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Using Machine Learning to Identify Social Risk Factors of Hypertension and Diabetes in New York City: Evidence to
Elizabeth Adamson1, Haoyang Li2, Zhenxing Xu2
1Novartis Foundation Basel Switzerland.
Background:
New 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.
Methods:
This 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.
Results:
SDoH explain 70% to 80% of the cross-tract variation in hypertension (R2=66.2%-79.5%, root mean square error=2.6-3.2) and diabetes (R2=72.9%-79.3%, root mean square error=2.1-2.2) prevalence in NYC. Normalized mean absolute Shapley Additive Explanations values showed that the top 10 SDoH contribute to most of the model's prediction: 73% (age adjusted) and 70% (unadjusted) for hypertension prediction and 73% (age adjusted) and 71% (unadjusted) for diabetes prediction. The top 10 SDoH with the highest Shapley Additive Explanations values center on domains like socioeconomic disadvantage, built environment, and commute time. The neighborhood-specific SDoH varied across census tracts and boroughs.
Conclusions:
This 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.
