Evidence map›Paper›PMID 39957331›Full record

ArticleDrug and alcohol review2025

Prioritising people who use drugs in health policy: An Australian Capital Territory case study.

Devin Bowles, Elisabeth Yar, Anke van der Sterren

Abstract read
In one paragraph

Article in Drug and alcohol review, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Devin BowlesACT Council of Social Service, Canberra, Australia.
Elisabeth YarAlcohol, Tobacco and Other Drug Association ACT, Canberra, Australia.ORCID 0000-0001-8678-9425
Anke van der SterrenAlcohol, Tobacco and Other Drug Association ACT, Canberra, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionGovernments are increasingly identifying priority populations on which to focus health policy and to measure health and wellbeing outcomes. Prioritising populations that are considered to be higher risk or to have particular needs that may not be captured within the parameters of health policy developed for the general population, is essential to health equity and efficient resource allocation. However, the criteria that governments use for prioritising populations is often vague or unspecified. To date, people who use drugs are almost never identified as a priority population in health policy, despite poor health and wellbeing outcomes.

methodsWe developed three-pronged criteria-disadvantage, prevalence/accessibility and amenability to change-for prioritising populations in government health policy. We used these criteria to compare people who access alcohol, tobacco and other drug (ATOD) services with populations which are prioritised within the Australian Capital Territory Government's Wellbeing Framework.

resultsUse of the criteria indicates that health policy prioritisation of people who access ATOD services is warranted. People who access ATOD services experienced worse health and wellbeing outcomes across all measures. DISCUSSION AND

conclusionsGiven increasingly explicit prioritisation of populations in health policy, there is an opportunity to advance health equity and embed policy efficiency through formal and transparent consideration of which populations to prioritise. Using set criteria for prioritising populations in health policy is possible, and could help identify populations often overlooked for prioritisation, such as people who access ATOD services.

Indexed as

Health PolicyHealth PrioritiesSubstance-Related DisordersAustraliaHealth Services AccessibilityHumanshealth equityhealth policypriority populationssubstance‐related disorderswellbeing

Identifiers

PMID39957331
PMCPMC11886498

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Registered trials

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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.