Massachusetts Prevalence of Opioid Use Disorder Estimation Revisited: Comparing a Bayesian Approach to Standard Capture-Recapture Methods.
Journal
Epidemiology (Cambridge, Mass.)
ISSN: 1531-5487
Titre abrégé: Epidemiology
Pays: United States
ID NLM: 9009644
Informations de publication
Date de publication:
01 11 2023
01 11 2023
Historique:
pmc-release:
01
11
2024
medline:
29
9
2023
pubmed:
28
9
2023
entrez:
27
9
2023
Statut:
ppublish
Résumé
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. 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. 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. 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. ClinicalTrials.gov Identifier: NCT04111939.
Sections du résumé
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.
Identifiants
pubmed: 37757873
doi: 10.1097/EDE.0000000000001653
pii: 00001648-202311000-00011
pmc: PMC10544852
mid: NIHMS1919737
doi:
Banques de données
ClinicalTrials.gov
['NCT04111939']
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
841-849Subventions
Organisme : NIDA NIH HHS
ID : UM1 DA049415
Pays : United States
Organisme : NIDA NIH HHS
ID : DP2 DA051864
Pays : United States
Organisme : NIDA NIH HHS
ID : R01 DA052214
Pays : United States
Organisme : NIDA NIH HHS
ID : UM1 DA049412
Pays : United States
Organisme : NIDA NIH HHS
ID : UM1 DA049417
Pays : United States
Organisme : NIDA NIH HHS
ID : UM1 DA049406
Pays : United States
Informations de copyright
Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.
Références
JAMA. 2017 Oct 24;318(16):1539-1540
pubmed: 28832871
Am J Public Health. 2018 Dec;108(12):1675-1681
pubmed: 30359112
Int J Epidemiol. 2001 Feb;30(1):12-4
pubmed: 11171841
Subst Use Misuse. 2022;57(5):815-821
pubmed: 35157549
Mol Psychiatry. 2022 Feb;27(2):787-792
pubmed: 34716409
Int Stat Rev. 2020 Aug;88(2):398-418
pubmed: 36081593
Annu Rev Stat Appl. 2018 Mar;5:95-118
pubmed: 30046636
Addiction. 2020 Dec;115(12):2393-2404
pubmed: 32392631
Subst Use Misuse. 2021;56(12):1752-1764
pubmed: 34315335
Health Place. 2012 Jul;18(4):824-31
pubmed: 22522099
Am J Epidemiol. 2022 Nov 19;191(12):2098-2108
pubmed: 36004683
Drug Alcohol Depend. 2020 Dec 1;217:108335
pubmed: 33248391
Drug Alcohol Depend. 2023 Jan 1;242:109710
pubmed: 36469995
Int J Epidemiol. 2001 Dec;30(6):1351-9; discussion 1359-60
pubmed: 11821345
Drug Alcohol Depend. 2013 Jul 1;131(1-2):143-8
pubmed: 23306097