Evidence map›Paper›PMID 38807133›Full record

ArticleHarm reduction journal2024

Medical cannabis use in Australia seven years after legalisation: findings from the online Cannabis as Medicine Survey 2022-2023 (CAMS-22).

Llewellyn Mills, Jonathon C Arnold, Anastasia Suraev, Sarah V Abelev, Cilla Zhou, Thomas R Arkell, Iain S McGregor, Nicholas Lintzeris

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In one paragraph

Article in Harm reduction journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

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

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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

8 authors.

Llewellyn MillsSpecialty of Addiction Medicine, Faculty Medicine and Health, University of Sydney, 591 South Dowling Street, Surry Hills, NSW, 2010, Australia. llew.mills@sydney.edu.au.
Jonathon C ArnoldLambert Initiative for Cannabinoid Therapeutics, The University of Sydney, Sydney, NSW, Australia.
Anastasia SuraevLambert Initiative for Cannabinoid Therapeutics, The University of Sydney, Sydney, NSW, Australia.
Sarah V AbelevLambert Initiative for Cannabinoid Therapeutics, The University of Sydney, Sydney, NSW, Australia.
Cilla ZhouLambert Initiative for Cannabinoid Therapeutics, The University of Sydney, Sydney, NSW, Australia.
Thomas R ArkellCentre for Human Psychopharmacology, Swinburne University of Technology, Hawthorn, VIC, Australia.
Iain S McGregor *Lambert Initiative for Cannabinoid Therapeutics, The University of Sydney, Sydney, NSW, Australia.
Nicholas Lintzeris *Specialty of Addiction Medicine, Faculty Medicine and Health, University of Sydney, 591 South Dowling Street, Surry Hills, NSW, 2010, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCannabis was legalised for medical purposes in 2016. Uptake was initially slow, but since 2019 there has been a large increase in the number of Australians who have been prescribed cannabis for medical reasons. Yet a significant number of consumers continue to treat their medical conditions via illicitly-sourced cannabis. Little is known about how these two groups of medical cannabis consumers differ.

methodsThe anonymous Cannabis-As-Medicine Survey 2022-2023 (CAMS-22) was available for completion online from December 2022 to April 2023 to adult Australians who had used cannabis to treat a medical condition in the previous year. Recruitment occurred through social media, consumer forums, and medical practices. Questions included demographic characteristics, patterns of cannabis use, conditions treated, and self-rated effectiveness.

resultsOf the 3323 respondents included in these analyses, 2352 (73%) mainly used prescribed medical cannabis, 871 (27%) mainly used illicit. Prescribed users were significantly more likely than illicit users to have had their health condition diagnosed (OR = 1.7, 95% CI 1.3, 2.2), to consume their cannabis via oral (OR = 1.9; CI 1.5, 2.4) or vaporised (OR = 5.2; CI 4.0, 6.8) routes, and to be sure of the composition of their medical cannabis (OR = 25.0; CI 16.7, 50.0). Prescribed users were significantly less likely to have used cannabis non-medically before medical use (OR = 0.6, CI 0.5, 0.7), consume cannabis via smoked routes (OR = 0.2, CI 0.1, 0.2), and to report any side effects (OR = 0.1; CI 0.1, 0.2). The most common conditions among both prescribed and illicit users were pain (37%), mental health (36%), and sleep (15%) conditions. Prescribed users were significantly more likely to use cannabis to mainly treat a pain (OR = 1.3; CI 1.1, 1.5) or sleep condition (OR = 1.4; CI 1.1, 1.7) and less likely to treat a mental health condition (OR = 0.8; CI 0.7, 0.9). There were no between-group differences in effectiveness with 97% saying medical cannabis had improved their symptoms.

conclusionsFrom a harm-reduction perspective there is much to recommend prescribed medical cannabis; it has fewer side-effects than illicit, is used more safely (oral or vaporised versus smoked routes), gives consumers greater certainty regarding the composition and quality of their medicine, and does not risk exposure to the criminal justice system. Of concern, however, is the apparent willingness of prescribers to prescribe for indications for which there is limited evidence of efficacy, such as mental health and sleep conditions.

Indexed as

Medical MarijuanaAdolescentAdultAgedAustraliaFemaleHumansMaleMiddle AgedSurveys and QuestionnairesYoung AdultMedical MarijuanaCannabisConsumer surveyMedical cannabisMedicinal cannabis

Identifiers

PMID38807133
PMCPMC11131250

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.