ArticleJournal of the American Heart Association2026
Using Machine Learning to Identify Social Risk Factors of Hypertension and Diabetes in New York City: Evidence to Support the HealthyNYC Initiative.
Article in Journal of the American Heart Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundNew York City (NYC) launched the HealthyNYC initiative in 2025, aiming to reduce cardiovascular disease and diabetes deaths by 5% by 2030. Effective place-based interventions require evidence to address social risk factors associated with hypertension and diabetes. This study aims to identify key social determinants of health (SDoH) associated with variation in hypertension and diabetes prevalence across census tracts in NYC.
methodsThis retrospective cohort study analyzed clinical data on 3.2 million NYC residents integrated with SDoH data at the census tract level. Using Extreme Gradient Boosting, we identified the top 10 SDoH most strongly associated with both age-adjusted and unadjusted prevalence across all census tracts in the city and neighborhood-specific SDoH for census tracts in the highest prevalence quintile for hypertension and diabetes.
resultsSDoH explain 70% to 80% of the cross-tract variation in hypertension (R
conclusionsThis study provides robust evidence that SDoH are strongly associated with prevalence of hypertension and diabetes and identifies neighborhoods where targeted, place-based approaches could be prioritized for implementing and testing.
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