Modeling suicide mortality in US counties using population socioeconomic indicators

Abstract

Background: Suicide is one of the leading causes of death in the United States and population risk prediction models can inform the type, location, and timing of public health interventions. Here, we report the development of a prediction model of suicide risk using population characteristics. Methods: All suicide deaths reported to the Nation Vital Statistics System between 2005-2019 were identified, and age, sex, race, and county-of-residence of the decedents were extracted to calculate baseline risk. County-wise annual measures of socioeconomic predictors of suicide risk (unemployment, weekly wage, poverty prevalence, median household income, and population density), along with two state-wise measures of prevalence of major depressive disorder and firearm ownership were compiled from public sources. Conditional autoregressive (CAR) models, which account for spatiotemporal autocorrelation in response and predictors, were used to estimate county-level risk. Results: Estimates derived from CAR models were more accurate than from models not adjusted for spatiotemporal autocorrelation. Inclusion of suicide risk/protective covariates further reduced errors. Suicide risk was estimated to increase with each standard deviation increase in firearm ownership (2.8%), prevalence of major depressive episode (1%) and unemployment (2.8%). Conversely, risk was estimated to decrease by 4.3% for each standard deviation increase in both median household income and population density. Increased heterogeneity of risk across counties was also noted. Conclusions: Area-level characteristics and the CAR model structure can estimate population-level suicide risk and thus inform decisions on resource allocation and focused interventions during outbreaks.