Spatial Patterns and Determinants of Diabetes Mellitus in Indian Adult Population: a Secondary Data Analysis from Nationally Representative Surveys

Introduction Diabetes mellitus (DM) is a major public health challenge around the world. It is crucial to understand the geographic distribution of the disease in order to pinpoint high-priority locations and focus intervention on the target populations. Hence, this study was carried out to determine the spatial pattern and determinants of type-2 DM in an Indian population using National Family Health Survey-4 (NFHS-4) and Longitudinal Aging Survey in India (LASI). Methods We have adopted an ecological approach, wherein geospatial analysis was performed using aggregated district-level data from NFHS-4 (613 districts) and LASI survey datasets (632 districts). Moran's I statistic was determined and Local Indicators of Spatial Association (LISA) maps were created to understand the spatial clustering pattern of DM. Spatial regression models were run to determine the spatial factors associated with DM. Results Prevalence of self-reported DM among males (15-50 years) and females (15-49 years) was 2.1% [95% confidence interval (CI) 2.0-2.3%] and 1.7% (95% CI 1.6-1.8%), respectively. Prevalence of self-reported DM among males and females aged 45 years and above was 12.5% (95% CI 11.5-13.5%) and 10.9% (95% CI 9.8-12%). Positive spatial autocorrelation with significant Moran's I was found for both males and females in both NFHS-4 and LASI data. High-prevalence clustering (hotspots) was maximum among the districts belonging to southern states such as Kerala, Tamil Nadu, Karnataka, and Andhra Pradesh. Northern and central states like Madhya Pradesh, Chhattisgarh, and Haryana mostly had clustering of cold spots (i.e., lower prevalence clustered in the neighboring regions). Conclusion DM burden in India is spatially clustered. Southern states had the highest level of spatial clustering. Targeted interventions with intersectoral coordination are necessary across the geographically clustered hotspots of DM.