Janni Leung

Randomized controlled trials (RCTs) are considered the gold standard for causal inference. With a sufficient sample size, randomization removes confounding up to the time of randomization and allows the treatment effect to be isolated. However, RCTs may have limited generalizability and transportability and are often not feasible in addiction research due to ethical or logistical constraints. The importance of observational studies from real‐world settings has been increasingly recognized in research on health. This paper provides an overview of modern approaches to designing observational studies that enable causal inference. It illustrates three key techniques, Directed Acyclic Graphs (DAGs), modified Disjunctive Cause Criterion and Target Trial Emulation, and discusses the strengths and limitations of their applications.

Issues Addressed The increase of youth vaping in Australia has raised concerns, especially with increasing promotions of vaping products. We observed the visibility of vaping products near high‐adolescent‐traffic areas. Methods We examined stores within walking distance of high schools and the central business district (CBD) in Brisbane, Australia, observing the number and proportion of stores selling or displaying vaping products. Using an observational approach, we analysed advertisements and open displays of vaping products at tobacconists, vape stores, and convenience stores. Results We identified 61 eligible stores located near schools or in the CBD. Overall, 20% of stores displayed vaping products externally, with higher proportions among vape stores (78%) compared to tobacconists (36%). Additionally, 46% of stores had visible interior displays, with vape stores (89%) displaying them more frequently than tobacconists (71%). Among the 28 stores displaying vaping products, 57% included warnings, one store featured only flavoured products, and 25% showcased nicotine‐containing products. Youth‐appealing features were present in all but one store, with cartoons (61%) as the most common feature. Conclusion Vapes were displayed near high schools and in the city, often featuring youth‐appealing features, such as colours and flavours. So What? The exposure to such promotions could normalise vaping and contribute to increased youth uptake. Continued monitoring of vaping product promotions is essential, particularly considering recent policy changes.

Issues Established literature suggests that electronic cigarettes (EC) are more effective than traditional nicotine replacement therapies (NRT) as a smoking cessation aid, but the factors that mediate this difference remain poorly understood. We examine how adverse events (AE) associated with EC use relative to NRTs differ, with the view that differences in AEs experienced may drive differences in use and compliance. Approach Papers for inclusion were identified via a three‐tiered search strategy. Eligible articles involved healthy participants and compared nicotine ECs to non‐nicotine ECs or NRTs and reported frequency of AE as an outcome. Random‐effects meta‐analyses were conducted to compare the likelihood for each of the AEs between nicotine ECs, non‐nicotine placebo ECs and NRTs. Key Findings A total of 3756 papers were identified, of which 18 were meta‐analysed (10 cross‐sectional and 8 randomised controlled trials). Meta‐analytic results found no significant difference in the rates of reported AEs (i.e., cough, oral irritation, nausea) between nicotine ECs and NRTs, and between nicotine and non‐nicotine placebo ECs. Implications The variation in the incidence of AEs likely does not explain user preferences of ECs to NRTs. Incidence of common AEs reported because of EC and NRT use did not differ significantly. Future work will need to quantify both the adverse and favourable effects of ECs to understand the experiential mechanisms that drive the high uptake of nicotine ECs relative to established NRTs. Conclusions There is inconclusive evidence on the incidence of AEs experience when using ECs compared to NRTs, possibly given the small sample size of studies.