IJCRR

IJCRR - 14(2), January, 2022

Pages: 08-16

Date of Publication: 16-Jan-2022

Geospatial Mapping of Genetic Diseases in the Southern Karnataka, India: A Novel Approach in Medical Technology

Author: Mohandas Aparna, Kumar Sunil D, Bhat Deepa, Murthy Narayana MR, Gopi Arun

Category: Healthcare

Abstract:Introduction: India records almost all known genetic disease, however surveillance activities and services are limited. Geographical information system (GIS) can act as a useful tool for surveillance. Aims: The aim of the study was to assess the geospatial distribution of genetic diseases in Southern Karnataka, India and to describe their clinico-epidemiological profile. Methodology: A cross-sectional study was conducted among 101 genetic disease patients attending the Genetic clinic of a tertiary care hospital. Data regarding the socio-demographic and clinico-epidemiological profile of the patients were collected using a semi-structured questionnaire. The geographical co-ordinates of the patients' addresses were recorded. Statistical analysis was performed using SPSS Version 22 and QGIS software Version 3.16.3 \"Hannover\" was used to create the maps. Qualitative variables were represented using proportions. Association between qualitative variables were inferred using Chi-square test. Results: Geospatial mapping showed that functional genetic diseases were more compared to structural genetic diseases. Among the 101 patients, 66(65.34%) were males, 34(33.66%) were females and 1(0.99%) belonged to the other gender. 17.82% were born of a 2nd degree and 25.74% from a 3rd degree consanguineous marriage. 3(2.97%) of mothers were above the age of 35 years. 75(74.25%) had gene defects, 13(12.87%) had chromosomal diseases, 7(6.93%) had mitochondrial diseases and 6(5.94%) had multifactorial diseases. Conclusion: It is evident from the geospatial mapping that both structural and functional genetic diseases have a widespread distribution in the population and is no longer a \"rare disease\". It is the need of the hour to expand surveillance activities.

Keywords: Congenital anomaly, Genetic diseases, Geographical information system, Geospatial mapping, Rare diseases, Surveillance

Full Text:

Introduction

Annually, 7.9 million children suffer from a grave congenital anomaly of genetic etiology which contributes to 3.3 million deaths. The major contributors of genetic diseases being a family history of such diseases, carrier state in the parents, consanguineous marriage, advanced maternal age, and maternal exposure to teratogenic drugs, radiations or chemicals, and infections like syphilis, rubella and use of recreational drugs, tobacco and alcohol.¹ Chromosomal disorder add to 6% of birth defects, Down syndrome being the most common of them.^1,2 The next major group are the single gene defects which account for 1 in 1000 live births, the two most predominant diseases being Sickle cell anemia and Thalassemia.^3,4 It is estimated that about 5% of the global population carries trait for hemoglobin disorders and 3 to 5 lakh children who are delivered annually suffer from them.^4,5

India as such has a high burden of Down syndrome, inborn errors of metabolism and hemoglobin disorders like Sickle cell anemia and Thalassemia.⁶ In India, Down syndrome affects 1 in every 900 children.⁷ As far as hemoglobin disorders are concerned, at one point of time there are about 1.5 lakh cases of Sickle cell anemia and 1 lakh cases of beta Thalassemia. It is more predominant amongst the scheduled subpopulation of the country, the prevalence of Thalassemia ranging between 4-17% amongst them.⁴ Another important group are the inborn errors of metabolism and in India, 5-15% of newborns, yearly, suffer from these. Newborn screening is a simple method of identifying the affected children to initiate early treatment and to prevent disabilities and also to offer prenatal diagnosis in the future pregnancies, however the services offered in India are limited.⁸

 Over the years, the burden of genetic diseases has increased, also the advancement in various dimensions has increased survival, adding on to the morbidity and impairment due to genetic diseases. While some countries, like in the United Kingdom have already incorporated neonatal and antenatal screening for hereditary diseases in their health system, the economically backward countries which contribute to nearly 94% of the infants with genetic defects, lack in public health policies for care and prevention of genetic diseases.⁹ Reducing the burden of genetic diseases should begin with active and passive surveillance in the community and at the hospital setting with the partnership of laboratories and research institutes. This shall help to lay the foundation by assessing the burden and trends in various geographic locations across the country. It is high time that genetic screening and counseling be incorporated into the existing health care delivery system. Expansion of diagnostics with adequate treatment and rehabilitative services also becomes a prerequisite.^10,11

Geographical information system (GIS) is a tool that combines spatial information with attributable data and assembles them as layers. GIS helps to build up these layers into visualizations using maps, which reveal patterns and relationships between spatial data and attribute data.¹² With regard to genetic diseases, GIS can be used to investigate the spatial form of distribution of these diseases and the associated spatial determinants which can play an important role in estimating the prevalence of genetic diseases across the country and also assessing the gaps in the existing service delivery system.¹³

With this background, the present study was conducted to assess the geospatial distribution of genetic diseases in Southern Karnataka, India and to describe their clinico-epidemiological profile.

Materials and Methods

Study design and population

This cross-sectional study was conducted for one and half years from January 2019 to July 2020 among patients attending the genetic clinic of a tertiary care hospital, in Southern Karnataka, India. Consecutive sampling was used to select the participants. Participants with genetic diseases who were willing to participate in the study were included and those who were not confirmed as having genetic diseases by laboratory diagnosis were excluded from the study.

Ethical statement

The study was commenced after presenting the protocol and obtaining ethical clearance from the institutional ethics committee. IEC NO: JSS/MC/PG/4623/2018-19

Before beginning the collection of data, the study details were explained to the patients or parents as relevant. Verbal assent was taken from children below 18 years and written informed consent was taken from parents/patients as appropriate.

Data collection

Data was collected by interview method using a semi-structured questionnaire regarding the sociodemographic factors (age, gender, religion, address, occupation, type of family) and family history, previous obstetric history and antenatal history. Relevant birth history, clinical and investigation history and diagnosis of the patient was recorded.

Final diagnosis of the patient was obtained and categorized as a structural or functional genetic disease.

Structural diseases- Refers to defects in body structure including skeletal system and organs. It includes external as well as internal defects. Includes neural tube defects, cleft lip, cleft palate, congenital heart diseases, Down syndrome, Edward syndrome, cystic kidney disease, skeletal and limb deformities and indeterminate sex.

Functional diseases- Functional defects in the neurological, muscular immunological or endocrine system. Includes metabolic disorders, cystic fibrosis, muscular dystrophy, behavioral disorders and neurological disorders.¹⁴

Statistical analysis

Data was entered into Microsoft Excel 2013 spreadsheet and analyzed using SPSS V.22 (Licensed to JSS AHER). Qualitative variables like gender, religion, place of residence, etc. were represented using proportions. Association between qualitative variables was inferred using the Chi-square test. A p value less than 0.05 was considered statistically significant.

Geospatial analysis

Freely accessible online sources were used to geocode the address of the study participants. The latitude and longitude were entered in Microsoft Excel and converted into CSV file (comma separated values) and exported into a free and open-source software version QGIS 3.16.3 “Hannover” (released on 15.01.2021) to create maps.

Results

Figure 1, depicts the map of southern Karnataka, India and the distribution of the study participants based on gender. The majority were males in the present study. Among the 101 study participants, the majority of the parents gave a history of non-consanguineous marriage (Figure 2). Figure 3, gives the classification of study participants based on the type of genetic diseases. In the study, functional genetic diseases were more, compared to structural genetic diseases.

Among the 101 study participants, 10(9.9%) were below 6 months, 40(39.60%) were between the age of 6 months to 1 year, 21(20.79%) were between 1 to 5 years, 13(12.87%) were between 5 to 10 years and 17(16.83%) were above the age of 10 years. 3(2.97%) of the mothers were above the age of 35 years at the time of conception. Among the 101 genetic disease patients, 56.43% were born of a non-consanguineous marriage, while 18.81% and 24.75% were born of a 2^nd and 3^rd degree consanguineous marriage, respectively. Among the mothers, 27.72% gave a history of previous abortions/neonatal death/stillbirths while 72.27% did not have such a history. 2(1.98%) of the mothers gave a history of fever during pregnancy. 73(72.27%) of the participants were of normal birth weight while 28(27.73%) had low birth weight. 89.10% of the study participants were term babies and 10.89% were preterm babies. In 11.88% of the cases, siblings had similar diseases. In the 101 study participants, 65(64.35%) had functional genetic diseases and 36(35.64%) had structural genetic diseases (Table 1).

On the classification of genetic diseases, 75(74.25%) had gene defects, 13(12.87%) had chromosomal diseases, of which Down syndrome (9.9%) was the majority, 7(6.93%) had mitochondrial diseases and 6(5.94%) had multifactorial diseases (Table 2).

Table 3, represents the association between socio-demographic and clinical-epidemiological factors with genetic diseases. There was a significant association between gender and socioeconomic class with genetic diseases. 3 (2.97%) of the mothers were above the age of 35 years and all of them had children with structural anomalies. There was a significant association between maternal age at conception and paternal medical conditions with genetic diseases in the children. 3(2.97%) of fathers had Diabetes Mellitus and they had children with structural anomalies.

Discussion

Geocoding is an important GIS tool used in public health for surveillance activities.¹⁵ Disease surveillance tracks data related to the incidence/ prevalence of the disease, its spread and possible risk factors which shall give the pattern and trend of the disease.¹⁶ In the present study, the locations of the patients were mapped. Functional genetic diseases accounted for the majority compared to structural genetic diseases. It is evident from the visualization that genetic diseases are not ‘rare’ as thought to be earlier, but rather have a widespread distribution across the population. GIS has an important role in scenarios wherein it can be utilized to study the distribution of genetic diseases, associated gene mutations and their trend over the years. This shall help to identify cluster areas as well as develop causal relationships.¹⁷In the study we have considered all genetic diseases, and it would require disease-specific genetic studies to make such conclusions.

Among 101 study participants, 65.34% were males, 33.66% were females and 1(0.99%) belonged to another gender. Similar findings were reported by Lavanya et al. and Jayasree et al.. in institutional-based studies in south India. It is suggested that females are affected more with lethal defects and cannot survive till term gestation.^18,19 Genetic diseases were more among the lower socioeconomic class in our study. This can be attributed to the predominance of consanguineous marriage and poor maternal health, added with higher exposure to environmental risk factors among them.^1,20

In the present study 97.02% of the mothers were below the age of 35 years at conception. The majority of the studies suggest the same, bulk of genetic diseases are reported among mothers less than 35 years, owing to the fact that in India, major proportion of deliveries take place before the mother reaches 35 years.^18,19 However among the 3% mothers above the age of 35 years, all had children with structural anomalies. This is consistent with the findings that advanced maternal age is associated with a higher risk of chromosomal abnormalities.¹ Among 101 study participants, the majority were born out of non-consanguineous marriage. Structural and functional genetic disease proportions were similar in non-consanguineous and consanguineous marriages. Jayasree et al. reported that only 1% of children with congenital anomalies were born out of a consanguineous marriage.¹⁹ Rama devi et al. suggest that in the background of various other environmental factors contributing to the burden of genetic diseases, the role of consanguinity may have been masked.²⁰ 27.72% of the mothers in our study gave a history of previous abortion/neonatal deaths and stillbirths. Jayasree et al. in a study in Kerala reported that 25.1% of the women who gave birth to a child with congenital anomaly had a previous history of abortions or intrauterine deaths (IUD) or neonatal deaths.¹⁹ This can be due to the fact that many genetic diseases are lethal and cause fetal death and hence women with bad obstetric history are at higher risk.²¹

In our study, 2.97% of fathers had Diabetes Mellitus and all of these children had structural genetic diseases. However, Mills et al. reported that children born to diabetic fathers had the same risk of developing malformations compared to non-diabetic fathers.²² 11.88% of the cases had similar conditions in the sibling. El Kowmi et al. reported that 6% of the affected children in their study had a history of an anomaly in the family, signifying that genetic diseases tend to recur and requires evaluation so as to prevent the same conditions in future pregnancies.²³

In the present study, 12.87% had chromosomal abnormalities, 74.25% had single gene defects, 6.93% had mitochondrial diseases and 5.94% had multifactorial diseases. Rama devi et al. in a study in Karnataka reported the prevalence of single-gene disorders to be higher followed by multifactorial disorders.²⁰ The most common chromosomal abnormality in our study was Down syndrome (9.9%). Similar findings were reported by Kaur et al., where the prevalence was 11% and Wojnik et al. who reported a prevalence of 30% among chromosomal disorders.^24,25

Among the single-gene disorders, the most common were the inborn errors of metabolism-organic acidemia (12.87%) and Lysosomal storage disorders (8.91%). Similar findings were quoted by Verma et al. from a multi-center study where the prevalence of organic acidurias were maximum (27.5%).²¹ The proportion of Duchenne muscular dystrophy was 5.94% and other common dystrophy was Limb-girdle musculodystrophy (1.98%) in this study. 2.97% had spinal muscular atrophy. Kaur et al. reported an overall proportion of muscular dystrophies to be 0.8% and Wojnik et al. reported the prevalence of spinal muscular atrophies to be 16% which is quite higher compared to our study, probably because of the difference in size of the sample and study setting.^24,25 1.98% of study participants had sickle cell anemia and Thalassemia in this study. The prevalence of the Beta-Thalassemia trait being 3-4% and Sickle cell anemia allele being 2-20% in India.⁶ 5.94% had multifactorial diseases in our study. Verma et al. quote a prevalence of 4.64%. The most common ones being cleft lip/cleft palate and multiple anomalies.²¹ Kaur et al. reported a proportion of 0.1% in her study while Rama devi et al. reported 2 in 12 cases of polygenic inheritance for cleft lip/cleft palate.^20,24

Limitations

Since this was a hospital-based study, the address provided by the patients were used for geocoding their locations and we could not confirm their accuracy of it. The sample was limited, hence we have given proportions for the various parameters assessed. It would require a community-based study to assess the prevalence of genetic diseases.

Conclusion

It is evident from the geospatial mapping that both structural and functional genetic diseases have a widespread distribution in the population and is no longer a “rare disease” as described earlier. Genetic diseases being a rising cause of childhood morbidity and mortality in India, it is the need of the hour to expand surveillance activities, incorporate newborn screening into the routine biochemical testing and to provide adequate diagnostic services to the suspected with preventive services like carrier screening and counselling.

Acknowledgement

We thank the study participants and their families for their time and co-operation.

Financial support

None

Conflict of interest

None

Author’s contribution

Mohandas Aparna: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing-original draft, writing-review and editing Kumar Sunil D: Conceptualization, Methodology, Supervision, Data curation, writing-review and editing Bhat Deepa: Conceptualization, Methodology, Investigation, writing-review and editing Murthy Narayana M.R: Methodology, Supervision, Data curation, writing-review and editing  Gopi Arun: Methodology, Data curation, Formal analysis, writing-review and editing

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