomplished by hashtags. The study made one day earlier U.S Presidential ballot demonstrated that Clinton’s supporters were more than trump. Since 60% of Instagram users were in the age between 18 and 35, their prediction was different from the real results. This shows clearly the inability to predict the result of Instagram to the whole society. Mohamed studied the Malaysian politician's storytelling.11,12 In this, all the post of politicians, including video, picture, and text content, have been analyzed. The post is divided into six categories, and the results are compared to each other. Recently, it has been shown the high correlation between different Indonesian party influencers and their presidential candidates on Twitter.13 Emotion analysis using text processing techniques also applied on the social network in, city event detection using expandable in initial event keywords has been studied.14-16 In this study, it found that Twitter is much better than Instagram in analyzing & predicting city events.17
Sentiment Classification Using Natural Language Processing (NLP)
As articulated by Lobur, natural language processing (NLP) is the domain in machine learning which is used in analytics.18 NLTK which is called the Natural language toolkit is a part of python’s library belonging to natural language processing. Natural language processing not only deals with text analytics but also plays an important part with research based on analysis of human languages. Preparing models for research based on human languages comes in computational linguistics. The major advantage of using NLTK is that it allows even a beginner programmer to understand concepts of natural language processing saving a lot of time from gathering information about it. Numerous advantage associated with using NLTK is it contains 60 corpora belonging to real-world data, collections of grammar, models which have been trained, functions which provide a path for performing general natural language processing tasks. The corpora used in the Natural language toolkit (NLTK) are generally divided into different categories for assisting its users. Though in other programming languages, natural language processing task can be accomplished. The major points which take python apart from other languages is a follows:
Better reading ability.
User-friendly object-oriented technique.
Ease of extensibility.
Better Unicode assistance.
A functionality-rich library.
NLTK has a vast source of libraries that are being updated with new functionalities over the period. This paper has provided a deep understanding of the functioning of the NLTK library. Tasks such as summarization of text, extraction of information, machine translation are performed by Natural Language Process as depicted in work from Zitnik.20 here, the author has carried out sentiment analysis using natural language processing toolkit to detect the language of the text and extract meaning out of it. For it, first, the language dataset has been cleaned in the pre-processing stage which was then followed by language detection and evaluation of the result. Though, this natural language toolkit library. The major limitation of this work is that it does not compares the performance of this library with other natural language toolkit libraries. Though, as an advantage, this library can be used for natural language processing courses for educational purposes. Moreover, this work has provided an understanding of the use of natural language processing in language detection which is used in this research article.
Feature Extraction for Sentiment Classification
As stated by Zhang, Jin and Zhon in their work that one of the most important models utilized for the categorization of the object is Bag of Words (BoW).21 The concept behind the Bow model is forming visual words by quantizing every extracted key point. After this, each picture is shown using visual words histogram. Joachims also worked upon the BoW model.23 He showed that the BoW model depicts the count of every word present in textual data. Ma et al.24 showed that a matrix depicting the count of words in textual data is created in the BoW model. Afterwards, the frequency of occurrence of these words is used as features to train the classifier. Thang Luong conducted a research where it is observed that the BoW model performed considerably well in compassion with other models on Chinese English language translation data.22 All these works have helped in understanding the concept behind BoW Model for feature extraction. Janani, emphasized various steps being taken while preprocessing the dataset.23 Various steps which were taken for the pre-processing dataset are stopped words removal, determination of sentence boundary, tokenization and stemming. Tokenization is one of the most important steps while pre-processing a dataset. It works in a manner that textual data is divided into small tokens. Each token represents a word from the textual document or language.
There are numerous libraries available in python such as NLTK word tokenize, Mila tokenizer, TextBlob tokenizer etc. which are used for tokenization. This work has helped in understanding the in-depth functioning of the Text Blob library for pre-processing phase.
TextBlob Approach Algorithm for Sentiment Classification
One of python’s libraries that use API for accessing methods to perform Natural language processing is called as TextBlob. A common challenge for work based on sentiment analysis is missing spelt words. This problem is addressed by Manushree, Adarsh and Kumar here, authors have compared TextBlob and SentiWordNet approach. Firstly, the dataset was pre-processed by removing stop words and unrequired data which could result in added computational cost in the performance of models.24,25 It was followed by aspect selection and based on it sentence extraction was done. Both the models were then used to calculate sentiment polarity and categorize the reviews into positive, negative and neutral categories. This work just focused on sentiment analysis of miss spelt words in the English language. The advantage of this work was that it performed sentiment analysis on miss spelt words in the English language. However, the limitation of this work is that it was unable to perform sentiment analysis on miss spelt words in other language using TextBlob.
Moreover, this work has helped in-depth understanding regarding the implementation of the TextBlob approach for the research project. The pre-processing of the textual data is of very importance in sentiment analysis as it reduces the size of textual data which is given as input to the model. Various steps are followed while pre-processing the textual data. The various pre-processing tasks performed for cleaning textual data are the determination of boundary of sentences, removal of stop words from natural language, stemming and tokenization. Tokenization involves splitting a sentence into tokens of each word belonging to the respective sentences. Janani et al.25 carried out work on certain tokenize and read the tokenized words. The advantage of this work is that it compared various good tokenization tools and it distinguished TextBlob from them. However, it was unable to read tokenized special characters which turned out as its limitation. This work has helped in understanding the major limitation of TextBlob.26
Python’s Regular Expression (REGEX/RE) Library
Stolee, concentrated mainly on regex, which is also called a regular expression, is a reflection of specific words search which helps in the identification of text through recognition of patterns in place of exact strings. REGEX library is commonly utilized for parsing textual data belong to general language. Regex is also called Python’s module. Even though regex is considered a versatile and powerful library it could be difficult to understand, this is one of its limitations. According to Spishak, Dietl and Ernst, The major advantage of python’s regular expression library is that it has a variety of applications as it has a powerful ability to fetch meaningful information from the given sentence.27 Regular expression is applicable in per-processing the data, MY QL injection, generation of test cases and intrusion detection in networks etc. According to Ganesh and Yeole, the major advantage of a regular expression library is that it has fast processing speed in terms of code execution, and it has very compressed code which reduces efforts of writing long codes for pre-processing of the dataset.28,29 Advantage fast processing and compresses coding.
ANALYSIS BASED ON THE LITERATURE REVIEW:
Step: 1 Setup of Twitter Application Programming Interface
To utilize Twitter API, we first need a Twitter Developer Application Page, to create a Developer account. After the application is approved, head to Developer Dashboard, Click on ‘Projects & Apps’ -> ‘Overview’(in that section) click the ‘New Project’ button to create a new project.30 Then again return to overview click on the Key icon to access the Keys and Tokens of the new project, upon clicking generate /regenerate we will have API Key, API Secret, Access Token & Access Token Secret to be popup, saved them for later use (Figure 1-4)
Step:2 Extracting & Pre-processing Tweets
Install and Import Libraries Before analysis, you need to install TextBlob and tweepy libraries using! Pip install command. Using the accessing credentials-4 codes generated earlier, we can set up the Twitter API authentication.
api_key = 'your_api_key_here'
api_key_secret = 'your_api_key_secret_here'
access_token = 'your_access_token_here'
access_token_secret = 'your_access_token_secret_here'
auth = tweepy.OAuthHandler(api_key, api_key_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
Step 3: Tweets Extraction
Key Considerations:
Rate limit of 900 API calls every 15 minutes
Twitter allows tweet extraction of the past 7 days alone. So need to extract each day for the record of the last 7 days.
The search term should be COVID -19 Covid vaccine.
Excluded retweets &tweets other than English.
from tweepy import
import pandas as pd
import csv
import re
import string
import preprocessor as p
consumer_key =
consumer_secret =
access_key=
access_secret =
auth=tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_key, access_secret)
api = tweepy.API(auth,wait_on_rate_limit=True)
csvFile = open('file-name', 'a')
csvWriter = csv.writer(csvFile)
search_words = "#" # enter your words
new_search = search_words + " -filter:retweets"
for tweet in tweepy.Cursor(api.search,q=new_search,count=100,
lang="en",
since_id=0).items():
csvWriter.writerow([tweet.created_at, tweet.text.encode('utf-8'),tweet.user.screen_name.encode('utf-8'), tweet.user.location.encode('utf-8')])
The output of the above code is a csv file.
Step 4: Tweet Pre-Processing
import preprocessor as p
# Clean tweet text with tweet-preprocessor
tweets_df['text_cleaned'] = tweets_df['text'].apply(lambda x: p.clean(x))
By using this tweet preprocessor, we can remove URL’s, Hashtag, Emoji’s, reserved words and mentions in the tweets.
Step 5: Sentiment Analysis Using TextBlob
from textblob import TextBlob
# Obtain polarity scores generated by TextBlob
tweets_df['textblob_score'] = tweets_df['text_cleaned'].apply(lambda x: TextBlob(x).sentiment.polarity)
# Set threshold to define neutral sentiment
neutral_thresh = 0.05
# Convert polarity score into sentiment categories
tweets_df['textblob_sentiment'] = tweets_df['textblob_score'].apply(lambda c: 'Positive' if c >= neutral_thresh else ('Negative' if c Englishhttp://ijcrr.com/abstract.php?article_id=3812http://ijcrr.com/article_html.php?did=3812[1] Kiaei SF, Dehghan RM, Farzi Sa. Designing and Implementing an Emotion Analytic System (EAS) on Instagram Social Network Data. Int J Web Res. 2019;2(2):9-14.
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareNew Learning from 2nd Wave of COVID-19: Global Importance of Social Awareness and Role of Health and Technology Sector
English0101Dr. Ajay G. PiseEnglishDr. Shilpa A. PiseEnglishCOVID-19 pandemic is continuing since January 2020 throughout
the world with loss of lives and significant economic loss as well
as turbulence in the every section in the world including educa-
tion and health. Irrespective of efforts taken up by World Health
Organisation, governments of each country, research institutes and
pharmaceutical industries, second wave of the COVID-19 heated
badly and exert loss more than the first one. This attributes to the ir-
rational behavior of human kinds and genetic feature of coronavirus
that can mutate at significant way and coming up with new deadly
variants with all new clinical manifestations. It is very essential to
be aware, up to date in terms of our social behaviors, knowledge
and attitude towards COVID-19 disease. In addition, use of tech-
nology can be a savior in this pandemic. Researchers are doing best
from their end and contributing in the knowledge of COVID-19
and social awareness. In this special issue, we are disseminating the
novel finding originated from laboratory experiments and survey-
based observations and information gathered in the form of review
articles. This collection of research and review articles are based on
basic life sciences as well as technology based researches.
Studies published in this special issue highlighted on the preva-
lence of psychological distress among medical undergraduate stu-
dents during the pandemic and therefore threat of reduced clinical
skills and possible course extension among undergraduate students
is in the mind. It is essential to develop positive attitude and satis-
factory preparedness for self-protection during this critical period
among these students as surveyed in one of the article published in
this issue. It is also important to consider psychological health of
medical/par-medical students as highlighted by Rosa et al. Knowl-
edge and awareness about social awareness is equally important
to win the battle against COVID-19. A survey-based study dem-
onstrated that awareness for social distancing was higher among
males than females and 65% participants were satisfied regarding
social distancing and 60% felt stress and anxiety. Awareness is also
important in the pregnant women regarding breastfeeding during
this pandemic. Various pros and cons of this and precautions are
suggested in the article.
There are number of novel pathological consequences that have
emerged over the last 15 months of this pandemic and are these
are also the cause of number of deaths and severe comorbidities.
We have published review articles and case studies to highlights
few of those comorbidities such as thrombosis, early pulmonary
hypertension, alterations in the blood cell count and imbalance in
the inflammatory markers such as C-reactive protein. Several thera-
peutics has been tried to combat primary as well as secondary com-
plications of COVI-19 and we have published articles on the same.
This issue also put a light of side effects of Vaccine that will surely
create awareness among the citizens. This pandemic also hampers
the routine diagnosis of other disease due to threat of spread and
unavailability of time slot. This is also studied in one of the article
to highlight the possible disadvantages such as limiting care for the
deadly disease like cancers. Dilemma related to results of diagnosis
test is also highlighted. Author suggested that quality control and
quality assurance of all processes should be done to check any pre-
analytical or analytical fallacies. Clinicians and patients both are to
be educated about the probable reasons of inconclusive tests.
COVID-19 pandemic brings up the new normal of online teach-
ing in the academics. However, 15 months world-wide experience
indicated that this is unable to replace classroom learning because
its implementation and effectiveness failed to meet policymakers’
expectations at the university level. Therefore, there is a need for
the increase in teachers’ competence and usage intensity regarding
online learning in faculties and universities. Other than education,
professional culture has also changed drastically such as work from
home, virtual meeting and conferences. Various authorities are ini-
tiating schemes to tackle the COVID-19 crisis. For the women Sau-
di women encouragement is provided in terms of flexible working
hours after the epidemic, part-time work arrangements, telecom-
muting, support during pregnancy, and parenting.
The software developed to propose an autonomous verification of
a passenger as it displays the passenger’s train/flight details and
boarding area by recognizing them from the passenger’s database.
This removes the need for manual checking of the passenger before
boarding. One of the studies evaluated the efficacy of Arogya-Setu
application developed by Government of India. Geospatial technol-
ogy used to develop contact tracing application for disease surveil-
lance and prevent the spread of COVID-19 disease. The automated
system is developed that for detecting the mask and non-mask in-
dividuals during. In addition, a suitable regression algorithm and
the prediction was compared with other regression algorithms for
predicting the number of cases which can definitely help to prepare
for the worst in terms of healthcare and economical losses.
This issue covers articles focused on COVID-19 and its impact
on social behavior, mental conditions, pathological severity, states
of comorbidities and various attempt to win the battle using tech-
nology. These articles are from researchers across the globe who
have presented finding from their regions and it is observed that
authorities and citizens globally facing the same consequences of
COVID-19 pandemics. I welcome you to read this special issue on
COVID-19 which can definitely help to enhance your knowledge
about this disease and can be a base or facilitator for your own
research.
EnglishCOVID-19 2nd wave,Arogya setu app ,psychological healthhttp://ijcrr.com/abstract.php?article_id=3813http://ijcrr.com/article_html.php?did=3813Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareAI-based Pandemic Trend Analysis
English131139Vergin RSMEnglish Anbarasi JLEnglish Graceline JSEnglish Valarmathi MLEnglish Mayank VEnglish Yash DEnglish Upender SEnglishIntroduction: The current outbreak of COVID-19 has caused the world to stop and go under lockdown and has quickly grown to become a pandemic. The clinicians and scientists in medical industries are observing the pandemic for screening the COVID -19 virus in a person. Objective: In these trying times, we thought of analysing the trends in COVID-19 cases in the USA, India and Brazil using Several Time Series, Machine Learning and Ensemble Learning algorithms to check out the trends. Methods: In this paper, Auto-Regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) under Time Series, Support Vector Regression (SVR) and Linear Regression under Machine Learning algorithms and Random Forest Regression, XGBoost and AdaBoost under Ensemble Learning were discussed. Results: After analyzing the results of all the algorithms, we observed that ARIMA and LSTM were performing better than the others for Time-Series Forecasting. This study would be valuable for medical Researchers and the Government in the future. Conclusion: Seven models, namely, ARIMA and LSTM models under time-series analysis models, support vector regression and linear regression under machine learning models and random forest regression, XGBoost and AdaBoost under ensemble learning were discussed. We first looked at the sample fits and then successfully forecasted the trends for the new cases, deaths and total cases for the next 30 days in the two countries with the highest number of cases, namely, India and the US. From the resultant graphs and table values, we could infer that overall, time-series models like ARIMA and LSTM perform the best in situations like these where data is continuous and forms a series
EnglishPandemic, COVID-19, Time Series, Machine Learning, Ensemble LearningINTRODUCTION
COVID–19 is a recently discovered disease that is caused by the newly discovered coronavirus or Novel SARS–CoV2 viruses. It causes infection in the respiratory tract which can be mild or in the worst case, fatal. This virus was first reported in Wuhan, the Hubei region of China in the middle of December from where it has quickly spread over the whole world to become a pandemic with countries like the US, India, Brazil, Russia and European countries being the worst hit. Medical facilities have been incapable of treating this alarming growth of new cases every day which gives us a picture of the seriousness of the situation we’re facing currently. Hence, we thought that if there is any estimated figure about the new cases, deaths and total cases, then it would be a great aid to the medical forces which proves to be our motivation for this research. Our work can help them in determining a better estimate of new cases so that they can make appropriate preparations accordingly. Our objective is to work on different time series, machine learning and ensemble learning algorithms and compare these algorithms.
For our study, Auto-Regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) under Time Series, Support Vector Regression (SVR) and Linear Regression under Machine Learning Algorithms and Random Forest Regression, XGBoost and AdaBoost under Ensemble Learning were discussed few algorithms used in this research work includes Auto-Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Support Vector Regression, Linear Regression, XGBoost, AdaBoost.1-3
RELATED WORK
The study of Time Series Analysis of the COVID – 19 pandemic is generally carried out for selecting the best-suited algorithm for growth prediction of the pandemic.4-6 It emphasizes the 2 major difficulties of time-series analysis that it can’t be applied to continuous data and the second one being, it includes some exogenous features which can’t be mapped to ML models. It covers 3 major ways, viz., by pure machine learning models like ARIMA or ‘Autoregressive Integrated Moving Average’, by RNNs or ‘Recurrent Neural Networks’ like LSTMs or ‘Long Short-Term Memory Networks’ and lastly, ‘Extreme Learning machines’ like ‘Online Sequential Extreme Learning Machines’.7 Application of artificial intelligence and computational intelligence techniques is diversified in various areas such as e-healthcare, smart city and smart grid, data processing, predictive maintenance etc.8-10 Likewise, the application of AI-based techniques plays a vital role in this research work.11
Our study dives into the analysis part of COVID – 19 focusing on Long Short-Term Memory Networks or LSTMs.12 The study first plots the general trend of COVID – 19 in India along with the fatality rates of various deadly viruses over the past 50 years. It then shows the main result plots according to the data have driven approach and the curve fitting methods. It includes prediction about estimated total confirmed cases, estimated recoveries, etc in the foreseeable future. Conclusions say that social distancing and announcing lockdowns can reduce its spread significantly.
Technical sense and the amount of mathematics involved in the algorithm are also very important to select the accurate and best-suited algorithm as per the nature of the dataset. An ‘Experimental Study’ goes on for the comparison of LSTM & ARIMA.13 A brief comparison is done based on various factors like size and structure of datasets, data pre-processing and assessment metrics. Conclusions are made from the comparison of the performance of both the models based on tuning of hyperparameters like the number of epochs, batch sizes, etc.
The study and analysis about AdaBoost mainly showcase three boosting algorithms, viz., AdaBoost.R2, AdaBoost.M1 and the main one AdaBoost.RT.14 They compare these three algorithms side by side and based on data sets and methods. AdaBoost.RT which is the newer one performs significantly better than the others and gives better results because the parameter log(1/beta) is always ensured to be non-negative and the harder examples are always given more emphasis in AdaBoost.RT.
The ARIMA model for new cases and new deaths daily during this period is more suitable for short-term prediction. ARIMA is the most common time series prediction model in the statistical model. It regards the data sequence formed by the prediction object over time as a random sequence. It analyses a portion of the data in the sequence to obtain specific parameters that describe the mathematical model of the sequence to achieve time series modelling and use the remaining data in the sequence to validate the model. It can be used to predict the subsequent values of the data series.4,15,16
Newly developed drugs usually take years to be successfully tested before coming to the market. X-ray images and computed tomography (CT) scans are widely used as the input of DL17 models to automatically detect the infected COVID-19 case. Infected COVID-19 patients normally reveal abnormalities in chest radiography images. A phone-based framework for COVID-19 detection and surveillance is proposed. The DL model can be trained in the cloud, even at a server collocated at the network edge, which is then pushed to mobile phones for further purposes.5-7
Visualization techniques used to visually represent the spread of COVID-19 pandemic 20 is also an important part of this study and is expected to demonstrate the utility of the new system in terms of comparing rate spread across different countries and different times. It should also indicate the rate and trend of spread over time and by comparing with past examples, the system should also be used to predict the future rate of spread. It will also be integrated into automatic speech recognition and text to speech features to disseminate information to people with different range of abilities.12-14,17
For analyzing the growth and trend of the ongoing pandemic COVID-19, it is shown that iterative weighting for fitting Generalized Inverse Weibull distribution is a better fit that can be obtained to develop a prediction framework.18 The study observes that using the iteratively weighted approach, the Inverse Weibull function fits the best to the COVID-19 dataset, as compared to the iterative versions of Gaussian, Beta (4-parameter), Fisher-Tippett (Extreme Value distribution), and Log-Normal functions. When applied to the same dataset, Iterative Weibull showed an average MAPE of 12% lower than non-iteratively weighted Weibull. An algorithm for iteratively weighted curve fitting using the GIW distribution (called” Robust Weibull”) is used.
The SIR (Susceptible, Infected, Recovered) model,22 commonly used to predict the growth of epidemic, is not much effective for today’s scenario because of the basic assumption made in the model that the total population under consideration does not change with time and is not valid under the current Indian circumstances. The rate of change of the total population, consisting of susceptible, infected and recovered (or dead) population, with time is zero is not valid. A simple model conceptualized based on an analogy to compound interest formula used in engineering economics, seems to work better in this case.18,19
The support vector regression is also being used as one of the algorithms. The idea of SVR is based on the computation of a linear regression function in a high dimensional feature space where the input data are mapped via a nonlinear function. SVR has been applied in various fields – time series and financial prediction, approximation of complex engineering analyses, convex quadratic programming and choices of loss functions, etc.20
PROPOSED WORK
Data Source
The data we used is the official data for COVID-19 taken from the World Health Organisation Database. The Dataset contains the data for COVID-19 patient counts from all over the world separated by country, and we’ve done the predictions for India, The US and Brazil only. The considered data is from the date the first COVID-19 case came in their respective countries to 7th of December, 2020 and we've forecasted the data for the next 19 days.7
Data Pre-processing
Firstly, we have cleaned the data and set the dates according to the U.K. format. Then we have imported the Date column along with the new cases column and total no. of deaths column. We have taken the Dates in variable X and no. of new cases or no. of deaths (depending upon what we are predicting) in variable Y. In some of the models like linear regression and random forest, the dates are accepted only in integers, therefore we have converted the dates in integers specifically for these models. Then we have taken the data, trained the data according to the chosen Machine learning model by splitting the dataset into a 90:10 ratio. 90% of the dataset trains the models and the rest 10% of the dataset compares the predicted outcomes with the actual statistics. After the comparison, we have used this data to perform forecasting for further dates. The training and testing split of ARMIA and LSTM was done without shuffling while the rest of the model was shuffled.
Time Series Algorithms
Auto-Regressive Integrated Moving Average (ARIMA)
Auto-Regressive Integrated Moving Average, in short ARIMA, is a popular machine learning model which is most suitable for forecasting a time series. It is a class of models that learns from its past values, errors and lags, and which the model uses to predict future outcomes.
Autoregressive models:
The terms auto regression indicates that it is a regression of itself. The equation for autoregressive model of order p is (1):
where yt-1, yt-2…yt-p are the past series values (lags), At is white noise (i.e. randomness) & is defined in (2):
Moving Average models :
Moving average models are regression-like models but instead of using past values, they use past forecast errors. The equation can be written as in (3) :
Here, is white noise and q is the order.
After combining autoregressive and moving average model we get ARIMA, whose equation can be written as in (4) :
where is the differenced series (it may have been different more than once)? This is called the ARIMA (p,d,q) model. (Here, p = order of the autoregressive part, d = degree of first differencing involved, q = order of the moving average part.)
Long Short-Term Memory (LSTM)
LSTM is a typical Recurrent Neural Network architectural algorithm which is known for its capability to store or rather ‘remember’ data for a specified period because of which these are widely used in time-series analysis, speech-recognition, or any other applications where data is continuous and needs to be remembered. Since LSTM is an RNN model, it is composed of many individual cells connected to form layers where the output from one cell is passed to the further cells. Talking about the structure of a single cell of an LSTM, it mainly comprises 3 gates, viz., the forget gate, input gate and the output gate. The forget gate is responsible for the ‘remembrance’ function. It does so by utilizing a sigmoidal function and compares the current data with the previous data and accordingly gives a value of 0 or 1 which are for forgetting and remembering respectively. The input gate determines up to what extent this passed data has to be remembered or forgotten and hence associates a weightage to the data being passed. Again, a sigmoidal function decides what data to keep and a hyperbolic tangent function decides how much data to keep. Finally, the output gate determines which part of the cell has to be passed as an output. It again utilizes sigmoidal and hyperbolic tangent functions to decide what to pass and how much to pass to the output layer. The LSTM model can be described by the following equations where (5) belongs to the forget gate, (6) and (7) belong to the input gate and (8) and (9) belong to the output gate.
Machine Learning Algorithms
Support Vector Regressor (SVR)
Support vector machines (SVMs), like random forest algorithms, are also a popular choice for classification and regression models. Support vector regression is the algorithm in SVM which is used for the regression problems. This regression model uses a hyperplane and fits it such that the hyperplane contains most of the points from the space. It also creates decision boundaries that enclose the points within an area and from which we determine the best fit line i.e. the hyperplane.
For this, we assume the hyperplane to be in the form as in (10):
Then, (11) become the equations of decision boundary (where d is the assumed distance from the hyperplane):
Therefore, any hyperplane that satisfies our SVR should satisfy the condition as in (12):
Then we focus on finding the decision boundary at the distance of ‘d’ from the assumed hyperplane such that data points that are closest to the hyperplane (or the support vectors) are within that decision boundary line. From here, we only consider the points which are within the decision boundary and the one that has the least error rate or are within the Margin of Tolerance (epsilon ε). This gives us the best possible hyperplane and thus gives us a better fitting model. Support vector regressor shows the presence of the non - linear nature of data in the dataset and makes an effective prediction model.
Linear Regression
Linear Regression is a common machine learning model and a well-known algorithm in statistics that assume a linear relationship between two variables. One variable is considered a dependent model while the other variable is considered to be an independent model, i.e. y can be calculated from the linear combination of x (the input variable). The equation of a linear regression line is of the form Y = a + bX, where X is the independent variable and Y is the dependent variable. Here, b is the slope of the line, and a is the intercept. Different techniques can be used to prepare or train the linear regression equation from data, the most common of which is called Ordinary Least Squares. The linear regression uses a linear equation that assigns one scale factor to each X, known as the coefficient and is represented by the capital Greek letter Beta (β). Forgiving an additional degree of freedom, one additional coefficient is also added (e.g. moving up and down on a 2-D plot) and is called the intercept or the bias coefficient.
Ensemble Learning Algorithms
Random Forest
Random forest is a supervised learning algorithm and a machine learning model which is popularly used for classification and regression in the data. Here, we have used the Random forest regression for the prediction of the data by training the older dataset. The random forest algorithm consists of many decision trees which work on the data and combine the result. The more trees are in the training of data, the more robust and effective the random forest regression will be. This algorithm creates decision trees on the dataset and then gets the prediction from every tree to select the best solution. First, we select random samples from the given dataset. Next, a random forest constructs a decision tree for every sample data. Then it will get the prediction result from every decision tree. Then, voting will be performed for every predicted result obtained from the decision trees. At last, the most voted prediction result is selected as the final prediction result. This model is very flexible and yields high accuracy results but is also complex which is the main disadvantage for the regression part that predicts these types of dataset a difficult and time-taking task.11-13
XGBoost
XGBoost, the acronym for eXtreme Gradient Boosting, is a decision-tree-based ensemble Machine Learning algorithm that works on a gradient boosting framework. It has recently become popular in machine learning for structured or tabular data processing. This algorithm uses gradient boosted decision trees which helps in the performance and reduces the time taken compared to other algorithms. The gradient boosting used in this model is mainly of three forms: (i) Gradient Boosting Machine. (ii) Stochastic Gradient Boosting. (iii) Regularized Gradient Boosting. One of the main highlights of this model is the use of parallel and distributed computing for faster learning. XGBoost decision trees use gradient boosting and advance regularisation which yields more accurate approximation. These approximations are ensemble and an optimal output is calculated while using parallel computing which makes the model faster for bigger datasets.
AdaBoost
AdaBoost is mainly compatible to work with either decision tree classifiers or random forest classifiers. In AdaBoost, the tree produced is generally based on only a single feature variable. Hence, it can have only two outputs at any given point in time. These types of tree structures with only one node and two leaves are called stumps and are said to be weak learners. An AdaBoost classifier is said to work by combining these stumps where every new stump is created by taking into account the results and errors of the previous stumps by refining them by using measures such as Gini index. Hence, an AdaBoost algorithm effectively acts as an ensemble learning algorithm and some stumps are said to have a greater say in the output. The final decision is then taken by a random forest tree which is a combination of all these stubs.20,21
RESULTS AND DISCUSSIONS
In this section, we have shown the graphs for the forecasting done by all the models for the next 30 days (1st Nov 2020 to 29th Nov 2020) along with the previous original data. After that, we have shown a performance metric table which evaluates the models based on four evaluation metrics, namely, R-squared, MSE, RMSE and MAE, although we’ll be presenting our inference based on MAE only because it shows considerable difference in its value for every model and every case. Hence, it would be easier to present our inferences according to that metric. Figure 1(a) details the forecasting of total deaths from COVID-19 in India and figure 1(b)details the forecasting of total COVID-19 cases in India. Table 1 details the performance comparison for every model for India.
INDIA
In Time Series Models, for cumulative cases, ARIMA gives the best results with MAE as 107740.030 followed by LSTM with MAE as 8616082.966. In cumulative deaths, ARIMA is far better than LSTM with MAE of ARIMA as 1315.637 and MAE for LSTM as 114679.320.
In Machine Learning Models, it is observed that for cumulative cases, the MAE for SVR and linear regression is 1719540.726 and 1508130.682 respectively, and for cumulative deaths, the MAE for SVR and linear regression is 37992.886 and 17203.704 respectively. From this, we can say that linear regression outperforms SVR in all cumulative cases and cumulative deaths
In Ensemble Learning Models, it is observed that for cumulative cases Random forest outperformed all the three models in this section with MAE as 30311.440 followed by XGBoost with MAE as 30531.9477, AdaBoost performed least best with MAE as 42925.859. For cumulative deaths Random Forest outperformed all the three models in this section with MAE as 459.386, followed by AdaBoost with MAE as 566.521, XGBoost performed least best with MAE as 631.741.
United States of America
In Time Series Models, it is observed that for cumulative cases, the MAE for ARIMA and LSTM are 757658.254 and 13695809.436 respectively, for cumulative deaths, the MAE for ARIMA and LSTM are 5275.695 and 180772.712 respectively. Hence from these values, we can infer that ARIMA outperforms LSTM in cumulative cases and cumulative deaths ( Fig 2a and b).
In the Machine Learning models for the cumulative cases, the performance comparison shows that the MAE for Support Vector regression is 38814.565 whereas the Linear regression model gave 17334.325 as MAE. Again, for the cumulative deaths, Support vector regression scored the MAE as 93497.918 and Linear regression with the MAE of 10546.31. Therefore, we can say that Linear Regression outperforms SVR in every case ( Table 2).
In the Ensemble Learning Models, it is observed that AdaBoost performs the best among the three ensemble learning models in Cumulative cases with the MAE of 547.453 whereas XGBoost gave the MAE of 566.871 and Random Forest MAE gave 584.867 which is the least among the three. For the Total deaths, XGBoost scored the MAE value as 1348.747 followed by AdaBoost as 1418.414 then Random Forest with the MAE value as 1446.547. Hence we can conclude that XGBoost performs the best in this case. Figure 2(a) details the forecasting of total deaths from COVID-19 in the USA and figure 2(b)details the forecasting of total COVID-19 cases in the USA. Table 2 details the performance comparison for every model for the USA
Brazil
In Time Series Models, it is observed that for cumulative cases, the MAE for ARIMA and LSTM are 212248.019 and 4521220.961 respectively, for cumulative deaths, the MAE for ARIMA and LSTM are 1999.133 and 82919.289 respectively. Hence from these values, we can infer that ARIMA outperforms LSTM in both cumulative cases and cumulative deaths ( Fig 3 a and b).
In the Machine Learning models for the cumulative cases, the performance comparison shows that the MAE for Support Vector regression is 2289047.022 whereas the Linear regression model gave 406039.526 as MAE. Again, for the cumulative deaths, Support vector regression scored the MAE as 57368.560 and Linear regression the least with the MAE of 7879.481 Therefore, we can say that Linear Regression outperforms SVR in every case (Table 3).
In the Ensemble Learning Models, it is observed that XGBoost performs the best among the three ensemble learning models in Cumulative cases with the MAE of 21933.902 whereas AdaBoost gave the MAE of 27662.759 which is the least among the three and Random forest MAE gave 23866.147. For the Total deaths, again AdaBoost scored the MAE value as 773.260 followed by XGBoost as 835.352 then Random Forest with the MAE value as 720.713. Hence we can conclude that Random forest performs the best in this case. Figure 3(a) details the forecasting of total deaths from COVID-19 in Brazil and figure 3(b)details the forecasting of total COVID-19 cases in Brazil. Table 2 details the performance comparison for every model for Brazil.
CONCLUSION
To conclude, in this paper, seven models, namely, ARIMA and LSTM models under time-series analysis models, support vector regression and linear regression under machine learning models and random forest regression, XGBoost and AdaBoost under ensemble learning were discussed. We first looked at the sample fits and then successfully forecasted the trends for the new cases, deaths and total cases for the next 30 days in the two countries with the highest number of cases, namely, India and the US. From the resultant graphs and table values, we could infer that overall, time-series models like ARIMA and LSTM perform the best in situations like these where data is continuous and forms a series. These are followed by the ensemble learning models like random forest regression, XGBoost and AdaBoost which perform the next best and finally followed by the machine learning models like support vector regression and linear regression performing not as per expectations. From our analysis, we can safely conclude that time-series models perform the best in situations where a continuous series of data is involved. As a caution about using these models, it is advised to not fully rely on the forecasting produced by the models every time as they highly depend upon the dynamics of the daily changing COVID-19 data.
Acknowledgement: Authors acknowledge the immense help received from the scholars whose articles are cited and included in references of this manuscript. The authors are also grateful to authors/editors/publishers of all those articles, journals and books from where the literature for this article has been reviewed and discussed."
Conflict of Interest: “The Author(s) declare(s) that there is no conflict of interest.
Englishhttp://ijcrr.com/abstract.php?article_id=3814http://ijcrr.com/article_html.php?did=3814
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareAnalysis of Top Ten Countries with Highest Number of COVID-19 Cases
English173177Ali ArshadEnglishIntroduction: Since December 2019, the whole world severely affected due to COVID-19 which impact all sectors of daily life ranging from social, economic, education health, medical, travel, games and leisure. Due to COVID-19 whole world facing an adverse crisis of all nature covering almost all areas of including social, economic, travelling, educations and even health. This study presents a detailed analysis related to the role of the total number of days to reach maximum daily cases of COVID-19 cases. Objective: To investigate the impact of temperature and the total number of days to reach the highest daily cases and mortality rate in the top ten affected worldly countries. Methods: A detailed analysis of the impact of temperature and the role of the total number of days to reach the peak in selected countries. The available data from WHO is used to analyze the impact of temperature and the total number of days in worldly top ten affected countries in the COVID-19 first wave. The number of days was counted between the date when the highest number of daily cases were witnessed and the date the first case was reported. Results: Percentage ratio analysis of total deaths till the day highest daily cases reported for each country both with total population and with accumulated cases up to the day of the highest number of cases. It is evident from Figure 7 (a) that the highest mortality rate is 0.1 percent of the population which is very low compared with total deaths of 3% reported so far worldwide 3-4. Moreover, the mortality rate with accumulated cases is found in 1 to 5 percent except for Peru which is 12 percent. Conclusion: In this research, the work period is considered in terms of the number of days for all regions for peak and found that an average (178.10±10.92) days took to reach the highest number of COVID-19 cases. Moreover, for most regions inversely proportional trend is observed between the highest number of daily cases and the total number of days to reach the peak. However, it is found that temperature is not correlated with the increase or decrease of daily cases and the number of days to attain peak.
English
Introduction
At end of 2019, the world witnessed a new virus which was named COVID-19 and it spread so quickly in the whole of the world. The countries imposed lock down the movement locally and internationally as well. The world is always on the verge of encountering health-related challenges which are evident by the novel wave of SARS (Severe Acute Respiratory Syndrome named COVID-191. This virus, which allegedly appeared in China in December 2019, was later (February 2020) termed as “Corona Virus Disease ‘19 (COVID-19)” by the World Health Organization (WHO). COVID-19 is the seventh variant of the coronavirus family1. Moreover, in March 2020, it gained eminence of pandemic2 and impacted widely exercised spheres of the human race beyond health such as social, economic, leisure and educational institutions, etc. This virus is found highly contagious 2 as by 30th October 2020, more than 39 million people across the globe were infected by this virus and caused 1.1 million deaths so far.3,4 Moreover, studies3,5 show people with underlying health issues (or chronic diseases) and the elderly are highly prone to this virus. Efforts have already been devoted by many researchers in identifying vital factors such as climate more specifically temperature and humidity,6-10 cultural customs of regional people such as smoking, drinking etc. 5 crucial and early actions such as lockdown, social distancing and SOP’s related to wearing of masks etc. to help to control the number of cases 11. In 14, the author presented the model to evaluates and predict the COVID-19 growth in Malaysia to limit the spread of the virus. Medical scientists put a lot of effects to create the cure for the COVID-19 disease and after almost one year several companies introduced a vaccine for the COVID-19. The various countries already started giving doses to the citizens to prevent the spread of the disease and save the life of the citizens.
In the presented study, we aim to investigate the total number of days took to reach to highest daily cases in the worlds top ten countries with the highest number of COVID-19 infected cases. The paper also talks about the death ratio to the number of COVID-19 infected cases and the total population of the cross ponding country. For analysis world, top ten countries are selected which are affected severely by this novel virus named COVID-19.
Material and Method
A detailed analysis related to the role of the total number of days to reach maximum daily cases for the world top ten countries with the highest number of COVID-19 cases is presented. Several days were counted between the date when the highest number of daily cases were witnessed and the date the first case was reported. For example, in the USA it took 161 days to reach the peak of the first COVID-19 from date February, 15th 2020 to date July, 24th 2020. Mean temperature on the days of incidence of the first case and highest daily cases are (15.70o C ±3.13o C) and (19o C ±1.94o C) respectively. Moreover, a mean number of the highest daily cases are found (35174.20±10644.93). However, the mean number of days took to reach the highest daily cases are (178.10±10.92). In Russia, it took 240 days to reach the highest number of daily cases with a temperature increase of 13o C (from 0o C to 13o C) whereas in Brazil roughly 136 days took with a temperature decrease of 3 units 13o C (from 26o C to 23o C). Also, India and Mexico are countries with the highest 97,859 and least 9,866. Worldometer and WHO webpage is used for the collection of COVID-19 data. Whilst temperature values are obtained from the Time and Date metrological webpage and values are recorded for the Capital city of each country. COVID-19 pandemic data such as date of the first case reported, accumulated cases, the highest number of daily cases, the date on the day highest cases hit, total population etc. For top ten countries with the highest number of cases are is collected from Worldometer and WHO 3-4 and is presented in Table-1. The mean temperature values are recorded from the meteorology webpage: Times and Date 12 for day first case arrived (with a mean temperature of 15.70±3.13) and on the day when peak (with a mean temperature of
19±1.94) is identified. World Bank webpage 13 was used to record the total population of countries considered with a mean population of (244586619±130114932.6), however, the population is also given in. 3,4
Results and Discussions
Four regions of the world North and South America, Europe and Asia includes 10 countries with the highest COVID-19 cases are identified and analyzed in this study. Seven countries are found with the highest daily cases below 30,000 whereas only three countries are with cases more than 70,000. In the European region, Russia was found to have the least number of highest daily cases peak occurred after 240 days. Then Spain which gained peak in more than 210 days, however in France peak reached in more than 180 days. In the European region and the inversely proportional trend is observed between days to reach a peak against the highest number of daily cases. India is the only country with the highest daily cases and representing the Asian region with a peak attained in 167 days. Finally, the South and North American region attain peaks from 150 to 180 days. No relation is observed in this presented study between temperature and the number of days for peak cases.
Amongst the top ten countries with the highest number of total COVID-19 cases, Brazil is the one which accomplished peak (defined as the highest number of daily cases) in the least number of days,e. 133. Whilst peak for Russia observed in a maximum of 240 days. Moreover, seven out of ten countries reached a peak in 133-169 days. Only three countries crossed beyond 210 days as is evident from Figure 1.
Moreover, it is clear from Figure 2 that the highest daily cases for six countries are in a range of (9500-16500), whereas only France has approximately 27000 cases and only three countries are with cases in the range of (70,500-98000).
Four regions of the world shown in Figure 3 (a-b) are identified covering North and South America, Europe and Asia including the top ten countries with the highest COVID-19 cases. Interestingly, an inversely proportional trend is observed for the Europe region between days to reach peak against the highest number of daily cases (see Figure 3 (a-b)).
Also, India is the sole country with the highest daily cases representing the Asian region with again the number of days in the lowest range. Also, from the South American region, Brazil is with the highest number of cases with a peak gained in the least number of days. Likewise, from the North American region, the USA is the highest number of cases with a peak gained in the higher end of the lowest range (i.e, 133-169) of the number of days.
Figure 4 (a-b), depicts an average of 150 to 180 days are required to reach a peak since the inception of the first case. Almost the same was observed for the country with the highest cases for the Asia region. For Europe, on average 210 days took to attain the peak of the cases since the first case was reported by the country. Factors such as temperature play a huge role in the rate of increase of infection cases6,8,10 which emphasize reflecting about average temperature on the day of inception and the day of peak and is presented in Figure 5. Considering leading countries in case of the highest number of daily such as India, USA and Brazil, it is evident since reporting of first case temperature is increased by 23o C up to the day to attain peak for the USA( Figure 5).
Negligible increase of 3o C is noticed for India which is the top leading country with the highest daily cases. Interestingly, in the case of Brazil, a decrease of 3o C is observed. Consequently, correlation of increase or decrease in temperature with increase or decrease in cases requires extensive study. In this regard, we suggest readers consult text11 with preliminary findings on this subject. Furthermore, considering Russia (an increase of 13o C with highest daily cases 13,634) and Peru (a decrease of 8o C with highest daily cases 10,143), in terms of cases no such variation, whereas the negatively correlated trend is evident for temperature profile.
Figure 6, presents a percentage ratio analysis of the highest daily cases with accumulated cases up to the day of highest cases in this regard. The percentage ratio ranges from 1 to 4 for all countries, however in the case of countries with the highest cases such as India, the USA and Brazil percentage ratio varies from 1.8 to 2.8. More, interestingly the percentage ratio of accumulated cases with total population varies from 0 to 2 which is not very alarming compared to the total population. Particularly, in the case of countries (such as Mexico, India and Russia) with a total population of 1.3-1.5 billion by 2020, the percentage ratio of accumulated cases to total population is below 0.5 for India and Mexico and below 1 for Russia, as is evident from the blue line in Figure 6.
Percentage ratio analysis of total deaths till the day highest daily cases reported for each country both with a total population (see Figure 7 (a)) and with accumulated cases up to the day of highest cases is presented (see Figure 7 (b)). It is evident from Figure 7 (a) that the highest mortality rate is 0.1 per cent of the population which is very low compared with total deaths of 3% reported so far worldwide 3-4. Moreover, the mortality rate with accumulated cases is found in 1 to 5 per cent except for Peru which is 12 per cent.
Strength of Study
The strength of this study can be measured based on the following factors. At first, data considered is taken from reliable sources such as WHO, Worldometer, World Bank and Date and Time1-3,11. Secondly, for reliable findings, a longer period consisting of eight months and the top ten countries with the highest infected cases were considered. Moreover, identification of the total number of days to reach peak for countries with most infected cases is investigated. As per our published literature, such an investigation is not made. Information related to the total number of days for the peak is highly valuable as (a) it is directly linked with a range of measures required for the management of outbreak of pandemic; (b) it will provide a good base for upgrading existing health facilities to cope with COVID-19 cases for future waves or reoccurrence; (c) effectiveness of proposed measures can be assessed and can be utilized in future; (d) readiness of health staff covering doctors, nurses etc. can be planned as per the highest number of daily cases.
Conclusions
In this research work time, span is considered in terms of the number of days for all regions for peak and found that an average (178.10±10.92) days took to reach the highest number of COVID-19 cases. Moreover, for most regions inversely proportional trend is observed between the highest number of daily cases and the total number of days to reach the peak. However, it is found that temperature is not correlated with the increase or decrease of daily cases and the number of days to attain peak. Various countries in the world are going through 3rd wave of COVID-19 and countries imposing complete lockdown in the country. Most of the Asian countries including India, Pakistan, Bangladesh, Nepal, Iran, etc. were severely affected during 3rd wave and countries already started Vaccination of the people. In future work, the 3rd wave data will be collected and investigated by using the same parameters. The finding presented in this paper will be further investigated by using the data from COVID-19 third wave and critical comparison will be analyzed and results will be presented in future research work.
Ethical Statement
Data utilized in this research related to Global pandemic COVID-19 infections are obtained from Worldometer, WHO and World Bank. Moreover, data related to temperature is obtained using webpage Time and Date. This data is publicly available and does not require ethical approval.
Acknowledgement
The author is thankful to the organizations from whom data is retrieved
Source(s) of support: Nil
Presentation at a meeting: Nil
Conflicting Interest (If present, give more details): Nil
Author Contribution:
The author of this paper collected the data from various sources related to COVID-19 cases of top ten countries and temperature of the capital city of cross-ponding countries to analyze the relation between weather and the number of cases and deaths. Also analyzed the cases to population ratio and death to population ratio to establish the connection of weather and population of top ten countries.
Englishhttp://ijcrr.com/abstract.php?article_id=4049http://ijcrr.com/article_html.php?did=4049
Alzahrani SI, Aljamaan IA, Al-Fakih EA. Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using the ARIMA prediction model under current public health interventions. J Infect Public Health. 2020;13(7):914-19.
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareForecasting of Covid-19 Cases in India by Time Series Analysis Using Autoregressive Integrated Moving Average Model
English178181Khobragade AshishEnglish Kadam DilipEnglishIntroduction: COVID-19 is caused by SARS-CoV-2, a coronavirus. Forecasting has an important role in the surveillance of new emerging diseases like COVID-19. Objective: The objective of the study was to forecast COVID-19 cases by using the ARIMA model. Methods: We have used the ARIMA model to forecast cases of COVID-19 occurring per day in India. A total of 50 observations were used to fit the model. Model is best fitted by using order (0,2,1) which has the lowest AIC value. Forecasted values were compared with actual values. Results: We have found that actual reported cases per day were within 95% CI of forecasted values. Conclusions: ARIMA model can be used to forecast over a short period. This model can be used to develop strategies for the containment of pandemics.
EnglishARIMA, COVID-19, Forecast, India, Model, Time series analysisIntroduction:
The first confirmed case of COVID-19 was reported from Wuhan city of China. Soon cases started spreading in China and nearby countries. It was announced as public health emergency of international concern (PHEIC) by the World Health Organization (WHO) in January 2020.1 Alert was also given by the WHO regarding the global spread of the disease in March 2020.1More than 151.8 million cases and 31.86 lakh deaths were reported from all over the world as on 2nd May 2021. Total 19.8 million confirmed cases and 2.18 lakh deaths have occurred in India as of 2nd May 2021.2
If we can forecast the new cases which will occur in near future, it will aid in planning the resources required to prevent the occurrence of further cases. Many forecasting models were tried in the case of COVID-19 all over the world and in India.3,4,5 In this study, we have tried a forecasting model based on autoregressive integrated moving average (ARIMA).
Objectives:
1. To develop a time series analysis (tsa) forecasting model of COVID-19 cases in India
2. To compare the actual cases of COVID 19 that occurred in India with the forecasted model.
Methodology:
The COVID-19 cases occurring daily in India were reported to the Government of India which has, in turn, appeared in the official dashboard. For the study purpose, the number of COVID-19 cases occurring daily from 17th February to 7th April 2021 per day in India was considered.2,6 The data is available freely on the Government of India portal. This data was extracted in an excel sheet date wise. This data for 50 days was used to predict the occurrence of new cases of COVID-19 for the next five days. The epidemic curve was plotted for the selected period. [Figure 1]
In time series analysis, an ARIMA model is used to forecast future data based on the past available data. The Time series model is forecasted using the ‘Forecast’ package in R software. Fifty data points (daily COVID-19 cases) were first converted to time series data using the ‘ts’ command by taking the start point and endpoint of the data. The frequency was taken as 365.25 as data was related to daily COVID cases. Non-seasonal ARIMA models are generally denoted by ARIMA order (p,d,q). This order has three components to forecast the model: p, d and q, where p= number of autoregressive terms, d=order of differencing and q=number of lagged forecast errors in prediction.
The pre-requisite to apply the ARIMA model on time series data is that time-series data should be stationary. Time series data is called stationary when it's mean; variance and autocorrelation are constant over some time. Hence, the Augmented Dickey-Fuller (ADF) test was applied on time series data to check for its stationarity. Differencing was done twice to make time-series stationary. Consecutive numbers were subtracted twice for second-order differencing. In this way by differencing, we have removed the trend and seasonality of the data and hence, the mean of the time series is now constant. The stationarity of the data was confirmed by conducting the ADF test again. If the p value is less than 0.05, the data is considered stationary.
The order of the ARIMA model was selected by plotting autocorrelation (ACF) and partial autocorrelation (PACF) graphs. P-value was obtained from pacf graph and q value from acf graph.[Figure 2 and 3] ARIMA model was fitted by using the auto Arima function. Akaike’s information criterion (AIC) was used for fitting the best model. Order having the lowest AIC value was selected as the best fitting model. Forecasting was done by using the fitted model for the next 5 days. The actual number of COVID-19 cases that occurred during this period was compared with forecasted data. The predicted model for the next 30 days was plotted graphically.
Statistical analysis: Statistical analysis was done by using R software version 3.6.1 using the ‘Forecast’ package.
Results: Original data was tested for its stationarity using Augmented Dickey-Fuller (ADF) test and the test results is as follows
ADF= 0.92, Lag order = 3, p-value = 0.99.
The data is not stationary as the ADF test p-value is more than 0.05. Hence, we have differenced the time series twice to make it stationary. After making differencing, ADF test results are as follows
ADF= -4.78, lag order=3, p value= 0.01. (p value < 0.05 is considered as significant)
The best fitted ARIMA model is (0,2,1) with the lowest AIC value of 965.79. The moving average (ma) coefficient for the fitted model is -0.8796 with a standard error of 0.0649. [Table 1]
The output of the forecasted model for the next 5 days is shown. The actual and predicted cases from 8th to 12th April 2021 is shown in table No.1. All the actual cases are within the range of 95% confidence interval of the predicted cases. [Table 2] Also predicted cases for the next 30 days (from 8th April to 7th May 2021) are plotted graphically [Figure 4].
Discussion:
In this study, we have used the ARIMA model to forecast cases of COVID-19. Without forecasting, it is very difficult to plan the strategies for the surveillance of the disease. When we have a forecasted data, public health surveillance can be carried out in the right direction and inculcate correct intervention measures. Hence, we have planned to do a time series analysis of COVID-19 cases to prove the hypothesis of whether these COVID-19 cases follows time series or not and to forecast the future trends.
We have taken COVID-19 cases that occurred in India as time series data of 50 days. As the data was not stationary, we have done differencing twice to take it stationary. We have used the ARIMA model to forecast using R software. The fitted model for that time series data in order (0,2,1). The AIC and BIC value is lowest for this order. ADF test was used to check stationarity. We have forecasted data for the next 5 days from 8th April to 12th April 2021. We have found that all of the actual cases reported are within the 95% confidence interval of the forecasted cases.
Different ARIMA models are fitted for different countries. In Saudi Arabia, the preferable ARIMA model is (2,1,1).7 Best fitted model for various countries are Italy (0,2,1), Spain (1,2,0) and France (0,2,1).8 Kabir et al. developed the ARIMA model for Nigeria. They used 39 observations to predict the corona cases.9 We have used 50 days of data to predict the daily cases of COVID-19. Amal et al. forecasted the cases for 10 days using ARIMA and NARANN model. In this study, they used only 1-month of data to predict the cases.10A model to predict cases and deaths were developed in Italy to predict. In this study, the model was fitted by using order (0,2,0) and (2,2,1).11 Similarly, one study forecasted COVID-19 cases for two days using ARIMA model of order (1,2,0) and (1,0,4).12 In our study, ARIMA model is best fitted by using order (0,2,1). Actual cases reported for the next 5 days are within a 95% confidence interval of the predicted values.
Previously many time series models were used for forecasting infectious disease surveillance. Public health experts can predict how much variability will be there in future regarding the pattern of the disease.13 Cases should be updated regularly so that if there is any change in the time trend of the disease, it will reflect in the model. The model will give a good prediction of the future trend of the disease. ARIMA model can be used for epidemiological surveillance of the new emerging diseases like COVID-19. So that correct intervention can be done at the correct time to prevent morbidity and mortality from the disease.
Conclusion: Actual cases of COVID-19 are within 95% CI of the predicted ARIMA model (0,2,1). ARIMA model can accurately predict the occurrence of COVID-19 cases. This model may be developed for state & district levels to predict COVID-19 cases.
Recommendation: Forecasting must be a part of routine surveillance activities in the pandemic situation of new emerging diseases like COVID-19.
Acknowledgement: We express gratitude to the Ministry of Health & Family Welfare, Government of India for the supporting data, used in this research.
Conflict of interest: None
Source of funding: Nil
Author’s contributions: Both authors have conceptualized the article. The manuscript was written by the 1st author and edited by the 2nd author. Data analysis was done by the 1st author.
Englishhttp://ijcrr.com/abstract.php?article_id=4050http://ijcrr.com/article_html.php?did=4050
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareEvaluation of the Effectiveness of Rapid Diagnostic Test Antibody, Serological Test, Reverse-Transcribed Polymerase Chain Reaction (RT-PCR) and Hematology in Non-Severe Covid-19 Patients
English182187Elisabeth LS SetianingrumEnglish Kartini LidiaEnglish Kristian RatuEnglishEnglishRapid diagnostic test, Antibody, RT-PCR, Serology, HematologyINTRODUCTION
COVID-19 is a zoonotic disease caused by a coronavirus which is similar to Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS), which can spread mainly from person to person through droplets from the nose or mouth through coughing, sneezing or talking and inhaling droplets from an infected person. COVID-19 originated from an outbreak in Wuhan, China, in December 2019 and continues to spread throughout the world, to Indonesia on March 2, 2020, and on April 9 2020 patient zero was recorded in Kupang City, East Nusa Tenggara. Until now, all hospital isolation rooms are filled with COVID-19 patients. 1,2,3
Clinical manifestations of COVID-19 vary widely, starting from the incubation period of approximately 3-14 days until the onset of symptoms, such as mild cough, runny nose, fever, anosmia, ageusia, or even no symptoms at all to severe respiratory symptoms such as acute respiratory distress syndrome (ARDS). From March to the end of December 2020 Kupang City started treating people with mild and severe symptoms. Based on the Guidelines for Handling COVID-19 of the Indonesian Ministry of Health 4th Edition, a screening tool such as the Rapid Diagnostic Test (RDT) antibody is used. If the result is reactive then it will be followed by a Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) examination. Antibody Rapid Diagnostic Test (RDT) is a simple, inexpensive and efficient screening kit. It employs whole blood or serum/blood plasma samples as a humoral immunological response. There are lots of commercial RDTs circulating the market with different brands, prices, sensitivities and specificities.1-5
The presence of antibodies as the body's humoral response to COVID19 infection shows whether there has been a recovery in the patient, besides that it can be used as a screening for asymptomatic conditions such as a study conducted by Guo et al. by detecting the emergence of Immunoglobulin (Ig) A, M, G. Another study by Tofe et al. on commercial RDT was tested on sensitivity and specificity by comparing RT-PCR examinations. The study by Elslande et al. compared seven commercial RDT IgM / IgG antibodies against the IgA / IgG Elisa tool to obtain the sensitivity and specificity of each RDT.5-8
Evaluation of the haematological profile of COVID-19 patients is also important for patients with mild or no symptoms. Haematological parameters such as haemoglobin, leukocytes, platelets, sedimentation rate (ESR), neutrophils, lymphocytes, monocytes and neutrophil lymphocytes ratio (NLR), platelet lymphocyte ratio (PLR) are used as biomarkers and prognostic factors to assess the severity of the disease in Covid-19 patients.9-12
This study will evaluate the use of several commercial RDT antibodies and compare them with the emerging serologic results examined using the Electro-Chemiluminescence Immunoassay (ECLIA) method and the suitability of the RT-PCR results in mild symptomatic and asymptomatic patients admitted to several Kupang city hospitals at the end of 2020 and the emerging haematological profile.
MATERIALS AND METHOD
The study design was a cross-sectional study that was conducted from October to December 2020 in several hospitals in the city of Kupang which treated patients with mild or no symptoms. The study was approved by the Ethics Commission of the Faculty of Medicine of the University of Nusa Cendana 2020. Research respondents were 34 subjects who had agreed and signed the informed consent.
The subjects were taken their blood samples for RDT antibody examination using blood serum (taken from centrifuged whole blood and separated the blood serum), where the reactive results on RDT antibodies must be followed by confirmatory or diagnostic tests which are the nasopharyngeal and oropharyngeal swabs for RT-PCR examination. Furthermore, for the LED examination which is the Complete Hematology, 3 ml of whole blood was taken and then processed.
The RDT antibody examination used the lateral flow method which analyzes the suitability of several commercial RDTs used in various health services with reactive results when antibodies are present and non-reactive if antibodies have not been formed. Subsequently, RT-PCR examination is carried out to check for any viruses in the patient's respiratory tract with a positive result if the virus is present (a patient confirmed with COVID-19) and negative if there is no virus.
Serological levels of SARS CoV2 were examined using the anti-SARS COV2 Electro-Chemiluminescence Immunoassay (ECLIA) method to detect antibodies in serum as an adaptive immune response to the SARS COV2 virus. This examination is a semiquantitative examination by comparing the electrochemiluminescence signal of the antigen-antibody reaction in a sample with a cut off index (COI) limit value if COI ≥ 1 indicates a reactive/positive state if COI 1, while respondents with serological levels of COI 1, but the appearance of antibodies in the body depending on nutritional factors, patient age, the severity of disease and treatment are given to patients. SARS COV2 antibody and serological RDT tests cannot be used to make a clinical diagnosis, because the acute infection period has passed, often causing false-positive results due to cross-reactions with other types of pathogens that cause coronavirus.15,18
RDT antibodies with the lateral flow method vary in the results released due to the differences in the quality of nitrocellulose and recombinant proteins and their stability when reagent stored. Besides that,false-negative results can occur in patients with severe symptoms. On the contrary, false-positive results can occur in cross-reactions to other types of coronavirus pathogens. Great suitability indicates that the selection of RDT antibodies used by the hospital in this study is quite good even though only a few reagents are used, however, the RDT antibody test is not recommended by WHO for clinical diagnosis because it shows more acute past infections.5,15,16,19
The comparison between RDT antibody and SARS CoV2 serology was 73.5%. This is because they both detect the presence of antibodies in the blood, where the antibodies that appear are IgM and IgG. IgM appears a few days after infection and is followed by the IgG, while serology tests can see the levels of antibodies that have been formed.5-7,16,20
Table 2 depicts the conformity between SARS CoV2 and RT-PCR serology was 70.6%, while the smallest conformity occurred between the use of RDT and the RT-PCR results of 47.1%. This is due to the differences in the examination samples used and different examination methods. The relatively high results between serology and RT-PCR results indicate that these two methods are acceptable to be used to see the emergence of antibodies and the acute phase that occurs while the virus is still present, meanwhile RDT results are less reliable in detecting the appearance of antibodies in a person's body.6,8,15,17
Antibodies are formed after the body forms immunity against viruses that enter the body. Due to the limitation of diagnostic tools such as rapid tests, it is important to continue the diagnosis with the RT-PCR examination (molecular test) to determine precisely the presence of the virus and the ability to replicate through the CT value of the SARS COV2 virus. According to WHO, molecular tests are the most recommended tests to diagnose COVID-19. Therefore, screened patients using the antibody Rapid test with reactive results should be carried on with RT-PCR examination. Those with a non-reactive rapid test result means that antibodies have not been formed now or in the past.6,18
Table 3 shows that the serological levels of SARS COV2 in the subjects had a mean value of 24.33 ± 39.129 where some of the patients had high serological levels of SARS COV2 (COI> 1), this shows the body's good immunological response to viruses in the body. Low antibody levels (COI 3.13. In this study, the average NLR of all subjects was 3.33 ± 5.23. This suggests an inflammatory state that has also occurred in the treated patient. One respondent experienced a high NLR increase up to 25.85 due to a state of neutrophilia and lymphocytopenia. According to Kazancioglu et al, (2021) in their study, the lymphocyte, NLR and PLR values ??provide more clinical value than other parameters in differentiating patients with COVID-19 from another influenza, whereas according to Ghahramani et al, (2020) NLR determines prognostic condition in a disease.2,9,10,12
In this study, an increased mean number indicates that inflammation has occurred, although many factors can influence this increase in ESR results. This examination has often been replaced by other inflammatory markers such as CRP, PCT, LDH and NLR which also have an increase in inflammatory processes / other acute bacterial infections.9,11,12,23 The platelet count in this study population had a normal mean number, in contrast to COVID-19 patients with severe symptoms of thrombocytopenia, as occurs in other severe infectious diseases. Increased Platelet Lymphocyte Ratio (PLR) is also more indicative of disease severity than NLR.9
CONCLUSIONS
RDT antibody and serology SARS COV2 detect the presence of antibodies that have been formed by the body approximately 1-2 weeks after the acute infection. RT-PCR is the best diagnostic to detect the presence of the virus in the respiratory tract during acute infection and is followed by the patient's clinical condition. Haematological analysis shows the prognostic and severity of COVID19 patients. Patients with mild symptoms / no symptoms do not have much difference in haematology results than normal people.
ACKNOWLEDGEMENTS
This research was fully funded and supported by the University of Nusa Cendana, Indonesia.
CONFLICT OF INTERESTS
There is no conflict of interests found during this study
ETHICS
This study has received ethical approval from the Health Research Ethics Commission of the Faculty of Medicine, University of Nusa Cendana.
AUTHOR CONTRIBUTIONS
Elisabeth was responsible in the whole project, including the protocol. Kartini and Kristian contributed in data analysis and writing. Elisabeth supervised the experiment and wrote the whole manuscript.
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14. Abbott Diagnostics. Abbott RealTime SARS-CoV-2: Instructions for Use. US Food Drug Adm website [Internet]. 2020;(Ic):1–12. Available from: https://www.fda.gov/media/136258/download
15. Yanti B, Ismida FD, Sarah KES. Differences in antigen, antibody, RT-PCR diagnostic tests and molecular rapid tests for Coronavirus Disease 2019. J Kedokt Syiah Kuala. 2020;20(3):172–1777.
16. Carter LJ, Garner L V., Smoot JW, Li Y, Zhou Q, Salveson CJ, et al. Assay Techniques and Test Development for COVID-19 Diagnosis. ACS Cent Sci. 2020;6(5):591–605.
17. Tang Y, Schmitz JE, Persing DH, Stratton CW. Laboratory Diagnosis of COVID-19: Current Issues and Challenges. J Clin Microbiol. 2020;58(6):1–9.
18. WHO.Recommended Use of Immunodiagnostic Tests at Health Care Facilities (Point of Care) for COVID-19. April 8th [Internet]. 2020; (Scientific Statement): 1–4. Available from: https://www.who.int/docs/default-source/searo/indonesia/covid19/saran-penggunaan-tes-imunodiagnostik-di-fasyankes-(point-of-care)-untuk-covid-19.pdf?sfvrsn=a428857b_2
19. Li Z, Yi Y, Luo X, Xiong N, Liu Y, Li S, et al. Development and clinical application of a rapid IgM-IgG combined antibody test for SARS-CoV-2 infection diagnosis. J Med Virol [Internet]. 2020;92(9):1518–24. Available from: http://dx.doi.org/10.1002/jmv.25727
20. Jacofsky D, Jacofsky E, Jacofsky M. Understanding Antibody Testing for COVID-19. One Heal. 2020;(January).
21. Lippi G, Mattiuzzi C. Hemoglobin value may be decreased in patients with severe coronavirus disease 2019. Hematol Transfus Cell Ther [Internet]. 2020;42(2):116–7. Available from: https://doi.org/10.1016/j.htct.2020.03.001
22. Cavezzi A, Troiani E, Corrao S. COVID-19: haemoglobin, iron, and hypoxia beyond inflammation. A narrative review. Clin Pract. 2020;10(2).
23. Terpos E, Ntanasis-Stathopoulos I, Elalamy I, Kastritis E, Sergentanis TN, Politou M, et al. Hematological findings and complications of COVID-19. Am J Hematol. 2020;95(7):834–847.
Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11Healthcare
A Study on Prevalence of Depression, Anxiety and Stress in Student Populations During the COVID - 19
English188193Achari KVEnglish
Background: The depression, anxiety and stress symptoms are increasing drastically among the student population due to COVID-19. These symptoms lead to change into life-threatening situations during COVID scenarios. Objectives: We studied the distribution of depression, anxiety and stress in a cohort of 1000 students population, from different institutions in central India. They were divided into four clusters based on their age, i.e. 15 – 16 y, 17 – 18 y, and 19 – 20 y. Material and Method: We administered the Depression Anxiety Stress Scale (DASS -21) to all student populations to determine their depression, anxiety and stress symptoms. Each student was classified either as mild or moderate or extreme type based on his/her performance. Result: Prevalence of mild-moderate and extreme in the studied population was 30.9%, 64.2% and 4.9%, respectively. The Chi-square test of stress revealed a significant association between stress and gender (p< 0.001) while it exhibited a non-significant association between anxiety and gender as well as depression and gender. The depression, anxiety and stress traits were compared using analysis of variance (ANOVA). Conclusion: Result depict that the independent factor age produced significant effects on depression (p< 0.001) anxiety (p< 0.001) and stress (p< 0.001) traits. In particular, irrespective of different age groups, the 19-20y age group exhibit more anxiety (p< 0.05) than that of their counterparts. However, a significant difference could not be validated within the age groups of depression and stress. The depression, anxiety and stress symptoms lead to change in psychiatric morbidity over time.
EnglishAnxiety, Depression, Stress, Student population
INTRODUCTION
Depression, anxiety and stress symptoms are common among student population concerning mental health and psychosocial functions. 1,2 The prevalence of depression and anxiety symptoms are 4.4 per cent and 3.6 per cent respectively worldwide.3,4The depression, anxiety and stress symptoms are increasing drastically between the age group of 15-18 years.5-8Twenty per cent of the Indian population is made up of adolescents of which around 17-25 per cent are suffering from depression, anxiety and stress symptoms.9,10,11Thesesymptoms can manifest in broad array of situations, ranging from life-threatening situations to school presentations and competitive examination. Depression, anxiety and stress symptoms can enhance the individual’s ability to cope, both in dangerous situations and in situations where the individual is facing a positive, yet challenging situation.15 However, depression, anxiety and stress symptoms are hallmarks and predictors of depression, anxiety and stress disorders.10Depression, anxiety and stress disorders are disabling for the individual.12,13,14Now a day, depression, anxiety and stress disorders were among the three leading causes for disability in adolescents.16-19Thedepression, anxiety and stress are found to be statistically significant with mental health.20The mental health and psychosocial functions lead to change in psychiatric morbidity over time.20,21Authors stated that around twenty per cent of the total population experienced mental disorders during their lifetime.22,23In the developing country one in five adolescents suffers from mental health problems, while it is 12-29 per cent in developing countries. Several studies indicate that the prevalence rates of individual disorders: Depression, anxiety, and stress (DAS) are growing among adolescents.22
The symptoms of depression mood disorders are dysphoria, hopelessness, devaluation of life, self-deprecation, lack of involvement, anhedonia and inertia.24,25Such short-term emotional responses can lead to change in serious health issues associated with impaired daily functions.21 Depression is a common mental health problem, which is moving towards twenty-five per cent of people worldwide.24Anxiety mental health problems can be defined as “a group of mental disorders characterized by an unpleasant feeling with uneasiness or worry about future events or the fear of responding to current events. It can occur without an identifiable triggering stimulus”.26Thus anxiety scale assesses skeletal muscular effects, autonomic arousal, situational anxiety and subjective performance of anxious effects. Around ten percent people are facing the anxiety health problem in the world.8Similarly stress can be defined as“when the person is not able to cope up with environmental conditions due to lack of compliance leads to change in psychological and biological features called stress”.6Because stress display judicious level of chronic non-specific arousal due to difficulty in relaxing, over reactiveness, impatient and nervous arousal. Therefore, depression, anxiety and stress exhibit worldwide mental health problems. 23,24
As regards the relationship between age and depression, anxiety and stress the conclusions are still not consistent. Several authors reported that people of advancing adolescent age tend to show greater depression, anxiety and stress.18,19,20, Mirzaei and coauthors19 stated that the prevalence of depression anxiety and stress is greater in older people than that of the younger population. Before that authors also stated that these symptoms commonly begin under the age of twelve and it extends up to eighteen.3,18,24However, it has not yet been con?rmed if age-associated depression, anxiety and stress could be imputed to the parallel changes in the central nervous system or the changes in work and/or hormonal environment.11,27
Further, in some studies, no signi?cant differences in the distribution of depression, anxiety and stress were found between females and males.12,17,18 In contrast, many reports suggest that females tend to score signi?cantly more towards depression, anxiety and stress than males.19,24,27The rate of anxiety is just double in females than that of males (female 4.6%; male 2.6% ) at the same time the symptoms of depression reveal around 5.1% in females and 3.6% in males worldwide. It has also been reported that the gender differences in depression, anxiety and stress could be explained as a by-product of socio-cultural in?uences and hormonal changes. The male-female divide in many distinct cultural and ethnic groups could in?uence the distribution pattern of depression, anxiety and stress as a function of gender. Physiologically, females that possess menstrual cycles, exhibit internal de-synchronization which lead to exhibit depression, anxiety, sleep problems and appetite disturbances.3Psychologists still have dif?culty in giving an exhaustive explanation for the relationship between depression, anxiety and stress preferences and gender. Therefore, a consensus is yet to emerge.21-27
The depression anxiety stress scale has been developed by several psychologists16,26 which can assess general distress or negative affect, physiological hyperarousal, and low levels of positive affect. Hence, Lovibond and Lovibond16developed the 42-item Depression, Anxiety, and Stress Scales (DASS-42), a self-report instrument with three dimensions. Further, the DASS-21 was developed from the original DASS-42 by selecting 7 of 14 items for each subscale with the highest loadings.16DASS-21 were designed to maximize measurement of the distinct features of depression, anxiety and stress, which typically co-occur in adults and to minimize measurement of what these states have in common. The original principal component analysis of DASS items revealed that a stable three-factor solution of depression, anxiety and stress was the optimal fit16 Recent research has applied the self-report version of the DASS to children and adolescents between the ages of 7 and 15.28.29Therefore DASS-21 is suitable for adolescent and young population.27Give background and then aim of the study here. Make the introduction slight shorter.
MATERIALS AND METHODS
Subjects
One thousand student population aged between 15 to 20 years (Median age = 17 y), including male and female volunteered to participate in this study (Table 1). The study was designed in such a way that it neither interferes nor disturbs the normal routine of the subjects. The DASS-21 inventory was administered to each subject belonging to each institution. They maintained dignity and confidentiality while responding to the different inventories. The subjects were interviewed invariably after the end of their study sessions. The time of the interview session varied generally as they were functioning at different institutions characterized by schedules of Schools or colleges.
All subjects signed an informed consent form before they participated in the study. They were assured that the responses obtained on the inventory would be kept firmly secret and under no circumstances would be made public or used for profit-making. Subjects were told to remain frank and give trustworthy answers and that their participation is for a noble cause, i.e., for science and society. The protocol of the study complied with the ethical standards of the journal.
Instrument/Inventory
The DASS-21 inventory was administered under standard conditions. A good “rapport” was established with the respondents both at individual and group levels.
Subjects were supplied with a personnel information sheet along with DASS-21 inventories. They were instructed to fill up the biographical data sheet first before proceeding to register their responses on the inventories. The biographic data sheet included different fields, such as name, fathers name, mothers name, date of birth, class, age, address, mobile number. They were told not to read the inventories unless asked for. The respondents were reminded not to ponder on each question, but to give the first response that occurs to them spontaneously as fast as possible. After the completion of the session, they were advised to check back and make sure that they have not missed any field of query on any one of the utensils.
Characteristics of the DASS-21 inventories and Determination of Scores
The DASS-21 inventory is widely used by researchers around the world. This is designed for assessing depression, anxiety and stress levels. The inventories consist of 21 questions each having four options. Each subject has the freedom to choose an answer, which he/she thinks to be the most appropriate for him/her, by putting a tick-mark inside the boxes drawn for each choice. Depression, anxiety and stress have seven items each. The original DASS-21 is in English,16whilein this study a modified Hindi version was used, as the participant were from Hindi speaking population.
The reliability of the DASS-21 scale in terms of internal consistencies was ascertained. The Cronbach’s alpha values of the entire scale were0.83, and for depression, anxiety and stress were 0.83, 0.85, and 0.80 respectively.
The DASS-21 scores were computed from the response sheet obtained from each subject. Concerning depression, anxiety and stress dimensions each subject was classified either as mild (score between 1 and 7), or moderate(score between 8 and 14) or extreme (score between 15 and 21).
Statistical Analysis
All data were stored in the form of records in database files. Descriptive statistics and Chi-square test was employed to analyze independence of attributes, such as depression, anxiety and stress dimension and student population (elaborate this, such as the function of gender: male vs female or age). The depression, anxiety and stress scores were subjected to ANOVA for multiple comparisons. Data were analyzed using software, namely SPSS (Version 20.0).
Results
The prevalence profile of the distribution of depression, anxiety and stress (DASS) traits, namely mild, moderate and extreme, among the student population is summarized in Table 1. Out of 1000 subjects, 309 (30.9%) were mild, 642 (64.2%) were moderate and 49 (4.9%)were extreme. The DASS score range of total subjects was 2 – 60 (mean ± SE: 26.5±0.30; N = 1000). However, the range for male was 5 – 60 (26.32±0.43; N = 468) and for females it was 2 – 55 (26.7±0.41; N = 532). Out of 468 (46.8%)male subjects, 148 (31.6%)were mild, 299 (63.9%)were moderate and 21 (4.5%)were extreme.Similarly, out of 532 (53.2%)female subjects, 161 (30.3%)were mild, 343 (64.5%)were moderate and 28 (5.2%)were extreme (Table 1).
Furthermore, a significant relationship was observed between different age groups and depression, anxiety and stress. In particular, irrespective of different age groups, the 19-20y age group exhibit more anxiety (p< 0.05) than that of their counterparts. However, a significant difference could not be validated within age groups of depression(p< 0.682)and stress(p< 0.815).
Distribution of depression traits
The prevalence profile of depression traits, namely mild, moderate and extreme, among the student population is summarized in Figures 1 and 4. Out of 1000 subjects, 390 (39.0%) were mild, 519 (51.9%) were moderate and 91 (9.1%) were extreme(Figure1). The depression score range of the total subjects was 0 – 21 (mean± SE: 8.93±0.13; N = 1000). However, the range for males was 1 – 21 (mean 9.01±0.18; N = 468) and for females it was 0 – 19 (mean 8.86±0.17; N = 532). Results of the Chi-square test indicated a statistically non-significant (p< 0.904) relationship between depression and gender (Figure 4). The depression traits were compared using an analysis of variance (ANOVA). Results depict that the independent factor produced significant effects on depression (p< 0.001) traits. Furthermore, a significant relationship was observed between different age groups and depression. In particular, irrespective of different age groups, the 15-16y age group exhibit more and 17-18 y group exhibit less depression (p< 0.682) than that of their counterparts.
Distribution of anxiety traits
The prevalence profile of anxiety traits, namely mild, moderate and extreme, among the student population is summarized in Figures 2 and 5. Out of 1000 subjects, 449 (44.9%) were mild, 498 (49.8%) were moderate and 53 (5.3%) were extreme (Figure1). The anxiety score range of the total subjects was 1 – 20 (mean± SE: 8.16±0.12; N = 1000). However, the range for males was 1 – 20 (mean 8.19±0.17; N = 468) and for females it was 1 – 19 (mean 8.12±0.16; N = 532). Results of the Chi-square test indicated a statistically significant (p< 0.805) relationship between anxiety and gender (Figure 5). The anxiety traits were compared using an analysis of variance (ANOVA). Results depict that the independent factor age produced significant effects on anxiety (p< 0.001) traits. Furthermore, a significant relationship was observed between different age groups and anxiety. In particular, irrespective of different age groups, the 19-20y age group exhibit more and 15-16 y group exhibit less anxiety (p< 0.05) than that of their counterparts.
Distribution of stress traits
Prevalence profile of stress traits, namely mild, moderate and extreme, among student population is summarized in Figures 3 and 6. Out of 1000 subjects, 337 (33.7%) were mild, 596 (59.6%) were moderate and 67 (6.7%) were extreme(Figure 3). The stress score range of the total subjects was 0 – 21 (mean 9.51±0.12; N = 1000). However, the range for males was 0 – 21 (mean 9.20±0.18; N = 468) and for females it was 1 – 19 (mean 9.77±0.16; N = 532). Results of the Chi-square test indicated a statistically significant (p< 0.001) relationship between stress and gender (Figure 6). The stress traits were compared using an analysis of variance (ANOVA). Results depict that the independent factor age produced significant effects on stress (p< 0.001) traits. Furthermore, a significant relationship was observed between different age groups and stress. In particular, irrespective of different age groups, the 15-16y age group exhibit more and the 19-20 y group exhibit less depression (p< 0.815) than that of their counterparts.
DISCUSSION
The results of the present study exhibited predominantly more moderate traits (64.2%)among the student population, irrespective of gender and age. The prevalence of depression (51.9%), anxiety (49.8%) and stress (59.6%) among the student population are greater than that of mild and extreme traits. This study also reported that the percentage of extreme (4.9%) student population [depression (9.1%), anxiety (5.3%) and stress (6.7%)] is very less due to high ability to cope with syllabus and pattern of examination. Nonetheless, the results of the present study contradict the results of an earlier study conducted in a different country in that the prevalence of the extreme traits was the highest in the adult medical student population of the northern part of the Indian subcontinent.24,26 The present results further contradict the ?ndings of Szabó andBhasin3,24withrespect to the distribution of extreme types. These authors reported a higher percentage of extreme traits than that of the present study. i.e. the frequency of the mild trait was conspicuously higher than the extreme trait. They were also stated that higher socio-economic background, qualified mothers, nuclear families and well-developed institutions of the students is also exhibited extreme traits of depression, anxiety and stress.3,15,24
In the present study, although the females had a higher mean score(52.2) as compared to the males, the results of the Chi-square test did not reveal a statistically significant association between gender and depression and anxiety. Only stress exhibit a statistically signi?cant association with gender. The present results are not in agreement with those published earlier.4,18,19,24These authors reported that females had higher depression, anxiety and stress scores than males. Moreover, in this study females exhibited more extreme traits (5.2%) as compared to the males (4.5%) and it was the opposite for the mild trait (females: 30.3%; males: 31.6%). However, our results based on gender-wise distribution are in agreement with those reported earlier.34
Furthermore, a signi?cant relationship was observed between age and depression, anxiety and stress scores. Although this study was large concerning sample size, the higher percentage of depression, anxiety and stress scores in comparable age groups could not be documented as has been reported in past studies.3,19The depression, anxiety and stress symptoms are elevating between the age group of 15-18 years.15,18,24, Around 25 per cent of adolescent and young people use to exhibit depression, anxiety and stress symptoms.15,23It has also been reported that the females who experienced depression, anxiety and stress symptoms attributed to hormonal ?uctuations during the menstrual cycle; females tend to experience depression than that of males.25,26Authors reported that rather than biological factors, environmental factors might also play an important role in the transcription of depression, anxiety and stress symptoms at an early age.25,26
CONCLUSION:
Although the results of the present study corroborate with most of the earlier?ndingsin terms of the prevalence of depression, anxiety and stress symptoms, it contradicts the same ?ndings as regards prevalence of depression, anxiety and stress scale concerning lower extreme traits among the student population of government institutions. Certainly, the image of life without stress is not possible in the COVID-19 scenario to develop a personality. Nevertheless, consistent stress may translate into anxiety which may lead to change into depression. Depression may lead to change into anxiety; therefore consistent positivity towards lifestyle is a prerequisite.
Acknowledgements
We are thankful to the students, teachers and principals of the schools for their invaluable support and cooperation during the study. We are also thankful to the Principal of GovernmentMadanLalShuklaPG College Seepat, Bilaspur CG India for allow performing the project work on the premises.
Declaration of Interest: The authors report no conflicts of interest. The author alone is responsible for the content and fieldwork. The entire work is self-funded by the author (Printing of questionnaires, Distribution and Collection of questionnaires, Interaction towards the students, writing work etc). The author alone did all the fieldwork, analysis and writing work.
Englishhttp://ijcrr.com/abstract.php?article_id=4052http://ijcrr.com/article_html.php?did=4052
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareEstimation of the Basic Reproduction Number (R0) for Covid-19 Epidemic at District Level in Rewa, Madhya Pradesh
English194200Singh S.English Sharma A.English Mishra A.English Patel M.English Derashri G.English Modi A.EnglishEnglishCOVID-19, R0 (Basic reproduction number), SIR model (Susceptible, infected and recovered), PythonINTRODUCTION- COVID-19 was announced as a global pandemic by World Health Organization (WHO) 1 owing to its highly contagious and pathogenicity that has been rapidly spreading throughout the world since its first reported outbreak in China in December 2019. In India, the first case of COVID-19 was reported on 30 January 2020 while in M.P. on 20 March 2020. On April 27, 2020, the first COVID-19 case was reported in Rewa, Madhya Pradesh. Understanding the epidemiology of COVID-19 at a local level is becoming increasingly important because even after five phases of lock-down after that unlocking in a phased manner, the daily rise of cases is alarming. To manage an epidemic like this, the administrations and health departments across the Rewa district with about 2,365,106 populations (as per census 2011), need forecasting about the said outbreak to tackle epidemic by decisive planning and management of the resource allocation for a COVID-19 case, which will help to improve survival rates.
Estimation of basic reproduction number R0 is calculated by either individual-level modelling (ILM) that is viewed as prospective or through population-level modelling (PLM) viewed as retrospective. In individual-level modelling (ILM) data are collected from the very beginning of an epidemic. All the contacts of patient zero (first infected individual) are traced and tested, and this carries on as the disease keeps transmitting while in population-level modelling (PLM) we use the variation in infected numbers within the population from one day to the next, frequently using adjusted cumulative models.2 individual-level modelling (ILM) and population-level modelling (PLM) do not give the same results, as they depend on the efficiency of contact tracing, the use of test results (and their accuracy) vs. symptoms, etc.
Either ILM or PLM R0 estimation depends upon some important factors like (a) average number of persons exposed by the infected person, (b) probability of infection after getting exposure, (c) total duration of infectiveness including asymptomatic period, (d) percentage of the people susceptible to getting the infection and total population size,(e) rate of removed persons that include recovery and death both. So we have to consider these factors in modelling and a different degree in different models.
R0 can be calculated from theoretical models for which some specific methods are like the survival function; next-generation method; Jacobean matrix eigenvalues; endemic equilibrium; meta-population models; partial differential equation models, hierarchical Bayesian regression and constant term-polynomial methods.3
Different models consider different numbers of factors along with different statistics like Frequentist or Bayesian, so estimates may be slight differences and do not agree.
For many diseases having R0 >1 but they do not always become epidemic and can die out while some disease having R0Englishhttp://ijcrr.com/abstract.php?article_id=4053http://ijcrr.com/article_html.php?did=4053
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Kanagarathinam K, Sekar K. Estimation of the reproduction number and early prediction of the COVID-19 in India using a statistical computing approach. Epidem Heal. 2020 May 09;42:e2020028.
Bhaskar A, Ponnuraja C, Srinivasan R, Padmanaban S. Distribution and growth rate of COVID-19 outbreak Tamil Nadu: A log-linear regression approach. Ind J Public Health. 2020;64(6):188.
Lv M, Luo X, Estill J, Liu Y, Ren M et al. COVID-19 in India using a statistical computing approach: a scoping review. Euro Surveill. 2020 Apr;25(15):2000125.
Khosravi A, Chaman R, Rohani-Rasaf M, Zare F, Mehravaran S et al. The basic reproduction number prediction of the epidemic size of the novel coronavirus (COVID-19) in Shahroud, Iran. Epidem Infect. 2020; J10(14):e115.
Agha Ali M, Kolifarhood G, Nikbakht R, Saadati HM, Hashemi Nazari SS. Estimation of the serial interval and reproduction number of COVID-19 in Qom, Iran, and three other countries: A data-driven analysis in the early of the outbreak. Transbound Emerg Dis. 2020;5(30): 342-345; doi: 10.1111/tbed.13656.
Delamater PL, Street EJ, Leslie TF, Yang YT, Jacobsen KH. The complexity of the Basic Reproduction Number. Emerg Infect Dis. 2019 Jan;25(1):1–4.
S. Zhang, M. Y. Diao, W. Yu, L. Pei, Z. Lin, D. Chen, Estimation of the reproductive number of novel coronavirus (COVID-19) and the probable outbreak size on the Diamond Princess cruise ship: A data-driven analysis, Int J Inf Dis. 2020; 93(2):201.
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareRecent Research on Effect of COVID-19 on Diabetic Patients
English201204MuditaEnglish RamneetEnglish Gupta K.English Gupta D.EnglishIntroduction: The current outbreak of the novel coronavirus disease 2019 (COVID-19) has left the whole world traumatized. It is prefixed with the word “novel” because it comes under the new strain of the virus that has not been reported before. This virus outbreak has disrupted human life in the most petrifying way worldwide. Numerous researchers have contributed since the beginning of this pandemic. Objective: The present study aims to analyze the work done in this field through a bibliometric review to investigate the association between COVID-19 and diabetes. Methods: We explored the Scopus database for publications and the time frame selected for review is the beginning of this pandemic to April 25, 2021. The inclusion criteria were met only by 60 papers and they came from 15 different countries. Results: The United States gave the highest number of publications with a count of 16 (26%) and followed by India with 8 publications (13%). This paper gives some major insights regarding the flow of current research using Biblioshiny. Conclusion: This study aims to explore the recent trends in fast-growing publications to present a bibliometric analysis of publications on diabetic people having coronavirus since its outbreak in December 2019.
English Bibliometric, COVID-19, Diabetes, Pandemic, Risk FactorINTRODUCTION
Coronaviruses are positive-sense single-stranded RNA viruses broadly spread worldwide. The groups of pneumonia cases of undetermined aetiology emerged in the Hubei Province of Wuhan in China in December 2019.1 An extensive sequencing examination of lower respiratory tract specimens revealed the coronavirus as a causative agent, which was called Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) that gave rise to a disease called COVID-19. 2, 3
COVID-19 is the most recently discovered disease that has affected more than one crore person worldwide. It has emerged as a quickly growing communicable disease which has been announced as a pandemic disease by the World Health Organization (WHO).
On 30 January 2020, WHO declared the outbreak of COVID-19 as a Public Health Emergency of International Concern, and this epidemic was promoted to pandemic on 11 March 2020. As of today (25.04.2021) in more than 200 countries, 146,054,107 confirmed cases are recorded with 3,092,410 deaths as shown in Fig. 1.4
Due to COVID-19, several nations have applied a lockdown system for the betterment of their people. Thus, this practice adversely affects human life socially, economically and emotionally.5,6,7
Diabetes is one of the main sources of mortality and morbidity on the planet and is mainly connected with significant cardiovascular and renal complications. The strength of the connection between the two pandemics, namely, COVID-19 and diabetes has been investigated in observational cohorts around the world. Diabetic patients have a high chance of risk of serious complications such as Adult Respiratory Distress Syndrome or failure of multi-organs. So, a bibliometric review is conducted to provide a summary of COVID-19 and diabetes. We believe that this analysis can contribute to future research by providing meaningful information and helps for better management of diabetic patients in COVID-19.
The evidence of epidemiologic recommends that an increased risk of communicable diseases is linked with diabetes. Also, diabetic patients are at high risk of pneumococcal bacteremia diseases and nosocomial bacteremia with a death rate as high as 50%.8 This ailment is correlated with several microvascular and macrovascular complications that affect the survival of patients.9 The pervasiveness of diabetes in humans affected by the virus is equal or slightly lower as compared to the general population as per various studies.10,11 A research in Wuhan exposed that out of the 41 COVID-19 cases, 32% had underlying disorders, and amongst them, 20% had diabetes.12 Therefore, diabetic patients are at high risk of hospitalization and mortality for the COVID-19 virus. So, patients having diabetes need to pay more attention if there is an occurrence of fast deterioration. Patients with diabetes should deal effectively with the difficulty of its treatment and management. This bibliometric analysis represents the most prominent references linked with diabetes and COVID-19 and helps in enhancing the understanding of research in the context of diabetic COVID-19 patients.
MATERIALS AND METHODS
All publications were extracted from the Scopus database which had been studied for this paper because Scopus is considered as the most extensive peer-reviewed journal database among others present in the world that fits best for scientific academic data.13 The search keywords were applied as follows: TITLE-ABS-KEY ("COVID-19") AND TITLE-ABS-KEY ("Diabetes"). The literature search was filtered to incorporate papers published till 10 June 2020. The following keywords ''pandemic", ''COVID-19", ''risk factor" and ''diabetes" were used with interposition of ''AND" Boolean operator. The following information was used: document title, year, author, source, keywords, citations, document and source type and affiliations. The software R-Studio is used to carry out statistical analysis. In this study, we applied computable methods for statistical analysis, including the Biblioshiny R package.14 We also used the scientific literature available on the US Centers for Disease Control and Prevention and WHO websites.
RESULTS
In total, 60 documents met the selection criteria. Publications came from many countries worldwide. In terms of document count, the United States (26%) is top in this list followed by India (13%), China (10%), Italy (10%), Brazil (8%) and others as shown in Fig. 2.
The seven most frequently used author keywords as shown in Fig. 3 are COVID-19 with 33 occurrences, diabetes with 30, telemedicine with 9, sars-cov-2 with 7, coronavirus with 7, pandemic with 5 and risk with 4 occurrences.
According to 60 documents, the top 10 cited sources are shown in Fig. 4. LANCET is at the top of this list followed by Diabetes Care and NEJM.
The documents that met our selection criteria were distinguished across nine document types. These 9 types of documents were article (35%), note (30%), letter (18%), review (8.3%), editorial (6.7%) and conference paper (1.7%) as shown in Fig. 5.
Fig. 6 summarizes the results matching the most relevant countries, their authors and affiliations. Y. Zhang has published maximum of papers from Shanghai Jiao Tong University School of Medicine of China followed by A.K. Singh, A. Hussain and R. Singh also published a good amount of papers from GD Hospital & Diabetes Institute, India.
The outcomes matching the most contributing countries, keywords and sources are summarized in Fig. 7. The most frequently used keyword is “COVID-19” by France and then followed by China, USA, UK, Switzerland, Italy and India and the Journal of Diabetes Science and Technology has maximum occurrences of this keyword followed by Diabetes and Metabolic Syndrome: Clinical Research and Reviews. The second most common keyword is “diabetes” used by China and then followed by France, the USA, the UK, India, Italy and Brazil and the Journal of Diabetes Science and Technology has the maximum occurrences of this keyword followed by Diabetes Research and Clinical Practice.
DISCUSSION
The progression of the immediate influence of the COVID-19 pandemic on diabetic patients is reviewed and interpreted by this bibliometric analysis. The included papers only cover the literature of COVID-19 & Diabetes and the focus of these research papers exhibited an outstanding rise in the number of papers. A huge inter-collaboration and intra-collaboration network between profoundly productive authors and organizations were found. This paper leads to intrinsic bias because we used only the Scopus database and results can vary according to other databases or on the inclusion of other search terms. Our bibliometric analysis gives a detailed quantitative review as well as confirms the feasibility of implementation and scale-up of networks.
CONCLUSION
The outbreak of COVID-19 has made a significant impact on human lives and increased the concern for public well-being. COVID -19 had been spreading rapidly and affecting a large number of people, so, it inspired researchers to research in this field. This paper aims to analyze 60 publications related to COVID -19 and diabetes using a bibliometric review. The subject areas with titles, keywords, and abstract criteria were utilized as a source for obtaining search results using Biblioshiny.
Among all countries, the United States contributed the most in terms of publications followed by India. COVID-19, diabetes and telemedicine became the most widely used keywords. LANCET is at the top of the list of cited sources followed by Diabetes Care and NEJM. 35% of the total documents are of article type and the authors from China and India published the maximum number of papers. Journal of Diabetes Science and Technology has maximum occurrences of the most widely used keywords. So, we can conclude that this analysis can be beneficial as it provides a global bibliometric evaluation of two pandemics, COVID-19 and Diabetes which may facilitate ongoing and future research.
ACKNOWLEDGEMENT
The authors acknowledge the immense help received from the scholars whose articles are cited and included in references of this manuscript. The authors are also grateful to authors/editors/publishers of all those articles, journals and books from where the literature for this article has been reviewed and discussed.
Conflict of Interest: The authors declare that they have no conflict of interest.
Financial Support: This Research project did not receive any grants from any specific funding agency or third party.
Ethical Approval: The contents of this manuscript do not involve any research involving human participants or animals performed by any of the authors.
AUTHOR CONTRIBUTION
All authors have contributed to the design, implementation, analysis and discussion of the results and also contributed to the writing of the manuscript. All authors have read and approved the final manuscript.
Englishhttp://ijcrr.com/abstract.php?article_id=4054http://ijcrr.com/article_html.php?did=4054
Rothan HA, Byrareddy SN. The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. J. Autoimmun. 2020:102433.
Yang X, Yu Y, Xu J, Shu H, Liu H, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centre, retrospective, observational study. Lancet Respir Med. 2020;24(7): 701-708.
World Health Organization [Internet]. Naming the coronavirus disease (COVID-19) and the virus that causes it. 2020. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(covid-2019)-and-the-virus-that-causes-it (Accessed 12 June 2020).
World Health Organization [Internet]. Coronavirus disease (COVID-19) Pandemic. 2020. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019 (Accessed 12 June 2020).
Koon OE. The impact of socio-cultural influences on the COVID-19 measures–reflections from Singapore. J. Pain Symptom Manag. 2020.
Nicola M, Alsafi Z, Sohrabi C, Kerwan A, Al-Jabir A, Iosifidis C, Agha M, Agha R. The socio-economic implications of the coronavirus and COVID-19 pandemic: a review. Int J Surg. 2020;6(2):671-675.
Kang L, Ma S, Chen M, Yang J, Wang YS. Impact on mental health and perceptions of psychological care among medical and nursing staff in Wuhan during the 2019 novel coronavirus disease outbreak: A cross-sectional study. Brain Behav Immun. 2020; 7(3):80-77.
American Diabetes Association [Internet]. 4. Comprehensive Medical Evaluation and Assessment of Comorbidities: Standards of Medical Care in Diabetes-2020. Diabetes Care. 2020; 43(Suppl 1): S37. Available from: https://care.diabetesjournals.org/content/43/Supplement_1/S37 (Accessed 12 June 2020).
Williams R, Karuranga S, Malanda B, Saeedi P, Basit A, Besançon S, Bommer C, Esteghamati A, Ogurtsova K, Zhang P, Colagiuri S. Global and regional estimates and projections of diabetes-related health expenditure: Results from the International Diabetes Federation Diabetes Atlas. Diabetes Res Clin Pract. 2020; 13:108072.
Li B, Yang J, Zhao F, Zhi L, Wang X, Liu L. Prevalence and impact of cardiovascular metabolic diseases on COVID-19 in China. Clin. Res. Cardiol. 2020; 109(5):531-538.
Fadini GP, Moriori ML, Longato E, Avogaro A. Prevalence and impact of diabetes among people infected with SARS-CoV-2. J Endocrinol Investig. 2020;43(6):867-869.
Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, Wang B, Xiang H, Cheng Z, Xiong Y, Zhao Y. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. J Ame Med Ass. 2020;323(11):1061-1069.
Klapka O, Slaby A. Visual Analysis of Search Results in Scopus Database. In International Conference on Theory and Practice of Digital Libraries 2018: 340-343.
Aria M, Cuccurullo C. bibliometric: An R-tool for comprehensive science mapping analysis. J Informer. 2017; 1;11(4):959-975.
Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareUncertainity at Rohingya Camps: Health Crisis of Rohingya Refugees in India Amid COVID-19 Pandemic
English205209Nancy PuriEnglish C. R. AkhouriEnglishIntroduction: Literature indicates that Rohingya refugees health has been severely impacted and effected by the novel corona virus disease (COVID- 19) in India. Aim: The aim and objective of the paper are to explore the risk factors faced by the Rohingyas refugees in India during the novel coronavirus (COVID- 19) crisis. Methods: A detailed and related Review of the literature of the previous work has been collected to extract the information about the vulnerable conditions of Rohingyas refugees in India amid novel coronavirus. Tables have been presented to highlight the issue. The paper is based on a qualitative research design. Data has been collected from official reports, documents, newspapers, journal articles, books based on Rohingyas refugees in India. Result: The evidence suggests that in overcrowded places or camps, COVID-19 viruses can spread more rapidly. Various international agencies and humanitarian organizations have adopted numerous preventive measures to curb the virus at the refugee’s camps in India. However, the Government of India has declared Rohingyas as illegal immigrants into India Conclusion: The paper will conclude with suggestions or recommendations to curb the virus to spread among the Rohingyas communities in India.
EnglishCOVID - 19, Crisis, Health, India, RohingyasINTRODUCTION
The world is facing an unprecedented crisis, namely COVID-19 also known as Novel Coronavirus disease, which is resulting in thousands of deaths in the world. In December 2019, the virus originated from Wuhan in the Hubei province of China and soon spread globally. On March 11, 2020, the World Health Organization (WHO) declared COVID- 19 a pandemic disease.1 The outbreak of a large-scale infectious disease has also spread to most of the South Asian countries. South Asia, being one of the most populated regions of the world comprising of the least developed nations of the world. India is one of the most populated countries in the South Asian region and such a country, it becomes difficult to combat the virus from spreading in the community. Though, in such populated countries, various refugees are residing in India such as Rohingya Refugees.2
Rohingyas are living illegally in India and the Government of India has denied them the status of refugees and they are continuously portrayed as “illegal immigrants.” India is hosting more than one million Rohingya refugees who fled from Bangladesh and Myanmar. However, many refugees are currently living in shelters that are overcrowded refugee camps. Maintain preventive measures such as social distancing, wearing the mask, washing hands and feet with soap are impossible for Rohingya refugees because they are living in such unhygienic and congested camps.3
While reviewing some of the available data on this subject, it was found to be inadequate as the pandemic is an evolving crisis that is still to be observed by social science researchers in terms of its impact and trajectory. In India, there are a large number of vulnerable populations living in overcrowded places and COVID- 19 is now a litmus test that how a country has overcome lethargic tendencies towards Rohingya refugees. This has particularly affected Rohingya refugees that reside in informal settlements, squatter camps, overcrowded places and the absence of social distancing in such camps. Due to the novel coronavirus outbreak, Rohingyas met with social crisis and discrimination on medical grounds in the host communities.4
The Government of India has implemented $2 billion packages for the COVID-19 Emergency Response and Health System which facilitates for the public. However, in such schemes, Rohingya refugees are not even mentioned. However, it has been observed that Refugees are often considered carriers of the virus.5 The health care personnel and social service personnel are interconnected to protect the life of the individuals. Though, in such COVID- 19 pandemic era, the life savings medicines are available based on the proof of Aadhar Cards of the individuals. Even medical centres asked for proof of permanent residence to contact and trace the people.6 Though, Rohingya refugees lack even this provision.
This paper is based on a review of the literature. Title and abstract were screened carefully and studies that are related to the paper were included. Initially, 30 articles were selected, some of them were found unrelated and some were duplicate. Hence, they were excluded from the study. Only full-text articles were reviewed by the authors and finally, 18 articles were selected for this study. After reviewing such articles, a gap has been found that Rohingya refugee’s health is more vulnerable due to the risk of COVID- 19 pandemic, as compared to the other communities living in India.
Research Questions
The research intends to seek answers to the following research questions. These are:
Why Rohingya’s health is extremely vulnerable to COVID- 19 pandemic?
What are the other health issues of Rohingya Refugees in the current pandemic phase?
METHODOLOGY
A detailed literature survey of previous work has been collected to extract the information about the vulnerable health conditions of Rohingyas refugees in India amid novel coronavirus. Tables and diagrams have been presented to highlight the issue. The paper is based on a qualitative research design. Data has been collected from official reports, documents, newspapers, journal articles, books based on Rohingyas refugees living in India during COVID- 19 pandemic era.
RESULTS AND DISCUSSIONS
Risk of COVID- 19 on Rohingya Refugees in India
The novel Coronavirus disease (COVID-19) is spread to almost the entire world. In early March 2020, the World Health Organization (WHO) declared novel coronavirus (COVID- 19) as pandemic disease. In India, numerous Rohingyas refugees are living in very congested and unhygienic camps with a dense population,7 of who carries the risk because they lack access to clean water, adequate sanitation system and medical facilities. India is facing a very uncertain future for the Rohingya refugees.8 Lack of COVID- 19 testing and medical facilities led to the spread of the virus very rapidly in the Rohingyas camps in India.9 As a result, it can lead to a risk of community transmission in the country. The Rohingya refugees’ camps are more prone to COVID- 19 infections because of the compact area and practically it is difficult to follow social distancing, hygiene rules and physical isolation etc. in such camps. (Figure 1). So, it is the biggest challenge to maintain social distancing at Rohingya refugee camps. In India, Rohingya refugees lack awareness and have insufficient knowledge about the symptoms of novel coronavirus (n- COV 2019). There are some other factors also that lead to the risks of community transmission.10 On the other hand, some Rohingya refugees hold a belief that COVID- 19 is a punishment sent by Allah.11 Rohingyas have a misconception that if they get infected by the virus they would be taken away by the Indian citizens and killed or
Little access to healthcare
In India, Rohingyas refugees are living in uncertain conditions and they have to face many barriers to access public health and social services. This implies the future hardship of vulnerable communities like Rohingya refugees. (Figure 2). These aspects significantly complicate the efforts to combat the COVID-19 pandemic in such a populated country.13 The increasing number of cases is raising a grave concern about the future trajectory of the outbreak.14 Due to the outbreak of coronavirus, Rohingya’s camps became weaker with the health system and poor baseline health status of the refugees.15 In an overcrowded place with a few public bathrooms are shared between wall to wall, in this way the virus can spread more rapidly and more easily.16 Another, the major risk faced by Rohingya refugees in India is the lack of resources like medicines, face masks, gloves and soaps. COVID- 19 Pandemic posed a substantial financial burden on the poor populations and the immigrants.17 Due to economic hardship, Rohingyas refugees have been exposed to prolonged mental health conditions in refugee camps.18 Rohingya refugees faced a lack of clean drinking water supply. World Health Organization predicts that people affected by novel Coronavirus disease (COVID-19) and some additional diseases such as tuberculosis are confronting the worst situation of a country. It is considered that tuberculosis and malaria are highly prevalent in refugee populations.19 Though, the vulnerable Rohingya refugees in India continues to be at the highest risk for exposure to COVID- 19 Pandemic.
Current Health Status of Rohingya refugees in India
Apart from COVID- 19 disease, Rohingya refugees in India are suffering from other such diseases as well. According to WHO 2020 data, nearly 54% of Rohingya refugees children, 60% of Rohingya refugees women and 10% of Rohingya refugees pregnant women’s are residing in India. According to a study, the major health problems prevailing among Rohingya refugees are unexplained fever (2,27,928), acute respiratory infection (2,23,651) and diarrhoea (1,92,560). In August 2020, the Rohingya refugees camps located in India are experiencing a sudden outbreak of diphtheria and measles were also spread among the community in June 2020. (Table 1). In India, the cases of tuberculosis (TB) in Rohingya refugees camps are highly prevalent among the vulnerable Refugees, because Myanmar is one of the top 30 countries with the highest TB has ridden country.20
According to a report of September 2020, it is estimated that in India approx. 51.5 % had hypertension and 14.2% had diabetes. Additionally, 36,930 refugees were suffering from injuries. Most of the Rohingya refugees in the congested camps are addicted to alcohol, tobacco etc. Nutritional deficiencies are exceedingly predominant among Rohingya refugees, particularly among children. In Rohingya refugees camps, it is found that children aged among six to 59 months are anaemic and one-fourth had Global Acute Malnutrition (GAM).21
MEASURES TO BE TAKEN
The International Organizations and the Government of India should address the prevailing conditions of Rohingya refugees and must provide humanitarian assistance to the vulnerable communities in India particularly the Rohingyas. In the COVID- 19 pandemic phase, it is necessary to provide services and assistance to lactating mothers, pregnant women’s and their reproductive health as well as to offer adequate provisions for the newborn child, particularly for Rohingya refugees. International health experts have to be prepared to guarantee satisfactory health promotions, advancement of cleanliness, and a domestic visit to pregnant Rohingya refugee women’s.22
During the lockdown phase, many of the Rohingya refugees faced mental health issues. So, International organizations must provide mental health services to Rohingya refugees. It is essential to provide proper and detailed information to Rohingya refugees about the risk factors related to COVID- 19 pandemic. In the current phase of the COVID- 19 pandemic, a quick measure need to be adopted for the upliftment of the refugees and it is necessary to provide reliable solutions that remain paramount. 23 Most of the Rohingya refugees has been illegally migrated from Bangladesh to India. So, both countries must also take proactive measures to stop infiltrating the migrants.
Thus, International Organizations should pay more attention to the collection of data. Rohingya refugees situation is aggravated due to financial constraints and unhealthy livings conditions in congested camps. All these factors worsen their access to health care facilities, making them prone to various dangers of life and diseases that can easily flare up in the camps. Thus, the Government of India and International Organizations should collaborate to assist and improve the health status of Rohingya refugees.
CONCLUSION
The paper is based on a Review of Literature. We emphatically prescribe context-specific procedures to address the health issues of the Rohingya refugees in India. COVID-19 undoubtedly had a signi?cant adverse impact on the everyday existence of the entire human society. Moreover, the COVID-19 has been declared as a pandemic disease by WHO in March 2020, which highlights it as a global threat, calls for a global response. India is one of the most populated countries in the South Asian region and various refugees are residing in India. In such countries, it becomes difficult to combat the spread of the virus. In India, Rohingya refugees are living illegally and are living in such congested and unhygienic camps, where they are prone to more risk of the virus. Rohingyas must be equipped with necessities such as safe food and water, a hygienic environment, and the essential proper awareness about the COVID-19 crisis must be provided by the Government of India and International Organizations. After recognizing all the problems and several severe consequences for refugees, there is an urgent need to take care of Rohingya refugees in India.
Conflict of Interest- There is no conflict of interest carried out in this research paper.
Source of Funding: None
AUTHORS CONTRIBUTION
Nancy Puri contributes to data collection and analysis of research data and is responsible for the findings of the article. Dr. C.R. Akhouri studied the concepts in the article.
ACKNOWLEDGEMENT
The authors acknowledged the immense help received from the scholars whose articles are cited and included in the references list of the manuscript. The authors are also grateful to authors/ editors/publishers of all those articles, journals and books from where the literature for this article has been reviewed and discussed.
Englishhttp://ijcrr.com/abstract.php?article_id=4055http://ijcrr.com/article_html.php?did=4055
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Wang C, Horby W, Peter HG, Frederick G, F George. A novel coronavirus outbreak of global health concern. The Lancet. 2020; 395(10223): 470-473
Li SS, Liddell BJ, Nickerson A. The relationship between post-migration stress and psychological disorders in refugees and asylum seekers. Curr Psy Rep. 2016; 18(9):82-92
Dookeran NM, Battaglia T, Cochran J, Geltman PL. Chronic disease and its risk factors among refugees and asylees in Massachusetts, 2001-2005. Prev Chronic Disease. 2010; 7(3): 51-55.
World Health Organization (WHO). Rohingya Refugees Crisis- WHO weekly situation report (June, 2020). https://reliefweb.int/
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WHO REPORT. 2020a, COVID- 19 India situation report November 2020: situation report 40 from https://www.who.int/docs/default-source/wrindia/situation-report/india-situation-report-40 (Retrieved on 29 November 2020)
WHO 2020b Novel Coronavirus Disease (COVID-19) October 2020. Situation update report – 36. from https://www.who.int/docs/default-source/wrindia/situation-report/india-situation-report-36-pdf (Retrieved on 30 November 2020)
Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareDrug Utilisation Study among COVID-19 Inpatients in a Tertiary Care Hospital in Eastern India
English210214Manjhi PKEnglish Singh SKEnglish Kumar REnglish Singh SEnglish Priya AEnglish NishiEnglishIntroduction: Coronavirus disease-2019 (COVID-19) is a global pandemic without any specific treatment to date. Drug utilisation plays a vital role in helping the health care system to understand the pattern of drug use. Aim: To assess the prescribing trends among COVID-19 patients admitted to the hospital. Methodology: This was a retrospective cohort study conducted at a tertiary hospital dedicated to COVID-19 patients. Patients admitted in AIIMS, Patna from March 2020 to October 2020 were analyzed for WHO core prescribing indicators and was compared with the standard WHO values. Results: The median value of drugs prescribed was found to be 7 (IQR-6-8). The most common drugs prescribed for COVID-19 were Azithromycin (25.95%), hydroxychloroquine (23.88%) followed by paracetamol (20.17%) and Heparin (16.35%). The percentage of drugs prescribed by generic name was 77.72%. The average number of drugs from NLEM was 92.56%. Drugs were classified based on the Anatomical Therapeutic and Chemical Classification (ATC) and the majority of the drugs belonged to the J category which is the anti-infective group of drugs. The defined daily dose (DDD) /100 bed-days of azithromycin and cefixime was 5.2 and 0.092, respectively. Conclusion: Polypharmacy being the common finding, the concept of generic prescribing was practised well. There was a lesser number of prescriptions containing drugs from the National List of Essential Medicines, and the number of prescriptions containing antibiotics and injections was following criteria. The WHO prescribing criteria were met with moderate adherence.
EnglishIntroduction:
SARS-CoV-2 coronavirus causing Coronavirus disease 2019 originated in Wuhan, China in December 2019 and has spread rapidly across the world due to its high transmissibility and pathogenicity.1 It was shortly declared as a Public Health Emergency of International Concern and On March 11, a global pandemic was declared.2 Since then many drugs have been tried to treat COVID-19 infection but the effort has mostly been focused on repurposing existing medicines. The drugs that may be reused are from different pharmacological categories i.e. antimalarial, anthelmintic, anti-protozoal, anti-HIVs, anti-influenza, antineoplastics, neutralizing antibodies, immunoglobulins, and interferons.3 These drugs have been used as a single agent or in combination, unfortunately, no medicine has yet been officially approved to treat COVID-19. The treatment guidelines for COVID-19 vary from one country to another. However, the treatment protocols across the countries are almost similar and vary very slightly and include hydroxychloroquine, chloroquine phosphate, Remedesivir, azithromycin, tocilizumab and lopinavir/ritonavir.4 In India COVID-19 treatment guideline has been changed several times based on the latest research data. Indian Council of Medical Research (ICMR), New Delhi recommended hydroxychloroquine for prophylactic purposes in asymptomatic healthcare workers and household contacts of laboratory-confirmed patients.5 The Government of India, Ministry of Health & Family Welfare, Directorate General of Health Services (Emergency Medical Relief (EMR) Division), recommended lopinavir/ritonavir after proper informed expressed consent from the patient.6 The Government of India, Ministry of Health & Family Welfare, Directorate General of Health Services (Emergency Medical Relief (EMR) Division), recommended HCQ for mild to moderate case, low molecular weight heparin and steroid for moderate to severe case of COVID-19.7 Spanish Society of Hospital Pharmacy recommended lopinavir/ritonavir, Remdesivir, hydroxychloroquine, chloroquine, darunavir/cobicistat, tocilizumab interferon and alfa-2B,beta-1B.8,9 US FDA has not yet approved any drug for COVID 19 although some information regarding the drugs recommended for the treatment has been provided by the Centers for Disease Control and Prevention and includes Remdesivir, chloroquine, hydroxychloroquine, and lopinavir/ritonavir.10 The drugs like Remdesivir, favipiravir, chloroquine phosphate, plasma, and traditional Chinese medicine was recommended by the National Health Commission of the People's Republic of China.11 At present there is the limited number of study which summarizes the drug Utilisation in corona infected patients in a tertiary care hospital in India. Drug utilisation focus on the factors related to the prescribing, dispensing, administering, and taking of medication, and the related events including medical and non-medical determinants of drug utilisation, the effects of drug utilisation, as well as how drug utilisation relates to the beneficial or adverse effects of drug use.12,13 Therefore this study has been designed to assess the prescribing trends among COVID-19 patients admitted to a tertiary care hospital in India. This study will be helpful to educate the prescribers, adherence towards rational drug therapy for safety and benefit to the patient.
Materials and methods:
This retrospective observational study was carried out at All India Institute of Medical Science (AIIMS), Patna, (Bihar) India. COVID-19 patients, admitted to AIIMS Patna from March 2020 to October 2020 were included in the study. Medical Records of (n = 300) COVID-19 patients from the Medical Record Department (MRD) were evaluated for 3 months. Data were recorded on a standardized format in which information on drug utilisation among COVID-19 patients which include patient’s demographic details (age, sex, duration of hospitalisation) and details of prescribed drugs (name, dose, therapeutic class, dosage form, route of administration, dosing frequency, etc.,) was retrieved from in-patient case files. Details of standard intravenous fluids, oxygen, vaccines and blood transfusion were not recorded. The prescription pattern was analysed using WHO’s core prescribing indicators. Drugs are divided based on pharmacological action on the organ or system and their therapeutic and chemical properties in the anatomical therapeutic chemical (ATC) classification.14The World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology defines the defined daily dose (DDD) as a statistical measure of drug consumption. For grouping related drugs, it is defined in conjunction with the ATC Code drug classification system. The DDD allows for drug usage comparisons between different drugs in the same group or between different health care environments which were calculated by the following formula.15
DDD/ 100 bed -days =
Drug consumption in mg x 100
DDD (mg) x no. of days in study period x total no. of beds x occupancy index
The total number of beds used was 300 and the average occupancy index was 0.85. The occupancy index for every month was calculated by the Medical Records department. The occupancy index is the average monthly occupancy index for the three months of our study period.
Statistical analysis: Data were captured on Microsoft excel. The results were expressed as actual numbers, means, median (IQR) frequency (percentages) using SPSS software version 16.0., and were presented using tables.
Results:
Three hundred medical records of patients with confirmed COVID-19 patients were evaluated. The demographic and clinical characteristics of the patients are shown in Table 1. Of the total study population, 71.33% were male. A median number of seven medications (IQR, 6–8) were administered for the patients. Diabetes (21%) and hypertension (21%) was the most common comorbidity followed by hypothyroidism (14.28%) and hypercholesterolemia (8.4 %).
The analysis of drug utilisation using WHO core prescribing indicators revealed that the percentage of encounters with antibacterials and injectables was 15.63% and 11.82%, respectively. The percentage of drugs prescribed with a generic name and from the Essential Drug List of India was 77.72% and 92.56%, respectively (Table 2).
Looking at the individual drugs the most commonly prescribed drugs for COVID-19 patients include azithromycin (25.95%) hydroxychloroquine (23.88%), followed by paracetamol (20.17 %) and heparin (16.35%) from antimicrobial, analgesics, anti-inflammatory and an anticoagulant class of drugs, respectively.[Table 3]
The World Health Organization (WHO) defines polypharmacy as “the administration of many drugs at the same time or the administration of an excessive number of drugs”. Polypharmacy may be defined as the administration of five or more than five drugs simultaneously which is evident in our study. [Table 4]
Table 5 shows the Defined daily doses (DDD) by anatomical therapeutic chemical (ATC) classification codes of various drugs utilized among COVID-19 inpatients.
Discussion:
Drug Utilisation study is an important tool for ensuring quality in hospital drug use. The WHO core indicators of prescribing practice assess healthcare providers' performance in key dimensions related to drug safety. As a result, the purpose of this study was to examine the prescribing indicator that will aid in the promotion of rational drug use to improve drug safety.
The average number of drugs per encounter in our study was 6.97, which was higher than the national average. WHO recommended range is 1.6-1.8.16 Our finding was higher than that of Hazra et al. (3.2)17 some international studies, such as Wang et al. (3.52),18 Bimo et al. (3.8)19, and other Indian studies, such as Rehan et al. (2.4)20 Tripathy et al.(2.9).21
The number of drugs prescribed in each encounter ranged from two to ten, with 27.33 % of encounters prescribing eight or more drugs, indicating a polypharmacy trend (Table 4). This was in line with Tripathy et al findings (30%).21 Polypharmacy has a variety of consequences, including adverse drug reactions, drug-drug interactions, therapeutic failure, and toxicity, reduces patient compliance, unnecessary drug costs, and the risk of bacterial resistance emergence when antibiotics from different classes are prescribed to the same patient without reason.
In our study, the percentage of drugs prescribed by generic names was 77.72%, which is less when compared to the standard WHO ideal value of 100%. It was higher than the findings of Chandelkar and Rataboli's study (0.05%)22, Rehan et al. (1.5%)20, Tripathy et al. (68 %),21 Hazra et al. (46.2 ),17 and other international studies.23,24
The analysis of two common expensive modes of drug administration such as antibiotics and injections showed that the percentage of encounters with antibiotics prescribed was 15.63%, which is less than the standard range of 20-26.8 % of the WHO prescribed values. The finding of other studies conducted in India such as Hazra et al. (72.8%),17 and Tripathy et al. (47.75 %) respectively.21 Lower rates of antibiotic prescribing in our study due to the viral origin of COVID-19.
The percentage of encounters with injections in our study was 11.82% which was lesser than the standard range of WHO ideal value (13.4-24.1%). It was higher to the findings from Tripathy et al. (8%)21 but very low compared to another region, South Ethiopia (38.1%)23 and Uganda (48 %).25
The percentage of drugs prescribed by the NLEM in our study was 92.56%, which was lower when compared to the ideal standard value of 100%. This finding was much more to findings from studies of other parts of India such as Hazra et al. (45.71%).17 The percentage of prescribing drugs from the essential drug list is lower in India17 compared to other countries such as Ethiopia (99%), South Ethiopia (99.6%),23 and Nepal (88%).19 This difference may be due to the lack of awareness of the essential drug list. Between the mean of the estimated parameters, demographic data, it is involved in all of this, is similar to the findings in other studies.26 Compared to studies from Dubai where the patient name was missing in 2.9%, age in 9.7%, and sex in 12%.27 Lack of demographic indicators will lead to the source of serious medication error such as dispensing of medication to the wrong patients. The diagnosis was mentioned in 100% of prescriptions which was slightly more than other study results Shipra et al. (64.66%).26 The doses of drugs were mentioned for 100% of the drugs which was similar to other study results such as Shipra et al. (100%).26 Dosage form was mentioned in 100% compared to other study results Shipra et al. (98.66%).26Duration of treatment was mentioned in 100% compared to 92.66% in Shipra et al.26 The DDD per 100 bed-days ranges from 23.96 to 0.03. Vitamin C was found to be the most commonly prescribed drug with DDD per 100 bed- days of 23.96 followed by antimicrobials 5.37. Among the antimicrobials, the defined daily dose (DDD) /100 bed-days of azithromycin and cefixime was 5.2 and 0.092, respectively. Our study reports the highest use of vitamin C. which plays a very important role as an immune modulator28,29 and since this study was performed during the COVID-19 pandemic where vitamin C was used very widely and this could be the possible reason behind the highest use. The DDD per 100 bed-days of azithromycin, cefixime and ceftriaxone, levofloxacin was found to be much lesser than other studies.30
This study has certain limitations as it is retrospective a time-bound study with a small sample size and among COVID- 19 patients. It was not possible to assess the rationality and quality of prescriptions. However, it helps to create a drug Utilisation database in a tertiary care teaching hospital of a developing country.
Conclusion: The WHO core prescribing indicators were met with moderate adherence. With the prevalence of polypharmacy, the concept of generic prescribing has been practised well. The prescription of NLEM drugs, antibiotics, and injections was within normal limits. Drug utilisation among covid-19 as well complied with AIIMS-Patna covid management protocol.
Acknowledgement: We thank all corona warriors of AIIMS Patna working at the front line against COVID-19. We acknowledge the medical record department staff of the hospital for cooperation in providing medical records.
Ethical Approval and informed consent: The study was approved by the Institutional Ethics Committee, AIIMS Patna (Ref. No. AIIMS/Pat/IEC/2020/647 Dated 25/01/2021). Waiver of informed consent was granted as patient details were anonymized and only medical records of hospitalized COVID-19 patients were analyzed.
Financial support of the study: Nil
Conflict of Interest: None declared
Author’s contribution: Manjhi PK analyzed data and wrote the manuscript. Singh SK wrote the introduction and Kumar R analyzed data. Priya A. and Nishi collected data from Medical Record Department. All authors read this manuscript.
Englishhttp://ijcrr.com/abstract.php?article_id=4056http://ijcrr.com/article_html.php?did=4056
Li H, Liu SM, Yu XH, Tang SL, Tang CK., Coronavirus disease 2019 (COVID-19): current status and future perspectives. Int J Antimicrob Agents. 2020;55(5):105951.
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Rameshrad M, Ghafoori M, Mohammadpour AH, Nayeri MJD, Hosseinzadeh H., A comprehensive review on drug repositioning against coronavirus disease 2019 (COVID19). Naunyn Schmiedebergs Arch Pharmacol. 2020;393 (7):1137-1152.
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National Taskforce for COVID-19 Advisory on the use of hydroxy-chloroquine as prophylaxis for SARS-CoV-2 infection 2020. Available from: https://www.mohfw.gov.in/pdf/AdvisoryontheuseofHydroxychloroquinasprophylaxisforSARSCoV2infection.pdf
Government of India Ministry of Health & Family Welfare Directorate General of Health Services (EMR Division). Guidelines on Clinical Management of COVID – 19 dated 17th March 2020.
Clinical Management Protocol: COVID-19. Ministry of Health & Family Welfare, Government of India directorate general of health services (EMR division), Available from: https://www.mohfw.gov.in/pdf/ClinicalManagementProtocolforCOVID19.pdf
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EAHP: COVID-19 Resource Centre. Available from: https://www.eahp.eu/hp-practice/hospitalpharmacy/eahp-covid-19-resource-centre
Centres for disease control and prevention: Information for Clinicians on Therapeutic Options for COVID-19 Patients. Available from: https://www.cdc.gov/coronavirus/2019ncov/hcp/therapeutic-options.html
National Health Commission of the People’s Republic of China. 2020. Potential Treatments for COVID-19. Available from: http://en.nhc.gov.cn/2020-03/19/c_77977.htm
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Isah AO, Ross-Degnan D, Quick J, Laing R, Mabadeje AF., The Development of Standard Values for the WHO Drug use Prescribing Indicators. ICUM/EDM/WHO. Available from: http://www.archives.who.int/prduc2004/rducd/ICIUM_
Hazra A, Tripathi SK, Alam MS., Prescribing and dispensing activities at the health facilities of a non-governmental organization. Natl Med J India. 2000;13(4):177-82.
Wang H, Li N, Zhu H, Xu S, Lu H, Feng Z., Prescription pattern and its influencing factors in Chinese county hospitals: A retrospective cross-sectional study. PLoS One. 2013;8(5):e63225.
Bimo., Report on Nigerian field test, INRUD news. In: How to Investigate Drug Use in Health Facilities. Vol. 3. Geneva: WHO; 1992. p. 9-10.
Rehan HS, Singh C, Tripathi CD, Kela AK., Study of drug utilisation pattern in dental OPD at the tertiary care teaching hospital. Indian J Dent Res. 2001;12(1):51-6.
Tripathy R, Lenka B, Pradhan MR., Prescribing activities at district health care centres of Western Odisha. Int J Basic Clin Pharmacol. 2015;4:419-21.
Chandelkar UK, Rataboli PV., A study of drug prescribing pattern using WHO prescribing indicators in the state of Goa, India. Int J Basic Clin Pharmacol. 2014;3:1057-61.
Desalegn AA., Assessment of drug use pattern using WHO prescribing indicators at Hawass university teaching and referral hospital, South Ethiopia: A cross-sectional study. BMC Health Serv Res. 2013;13:170.
Menik HL, Isuru AI, Sewwandi S., A survey: Precepts and practices in drug use indicators at Government Healthcare Facilities: A Hospital-based prospective analysis. J Pharm Bioallied Sci. 2011;3:165-9.
Bannenberg WJ, Forshaw CJ, Fresle D, Salami AO, Wahab HA., Evaluation of the Nile Province Essential Drug Project. Geneva: WHO; 1991.
Shipra J, Zafar YK, Prerna U, Kumar A., Assessment of prescription pattern in a private teaching hospital in India. Int J Pharm Sci. 2013;3(3):219-22.
Sharif S, Al-Shaqra M, Hajjar H, Shamoun A, Wess L., Patterns of drug prescribing in a hospital in Dubai, United Arab emirates. Libyan J Med. 2008;3(1):10-2.
Carr AC, Maggini S., Vitamin C and Immune Function. Nutrients. 2017 Nov 3;9(11):1211.
Chambal S, Dwivedi S, Shukla KK, John PJ, Sharma P., Vitamin C in disease prevention and cure: an overview. Indian J Clin Biochem. 2013;28(4):314-328.
Mittal N, Mittal R, Singh I, Shafiq N, Malhotra S., Drug utilisation study in a tertiary care centre: recommendations for improving hospital drug dispensing policies. Indian J Pharm Sci. 2014;76(4):308-314.
Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareSpectrum of Atypical HRCT Chest Imaging Features in Covid 19 Patients from Eastern India -A Revelation
English215219Rohit AroraEnglish Kamal K. SenEnglish Sangram PandaEnglish Sudhansu Sekhar MohantyEnglish Mayank GoyalEnglish Roopak DubeyEnglishBackground: Coronavirus Disease 2019 (COVID-19), a severe respiratory syndrome is a pandemic, known to affect patients of all age groups with varied imaging features. Aim and Objective: To identify and categorize the additional Atypical imaging features detected in COVID 19 patients from eastern India. Method: HRCT images of 1300 COVID-19 patients without any known co-morbid conditions and showing positive HRCT findings were analyzed and evaluated for prevalence of atypical imaging features. HRCT images were categorized into typical, atypical and indeterminate. Further the additional atypical features were evaluated. Results: Out of 1300 patients, 320 (24.6%) patients showed atypical imaging features, 860 patients (64.6%) were in the Typical and 140 (10.7%) were in the indeterminate category. Amongst patients with atypical imaging features, we found that isolated lobar or segmental consolidation without associated GGO’s prevalent in 5.6% of patients, discrete pulmonary nodules which include both centrilobular and tree-in-bud nodular patterns in 42%, mediastinal/hilar lymphadenopathy in 9.3%. About 11% of patients had pleural effusion and 1.2% demonstrated pneumothorax and pneumomediastinum. Linear or subsegmental atelectasis was noted in 66% of patients. Conclusion: Significantly higher additional atypical features like atelectatic bands & subpleural curvilinear atelectasis (66%), followed by discrete nodules (42%) were associated with COVID 19 diagnosis in the absence of any known co-morbid conditions. We propose that imaging findings that have not been categorized under any of the existing four groups, be incorporated in either a mixed category or added to any of the existing groups, in the current imaging-based classification for COVID 19.
EnglishAtypical, COVID 19, HRCT, GGO, RT-PCRINTRODUCTION
Coronavirus infection has become a global concern since the outbreak of Severe Acute Respiratory Syndrome Coronavirus (SARS-COV-1) in 2002-2003.1,2 and the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) in 2012.3,4 In late December 2019, an outbreak of pneumonia was reported in Wuhan, China caused by novel coronavirus 2019-nCoV, currently designated as a severe acute respiratory syndrome (SARS-COV-2) by the International Committee on Taxonomy of Viruses (ICTV).5 The disease has now been officially named COVID 19 by World Health Organization. On January 7, 2020 a novel coronavirus has been identified as a causative agent by viral typing.6
Initially, it caused an outbreak of pneumonia in china and thereafter had spread globally with nearly 9 million confirmed cases and 470,000 deaths till June 23,2020.7 With the growing global concerns about the COVID-19 outbreak, it necessitates a comprehensive understanding of the hallmark and/ or atypical imaging features for an early diagnosis. In a study by Simpson et al. in the year 2020 imaging findings were categorized into four groups: Typical, Atypical, Indeterminate and Negative for COVID 19.8 It provides a set protocol that can reduce variation in reporting. The primary findings on chest radiographs and CT is that of atypical pneumonia or organizing pneumonia.
Reverse transcriptase-polymerase chain reactions (RT-PCR), which serves as a gold standard, has a sensitivity of about 71 per cent,9 while High-Resolution Computed Tomography (HRCT) Thorax appears to have a much higher sensitivity as analysed by Fang et al. who reported a 98 % sensitivity for the diagnosis of COVID-19.10 RT PCR for COVID-19 takes about 2-3 days for the results to come and hence clinicians are dependent on accurate diagnostic imaging for isolation & specific management.11
In this study, we have attempted to analyse HRCT images of 1300 COVID-19 and HRCT positive patients and evaluated the prevalence of the atypical imaging features amongst our study group. In addition, imaging findings amongst the patients from this part of the country, which do not fit under any of the existing groups necessitated inclusion in a separate category. Hence, we propose a modification of the available criteria for categorizing COVID 19 based on imaging and incorporation of these imaging features.
MATERIALS AND METHODS
Patient population and study design
This is a prospective study conducted in Odisha COVID Hospital, KIMS, India.
1300 consecutive COVID positive (RT-PCR ) patients without known co-morbid conditions with positive imaging features on HRCT Thorax were included in our study group.
Computerised Tomography ( CT) Acquisition Technique
Chest CT acquisitions were obtained with the patients in the supine position during end inspiration. Evaluation is done with 64-slice CT Siemens Somatom go. Up having 2.2 cm stellar detector with Sinogram Affirmed Iterative Reconstruction (SAFIRE) software dedicated only to patients of COVID 19. The scan was performed with the following technical parameters tube voltage 100-120 V; tube current modulation 180-400 mAs; automated exposure control; collimation width 64 X 0.625 mm; interslice gap 0mm; reconstruction algorithm: iterative-based reconstruction. Reconstructions were obtained at a slice thickness of 1.25 mm.
The scanning range covered the area from the level of the thoracic inlet to the diaphragm.
Computerised Tomography (CT) Image Analysis
All the CT images were viewed by two Residents involved in the study, followed by two Senior Radiologists. Radiological findings were classified into three groups viz. Typical, Atypical and Indeterminate for COVID 19 (Table 1), similar to an earlier study by Simpson S et.al. Negative for COVID 19 pneumonia were excluded in our study population. All data were anonymized and collected in a shared database. In our study the following atypical features were considered for analysis:
Presence of
a) Soft tissue nodules
b) Pleural effusion or pleural thickening
c) Mediastinal or hilar lymphadenopathy (> 10mm in short axis diameter)
d) Isolated consolidation
e) Presence of atelectasis
f) Presence subpleural linear or curvilinear opacification
g) Underlying lung diseases like fibrosis, bronchiectasis changes or emphysema
h) Pleural effusion or pleural thickening, lymphadenopathy and pneumothorax or pneumo-mediastinum in addition to ground glass opacifications (GGO’s).
The nodule is around or irregular opacity of less than 3 cm in diameter with sharp or ill-defined margins. They are classified as centrilobular or tree-in-bud and discrete nodules.12 GGO represent filling of alveolar space with pus, oedema, haemorrhage or cells causing haziness with preserved broncho-vascular marking.13,14 Consolidation refers to opacification of the alveolar space with the abutment of broncho-vascular markings.15,16 Sub pleural lines (also known as pleural lines) refers to thin linear or curvilinear opacities, 1-3 mm in thickness, lying less than 1 cm from and parallel to the pleural surface.
RESULTS
1300 positive COVID-19 patients, with positive imaging findings were analysed. We found that 320 patients (24.6%) showed atypical imaging features, 860 patients (64.6%) were in the Typical and 140 (10.7%) were in the indeterminate category (Fig.1).
We found that of the 24.6% patients having atypical imaging findings on HRCT, Isolated lobar or segmental consolidation without associated GGO’s was noted in 5.6% of patients, 42.8% had discrete pulmonary nodules which includes both centrilobular and tree-in-bud nodular patterns, 9.3% presented with mediastinal lymphadenopathy/ hilar lymphadenopathy. About 11.2% had pleural effusion and 1.2% demonstrated pneumothorax and pneumomediastinum. Linear or subsegmental atelectasis was noted in 66.2% of patients (Table 1).
About 86% of the patients showing atypical imaging features on HRCT belonged to the adult age group while 9% and 5% belonged to the elderly and paediatric age group respectively.
We also found the presence of GGOs in addition to discrete nodules in 73, hilar or mediastinal lymphadenopathy in 42, pneumothorax or pneumomediastinum in 1 and pleural effusion in 7 patients and about 212 patients showed linear or subsegmental atelectasis (Table 2), probably due to partly resolving pneumonia or early fibrosis.
DISCUSSION
Established guidelines are the need of the hour for a comprehensive understanding of Typical & atypical imaging features on CT, for an early diagnosis and effective patient management.
The commonest imaging findings noted on HRCT in our study population were multifocal GGO’s of rounded morphology, GGO’s with associated interlobular septal thickening termed as “crazy paving appearance” and GGO’s associated with air space consolidation. There was the involvement of multiple lobes especially lower lobes with a peripheral distribution and basal zone predilection in the majority of the cases. Other features included halo-sign, reverse halo-sign, non-rounded or non-peripheral GGO’s with or without consolidation lacking specific distribution, coarse linear or curvilinear opacities or fine subpleural reticulations, isolated consolidation without GGO, discrete small nodules (centrilobular or tree in bud), pleural effusion, pneumothorax/ pneumomediastinum, bronchiectasis changes and lymphadenopathy.
320 patients showed atypical imaging features which accounts for about 24.6 % of the total study sample. Atypical imaging features (Fig. 2) in this study group were, isolated lobar or segmental consolidation without GGO’s in 18 patients, discrete pulmonary nodules, both centrilobular and tree-in-bud patterns in 137, hilar or mediastinal lymphadenopathy in 30, pleural effusion or pleural thickening in 36 and pneumothorax or pneumo-mediastinum in 4 patients. 212 patients showed linear or subsegmental atelectasis.
In a study by Federica Ciccarese et al., it was stated that 7 out of 211 (3.3%) COVID positive patients had atypical imaging features. However, 60 out of 249 (24 %) patients showed atypical imaging features who were negative on RT-PCR17 indicating that these features have a more common association with a non-COVID aetiology. In another study by Sudhir Bhandari et al. on 80 COVID patients revealed that about 2.5% of patients had atypical imaging features on HRCT.18 In another retrospective study on 96 suspected COVID patients by de Jaegere et al. (RSNA) found that amongst 45 RT-PCR positive patients, 2.5-5.3% showed atypical imaging features on HRCT.19 This depicts a significantly higher prevalence of atypical imaging features in COVID 19 patients in our study sample, as compared to the previous studies. Hence it is felt that atypical imaging features may not be such an uncommon association in COVID 19, as previously conceptualized.
As per the presently available literature, the Chest CT severity score does not include certain atypical imaging features like pleural effusion, pneumothorax or pneumomediastinum and lymphadenopathy.20 At present, atypical features on imaging is thought to be associated with non- COVID 19 aetiology, like Tuberculosis or other viral infections, aspiration pneumonia and metastasis.21 But in our study we found an association of additional atypical imaging features in a significant number of RT-PCR positive patients, without any comorbidities like tuberculosis, chronic kidney diseases, hepatitis and immunocompromised conditions like HIV, Hepatitis B and patients on immunosuppressants. Hence images must be analysed meticulously in order not to overlook these features that will help in accurate CT staging, standardisation and enhance the diagnostic efficacy.
Atypical imaging features were most commonly seen in the adult age group and less commonly in the paediatric and elderly age groups in our study population. The probable cause of this adult involvement could be due to higher exposure to the infected population.
We also found that certain additional imaging findings were overlapping and were not categorized under any of the types i.e. Typical, Atypical, Indeterminate for COVID as per present imaging-based classification. These findings are as follows:
Unaccompanied ground glass opacifications and nodules
Ground glass opacifications and pleural effusion/ pleural thickening
Ground glass opacifications and pneumothorax/ pneumomediastinum
Ground glass opacifications and hilar/ mediastinal lymphadenopathy
Linear or Sub-segmental atelectasis
CONCLUSION
In our study group in this part of the country, the Typical features were 64.6%, indeterminate 10.7% and Atypical 24.6%. Significantly higher atypical features like atelectatic bands & subpleural curvilinear atelectasis (66%), followed by discrete nodules (42%) were more in favour of COVID 19 in absence of any other known co-morbid conditions like tuberculosis, chronic kidney diseases, hepatitis and immunocompromised conditions like HIV, Hep B and patients on immunosuppressants. It was noted that the prevalence of atypical features was more prevalent in the adult population.
We propose that the imaging findings found in our study, which have not been categorized under any of the four groups, need to be incorporated in either a mixed category or any of the existing groups in the current imaging, based classification for COVID 19.
In this pandemic situation, all patients with respiratory tract infection, fever, dyspnoea & HRCT features of COVID, including these atypical features, may be subjected to RT PCR to rule out COVID 19. Faster diagnosis, early isolation to restrict the spread of the disease will help society at large, besides helping in specific management.
Conflict of Interest: Nil
Source of funding: Nil
Authors' contributions –
Arora R, Sen KK, Panda S, Mohanty SS, Goyal M, Dubey R.
Arora R - Primary and corresponding author is responsible for ensuring that the descriptions are accurate and agreed by all authors,
Sen KK – Guide for manuscript preparation,
Panda S and Mohanty SS had made substantial contributions to all of the following: (1) the conception and design of the radiological work; (2) the acquisition, analysis, and interpretation of radiological data; and (3) drafting the work and revising it
Goyal M and Dubey R had made substantial contributions to (1) acquisition, analysis, and interpretation of clinico-laboratory data and (2) drafting the work and revising it.
All authors approved the submitted version. All authors have agreed both to be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature.
Figure 2: Axial section through HRCT chest demonstrating atypical imaging features. (A) Mediastinal lymphadenopathy; (B) Ground glass opacifications, pneumothorax, pneumomediastinum and subcutaneous emphysema; (C) Peripheral confluent ground glass opacifications; (D) Subpleural curvilinear opacifications; (E) Isolated segmental consolidation; (F) Tree-in bud nodules.
Englishhttp://ijcrr.com/abstract.php?article_id=4057http://ijcrr.com/article_html.php?did=4057
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareClinical Presentation and Outcomes of COVID-19 in Pediatric Age Group: An Observational Study of 93 Children from North India
English220226Motiani PEnglish Shukla UEnglish Madan JEnglish Bhakri B KEnglish Singh D KEnglish Jain DEnglishIntroduction: Worldwide the data regarding the epidemiological features and transmission patterns of COVID-19 infection among children is evolving. We present our observations on the presentation and clinical course of COVID-19 infection among children. Methods: Retrospective analysis of records of children admitted with COVID infection in a tertiary care COVID facility in north India. Results: Records from 93 case files were analysed. Most of the children were infected after close contact with an infected family member. The majority of them were males and asymptomatic. Younger anaemic children were more likely to be symptomatic. Only a small proportion (EnglishCOVID-19, Pediatric, North IndiaINTRODUCTION
The epidemiological information for COVID-19 is still evolving. While a growing number of studies from across the globe have widely discussed the presentation of COVID-19 since its outbreak, very limited data is available on epidemiological features and transmission patterns among children.1-3 Worldwide, children of all ages and with a wide range of demographic and socioeconomic characteristics and ethnicity appear susceptible.4,5,6 Majority of available literature suggest relatively better disease outcomes among children, with more than 90% of them being either asymptomatic or presenting with mild or moderate disease.7,8 However, despite being assured, the available quantum of literature on pediatric COVID-19 infection is not yet enough to be considered conclusive. There is a need of generating further data with particular emphasis on the populous tropical Indian subcontinent region.
We present our observations on the presentation and clinical course of COVID-19 infection among children (birth to 18 years of age) from a tertiary care teaching hospital in India.
METHOD
This was an observational study involving the analysis of records of children (up to 18 years of age) admitted at our hospital between 1st April 2020 and 31st July 2020. Data were collected retrospectively for patients admitted till 23rd June 2020 and prospectively for children admitted subsequently. The study was approved by the Ethics committee of the institute. Ours is an exclusive paediatric super-speciality teaching hospital located in north India which was designated as an exclusive COVID care facility in early April 2020. The hospital offered comprehensive care for children including a critical care facility. Children were either diagnosed by contact tracing following the detection of COVID-19 infection among any of their close contacts or by the screening of the children visiting our facility with signs and symptoms conducive to the possibility of COVID-19 infection. Records from children diagnosed with COVID-19 infection either with:
Rapid Antigen Test-A rapid, qualitative, chromatographic immunoassay was done using a card-based sensor of the make SD Biosensor, with a turnaround time of 30 mins.
TruNAT test – Done in a 2 step process using TruelabR AUTO V2 Universal Cartridge based Sample Prep Device and TruelabTM Quattro Real-time Quantitative micro PCR analyzer, with appropriate Kits, from M/s Molbio Diagnostics Private Limited or
Reverse transcriptase-polymerase chain reaction test (RT-PCR) involving Extraction using ZYBIO Nucleic Acid Purification system; and Amplification using The AriaMx Real-Time PCR system from M/s Agilent Technologies or CFX96 from M/s Biorad with various ICMR Approved kits.
As per the guideline released by the Ministry of Health & Family Welfare, the presentation of illness among children was defined either asymptomatic or with mild, moderate or severe symptoms.9 Children were managed as per the standard guidelines as adopted by the administration of the state from time to time.10
The recorded parameters included demographic (age, gender, residential location, history of travel during last 2 weeks, either international or to any area designated as disease hotspot, close contact with a person tested positive for COVID-19), clinical details (parameters at the time of admission, evidence of past BCG immunization, presence of comorbidities, duration of hospital stay and outcome) and laboratory parameters (haematological, liver and renal functions and chest radiograms) which were performed as and when indicated as per standard practice.
The data were recorded in Microsoft Excel sheets and analyzed using SPSS software. Continuous measures were depicted as mean (SD) or median (IQR) as applicable and analyzed using the test. Categorical measures were depicted as proportions and analyzed using either the ‘Chi square’ or ‘Fisher exact’ test. Correlation among the parameters was studied using Pearson’s correlation coefficient. The ‘p’ value of Englishhttp://ijcrr.com/abstract.php?article_id=4058http://ijcrr.com/article_html.php?did=40581: Hoang A, Chorath K, Moreira A, Evans M, Burmeister-Morton F, Burmeister F, Naqvi R, Petershack M, Moreira A. COVID-19 in 7780 pediatric patients: A systematic review. E Clin Med. 2020 Jun 26;24:100433.
2. Zhang L, Peres TG, Silva MVF, Camargos P. What we know so far about Coronavirus Disease 2019 in children: A meta-analysis of 551 laboratory-confirmed cases. Pediatr Pulmonol. 2020 Aug; 55(8):2115-2127.
3: Kuttiatt VS, Abraham PR, Menon RP, Vaidya PC, Rahi M. Coronavirus disease 2019 in children: Clinical & epidemiological implications. Indian J Med Res. 2020 Jul & Aug;152(1&2):21-40.
4. Liguoro I, Pilotto C, Bonanni M, Ferrari ME, Pusiol A, Nocerino A, Vidal E, Cogo P. SARS-COV-2 infection in children and newborns: a systematic review. Eur J Pediatr. 2020 Jul; 179(7):1029-1046.
5: Mustafa NM, A Selim L. Characterisation of COVID-19 Pandemic in Paediatric Age Group: A Systematic Review and Meta-Analysis. J Clin Virol. 2020 Jul;128:104395.
6: Patel NA. Pediatric COVID-19: Systematic review of the literature. Am J Otolaryngol. 2020 Sep-Oct;41(5):102573
7. Saleem H, Rahman J, Aslam N, Murtazaliev S, Khan S. Coronavirus Disease 2019 (COVID-19) in Children: Vulnerable or Spared? A Systematic Review. Cureus. 2020 May 20; 12(5):e8207.
8. Ludvigsson JF. A systematic review of COVID-19 in children shows milder cases and a better prognosis than adults. Acta Paediatr. 2020 Jun; 109(6):1088-1095.
9. Revised guidelines for Home Isolation of very mild/pre-symptomatic COVID-19 cases. https://www.mohfw.gov.in/pdf/Revised guidelines for Home Isolation of very mild presymptomatic COVID 19 cases 10May2020.pdf
10. Sankar J, Dhochak N, Kabra SK, Lodha R. COVID-19 in Children: Clinical Approach and Management. Indian J Pediatr. 2020 Jun;87(6):433-442.
11. Mahajan P, Kaushal J. Epidemic Trend of COVID-19 Transmission in India during Lockdown-1 Phase. J Community Health. 2020 Jun 23:1-10.
12. Dong Y, Mo X, Hu Y, Qi X, Jiang F, Jiang Z, Tong S. Epidemiology of COVID-19 Among Children in China. Paediatrics. 2020 Jun;145(6):e20200702
13. Götzinger F, Santiago-García B, Noguera-Julián A, Lanaspa M, Lancella L, Calò Carducci FI, et al. COVID-19 in children and adolescents in Europe: a multinational, multicentre cohort study. Lancet Child Adolesc Health. 2020 Sep; 4(9):653-661.
14. CDC COVID-19 Response Team. Coronavirus Disease 2019 in Children - United States, February 12-April 2, 2020. MMWR Morb Mortal Wkly Rep. 2020 Apr 10;69(14):422-426.
15. Kim L, Whitaker M, O'Halloran A, Kambhampati A, Chai SJ, Reingold A, et al, COVID-NET Surveillance Team. Hospitalization Rates and Characteristics of Children Aged Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11HealthcareEvaluation of Health Problems of Post COVID-19 Discharged Patients from Dedicated COVID Health Centre (DCHC), (Nootan General Hospital, Visnagar)
English227231Pandya VijayEnglish Dave BhargavEnglish Patel NikhilEnglish Prajapati ManishaEnglishIntroduction: Coronavirus disease 2019 (COVID-19) is a perilous condition with more than 136 million cases and 2.9 million deaths worldwide. It is now quite clear that the impact of COVID-19 lies way beyond the treatment phase so, it is very crucial to understand post COVID-19 outcome in recovered patients to understand if they can develop any other complications. Objectives: To evaluate the health problems in post COVID-19 discharged patients Methods: A Cross-sectional study conducted on post-recovery discharged patients between 20th July to 15th November (201 patients) from Nootan General Hospital, Visnagar. Result: 201 Participants (126 Male and 75 Female) were included in the study with highest numbers from 14-60 years (61.69%) age group. Total 70 (34.82%) participants had Diabetes and out of 201 participants, 123 (61.19%) required O2. More than two-third (69.65%) of participants required hospitalization between 5-10 days. Most common complaint at the time of interview was Weakness (71.64%) followed by Fatigue (60.69%), Cough (39.80%), Loss of Appetite (38.81%). Statistically, significant difference was observed between Male and Females (pEnglishComplication, COVID-19, Health status, Pandemic, Post recovery, SymptomsIntroduction:
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is causing a disease named as Coronavirus disease 2019 (COVID-19).On 30th January 2020, World Health Organization (WHO) has declared this outbreak a global health emergency. Case fatality rate of COVID-19 is about 2-3% and proportion of mild cases is around 85%. 1As on 14/04/2021 about 136 million cases have occurred around the world. In India, until now around 14 million people have suffered from COVID-19 and out of them 12 million people have successfully recovered. India has the second-highest no. of COVID-19 case burden after US in world. In Gujarat, until now 3,67,616 cases have occurred and out of them, 3,23,371 patients have recovered with 88% recovery rate. In Mehsana district 8843 cases had been recorded till now and from them, 7809 patients have recovered so, recovery rate is 89%.1,2,3It is quite clear now that the impact of COVID-19 lies way beyond the treatment phase, even for the majority who are affected by milder form of the disease. It is now known that COVID-19 is not only affecting lungs but it’s a multiorgan disease, it seems now necessary to take follow-up of these recovered patients and perform thorough assessment for detection of any further complications which will guide us for proper management.4
Aim:
Evaluation of health problems in post COVID-19 discharged patients
Objectives:
To know socio-demographic and hospitalization profile of discharged patient
To know the present condition of patient in terms of medical health problems and complications
To know about current treatment and preventive practices of participants
Methodology:
A Cross-sectional study was conducted with study duration between 10th November to 10th December 2020
Sample size: All COVID-19 positive discharged patients from 20th July 2020 to 15th November 2020 from Nootan general hospital, Visnagar were included in the study
Data collection: A performed semi-structured questionnaire was used for data collection. Details about COVID-19 positive discharged patients were taken from Nootan General Hospital, Visnagar and data collection was done by 2 methods
Telephonic interview: After taking informed verbal consent questions were asked to study participants telephonically
Personal interview: 20% of finalized sample size patients were included in the study through personal interview. Informed and written consent was taken from them.
(personal interview was done either in hospital premises at the time of follow-up visit of patient/ Home visit of patient was done and interview was conducted)
Inclusion criteria:
All COVID 19 positive discharged patients from Nootan General hospital, Visnagar
Patients who gave consent to participate in the study
Analysis: Data was entered in Microsoft excel 2016 and analysis was done by Epi-info software version 7.2. Qualitative variables were entered in terms of frequency and percentage. Quantitative variables were entered in terms of mean and standard deviation
Statistical analysis was performed by using appropriate parametric and non-parametric tests. pEnglishhttp://ijcrr.com/abstract.php?article_id=4059http://ijcrr.com/article_html.php?did=40591. WHO Coronavirus (COVID-19) Dashboard [Internet]. Covid19.who.int. 2021 [cited 15 April 2021]. Available from: https://covid19.who.int/
2. Coronavirus in India: Latest Map and Case Count [Internet]. Covid19india.org. 2020 [cited 15 April 2021]. Available from: https://www.covid19india.org/
3. Coronavirus in India: Gujarat [Internet]. 2020 [cited 15 April 2021]. Available from: https://www.covid19india.org/state/GJ
4. Balachandar V, Mahalaxmi I, Subramaniam M, Kaavya J, Senthil Kumar N, Laldinmawii G, et al. Follow-up studies in COVID-19 recovered patients - is it mandatory? Sci Total Environ 2020 Aug;729:139021.
5. Carfì A, Bernabei R, Landi F. Persistent Symptoms in Patients After Acute COVID-19. JAMA 2020 Aug 11;324(6):603.
6. Luo S, Guo Y, Zhang X, Xu H. A follow-up study of recovered patients with COVID-19 in Wuhan, China. Int J Infect Dis 2020 Oct;99(January):408–9.
7. Puntmann VO, Carerj ML, Wieters I, Fahim M, Arendt C, Hoffmann J, et al. Outcomes of Cardiovascular Magnetic Resonance Imaging in Patients Recently Recovered From Coronavirus Disease 2019 (COVID-19). JAMA Cardiol 2020 Nov 1;5(11):1265.
8. Liang L, Yang B, Jiang N, Fu W, He X, Zhou Y, et al. Three-month Follow-up Study of Survivors of Coronavirus Disease 2019 after Discharge. J Korean Med Sci. 2020;35(46):1–15.
9. Halpin S, O’Connor R, Sivan M. Long COVID and chronic COVID syndromes. J Med Virol. 2021;93(3):1242–3
10. COVID-19 (coronavirus): Long-term effects [Internet]. Mayo Clinic. 2021 [cited 15 April 2021]. Available from: https://www.mayoclinic.org/diseases-conditions/coronavirus/in-depth/coronavirus-long-term-effects/art-20490351
Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11Healthcare
Management of Ellis Class IV Fracture - A Case Report
English232234Gayatri PendseEnglish Sumita BhagwatEnglish Priyanka PanikkarEnglish Simran ShahEnglish Pragya JainEnglish Anas AnsariEnglish
Introduction: Crown fracture is the most common traumatic injury which affects permanent teeth. Most commonly affected teeth are maxillary incisors, accounting for 96% of all crown fractures. Children and adolescents usually suffer from traumatic injuries, with boys being affected more commonly than girls. Missing tooth structure causes emotional trauma to the children. Re-habilitation of both esthetics and function is the principal objective of the treatment in such cases. Direct composite restorations and indirect ceramic restorations is the primary line of treatment for restoring anterior teeth after fracture when it is not possible to reattach the tooth fragment. The treatment options in uncomplicated coronal fractures depend on various factors such as the amount of residual dentinal enamel tissue, the relationship with the gingival profiles, and the age of the patient. Case Report: This case report describes the clinical procedure involved in the treatment of a complicated fracture in the maxillary left central incisor in a 17-year-old female patient, due to accidental fall. After clinical and radiograph examination Ellis class III fracture was diagnosed. Endodontic treatment was carried out followed by post-endodontic restoration. Conclusion: Composite resins have proven themselves as one of the most important tools in the clinician’s armament. Reliable strength and a realistic aesthetic result is achievable. The advantage of this technique is closely associated with satisfactory results, combined with the dexterity, skill and mastery of technique employed by the professional.
Englishhttp://ijcrr.com/abstract.php?article_id=4691http://ijcrr.com/article_html.php?did=4691
1. Dietschi D, Jacoby T, Dietschi JM, Schatz JP. Treatment of traumatic injuries in the front teeth: Restorative aspects in crown fractures. Pract Periodontics Aesthet Dent. 2000;12:751–8.
2. Goenka P, Marwah N, Dutta S. Biological approach for management of anterior tooth trauma: Triple case report. J Indian Soc Pedod Prev Dent. 2010;28(3):223–9. doi:10.4103/0970- 4388.73791.
3. Zerman N, Cavalleri G. Traumatic injuries to permanent incisors. Endod Dent Traumatol. 1993;9(2):61–4. doi:10.1111/j.1600- 9657. 1993.tb00661. x.
4. Garoushi S, Vallittu PK, Lassila LV. Continuous and Short Fiber Reinforced Composite in Root Post-Core System of Severely Damaged Incisors. Open Dent J. 2009;3(1):36–41. doi:10.2174/ 1874210600903010036.
5. Apponi R, Murri Dello Diago A, Colombini V, Melis G. Direct versus Indirect Techniques to Manage Uncomplicated Crown Fractures of Anterior Teeth Following Dentoalveolar Trauma. Dent J (Basel). 2021 Jan 20;9(2):13. doi: 10.3390/dj9020013. PMID: 33498541; PMCID: PMC7909509.
6. Kumar SC, Rao A, Sheila K, Reddy HG. Multidisciplinary Approach in Management of Fractured Central Incisor through Composite Plug Stabilization - A Case Report. J Int Oral Health. 2013;5:79–82.
7. Andreasen JO, Andreasen FM. Textbook and color atlas of traumatic injuries to the teeth. 4th ed. Oxford: Blackwell; 2007.
8. Abu-Hussein M, Nezar W, Azzaldeen A, Mai A. Prevalence of Traumatic Dental Injury in Arab Israeli Community Journal of Dental and Medical Sciences 2016; 15(7): 91-98
9. Chan DCN, Cooley RL. Direct Anterior Restorations. In: Schwartz RS, Summitt JB, Robbins JW, editors. Fundamentals of operative dentistry. A contemporary approach. Illinois: Quintessence Publishing; 1996: 187– 205.
10. Abu-Hussein M, Nezar W, Azzaldeen A. Gummy Smile and Optimization of Dentofacial Esthetics Journal of Dental and Medical Sciences. 2015; 14(4): 24-28DOI: 10.9790/0853-14462428.
11. Abu- Hussein M, Abdulgani A, Watted N. Restoring Fractured Anterior Tooth Using Direct Composite Restoration: A Case Report. Global Journal of Dental Sciences. 2019; 1:1
12. Nathanson D. Current developments in esthetic dentistry. Curr Opin Dent.1991; 1(2): 206-211.
Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11Healthcare
Management of Blunderbuss Canal: A Case Report
English235237N. VimalaEnglish Nikita TopraniEnglish Preethi DurairajEnglish Vashna UpadhyayaEnglish Gunjan PagariyaEnglish Smriti BalajiEnglish
Introduction: An immature permanent tooth having a blunderbuss canal and open apex can be an endodontic challenge because of difficulty in obtaining an apical seal, and existing thin radicular walls which are susceptible to fracture. Teeth with open apices, such as in immature teeth are clinical cases with difficult immediate resolution. With the use of mineral trioxide aggregate (MTA) and similar calcium silicate cements, it is possible to get a better prognosis of the treated teeth. MTA sealing ability has been shown to be superior to that of super EBA and other cements and was not affected by blood contamination. Case Report: A 15-year-old patient came with a chief complaint of fractured maxillary anterior teeth due to trauma. Examination showed Ellis Class II Fracture on tooth 11. Radiograph showed a blunderbuss canal with open apex and periapical radiolucency. Root canal treatment was planned. Upon access, weeping canal was seen. Circumferential canal preparation was employed due to thin dentinal walls. Conclusion: When attempts to achieve abiogenesis fails or the pulp is necrotic, apexification must be done. Apical surgery is now considered a predictable treatment option to save a tooth with apical pathology that cannot be managed by conventional, non-surgical endodontics.
EnglishBlunderbuss Canal, Open Apex and Periapical Radiolucency, Immature Permanent Tooth, Ellis Class II Fracture, Mineral Trioxide Aggregate, Dentinal Wallshttp://ijcrr.com/abstract.php?article_id=4692http://ijcrr.com/article_html.php?did=4692Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52412nd Wave of COVID-19: Role of Social Awareness, Health and Technology SectorEnglishN2021June11Healthcare
A Pearl in a Shell: A Case Report
English238240Mayura MahajanEnglish Subraj ShettyEnglish Swati Shrikant GotmareEnglish PriyadharshiniEnglish Mohammed Affaan SyedEnglish Megha Nishant LalaiEnglish
Introduction: Oral and maxillofacial trauma causes the most obvious damage which is trauma to the tooth and its supporting structures. These unnoticed fractures can also cause soft tissue lacerations. Clinical examination thus plays an important role in these emergency situations. Therefore, identifying the cause, early diagnosis and surgical removal are the necessary steps to be implemented. Case Report: This paper discusses one such case of a traumatic fibroma being developed due to a hit in a pediatric patient. A 13-year-old male patient was referred to the department of Oral pathology with a swelling on the lower lip. Patient gave a history of a cricket bat being hit on his face which led to the development of swelling. An immediate diagnostic approach was carried out in order to preserve the anatomy and morphology of the tooth. Conclusion: By this paper we focus on dentofacial injuries which include the displacement of tooth fragments. These soft tissue lacerations should be advocated with immediate effect with proper clinical, radiological and histopathological examination.
EnglishDental Fragment, Lower Lip, Fibrous Scar, Dentofacial Trauma, Dentofacial Injuries, Soft Tissue Lacerationshttp://ijcrr.com/abstract.php?article_id=4693http://ijcrr.com/article_html.php?did=4693
1. Taran A, Har-Shai Y, Ullmann Y, Laufer D, Peled IJ. Traumatic self-inflicted bite with embedded tooth fragments in the lower lip. Ann Plast Surg. 1994;32(4):431–3.
2. da Silva AC, de Moraes M, Bastos EG, Moreira RWF, Passeri LA. Tooth fragment embedded in the lower lip after dental trauma: case reports. Dent Traumatol. 2005;21(2):115–20.
3. de Santana Santos T, Melo AR, Pinheiro RTA, Antunes AA, de Carvalho RWF, Dourado E. Tooth embedded in tongue following firearm trauma: report of two cases. Dent Traumatol. 2011;27(4):309–13.
4. Lin CJ, Su WF, Wang CH. A foreign body embedded in the mobile tongue masquerading as a neoplasm. Eur Arch Otorhinolaryngol. 2003;260(5):277–9.