Estimating the prevalence of problem drug use from drug-related mortality data.
Bayesian analysis
capture-recapture
hidden populations
indirect estimation
multiplier methods
synthetic estimation
Journal
Addiction (Abingdon, England)
ISSN: 1360-0443
Titre abrégé: Addiction
Pays: England
ID NLM: 9304118
Informations de publication
Date de publication:
12 2020
12 2020
Historique:
received:
21
08
2019
revised:
05
11
2019
accepted:
04
05
2020
pubmed:
12
5
2020
medline:
18
5
2021
entrez:
12
5
2020
Statut:
ppublish
Résumé
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'. 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. 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. 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.
Sections du résumé
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.
Identifiants
pubmed: 32392631
doi: 10.1111/add.15111
pmc: PMC7613965
mid: EMS158434
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2393-2404Subventions
Organisme : Department of Health
ID : RP-PG-0616-20008
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_U145079307
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_00002/11
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/M014533/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/N00616X/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : G1000021
Pays : United Kingdom
Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2020 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.
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