A Bayesian Multi-State Data Integration Approach for Estimating County-level Prevalence of Opioid Misuse in the United States

2026-01-08

AI Summary

This study addresses the critical gap in nationwide county-level estimates of opioid misuse prevalence, which has hindered targeted local interventions. To overcome this limitation, the authors propose a scalable, parsimonious, and highly generalizable Bayesian multi-source data fusion framework that integrates national survey data, existing state- and county-level estimates where available, and county-level covariates. The model jointly characterizes opioid misuse prevalence and overdose mortality while incorporating a horseshoe+ sparsity-inducing prior to enable flexible shrinkage of regression coefficients. This approach yields, for the first time, comprehensive prevalence estimates for all U.S. counties. Cross-validation demonstrates high predictive accuracy, establishing a foundational dataset to inform public health policy and precision intervention strategies.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: State Estimation
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Drug overdose deaths, including from opioids, remain a significant public health threat to the United States (US). To abate the harms of opioid misuse, understanding its prevalence at the local level is crucial for stakeholders in communities to develop response strategies that effectively use limited resources. Although there exist several state-specific studies that provide county-level prevalence estimates, such estimates are not widely available across the country, as the datasets used in these studies are not always readily available in other states, which, therefore, has limited the wider applications of existing models. To fill this gap, we propose a Bayesian multi-state data integration approach that fully utilizes publicly available data sources to estimate county-level opioid misuse prevalence for all counties in the US. The hierarchical structure jointly models opioid misuse prevalence and overdose death outcomes, leverages existing county-level prevalence estimates in limited states and state-level estimates from national surveys, and accounts for heterogeneity across counties and states with counties'covariates and mixed effects. Furthermore, our parsimonious and generalizable modeling framework employs horseshoe+ prior to flexibly shrink coefficients and prevent overfitting, ensuring adaptability as new county-level prevalence data in additional states become available. Using real-world data, our model shows high estimation accuracy through cross-validation and provides nationwide county-level estimates of opioid misuse for the first time.

Problem

• opioid misuse
• county-level prevalence
• data integration
• public health
• overdose deaths

Innovation

• Bayesian hierarchical modeling
• multi-state data integration
• county-level prevalence estimation
• horseshoe+ prior
• opioid misuse