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Aim: To synthesize international findings on the alcohol-dementia relationship, including representation from low- and middle-income countries., Methods: Individual participant data meta-analysis of 15 prospective epidemiological cohort studies from countries situated in six continents. Cox regression investigated the dementia risk associated with alcohol use in older adults aged over 60 years. Additional analyses assessed the alcohol-dementia relationship in the sample stratified by sex and by continent. Participants included 24 478 community dwelling individuals without a history of dementia at baseline and at least one follow-up dementia assessment. The main outcome measure was all-cause dementia as determined by clinical interview., Results: At baseline, the mean age across studies was 71.8 (standard deviation = 7.5, range = 60-102 years), 14 260 (58.3%) were female and 13 269 (54.2%) were current drinkers. During 151 636 person-years of follow-up, there were 2124 incident cases of dementia (14.0 per 1000 person-years). When compared with abstainers, the risk for dementia was lower in occasional [hazard ratio (HR) = 0.78; 95% confidence interval (CI) = 0.68-0.89], light-moderate (HR = 0.78; 95% CI = 0.70-0.87) and moderate-heavy drinkers (HR = 0.62; 95% CI = 0.51-0.77). There was no evidence of differences between life-time abstainers and former drinkers in terms of dementia risk (HR = 0.98; 95% CI = 0.81-1.18). In dose-response analyses, moderate drinking up to 40 g/day was associated with a lower risk of dementia when compared with lif-time abstaining. Among current drinkers, there was no consistent evidence for differences in terms of dementia risk. Results were similar when the sample was stratified by sex. When analysed at the continent level, there was considerable heterogeneity in the alcohol-dementia relationship., Conclusions: Abstinence from alcohol appears to be associated with an increased risk for all-cause dementia. Among current drinkers, there appears to be no consistent evidence to suggest that the amount of alcohol consumed in later life is associated with dementia risk., (© 2022 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.)

Background and Aims: Alcohol consumption is increasing among older adults. Rethink My Drink is a brief internet-delivered intervention to reduce alcohol consumption and related harms, adapted specifically for older adults. This protocol for a large-scale randomised controlled trial will evaluate whether Rethink My Drink is effective in reducing alcohol consumption and cognitive decline in a sample of older risky drinkers, compared with an active control., Design: 1:1 parallel group, randomised controlled trial., Setting: Online trial in Australia., Participants: Hazardous or harmful drinkers (defined as those scoring ≥5 on the Alcohol Use Disorders Identification Test [AUDIT]) age 60 to 75 years old (n = 842). Participants will be recruited from August 2021 to August 2022 through online social media advertisements and community networks., Intervention and Comparator: Participants will be randomly allocated to receive access to Rethink My Drink (intervention) or Alcohol: The Facts (comparator), an online patient information booklet provided by New South Wales (NSW) Health., Measurements: Primary outcomes include (i) average weekly standard drinks and (ii) rate of cognitive decline. Secondary outcomes include (i) typical quantity of drinks per drinking day; (ii) heavy episodic drinking; (iii) age-specific risky drinking; (iv) alcohol-related harms; (v) subjective cognitive complaints; and (vi) quality of life. All primary and secondary outcomes will be assessed at baseline, post-intervention (4 weeks) and 12 months. Effectiveness will be evaluated using multilevel linear regression, adjusting for baseline demographic differences. Bonferroni adjustments will be used to control for multiple comparisons. Multiple imputation, regression weighting and sensitivity analyses will assess the effect of attrition., Comments: This will be the first large-scale trial, internationally, to examine whether a brief internet-delivered intervention is effective in reducing alcohol consumption and cognitive decline among older adults. If successful, the intervention will provide an accessible and highly scalable treatment to reduce risky alcohol consumption in older adulthood., (© 2021 Society for the Study of Addiction.)

Background and Aims: The idea that cannabis is a 'gateway drug' to more harmful substances such as opioids is highly controversial, yet has substantially impacted policy, education and how we conceptualize substance use. Given a rise in access to cannabis products and opioid-related harm, the current study aimed to conduct the first systematic review and meta-analysis on the likelihood of transitioning from cannabis use to subsequent first-time opioid use, opioid use disorders (OUD), dependence or abuse., Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, pubMed/MEDLINE, Scopus, EMBASE, PsychINFO, Cochrane Central Register of Controlled Trials and Informit Health Collection were searched for full-text articles assessing the likelihood of transitioning from cannabis to subsequent opioid use, and from opioid use to OUD, abuse or dependence given prior cannabis use. Analysis of subpopulations within studies were discussed narratively, and E-values were calculated to assess the potential influence of unmeasured confounding., Findings: Six studies provided relevant data from the United States, Australia and New Zealand between 1977 and 2017, a total sample of 102 461 participants. Random-effects analysis of the adjusted pooled effect size indicates that the likelihood of transitioning from cannabis to opioid use, relative to non-cannabis users, is odds ratio (OR) = 2.76, 95% confidence interval (CI) = 2.26-3.36, whereas the likelihood of transitioning from opioid use to OUD, abuse or dependence given prior cannabis use is OR = 2.52, 95% CI = 1.65-3.84. While the evidence was determined to be of low quality with moderate risk of bias, E-values suggest that these findings are robust against unmeasured confounding., Conclusion: A systematic review and meta-analysis found that while people who use cannabis are disproportionately more likely to initiate opioid use and engage in problematic patterns of use than people who do not use cannabis, the low quality of the evidence must be considered when interpreting these findings., (© 2021 Society for the Study of Addiction.)

Background and Aims: The experience of alcohol use among adolescents is complex, with international differences in age of purchase and individual differences in consumption and consequences. This latter underlines the importance of prediction modeling of adolescent alcohol use. The current study (a) compared the performance of seven machine-learning algorithms to predict different levels of alcohol use in mid-adolescence and (b) used a cross-cultural cross-study scheme in the training-validation-test process to display the predictive power of the best performing machine-learning algorithm., Design: A comparison of seven machine-learning algorithms: logistic regression, support vector machines, random forest, neural network, lasso regression, ridge regression and elastic-net., Setting: Canada and Australia., Participants: The Canadian sample is part of a 4-year follow-up (2012-16) of the Co-Venture cohort (n = 3826, baseline age 12.8 ± 0.4, 49.2% girls). The Australian sample is part of a 3-year follow-up (2012-15) of the Climate Schools and Preventure (CAP) cohort (n = 2190, baseline age 13.3 ± 0.3, 43.7% girls)., Measurements: The algorithms used several prediction indices, such as F 1 prediction score, accuracy, precision, recall, negative predictive value and area under the curve (AUC)., Findings: Based on prediction indices, the elastic-net machine-learning algorithm showed the best predictive performance in both Canadian (AUC = 0.869 ± 0.066) and Australian (AUC = 0.855 ± 0.072) samples. Domain contribution analysis showed that the highest prediction accuracy indices yielded from models with only psychopathology (AUC = 0.816 ± 0.044/0.790 ± 0.071 in Canada/Australia) and only personality clusters (AUC = 0.776 ± 0.063/0.796 ± 0.066 in Canada/Australia). Similarly, regardless of the level of alcohol use, in both samples, externalizing psychopathologies, alcohol use at baseline and the sensation-seeking personality profile contributed to the prediction., Conclusions: Computerized screening software shows promise in predicting the risk of alcohol use among adolescents., (© 2018 Society for the Study of Addiction.)