Spatial Disparities in Mental Distress Across the United States: A Multiscale Geographic Weighted Regression Analysis

Keywords: Mental Distress, MGWR, Spatial Disparities
Abstract Type: Poster Abstract

Abstract

Understanding geographic disparities in mental distress is crucial for designing effective public health interventions. This study employs a Multiscale Geographic Weighted Regression (MGWR) model to investigate the spatial distribution of factors influencing mental distress across 3108 counties in the contiguous United States. Using data from the 2022 County Health Rankings and the 2015–2019 American Community Survey, the analysis considers a wide array of variables, including smoking, alcohol consumption, unemployment, marital status, household income, mental health provider density, social associations, internet access, sleep insufficiency, gender ratio, and minority population. Findings reveal significant spatial clustering, with high mental distress levels prevalent in southeastern and Appalachian regions and low levels observed in the northern central states. Key determinants exhibit spatially varying relationships; for example, smoking consistently correlates positively with high mental distress, while higher internet access and median household income are associated with reduced distress. The MGWR model explains over 90% of spatial variability in many regions, validated through Moran’s I index and residual mapping. These results highlight the need for geographically tailored public health strategies to address mental health disparities. By identifying regional patterns, this study contributes to evidence-based policymaking aimed at mitigating mental distress and promoting mental health equity.