A machine learning approach was proposed to prioritize place-based prevention in New York City, aiming to reduce cardiovascular disease and diabetes deaths by 5% by 2030.
Describes the HealthyNYC initiative, which uses a machine learning approach to prioritize place-based prevention to reduce cardiovascular disease burden.
Cardiovascular disease (CVD) is a leading cause of death in New York City (NYC), accounting for nearly 27% of all deaths in 2021, with substantial disparities driven by social determinants of health (SDoH).1 In response, NYC launched HealthyNYC in 2025, a citywide population health initiative aiming to reduce deaths from CVD and diabetes by 5% by 2030. The initiative emphasizes place-based prevention, including expanded prevention activities and social supports, improved access to healthy foods, and targeted action on unmet material needs in high-burden neighborhoods.
Li et al. (2026) studied Cardiovascular disease. Machine learning approach for place-based prevention was evaluated. A machine learning approach was proposed to prioritize place-based prevention in New York City, aiming to reduce cardiovascular disease and diabetes deaths by 5% by 2030.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: