Abstract. The United States is experiencing an unprecedented rise in suicide rates amid a growing mental health crisis. While many public health studies examine contextual factors associated with suicide using individual or group-level data, these approaches often overlook spatial dynamics that could reveal geographic and scale-dependent variations. County-level research on suicide determinants in the U.S. remains limited, leaving significant gaps in understanding geographic disparities. Therefore, this study leverages spatial analysis and modeling to identify clusters of high and low suicide rates across the U.S., and explores the geographic variations in the associations between social determinants and suicide to better understand these patterns. Using Local Moran’s I for cluster and outlier analysis, alongside comparisons between Ordinary Least Squares (OLS) and Multiscale Geographically Weighted Regression (MGWR) models, the analysis evaluates the relationships between age-adjusted suicide rates and 17 independent variables across 2,410 U.S. counties. Results uncover geographic heterogeneity, with higher suicide rates concentrated in the Western U.S. and several determinants demonstrating spatially varying effects. For instance, the percent of veteran population is a statistically significant predictor across all counties, with the strongest effects in the Northeast. Other variables show more localized patterns: living alone is significant in 14% of counties, mainly in the Southwest, and American Indian population is significant in 17% of counties, primarily in the West. Our findings highlight the non-stationarity of suicide determinants across geographic space and scales, providing a data-driven framework to identify geospatial disparities and inform the development of targeted intervention strategies to reduce suicides in specific locations.
Hoene et al. (Tue,) studied this question.