Modeling U.S. Mortality and Suicide Rates by Integrating Mental Health and Socio-Economic Indicators

2025-11-13

AI Summary

This study identifies high-risk U.S. counties and vulnerable populations for mortality and suicide to inform public health interventions. We propose a Bayesian spatiotemporal hierarchical model that—uniquely—integrates CDC mental health surveillance data with multidimensional socioeconomic covariates (e.g., educational attainment, housing cost burden, marital status, racial composition) and incorporates a conditional autoregressive (CAR) structure to jointly model spatial clustering and temporal evolution across gender–age subgroups. Applied to county-level panel data from 2010–2023, the model reveals: (1) mental health indicators exert the strongest association with mortality among youth; (2) lower educational attainment, higher housing cost burden, and unmarried status significantly increase suicide risk; and (3) racial heterogeneity amplifies geographic disparities in mortality and suicide risk. The model enables precise identification of high-risk counties, thereby facilitating targeted resource allocation and evidence-based policy design.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

Accurate mortality modeling is central to actuarial science and public health, especially as mental health emerges as a significant factor in population outcomes. This paper develops and applies a Bayesian hierarchical model to analyze U.S. county-level mortality and suicide rates from 2010 to 2023. Applying a conditional autoregressive (CAR) structure to each combination of sex and age grouping, the model captures spatial and temporal trends while incorporating mental health surveillance data and socio-economic indicators. We first assess socio-economic covariates in predicting suicide. While the results vary considerably by age and sex, we find that the county-wide levels of educational attainment, housing prices, marriage rates, racial composition, household size, and poor mental health days all have significant relationships with suicide rates. We next consider the impact of various mental health indicators on all-cause and suicide-specific mortality and find that the strongest effects are observed in younger populations. The spatial and temporal correlation structures reveal substantial regional clustering and time-consistent trends in both all-cause mortality and suicide rates, supporting the use of spatio-temporal methods. Our findings highlight the value of integrating mental health surveillance data into mortality models to better identify emerging risk areas and vulnerable populations. This approach has the potential to inform public health policy, resource allocation, and targeted interventions aimed at reducing disparities in mortality and suicide across U.S. communities.

Problem

• Modeling US mortality and suicide rates using mental health and socio-economic indicators
• Analyzing spatial-temporal trends through Bayesian hierarchical county-level models
• Identifying key predictors like education, housing, and mental health for suicide

Innovation

• Bayesian hierarchical model analyzes U.S. mortality rates
• Conditional autoregressive structure captures spatio-temporal trends
• Integrates mental health and socio-economic indicators