Estimating Opioid Misuse in Subpopulations: A Bayesian Factor Analysis Approach Using Capture-Recapture and Linked Health Data

Music City Center 

The ongoing opioid crisis has highlighted the urgent need for accurate surveillance systems to monitor substance misuse and inform public health interventions. However, fragmented and incomplete data sources hinder reliable estimation of disease burden, particularly among key subpopulations. We propose a Bayesian hierarchical factor analysis framework to estimate subpopulation-specific prevalence by jointly modeling their interaction with multiple administrative health data sources within a capture-recapture (CRC) framework. The model accounts for group-specific detection probabilities, referral relationships among data sources, and latent heterogeneity in healthcare-seeking behavior. These detection processes are embedded within a higher-level model for underlying prevalence. Simulation studies show that our approach improves estimation efficiency, especially for small subgroups, and resolves the model-fitting issues (e.g., zero cells) often encountered in stratified CRC methods. Applying the method to the Massachusetts Public Health Data Warehouse (MA PHD), we demonstrate that it provides more stable and interpretable estimates than conventional stratified log-linear CRC approaches, particularly when detection probabilities across sources are similar. This flexible and robust framework enables the use of linked administrative data to identify high-risk populations and supports the development of more efficient public health strategies.