Estimating the burden of the opioid epidemic for adults and adolescents in Ohio counties.

Bayesian hierarchical modeling disease mapping multivariate opioids spatial analysis substance-related disorders

Journal

Biometrics
ISSN: 1541-0420
Titre abrégé: Biometrics
Pays: United States
ID NLM: 0370625

Informations de publication

Date de publication:
06 2021
Historique:
revised: 19 03 2020
received: 13 06 2019
pubmed: 16 5 2020
medline: 26 10 2021
entrez: 16 5 2020
Statut: ppublish

Résumé

Quantifying the opioid epidemic at the local level is a challenging problem that has important consequences on resource allocation. Adults and adolescents may exhibit different spatial trends and require different interventions and resources so it is important to examine the problem for each age group. In Ohio, surveillance data are collected at the county level for each age group on measurable outcomes of the opioid epidemic, overdose deaths, and treatment admissions. However, our interest lies in quantifying the unmeasurable construct, representing the burden of the opioid epidemic, which drives rates of the outcomes. We propose jointly modeling adult and adolescent surveillance outcomes through a multivariate spatial factor model. A generalized spatial factor model within each age group quantifies a latent factor related to the number of opioid-associated treatment admissions and deaths. By assuming a multivariate conditional autoregressive model for the spatial factors of adults and adolescents, we allow the adolescent model to borrow strength from the adult model (and vice versa), improving estimation. We also incorporate county-level covariates to help explain spatial heterogeneity in each of the factors. We apply this approach to the state of Ohio and discuss the findings. Our framework provides a coherent approach for synthesizing information across multiple outcomes and age groups to better understand the spatial epidemiology of the opioid epidemic.

Identifiants

pubmed: 32413155
doi: 10.1111/biom.13295
pmc: PMC7666653
mid: NIHMS1603526
doi:

Substances chimiques

Analgesics, Opioid 0

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

765-775

Subventions

Organisme : NIDA NIH HHS
ID : R21 DA045236
Pays : United States

Informations de copyright

© 2020 The International Biometric Society.

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Auteurs

David Kline (D)

Department of Biomedical Informatics, Center for Biostatistics, The Ohio State University, Columbus, Ohio.

Staci A Hepler (SA)

Department of Mathematics and Statistics, Wake Forest University, Winston-Salem, North Carolina.

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Classifications MeSH