A Bayesian Information Synthesis Framework for Opioid Use Disorder Prevalence EstimationPresentation

Oregon Convention Center 

Identifying the prevalence of OUD in the population is a critical public health activity for prevention and intervention. While tracking OUD prevalence is critical, it is challenging. Survey data are most often used to assess OUD, but are known to seriously underestimate true prevalence of OUD. Administrative records and treatment datasets represent only a minority of individuals with OUD. Other methods have been proposed for OUD prevalence estimation, such as capture-recapture, venue-based methods, network methods, and multiplier methods. In this talk, I will present a Bayesian framework that connects multiple data sources that reveal widespread and heterogeneous patterns of under-diagnosis of OUD in New York State.