Evidence map›Paper›PMID 39916423›Full record

Trial reportDrug and alcohol review2025

What influences Australian pharmacists' decisions to supply opioids? Results from a survey and randomised controlled factorial experiment.

Louisa Picco, Ting Xia, Rose Laing, Elizabeth Grist, Jana Dostal, Suzanne Nielsen

Abstract readRandomized Controlled Trial
In one paragraph

Trial report 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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Trial
  2. Article
  3. 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

6 authors.

Louisa PiccoMonash Addiction Research Centre, Eastern Health Clinical School, Monash University, Melbourne, Australia.ORCID 0000-0001-7593-3209
Ting XiaMonash Addiction Research Centre, Eastern Health Clinical School, Monash University, Melbourne, Australia.ORCID 0000-0001-5033-6248
Rose LaingMonash Addiction Research Centre, Eastern Health Clinical School, Monash University, Melbourne, Australia.
Elizabeth GristMonash Addiction Research Centre, Eastern Health Clinical School, Monash University, Melbourne, Australia.
Jana DostalMonash Addiction Research Centre, Eastern Health Clinical School, Monash University, Melbourne, Australia.ORCID 0009-0009-2044-5326
Suzanne NielsenMonash Addiction Research Centre, Eastern Health Clinical School, Monash University, Melbourne, Australia.ORCID 0000-0001-5341-1055

Funding

National Health and Medical Research Council 2016909National Health and Medical Research Council 2025894
6 · The paper itself

Abstract

introductionPrescription drug monitoring programs (PDMP) identify medication-related risks to support clinical decision-making. This study aims to report prescription medication refusal rates based on PDMP alerts and determine how PDMP alerts and other characteristics influence pharmacists' decisions to supply opioids.

methodsPharmacists completed an online survey and randomised controlled factorial experiment. Generalised linear models explored differences in the proportion of refusals by state. Pharmacists indicated their likelihood to supply opioids based on six clinical vignettes. Mixed-effect linear regression models explored the association between vignette and pharmacy-related characteristics and the likelihood to supply opioids.

resultsData related to 598 pharmacists (n = 3370 vignettes). Jurisdiction was associated with refusals; Western Australian pharmacists had significantly lower odds of refusing supply compared with Victorian pharmacists (odds ratio = 0.186, 95% confidence interval 0.104-0.331). The factorial experiment revealed the strongest predictors of reduced likelihood to supply were the PDMP high dose (β = -2.76, p < 0.001) and multiple prescriber (β = -3.79, p < 0.001) alerts. Unemployment (β = -0.0421, p < 0.001), hepatitis C (β = -0.260, p = 0.009), depression (β = -0.301, p = 0.003), high opioid dose (β = -0.259, p = 0.002) and co-prescribed opioids with benzodiazepines (β = -0.478, p = 0.001) resulted in smaller reductions in the likelihood to supply. Older patient age, patient familiarity and rural/remote pharmacies were associated with significant, albeit small increases in the likelihood to supply with respective 0.293-, 0.250- and 0.339-unit increases. DISCUSSION AND

conclusionsJurisdictional differences in refusal to supply were observed, while PDMP alerts were the strongest predictor of reduced likelihood to supply opioids in the factorial experiment. Unintended consequences of PDMPs including abrupt opioid discontinuation observed elsewhere, should be avoided following the implementation of this supply-side policy in Australia.

Indexed as

Analgesics, OpioidDecision MakingPharmacistsPrescription Drug Monitoring ProgramsAdultAustraliaFemaleHumansMaleMiddle AgedSurveys and QuestionnairesAnalgesics, Opioidclinical decision‐makingopioidsPDMPpharmacistsreal‐time prescription drug monitoring program

Identifiers

PMID39916423
PMCPMC11886474

What OpenQuestion holds

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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.