Evidence map›Paper›PMID 39932804›Full record

ArticleDrug and alcohol review2025

Unmet treatment need: The size of the gap for alcohol and other drugs in Australia.

Alison Ritter, Keelin O'Reilly

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 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
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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

2 authors.

Alison RitterDrug Policy Modelling Program, Social Policy Research Centre, UNSW Sydney, Sydney, Australia.ORCID 0000-0001-9540-1920
Keelin O'ReillyDrug Policy Modelling Program, Social Policy Research Centre, UNSW Sydney, Sydney, Australia.ORCID 0000-0001-5361-2316

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAssessing unmet demand for alcohol and other drug (AOD) treatment requires accurately counting those in treatment and determining those in need of treatment. Using updated epidemiological and treatment data, this study sought to provide an updated estimate of the unmet demand for AOD treatment in Australia.

methodsAustralian prevalence rates for alcohol, cannabis, methamphetamine and opioid use disorders were obtained from the Global Burden of Disease and research studies. The estimated proportion of people who would likely not seek or need treatment were subtracted from the diagnosed population. The number of people receiving treatment was estimated using AOD treatment databases and previous research.

resultsAn estimated 752,812 to 1,291,119 people met criteria for a substance use disorder in Australia in 2023. Removing the proportion of people who would not need or seek treatment resulted in between 406,697 and 668,497 people as the potential treatment population. The number of people who received treatment in Australia was estimated at 198,731 people. The unmet demand for AOD treatment was estimated to be between 207,966 and 469,767 in 2023. DISCUSSION AND

conclusionsIn Australia, we only treat between 30% and 48% of the population who would seek and benefit from AOD treatment. This is a conservative analysis that assumes only 40% of the alcohol and cannabis use disorder population would seek treatment. The findings from this analysis highlight the continued significant unmet treatment needs of people with substance use disorders. Treatment resources need to be doubled in order to address this unmet treatment population.

Indexed as

AlcoholismHealth Services Needs and DemandSubstance-Related DisordersAdultAustraliaFemaleHumansMalePrevalencealcoholdrugshealthcare planningtreatment demandtreatment need

Identifiers

PMID39932804
PMCPMC11886497

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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.