Paper Information

Mental health is a critical component of overall well-being, profoundly influencing how individuals think, act, and interact in daily life. In the United States, an estimated 57.8 million adults experience a mental illness, representing 22.8% of the adult population. Access to mental health care is particularly challenging in the Great Plains and Rocky Mountains, where high costs and limited treatment centers exacerbate disparities. Understanding the spatial distribution of mental health outcomes in these regions is essential for guiding resource allocation and informing targeted interventions. In this study, we investigate the spatio-temporal dynamics of frequent mental distress (FMD) across counties in the Great Plains and Rocky Mountains from 2019 to 2023. We examine how socioeconomic, demographic, and healthcare-related variables influence mental health outcomes. To account for spatial dependence and spatiotemporal heterogeneity, we use Moran's I, ordinary least squares (OLS), geographically and temporally weighted regression (GTWR) and multiscale geographically and temporally weighted regression (MGTWR) models. Results revealed persistent spatial clustering of FMD, with higher levels concentrated in counties within Kansas, Nebraska, Oklahoma, and Texas and lower levels observed in North Dakota, Wyoming, and Montana. Significant spatial heterogeneity was identified in the associations between FMD and unemployment, poverty, uninsured population, educational attainment, and racial and ethnic composition. Compared with OLS, both GTWR and MGTWR improved model performance and better captured localized variation in FMD associations. Temporal variation was comparatively limited relative to the pronounced spatial heterogeneity observed across counties. These findings suggest that mental health disparities across the Great Plains and Rocky Mountains are associated with geographically uneven socioeconomic and structural conditions. The study highlights the importance of accounting for spatial non-stationarity in population mental health research and demonstrates the value of geographically weighted approaches for identifying localized disparities and informing targeted public health interventions.