Estimating the prevalence of problem drug use from drug‐related mortality data.
Background and Aims: Indirect estimation methods are required for estimating the size of populations where only a proportion of individuals are observed directly, such as problem drug users (PDUs). Capture–recapture and multiplier methods are widely used, but have been criticized as subject to bias....
| Publicado en: | Addiction Vol. 115; no. 12; pp. 2393 - 2405 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Dec2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=146927215&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146927215 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09652140 AIO jtl: Addiction issn: 09652140 maglogo: Y pubinfo: dt: Dec2020 vid: 115 iid: 12 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 146927215 146927215 146927215 10.1111/add.15111 146927215 ppf: 2393 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Estimating the prevalence of problem drug use from drug‐related mortality data. aug: au: Jones, Hayley E. Harris, Ross J. Downing, Beatrice C. Pierce, Matthias Millar, Tim Ades, A. E. Welton, Nicky J. Presanis, Anne M. De Angelis, Daniela Hickman, Matthew affil: Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK sug: subj: Drug Toxicity Mortality Data Collection Methods Persons Who Use Intravenous Drugs Databases, Health Human Prospective Studies Models, Statistical Regression Descriptive Statistics Adolescence Adult Middle Age Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years ab: Background and Aims: Indirect estimation methods are required for estimating the size of populations where only a proportion of individuals are observed directly, such as problem drug users (PDUs). Capture–recapture and multiplier methods are widely used, but have been criticized as subject to bias. We propose a new approach to estimating prevalence of PDU from numbers of fatal drug‐related poisonings (fDRPs) using linked databases, addressing the key limitations of simplistic 'mortality multipliers'. Methods: Our approach requires linkage of data on a large cohort of known PDUs to mortality registers and summary information concerning additional fDRPs observed outside this cohort. We model fDRP rates among the cohort and assume that rates in unobserved PDUs are equal to rates in the cohort during periods out of treatment. Prevalence is estimated in a Bayesian statistical framework, in which we simultaneously fit regression models to fDRP rates and prevalence, allowing both to vary by demographic factors and the former also by treatment status. Results: We report a case study analysis, estimating the prevalence of opioid dependence in England in 2008/09, by gender, age group and geographical region. Overall prevalence was estimated as 0.82% (95% credible interval = 0.74–0.94%) of 15–64‐year‐olds, which is similar to a published estimate based on capture–recapture analysis. Conclusions: Our modelling approach estimates prevalence from drug‐related mortality data, while addressing the main limitations of simplistic multipliers. This offers an alternative approach for the common situation where available data sources do not meet the strong assumptions required for valid capture–recapture estimation. In a case study analysis, prevalence estimates based on our approach were surprisingly similar to existing capture–recapture estimates but, we argue, are based on a much more objective and justifiable modelling approach. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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