SPATIAL MODELING OF THE SOCIAL HEALTH DETERMINANTS IMPACT ON THE EPIDEMIOLOGY OF DISEASES IN LOW- MIDDLE- AND HIGH-INCOME SETTINGS IN NORTH MAHARASHTRA

Abstract

In this study, Researchers examine the spatial distribution of social determinants of health (SDOH) and how they relate to disease epidemiology in low-, middle- and high-income settings across North Maharashtra. The research uses advanced spatial modeling techniques to demonstrate strong correlations between socioeconomic factors, including education, income, and healthcare access, and health outcomes. A spatial epidemiology theoretical framework employs SDOH domains to identify geographic variation in chronic and infectious disease prevalence. The economic gradient displays are consistent with disparities in health outcomes, and have implications for localized interventions to address public health equities. Using multi-level data sources and machine learning methods predicted key predictors of health (education, income) with strong predictive models (R² up to 0.845). The policy implications of the five prior factors in the study stress the importance of targeted community-based initiatives, enhanced social support networks, and integrated data systems used by the healthcare system. The essential interplay of spatial and social factors in shaping health outcomes is emphasized, while highlighting the necessity of future research establishing causal links, and the implications of these findings are suggested to be applicable to a variety of socioeconomic settings worldwide.