Evidence map›Paper›PMID 32392631›Full record

ArticleAddiction (Abingdon, England)2020

Estimating the prevalence of problem drug use from drug-related mortality data.

Hayley E Jones, Ross J Harris, Beatrice C Downing, Matthias Pierce, Tim Millar, A E Ades, Nicky J Welton, Anne M Presanis, Daniela De Angelis, Matthew Hickman

Abstract read
In one paragraph

Article in Addiction (Abingdon, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. The Socioeconomic Impact of Long-Acting Injectable Buprenorphine.ClinicoEconomics and outcomes research : CEOR · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Hayley E JonesPopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.ORCID 0000-0002-4265-2854
Ross J HarrisCentre for Infectious Disease Surveillance and Control, Public Health England, London, UK.
Beatrice C DowningPopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Matthias PierceDivision of Psychology and Mental Health, School of Health Sciences, University of Manchester, Manchester, UK.
Tim MillarDivision of Psychology and Mental Health, School of Health Sciences, University of Manchester, Manchester, UK.
A E AdesPopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Nicky J WeltonPopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Anne M PresanisMRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
Daniela De AngelisCentre for Infectious Disease Surveillance and Control, Public Health England, London, UK.
Matthew HickmanPopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.ORCID 0000-0001-9864-459X

Funding

Department of Health RP-PG-0616-20008Medical Research Council G1000021Medical Research Council MC_U145079307Medical Research Council MC_UU_00002/11Medical Research Council MR/M014533/1Medical Research Council MR/N00616X/1
6 · The paper itself

Abstract

BACKGROUND AND

aimsIndirect 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'.

methodsOur 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.

resultsWe 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.

conclusionsOur 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.

Indexed as

AdolescentAdultBayes TheoremEnglandEpidemiologic MethodsFemaleHumansMaleMiddle AgedOpioid-Related DisordersPrevalenceRegistriesYoung AdultBayesian analysiscapture-recapturehidden populationsindirect estimationmultiplier methodssynthetic estimation

Identifiers

PMID32392631
PMCPMC7613965

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.