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Background and Aims: Changes in cannabis legalization and availability in Australia necessitate monitoring use and attitudes. We estimated age–period–cohort effects of past‐year cannabis use and attitudes toward criminalization and legalization. Design Analysis of six waves of the Australian National Drug Strategy Household Survey (NDSHS) every 3 years (2001–2016). Setting and participants: The study was nationally representative of Australian households using multistage random sampling, totaling 145 168 respondents 18–79 years old. Data were collected using telephone, face‐to‐face, and drop‐and‐collect. Sample sizes per analysis varied based on data availability (~107 000–127 000 per model). Measurements Six waves of data for past‐year cannabis use (by sex and education), attitudes toward criminalization and legalization. Findings Past‐year cannabis use decreased in young adults ages 18–35 from 2001–2016 (25.1%–18.6%) and increased in middle adults ages 36–55 (8.6%–10.1%) and older adults ages 56–79 (0.6%–3.0%). We observed a positive period effect and negative cohort effect for recent cohorts for past‐year use (e.g. 1955 cohort had 1.41 (95% CI: [1.11, 1.70]) increased log odds vs. 1998 cohort had −2.86 (95% CI: [−3.17, −2.55]) increased log odds) compared with the mean across years. Results were consistent by sex and varied by education. We observed a negative period effect for criminalization favorability (0.14 (95% CI: [0.003, 0.28]) increased log odds in 2001 vs. ‐0.31 (95% CI: [−0.45, −0.17]) increased log odds in 2016) and positive cohort effect for recent cohorts. Last, we observed a positive period effect for legalization support (−0.03 (95% CI: [−0.20, 0.14]) increased log odds in 2001 vs. 0.38 (95% CI: [0.22, 0.55]) increased log odds in 2016) and negative cohort effect for recent cohorts. Conclusion: Cannabis use appears to be increasing in Australia among adults over 35, while decreasing among adolescents and young adults. Legalization support also appears to have been increasing since 2007, signaling discordance between use and attitudes among adolescents and young adults, and potentially predicting increases in use over time. [ABSTRACT FROM AUTHOR]

The opioid overdose crisis is driven by an intersecting set of social, structural, and economic forces. Simulation models are a tool to help us understand and address thiscomplex, dynamic, and nonlinear social phenomenon. We conducted a systematic review of the literature on simulation models of opioid use and overdose up to September 2019. We extracted modeling types, target populations, interventions, and findings; created a database of model parameters used for model calibration; and evaluated study transparency and reproducibility. Of the 1,398 articles screened, we identified 88 eligible articles. The most frequent types of models were compartmental (36%), Markov (20%), system dynamics (16%), and agent-based models (16%). Intervention cost-effectiveness was evaluated in 40% of the studies, and 39% focused on services for people with opioid use disorder (OUD). In 61% of the eligible articles, authors discussed calibrating their models to empirical data, and in 31%, validation approaches used in the modeling process were discussed. From the 63 studies that provided model parameters, we extracted the data sources on opioid use, OUD, OUD treatment, cessation or relapse, emergency medical services, and death parameters. From this database, potential model inputs can be identified and models can be compared with prior work. Simulation models should be used to tackle key methodological challenges, including the potential for bias in the choice of parameter inputs, investment in model calibration and validation, and transparency in the assumptions and mechanics of simulation models to facilitate reproducibility., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)