ArticleAmerican journal of epidemiology2024
Estimation of the prevalence of opioid misuse in New York State counties, 2007-2018: a bayesian spatiotemporal abundance model approach.
Article in American journal of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Estimating community-level prevalence of opioid use disorder: Extrapolating from Medicaid claims data and other publicly available data sources in Ohio, USA.Addiction (Abingdon, England) · 2026Article
- Rates of Receiving Medication for Opioid Use Disorder and Opioid Overdose Deaths During the Early Synthetic Opioid Crisis: A County-level Analysis.Epidemiology (Cambridge, Mass.) · 2025Article
- Invited commentary: motivating better methods-and better data collection-for measuring the prevalence of drug misuse.American journal of epidemiology · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
An important challenge to addressing the opioid overdose crisis is the lack of information on the size of the population of people who misuse opioids (PWMO) in local areas. This estimate is needed for better resource allocation, estimation of treatment and overdose outcome rates using appropriate denominators (ie, the population at risk), and proper evaluation of intervention effects. In this study, we used a bayesian hierarchical spatiotemporal integrated abundance model that integrates multiple types of county-level surveillance outcome data, state-level information on opioid misuse, and covariates to estimate the latent (hidden) numbers of PWMO and latent prevalence of opioid misuse across New York State counties (2007-2018). The model assumes that each opioid-related outcome reflects a partial count of the number of PWMO, and it leverages these multiple sources of data to circumvent limitations of parameter estimation associated with other types of abundance models. Model estimates showed a reduction in the prevalence of PWMO during the study period, with important spatial and temporal variability. The model also provided county-level estimates of rates of treatment and opioid overdose using the numbers of PWMO as denominators. This modeling approach can identify the sizes of hidden populations to guide public health efforts in confronting the opioid overdose crisis across local areas. This article is part of a Special Collection on Mental Health.
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