Prevalence and detecting spatial clustering of diabetes at the district level in India

Abstract

Background

Diabetes is increasingly becoming a prevalent disease in developing countries such as India. This has raised great concern among public health practitioners, researchers and policy makers. The present study aims to examine the geographical variations in the prevalence of diabetes and its contextual risk factors including production and consumption of different food groups in the districts of India.

Methods

We used state- and district-level data from multiple sources. These include National Family Health Survey-4 (2015–2016), socio-economic data from the census of India (2011), per capita crop production from the Ministry of Agriculture and Farmers and consumption of different food groups from the 68th round of the National Sample Survey. The study adopted advanced spatial statistical analysis including the Moran index and spatial regression to fulfil the objectives of the article.

Results

The study reveals the remarkable geographical variations in diabetes risk in the districts of India. Districts from the coastal area have more diabetes prevalence. Per capita calorie consumption, calorie intake from sugar and production of sugar crops in districts are significantly positively related to diabetes prevalence. On the other hand, protein intake and calorie consumption from pulses and nuts and milk and dairy products reduce the risk of diabetes.

Conclusion

The study recommends that public health programmes target hot-spot districts with high prevalence of diabetes and encourage people to increase consumption of protein-rich diets including pulses, nuts and milk products to reduce diabetes prevalence.

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Data availability

This article used state/district-level data from multiple sources. State/district factsheets of National Family Health Survey-4 (2015–2016) can be downloaded from the website “http://rchiips.org/nfhs/”. Socio-economic indicators for all districts were compiled from http://www.censusindia.gov.in/2011-Common/CensusData2011.html (census of India, 2011). Furthermore, the article used per capita crop production from the website of the Ministry of Agriculture and Farmers (http://aps.dac.gov.in/APY/Public_Report1.aspx), and consumption of different food groups at the state-level was compiled from the National Sample Survey Office (NSS Report no. 560).

References

  1. Agrawal S, Millett CJ, Dhillon PK, Subramanian SV, Ebrahim S (2014) Type of vegetarian diet, obesity and diabetes in adult Indian population. Nutr J 13(1):89. https://doi.org/10.1186/1475-2891-13-89

    Article  PubMed  PubMed Central  Google Scholar 

  2. Akhtar SN, Dhillon P (2017) Prevalence of diagnosed diabetes and associated risk factors: evidence from the large-scale surveys in India. J Soc Health and Diabetes 5(1):28. https://doi.org/10.4103/2321-0656.194001

    Article  Google Scholar 

  3. Alkerwi A, Bahi IE, Stranges S, Beissel J, Delagardelle C, Noppe S, Kandala NB (2017) Geographic variations in cardiometabolic risk factors in Luxembourg. Int J Environ Res Public Health 14:648. https://doi.org/10.3390/ijerph14060648

    Article  PubMed Central  Google Scholar 

  4. Anjana RM, Ali MK, Pradeepa R, Deepa M, Datta M, Unnikrishnan R, Rema M, Mohan V (2011) The need for obtaining accurate nationwide estimates of diabetes prevalence in India— rationale for a national study on diabetes. Indian J Med Res 133(4):369–380

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Anjana RM, Deepa M, Pradeepa R, Mahanta J, Narain K, Das HK et al (2017) Prevalence of diabetes and prediabetes in 15 states of India: results from the ICMR–INDIAB population-based cross-sectional study. Lancet Diabetes Endocrinol 5:585–596. https://doi.org/10.1016/S2213-8587(17)30174-2

    Article  PubMed  Google Scholar 

  6. Anselin L (1988) Spatial econometrics: methods and models. Springer, Netherlands

    Google Scholar 

  7. Auchincloss AH, Gebreab SY, Mair C, Roux AVD (2012) A review of spatial methods in epidemiology, 2000–2010. Annu Rev Public Health 33:107–122. https://doi.org/10.1146/annurev-publhealth-031811-124655

    Article  PubMed  PubMed Central  Google Scholar 

  8. Borah M, Goswami RK (2017) Sociodemographic and clinical characteristics of a diabetic population at a tertiary care center in Assam, India. Journal of Social Health and Diabetes 5(1):37. https://doi.org/10.4103/2321-0656.193997

    Article  Google Scholar 

  9. Chow CK, Raju PK, Raju R, Reddy KS, Cardona M, Celermajer DS, Bruce C (2006) The prevalence and management of diabetes in rural India. Diabetes Care 29:1717–1718. https://doi.org/10.2337/dc06-0621

    Article  PubMed  Google Scholar 

  10. Dagogo-Jack S (2006) Primary prevention of type-2 diabetes in developing countries. J Natl Med Assoc 98:415–419

    PubMed  PubMed Central  Google Scholar 

  11. Diamond J (2003) The double puzzle of diabetes. Nature 423(6940):599–602

    CAS  PubMed  Google Scholar 

  12. Food and Agriculture Organisation (2010). FAOSTAT Commodity list http://www.fao.org/economic/ess/ess-standards/commodity/comm-chapters/en/. Accessed 05 January, 2019

  13. Gulati S, Misra A (2014) Sugar intake, obesity, and diabetes in India. Nutrients 6(12):5955–5974. https://doi.org/10.3390/nu6125955

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. International Diabetes Federation (2013) IDF Diabetes Atlas. In: 6th edition. https://idf.org/e-library/epidemiology-research/diabetes-atlas/19-atlas-6th-edition.html Accessed 05 January 2019

  15. Jayawardena R, Ranasinghe P, Byrne NM, Soares MJ, Katulanda P, Hills AP (2012) Prevalence and trends of the diabetes epidemic in South Asia: a systematic review and meta-analysis. BMC Public Health 12:380

    PubMed  PubMed Central  Google Scholar 

  16. Joner G, Sovik O, Riise T (1998) Clustering of type one diabetes mellitus in Norway. Diabetologia 41(Suppl 1):A21–A75

    Google Scholar 

  17. King H, Aubert RE, Herman WH (1998) Global burden of diabetes, 1995-2025: prevalence, numericl estimates, and projections. Diabetes Care 21(9):1414–1431

    CAS  PubMed  Google Scholar 

  18. Liu S, Manson JE, Stampfer MJ, Hu FB, Giovannucci E, Colditz GA et al (2000) A prospective study of whole-grain intake and risk of type 2 diabetes mellitus in US women. Am J Public Health 90(9):1409–1415

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Malik VS, Popkin BM, Bray GA, Després JP, Willett WC, Hu FB (2010) Sugar-sweetened beverages and risk of metabolic syndrome and type 2 diabetes: a meta-analysis. Diabetes Care 33(11):2477–2483

    PubMed  PubMed Central  Google Scholar 

  20. Mattei J, Malik V, Wedick NM, Hu FB, Spiegelman D, Willett WC, Campos H (2015) Reducing the global burden of type 2 diabetes by improving the quality of staple foods: the global nutrition and epidemiologic transition initiative. Glob Health 11(1):23. https://doi.org/10.1186/s12992-015-0109-9

    Article  Google Scholar 

  21. Ministry of Agriculture and Farmers Welfare, Govt. of India (2010) Crop production statistics information system 2010–11: District-wise crop production statistics, New Delhi. http://aps.dac.gov.in/APY/Public_Report1.aspx. Accessed on 06 August 2017

  22. Mohan V, Sandeep S, Deepa R, Shah B, Varghese C (2007) Epidemiology of type 2 diabetes: Indian scenario. Indian J Med Res 125(3):217–230

    CAS  PubMed  Google Scholar 

  23. National Sample Survey Organization (NSSO) (2014) Ministry of Statistics and Programme Implementation (MOSPI). Nutritional Intake in India 2011–12. Report No.560. New Delhi

  24. Popkin BM, Horton S, Kim S, Mahal A, Shuigao J (2001) Trends in diet, nutritional status, and diet-related noncommunicable diseases in China and India: the economic costs of the nutrition transition. Nutrition Res 59(12):379–390

    CAS  Google Scholar 

  25. Prasad RB, Groop L (2015) Genetics of type 2 diabetes—pitfalls and possibilities. Genes 6(1):87–123. https://doi.org/10.3390/genes6010087

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. Ramachandran A, Snehalatha C, Kapur A, Vijay V, Mohan V, Das AK et al (2001) High prevalence of diabetes and impaired glucose tolerance in India: National Urban Diabetes Survey. Diabetologia 44(9):1094–1101

    CAS  PubMed  Google Scholar 

  27. Samuelsson U, Johansson C, Carstensen J, Ludvigsson J (1994) Space-time clustering in insulin-dependent diabetes mellitus (IDDM) in south-East Sweden. Int J Epidemiol 23(1):138–142

    CAS  PubMed  Google Scholar 

  28. Santos JL, Pérez-Bravo F, Carrasco E, Calvillán M, Albala C (2001) Low prevalence of type 2 diabetes despite a high average body mass index in the Aymara natives from Chile. Nutrition 17(4):305–309

    CAS  PubMed  Google Scholar 

  29. Snowdon DA, Phillips RL (1985) Does a vegetarian diet reduce the occurrence of diabetes? Am J Public Health 75(5):507–512

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Somannavar S, Ganesan A, Deepa M, Datta M, Mohan V (2009) Random capillary blood glucose cut points for diabetes and pre-diabetes derived from community-based opportunistic screening in India. Diabetes Care 32(4):641–643. https://doi.org/10.2337/dc08-0403

    CAS  Article  PubMed  Google Scholar 

  31. Tonstad S, Butler T, Yan R, Fraser GE (2009) Type of vegetarian diet, body weight, and prevalence of type 2 diabetes. Diabetes Care 32(5):791–796. https://doi.org/10.2337/dc08-1886

    Article  PubMed  PubMed Central  Google Scholar 

  32. Turi KN, Toussaint DSG (2017) Spatial spillover and the socio-ecological determinants of diabetes-related mortality across US counties. Appl Geogr 85:62–72. https://doi.org/10.1016/j.apgeog.2017.05.005

    Article  Google Scholar 

  33. Vos T, Barber RM, Bell B, Bertozzi-Villa A, Biryukov S, Bolliger I, Duan L (2015) Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: a systematic analysis for the global burden of disease study 2013. Lancet 386(9995):743

    Google Scholar 

  34. World Health Organization (WHO) (2016) Global Report on Diabetes, Geneva http://apps.who.int/iris/bitstream/handle/10665/204871/9789241565257_eng.pdf;jsessionid=683202F3A1F12D05926016C769AB83A0?sequence=1. accessed 06 January 2019

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Acknowledgements

This research received no specific grant from any funding agency, commercial entity or not-for-profit organisation. We thank the reviewers from the journal for giving their useful comments for the improvement of this article.

Author information

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Authors

Contributions

Conceived and designed the experiments: KG, PD. Compiled the data: KG. Analysed the data: KG. Writing-original draft: KG, PD. Writing-review and editing: PD, GA.

Corresponding author

Correspondence to Preeti Dhillon.

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Competing interests

We declare that no competing interests exist.

Conflict of interests

The authors declare that they have no conflict of interest.

Ethical statement

The authors assert that all procedures contributing to this work fulfil the ethical standards of the National Family Health Survey. The protocol for the NFHS-4 survey, including the content of all the survey questionnaires, was approved by the IIPS Institutional Review Board and the ICF Institutional Review Board. The protocol was also reviewed by the US Centers for Disease Control and Prevention (CDC).

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Cite this article

Ghosh, K., Dhillon, P. & Agrawal, G. Prevalence and detecting spatial clustering of diabetes at the district level in India. J Public Health (Berl.) 28, 535–545 (2020). https://doi.org/10.1007/s10389-019-01072-6

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Keywords

  • Diabetes
  • Spatial
  • Dietary groups
  • India
  • Districts