Evaluation of A Spatial Microsimulation Framework for Small-Area Estimation of Population Health Outcomes Using the Behavioral Risk Factor Surveillance System

The field of population health addresses a wide spectrum of challenges, spanning infectious and chronic diseases to mental health and health risk behaviors such as smoking and alcohol consumption (Sharma et al., 2025). A common barrie r to addressing these issues is the lack of ground truth data capturing health outcomes and behaviors at fine geographic scales. This limits both local and national health decision - makers in planning and management efforts, such as identify ing health inequalities or targeting interventions where they are most needed (Rahman, 2017; Wang, 2018) . T o fill this gap, researchers use small area estimation (SAE), a collection of statistical methods that combine survey and geographic data to generate estimates of population - level health outcomes at various spatial scales (RTI International, 2025) . There are numerous methods for generating SAE of health outcomes, which can generally be grouped into two main approaches: direct and indirect model - based estimates (Rahman, 2017) . Direct estimates are calculated using only the survey responses from individuals or households sampled within the specified geographi c areas (counties, states) to estimate disease prevalence or other population characteristics.