Intersectional inequities and longitudinal prevalence estimates of opioid use disorder in Massachusetts 2014-2020: a multi-sample capture-recapture analysis.

Capture-recapture Health disparities Healthcare based surveillance data Opioid use disorder Population size estimation

Journal

Lancet regional health. Americas
ISSN: 2667-193X
Titre abrégé: Lancet Reg Health Am
Pays: England
ID NLM: 9918232503006676

Informations de publication

Date de publication:
Apr 2024
Historique:
received: 17 09 2023
revised: 18 02 2024
accepted: 20 02 2024
medline: 21 3 2024
pubmed: 21 3 2024
entrez: 21 3 2024
Statut: epublish

Résumé

As overdoses continue to increase worldwide, accurate estimates are needed to understand the size of the population at risk and address health disparities. Capture-recapture methods may be used in place of direct estimation at nearly any geographic level (e.g., city, state, country) to estimate the size of the population with opioid use disorder (OUD). We performed a multi-sample capture-recapture analysis with persons aged 18-64 years to estimate the prevalence of OUD in Massachusetts from 2014 to 2020, stratified by sex and race/ethnicity. We used seven statewide administrative data sources linked at the individual level. We developed log-linear models to estimate the unknown OUD-affected population. Uncertainty was characterized using 95% confidence intervals (95% CI) on the total counts and prevalence estimates. The estimated OUD prevalence increased from 5.47% (95% CI = 4.89%, 5.98%) in 2014 to 5.79% (95% CI = 5.34%, 6.19%) in 2020. Prevalence among Hispanic females doubled (2.46% in 2014 to 4.23% in 2020) and prevalence rose to nearly 10% among Black non-Hispanic males and Hispanic males from 2014 through 2019. Estimates for Black non-Hispanic females more than doubled from 2014 through 2019 (3.39% to 7.09%), and then decreased to 5.69% in 2020. This study is the first to provide OUD prevalence trend estimates by binary sex and race/ethnicity at a state level using capture-recapture methods. Using these methods as the international overdose crisis worsens can allow jurisdictions to appropriately allocate resources and targeted interventions to marginalised populations. NIDA.

Sections du résumé

Background UNASSIGNED
As overdoses continue to increase worldwide, accurate estimates are needed to understand the size of the population at risk and address health disparities. Capture-recapture methods may be used in place of direct estimation at nearly any geographic level (e.g., city, state, country) to estimate the size of the population with opioid use disorder (OUD). We performed a multi-sample capture-recapture analysis with persons aged 18-64 years to estimate the prevalence of OUD in Massachusetts from 2014 to 2020, stratified by sex and race/ethnicity.
Methods UNASSIGNED
We used seven statewide administrative data sources linked at the individual level. We developed log-linear models to estimate the unknown OUD-affected population. Uncertainty was characterized using 95% confidence intervals (95% CI) on the total counts and prevalence estimates.
Findings UNASSIGNED
The estimated OUD prevalence increased from 5.47% (95% CI = 4.89%, 5.98%) in 2014 to 5.79% (95% CI = 5.34%, 6.19%) in 2020. Prevalence among Hispanic females doubled (2.46% in 2014 to 4.23% in 2020) and prevalence rose to nearly 10% among Black non-Hispanic males and Hispanic males from 2014 through 2019. Estimates for Black non-Hispanic females more than doubled from 2014 through 2019 (3.39% to 7.09%), and then decreased to 5.69% in 2020.
Interpretation UNASSIGNED
This study is the first to provide OUD prevalence trend estimates by binary sex and race/ethnicity at a state level using capture-recapture methods. Using these methods as the international overdose crisis worsens can allow jurisdictions to appropriately allocate resources and targeted interventions to marginalised populations.
Funding UNASSIGNED
NIDA.

Identifiants

pubmed: 38510791
doi: 10.1016/j.lana.2024.100709
pii: S2667-193X(24)00036-X
pmc: PMC10951507
doi:

Types de publication

Journal Article

Langues

eng

Pagination

100709

Informations de copyright

© 2024 The Authors.

Déclaration de conflit d'intérêts

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. White reports grant support for the research presented in this manuscript, which was paid to her respective institution. Dr. Kline reports grant support from National Institute on Drug Abuse for the research presented in this manuscript (R01DA052214). Dr. Barocas also reports membership on the scientific advisory committee for eMed. All other authors have declared that no financial support was received for the research of this manuscript and that there are no financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this paper.

Auteurs

Jianing Wang (J)

Biostatistics Center, Massachusetts General Hospital, Boston, MA, USA.

Dana Bernson (D)

Office of Population Health, Massachusetts Department of Public Health, Boston, MA, USA.

Elizabeth A Erdman (EA)

Office of Population Health, Massachusetts Department of Public Health, Boston, MA, USA.

Jennifer Villani (J)

National Institute on Drug Abuse, National Institutes of Health, Bethesda, MD, USA.

Redonna Chandler (R)

National Institute on Drug Abuse, National Institutes of Health, Bethesda, MD, USA.

David Kline (D)

Division of Public Health Sciences, Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA.

Laura F White (LF)

Biostatistics Department, School of Public Health, Boston University, Boston, MA, USA.

Joshua A Barocas (JA)

Divisions of General Internal Medicine and Infectious Diseases, Department of Medicine, University of Colourado School of Medicine, Aurora, CO, USA.

Classifications MeSH