Data-Driven Population Health Analytics for Identifying High-Risk Groups and Health Disparities - Rahman, Mahzabin Binte; Yasin, Mohammad; Ahmed, Md Parvez (American Journal of Botany and Bioengineering, 2024)
Practices of population health management are now more and more dependent on the mass levels of surveillance data to inform the identification of vulnerable groups and intervene in enduring health disparities. This research uses data on the Behavioral Risk Factor Surveillance System (BRFSS) collected during 2011 -2021 to provide a comprehensive analysis of the population in order to determine high-risk groups and explore socioeconomic and demographic disparities in health in the United States. This dataset contains more than 2.2 million records of aggregation of all 50 states, the District of Columbia, and U.S. territories, and contains behavioral risk factors, chronic health status, preventive service use, and social determinants of health. Through multivariate regression analysis, trend models and stratified demographic comparisons, this study is able to measure the prevalence of chronic diseases (diabetes, cardiovascular disease, obesity, and depression) as a function of income, education level, race/ethnicity, age cohorts, and geographic regions. This study also examines behavioral determinants which include smoking, physical inactivity and healthcare access limitation in order to evaluate their relation with negative health outcomes. Temporal analysis identifies health indicators changes in the last decade, the COVID-19 era, and assesses the changing patterns of disparity. The results will likely emphasize the disproportionate burden of diseases in low-income groups, racial and ethnic minorities, and people with lower education levels, which will support the importance of social determinants in health outcome determination. This study will help make evidence-based policy, intervention-based, and equitable resource distribution, as it combines information-driven population health analytical data with disparity-based evaluation. The findings are useful to the public health officials, health care systems and policymakers on reducing the number of preventable health inequities and enhancing the long term population health outcomes.