Small Area Estimation

A spatial capture-recapture approach for estimating opioid use disorder prevalence in small areas using administrative data
Working Paper
Jianing Wang, Shariq Mohammed, Laura F. White, David Kline
Massachusetts prevalence of Opioid Use Disorder estimation revisited: Comparing a Bayesian approach to standard capture-recapture methods
Working Paper abstract Accurate estimation of the prevalence of people with opioid use disorder (OUD) is critical to the success of treatment and resource planning. Various indirect estimation approaches have been used but are subject to issues related to data availability and infrastructure.
Jianing Wang, Nathan Doogan, Katherine Thompson, Dana Bernson, Daniel Feaster, Jennifer Villani, Redonna Chandler, Laura F. White, David Kline, Joshua A. Barocas
The prevalence of opioid use disorder in Kentucky’s counties: A two-year multi-sample capture-recapture analysis
abstract Background Kentucky has one of the highest opioid overdose mortality rates in the United States. Accurate estimates of people with opioid use disorder (OUD) are critical to plan for the scope of interventions required to reduce overdose and opioid misuse.
Katherine Thompson, Joshua A Barocas, Chris Delcher, Jungjun Bae, Lindsey Hammerslag, Jianing Wang, Redonna Chandler, Jennifer Villani, Sharon Walsh, Jeffery Talbert
Cite Link
A Spatial Capture-Recapture Approach for Estimating Opioid Use Disorder Prevalence in Small Areas Using Administrative Data
We propose a two-stage Bayesian hierarchical model with spatial smoothing using a capture-recapture data structure to estimate community-specific opioid use disorder (OUD) prevalence. We explicitly model the hidden prevalence of interest and the associated detection model that describes individual detection histories across data sources.
Sep 30, 2022
A Spatial Capture-Recapture Approach for Estimating Opioid Use Disorder Prevalence in Small Areas Using Administrative Data