Population health data analytics can optimize preventive resource allocation, addressing a 21-year life expectancy gap linked to social determinants of health in D.C.
Can population health data analytics optimize preventive resource allocation and reduce health inequities in Washington, D.C.?
Integrating population health data analytics and geospatial modeling may help target preventive resources to reduce severe health disparities and chronic disease burden in Washington, D.C.
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The persistent disparities in health outcomes across neighborhoods in Washington, D.C.underscore the urgent need for data-driven strategies to promote health equity. This projectexamines how population health data analytics can optimize preventive resource allocation inD.C., where social determinants of health (SDoH), including income, race, education, and accessto nutritious food, contribute to a 21-year life expectancy gap between predominantly white andBlack communities. Through a structured review of literature, public health reports, andgeospatial datasets, the project examines how predictive analytics, risk stratification, andgeospatial modeling can identify high-risk areas and inform targeted preventive interventions.The focus is on translating complex population-level data into actionable strategies that improveaccess to care, enhance disease prevention, and reduce inequities in chronic disease burden. Thefindings are expected to demonstrate how integrating data systems across public healthagencies, healthcare providers, and community organizations can strengthen Washington, D.C.’scapacity to deliver equitable, preventive healthcare services and guide sustainable policy change.
Gowthami Vasamsetti (Wed,) reported a other. Population health data analytics can optimize preventive resource allocation, addressing a 21-year life expectancy gap linked to social determinants of health in D.C.