Diabetes is a leading contributor to cardiovascular disease and mortality; social determinants of health (SDOH) are associated with disparities in diabetes risk. Quantifying the cumulative impact of SDOH and identifying the SDOH most associated with diabetes prevalence at the neighborhood level can help policy-makers design and target local interventions to mitigate these disparities. Machine learning (ML) methods can provide novel insights and help inform public health intervention strategies in a place-based manner. In a cross-sectional study, we used gradient boosting ML models to estimate the cumulative contribution of a set of SDOH variables to diabetes prevalence (%) at the census tract level within New York City (NYC); Shapley Additive Explanations were used to assess the magnitude and shape of relationships between our SDOH variables and model-predicted NYC diabetes prevalence. SDOH measures included socioeconomic position, educational attainment, food access, air quality, neighbourhood environment, housing conditions and insurance coverage.
This media asset is free for editorial broadcast, print, online and radio use. It is restricted for other purposes.
