Key Findings

This is a quick summary of the main discovery for each research paper we have published, organized issue by issue. Each key finding is below the article title, with a link to the abstract. 


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Current List - October 2023

A randomised controlled trial testing acceptance of practitioner-referral versus self-referral to stop smoking services within the Lung Screen Uptake Trial

Practitioner-referral and self-referral smoking cessation strategies were highly accepted among lung cancer screening participants who self-reported smoking or met a CO cut-off.

Link to Abstract

Approach-avoidance behavior and motor-specific modulation towards smoking-related cues in smokers

There appear to be motor-specific modulatory mechanisms in the primary motor cortex for the approach-avoidance bias behavior of smokers.

Link to Abstract

Cohort effects of women's mid-life binge drinking and alcohol use disorder symptoms in the United States: Impacts of changes in timing of parenthood

In the US, subgroups of women at highest risk of excessive drinking appear to be increasing in size.

Link to Abstract

Drinking motives, personality traits and life stressors—identifying pathways to harmful alcohol use in adolescence using a panel network approach

Heavy/frequent alcohol use and social drinking motives may be key targets to prevent development of alcohol-related problems in late adolescence.

Link to Abstract

Estimating the true effectiveness of smoking cessation interventions under variable comparator conditions: A systematic review and meta-regression

Comparator variability and underreporting of comparators obscures the interpretation, comparison and generalisability of behavioural smoking cessation trials.

Link to Abstract

How does a family history of psychosis influence the risk of methamphetamine-related psychotic symptoms: Evidence from longitudinal panel data

A family history of psychosis is not necessary for people to have psychotic symptoms during periods of methamphetamine use.

Link to Abstract

Identifying genetic loci and phenomic associations of substance use traits: A multi-trait analysis of GWAS (MTAG) study

Multi-trait analysis of GWAS boosted the number of loci found for substance use traits.

Link to Abstract

Increase in educational inequalities in alcohol-related mortality in Spain during a period of economic growth

In Spain from 2012-2019, changes in mortality risk from alcohol-related causes were unfavourable among low- and medium-educated people.

Link to Abstract

Perceptions about levels of harmful chemicals in e-cigarettes relative to cigarettes, and associations with relative e-cigarette harm perceptions, e-cigarette use and interest

US adult smokers and young adult non-smokers do not appear to think that e-cigarettes have fewer harmful chemicals than cigarettes.

Link to Abstract

Postoperative buprenorphine continuation in stabilized buprenorphine patients: A population cohort study

In Ontario, Canada from 2012-2018, most patients on continuous preoperative buprenorphine therapy continued buprenorphine use after surgery.

Link to Abstract

Prevalence of opioid dependence in New South Wales, Australia, 2014–16: Indirect estimation from multiple data sources using a Bayesian approach

A Bayesian approach estimates the prevalence of opioid dependence in NSW, Australia in 2016 was 0.92%.

Link to Abstract

Social and structural determinants of injection drug use-associated bacterial and fungal infections: A qualitative systematic review and thematic synthesis

Injecting-related bacterial and fungal infections are shaped by modifiable social-structural factors.

Link to Abstract

Systematic assessment of non-medical use of prescription drugs using doctor-shopping indicators: A nation-wide, repeated cross-sectional study

Doctor-shopping in France mainly involves opioid maintenance drugs, some opioids analgesics, some benzodiazepines and Z-drugs, and pregabalin.

Link to Abstract

Who responds to a multi-component treatment for cannabis use disorder? Using multivariable and machine learning models to classify treatment responders and non-responders

Multivariable/machine learning models can improve upon chance prediction of treatment response to outpatient cannabis use disorder treatment.

Link to Abstract