Massachusetts Prevalence of Opioid Use Disorder Estimation Revisited: Comparing a Bayesian Approach to Standard Capture-Recapture Methods

Jianing Wang, Nathan Doogan, Katherine Thompson, Dana Bernson, Daniel Feaster, Jennifer Villani, Redonna Chandler, Laura F. White, David Kline, Joshua A. Barocas

Research output: Contribution to journalArticlepeer-review

Abstract

Background: The National Survey on Drug Use and Health (NSDUH) estimated the prevalence of opioid use disorder (OUD) among the civilian, noninstitutionalized people aged 12 years or older in Massachusetts as 1.2% between 2015 and 2017. Accurate estimation of the prevalence of OUD is critical to the success of treatment and resource planning. Various indirect estimation approaches have been used but are subject to data availability and infrastructure-related issues. Methods: We used 2015 data from the Massachusetts Public Health Data Warehouse (PHD) to compare the results of two approaches to estimating OUD prevalence in the Massachusetts population. First, we used a seven-dataset capture-recapture analysis under log-linear model parameterization, controlling for the source dependence and effects of age, sex, and county through stratification. Second, we applied a benchmark-multiplier method in a Bayesian framework by linking health care claims data to death certificate data assuming an extrapolation of death rates from observed untreated OUD to unobserved OUD. Results: Our estimates for OUD prevalence among Massachusetts residents (aged 18-64 years) were 4.62% (95% CI = 4.59%, 4.64%) in the capture-recapture approach and 4.29% (95% CrI = 3.49%, 5.32%) in the Bayesian model. Both estimates were approximately four times higher than NSDUH estimates. Conclusion: The synthesis of our findings suggests that the disease surveillance system misses a large portion of the population with OUD. Our study also suggests that concurrent use of multiple methods improves the justification and facilitates the triangulation and interpretation of the resulting estimates. Trial registration: ClinicalTrials.gov Identifier: NCT04111939.

Original languageEnglish
Pages (from-to)841-849
Number of pages9
JournalEpidemiology
Volume34
Issue number6
DOIs
StatePublished - Nov 1 2023

Bibliographical note

Publisher Copyright:
© 2023 Lippincott Williams and Wilkins. All rights reserved.

Keywords

  • Bayesian benchmark-multiplier method
  • Capture-recapture analysis
  • Opioid use disorder prevalence estimation
  • Surveillance data integration

ASJC Scopus subject areas

  • Epidemiology

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