Addiction最新文献

{"title":"From stigma to support: A new vision for alcohol use disorder treatment and recovery, Mashal Khan and Jonathan Avery, Cham, Switzerland: Springer, 2024, ISBN: 9783031735523","authors":"Georgia Foote","doi":"10.1111/add.70137","DOIUrl":"https://doi.org/10.1111/add.70137","url":null,"abstract":"","PeriodicalId":109,"journal":{"name":"Addiction","volume":"120 10","pages":""},"PeriodicalIF":5.3,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145037806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"We need better measures to understand the influence of social media on substance use","authors":"Benjamin Riordan, Damian Scarf, Megan Strowger, Gedefaw Alen, Taylor Winter, Emmanuel Kuntsche","doi":"10.1111/add.70138","DOIUrl":"10.1111/add.70138","url":null,"abstract":"<p>Social media use is extremely common, with an estimated 63.9% of the global population using social media in 2024 [<span>1</span>]. Adolescents and young adults (aged 16–24 years) stand out compared to other age groups, and spend approximately 180 minutes per day on social media [<span>2</span>]. Together with the reported impact on wellbeing, policymakers globally are considering drastic changes to limit youth access to social media platforms [<span>3</span>]. For instance, Australia recently passed a law to ban people under 16 years of age from using social media and France, the United Kingdom and Norway are also considering implementing age restrictions [<span>3</span>].</p><p>To justify these bans, policymakers have cited evidence of the link between social media use and negative health outcomes among young people, including the impact on substance use [<span>4</span>]. Indeed, a recent systematic review and meta-analysis found a positive link between social media use and alcohol, drugs, tobacco and vaping [<span>5</span>]. However, there is an ongoing debate about the quality of the evidence assessing the impact of social media use, with concerns about: small effects, a lack of theoretical models, the design (often cross-sectional), failing to control for confounders or consideration of the positive effects of social media use [<span>6-8</span>].</p><p>One of the most critical methodological limitations is with how social media use or exposure to certain content on social media has been measured [<span>7, 8</span>]. Recent reviews have found that most studies used self-reported social media use (e.g. how much time/how often do you use social media) and exposure to substance use (e.g. how often do you see alcohol on social media) [<span>4, 5</span>]. For example, in a recent meta-analysis, only six of 94 studies used an objective assessment of social media use [<span>7</span>]. Unfortunately, self-reports for digital media use in general and exposure to substance-related content have seldom shown comparable accuracy to objective measures [<span>8</span>]. In this editorial, we briefly overview the discrepancy between self-report and objective measures and identify alternative options for measuring social media use or exposure to substance-related content.</p><p>Parry <i>et al</i>. [<span>8</span>] meta-analysed 45 studies that measured both self-report and logged digital media use (including 4 that measured social media use) and found only a modest correlation between the two measures. They concluded that ‘when asked to estimate their usage, participants are rarely accurate.’ More recent studies have found similar discrepancies when comparing objective and self-reported social media use [<span>9-11</span>]. There are a number of reasons why self-report may be inaccurate, like recall bias (social media is used so frequently that it is unmemorable) or limitations with the measures used (e.g. asking participants to report their social media u","PeriodicalId":109,"journal":{"name":"Addiction","volume":"120 11","pages":"2162-2164"},"PeriodicalIF":5.3,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/add.70138","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144606903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing cognitive behaviour therapy for gaming disorder: A call for collaborative action","authors":"Marcela Radunz, Matthew W. R. Stevens, Sanni Behm, Zsolt Demetrovics, Paul Delfabbro, Daniel L. King","doi":"10.1111/add.70142","DOIUrl":"10.1111/add.70142","url":null,"abstract":"<p>Gaming is a globally popular form of digital entertainment, but there is growing recognition of the negative consequences associated with problematic use. Gaming disorder (GD) – the most severe form of problem gaming – is a behavioural addiction sharing diagnostic features with other recognized addictive disorders, including gambling and substance use [<span>1</span>]. Although GD has attracted some academic debate [<span>2</span>], it has gained acceptance in major health nomenclature, including the current International Classification of Diseases (ICD-11).</p><p>In parallel with efforts to measure and diagnose GD effectively, increasingly there are more studies of possible treatments, including case reports [<span>3</span>], feasibility studies [<span>4</span>], non-randomized trials [<span>5</span>] and randomized controlled trials [<span>6</span>]. An inspection of recent literature reveals at least 12 systematic treatment reviews, see [<span>7, 8</span>], for example, as well as reviews on treatment for ‘internet addiction’ (an umbrella term commonly used to encompass GD) [<span>9</span>]. Although many treatment approaches exist [<span>10</span>], the evidence seems to primarily favour cognitive behavioural therapy (CBT). CBT has the largest evidence base and has consistently demonstrated efficacy in reducing GD symptoms and associated comorbidities [<span>11, 12</span>], and may also supplement pharmacological interventions [<span>7</span>].</p><p>While advancements in GD treatment research are promising, in this letter we draw attention to a critical roadblock to progress: the ‘silo effect’, where information and guidance on CBT approaches for GD are not widely shared or available. Unlike other addictive disorders, such as gambling disorder – where treatment manuals, protocols, and resources are published and freely available – similar information for GD is scarce. Published works vary in their reporting; providing either brief overviews of CBT protocols or modules or tabular summaries of session themes and objectives [<span>13</span>], and many studies are published in non-English languages. No standardized treatment guidelines [e.g. akin to the UK National Institute for Health and Care Excellence (NICE) guidance] and very few practical clinical texts (e.g. books or specialist book chapters) exist for GD. As a result, it is unclear how clinicians in different settings (e.g. inpatient vs outpatient) should administer CBT, and how therapy delivery should vary across individuals, groups and families, or depending upon the individual characteristics (e.g. age, gender, comorbidity, intellectual ability, etc.). At present, therefore, it is often difficult to determine the optimal approach or best practice in CBT delivery for GD.</p><p>To advance the promising international work in this area, there is a need for a collective focus on and commitment to principles of transparency, standardization and collaboration. We propose the following pra","PeriodicalId":109,"journal":{"name":"Addiction","volume":"120 10","pages":"2152-2153"},"PeriodicalIF":5.3,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/add.70142","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How changes in post-traumatic stress disorder (PTSD) severity mediate substance use disorder (SUD) severity during and after treatment for co-occurring PTSD and SUD: Results from Project Harmony","authors":"Denise A. Hien,&nbsp;Jordan A. Gette,&nbsp;Shannon M. Blakey,&nbsp;Marilyn L. Piccirillo,&nbsp;Sudie E. Back,&nbsp;Alexandria G. Bauer,&nbsp;Chantel T. Ebrahimi,&nbsp;Robyn A. Ellis,&nbsp;Therese K. Killeen,&nbsp;Elizabeth A. Lehinger,&nbsp;Teresa López-Castro,&nbsp;Sonya B. Norman,&nbsp;Lesia M. Ruglass,&nbsp;Tanya C. Saraiya,&nbsp;Lissette M. Saavedra,&nbsp;Antonio A. Morgan-López","doi":"10.1111/add.70126","DOIUrl":"10.1111/add.70126","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background and Aims</h3>\u0000 \u0000 <p>Post-traumatic stress disorder (PTSD) commonly co-occurs with substance use disorders (SUD). Comorbid PTSD and SUD (PTSD+SUD) is associated with greater severity and impairment and poorer treatment outcomes. Several interventions exist to treat PTSD, SUD and PTSD+SUD; however, research has yet to elucidate the indirect pathways underlying treatment for PTSD+SUD. The present study examined how changes in PTSD severity relate to changes in SUD severity across treatment types during and post-treatment.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Observational study using data collected as part of Project Harmony, a virtual clinical trial employing integrative data analysis to compare treatment effectiveness of PTSD+SUD interventions from 36 randomized controlled trials for PTSD+SUD (<i>n</i> = 4046). Multilevel mediated linear growth modeling was used to examine potential outcomes mediation. Each of the eight active treatments was compared to treatment as usual (TAU) for both alcohol and drug use outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Alcohol use severity outcomes were fully or partially mediated by changes in PTSD severity for trauma-focused psychotherapy + AUD medication [<i>ab</i> = −0.16 (95% confidence interval = −0.30 to −0.04)]; other treatments with mediation effects included trauma-focused integrated psychotherapy, AUD medications and PTSD medications. Drug use severity outcomes were fully or partially mediated by changes in PTSD severity for trauma-focused psychotherapy + AUD medication [<i>ab</i> = −0.08 (−0.18 to −0.001)]; other treatments with mediation effects on drug use severity included trauma-focused integrated psychotherapy, AUD medications, PTSD medications and placebo medications.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Among people with co-occurring post-traumatic stress disorder (PTSD) and substance use disorders (SUD), reductions in alcohol and drug use severity appear to be mediated by reductions in PTSD during treatment. For those with drug use disorders, PTSD reductions appear to mediate further SUD reductions after treatment.</p>\u0000 </section>\u0000 </div>","PeriodicalId":109,"journal":{"name":"Addiction","volume":"120 11","pages":"2245-2257"},"PeriodicalIF":5.3,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/add.70126","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The International Scientific Forum on Alcohol Research (ISFAR) critiques of alcohol research: Promoting health benefits and downplaying harms","authors":"James M. Clay,&nbsp;Tim Stockwell,&nbsp;Su Golder,&nbsp;Keegan Lawrence,&nbsp;Jim McCambridge,&nbsp;Nicole Vishnevsky,&nbsp;Alexandra Zuckermann,&nbsp;Timothy Naimi","doi":"10.1111/add.70132","DOIUrl":"10.1111/add.70132","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background and Aims&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The International Scientific Forum on Alcohol Research (ISFAR), many of whose members are linked to the alcohol industry, has published over 280 critiques on alcohol and health research. This study investigated whether ISFAR critiques favour studies reporting alcohol's health benefits while being more critical of those identifying harms. We also examined whether industry-funded studies are more likely to report benefits, and whether ISFAR's critiques reflect the methodological rigor of the studies they assess.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;We analysed 268 ISFAR critiques published between April 2010 and January 2024, manually coding each underlying study for its content (whether the original study reported alcohol-related health benefits or harms) and each critique for its tone (positive or negative). Sentiment analysis (SA) algorithms were applied to critique summaries to assess tone using automated methods. Study authors were examined for prior receipt of alcohol industry funding. AMSTAR-2 and ROBIS tools evaluated risk of bias in 36 systematic reviews and meta-analyses favoured (&lt;i&gt;n&lt;/i&gt; = 24) or criticised (&lt;i&gt;n&lt;/i&gt; = 12) by ISFAR.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Studies reporting health benefits had higher odds of receiving positive reviews from ISFAR [odds ratio (OR) = 6.50, 95% confidence interval (95% CI) = (3.62–12.00)], as did studies minimising alcohol harms [OR = 2.47, 95% CI = (1.40–4.45)]. Studies reporting health harms had higher odds of receiving negative critiques [OR = 0.29, 95% CI = (0.15–0.14)], as did studies minimising health benefits [OR = 0.21, 95% CI = (0.10–0.41)]. Algorithmic SA replicated these patterns, though the correlation with manual coding was modest [&lt;i&gt;r&lt;/i&gt; = 0.20, 95% CI = (0.08–0.32)]. Studies with industry ties had higher odds of minimising alcohol-related harms [OR = 1.90, 95% CI = (1.04–3.50)], and those co-authored by ISFAR members had higher odds of reporting a J-shaped relationship between alcohol use and health [OR = 2.52, 95% CI = (1.00–6.48)]. No association was found between ISFAR sentiment and study quality as independently assessed by AMSTAR-2 and ROBIS (BF&lt;sub&gt;01&lt;/sub&gt; = 6.13–6.21).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusion&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Critiques from The International Scientific Forum on Alcohol Research (ISFAR) consistently promote alcohol's purported health benefits while minimising evidence of harm, regardless of study quality. These patterns provide a valuable resource for industry actors to shape public perception, down","PeriodicalId":109,"journal":{"name":"Addiction","volume":"120 11","pages":"2319-2328"},"PeriodicalIF":5.3,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/add.70132","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144590091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the impact of vaping facts films on vaping harm perceptions among young adults in the UK: A randomized on-line experiment","authors":"Mohammad Alharbi,&nbsp;Emma Ward,&nbsp;Caitlin Notley,&nbsp;Martin Dockrell,&nbsp;Eve Taylor,&nbsp;Katherine East","doi":"10.1111/add.70119","DOIUrl":"10.1111/add.70119","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Aim&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Measure the impact of brief, academic-led, evidence-based social media videos on vaping harm perceptions among young adults.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Design&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;On-line between-subjects experimental study. Participants were randomized to one of two conditions: experimental (exposed to one of eight brief videos, designed for social media, with academic experts addressing vaping harms) or control. Before and after exposure to the videos, all participants answered questions about their perceptions of vaping and smoking and socio-demographics.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Setting&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Qualtrics on-line survey platform.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Participants&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;593 young adults aged 18–30 years who resided in the UK (49.7% female, 49.2% male; 8.9% exclusively smoked, 32% exclusively vaped, 28.7% did both and 30.4% did neither). Participants were randomly assigned to intervention (&lt;i&gt;n&lt;/i&gt; = 279) or control (&lt;i&gt;n&lt;/i&gt; = 314) groups.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Measurements&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The primary outcome was the perception that vaping is less harmful than smoking. Secondary outcomes were perceptions that vaping is harmful, vaping is addictive and responses (true, false) to statements that were matched to the videos (e.g. vaping causes cancer, vaping causes lung injuries).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Findings&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;After exposure to an expert video, compared with those in the control group, participants in the intervention group had over three times the odds of perceiving vaping as less harmful than smoking [82.1% vs 57.6%; adjusted odds ratio (AOR) = 3.69; 95% confidence interval (95% CI) = 2.49–5.47; &lt;i&gt;P&lt;/i&gt; &lt; 0.001]. Perceptions that the following statements are false were also higher after viewing expert videos than control videos: vaping causes lung injury, vaping leads to cancer, nicotine is harmful when used in ways that does not involve smoking tobacco, pregnant women should not vape, vaping will not help you quit smoking, vaping has no place on the NHS (all &lt;i&gt;P&lt;/i&gt; &lt; 0.05). Participants exposed to the ‘vaping is as harmful as smoking’ misconception video had the highest odds of accurately perceiving vaping as less harmful than smoking (AOR = 13.92; 95% CI = 3.26–59.37; &lt;i&gt;P&lt;/i&gt; &lt; 0.001). Videos specifically targeting individual misconceptions (e.g. ‘vaping causes lung injury’ or ‘","PeriodicalId":109,"journal":{"name":"Addiction","volume":"120 11","pages":"2202-2214"},"PeriodicalIF":5.3,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/add.70119","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144590090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transporting the effect of the ASSIST school-based smoking prevention intervention to the Smoking, Drinking and Drug Use Among Young People in England Survey (2004–2021): A secondary analysis of a randomized controlled trial","authors":"James White","doi":"10.1111/add.70141","DOIUrl":"10.1111/add.70141","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Aims&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To conduct exploratory analyses into the transported effect of the ASSIST (A Stop Smoking in Schools Trial) school-based smoking prevention intervention on weekly smoking in young people between 2004 and 2021.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Design&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Secondary analysis of a cluster randomized control trial (cRCT).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Setting&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;England and Wales.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Participants&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;ASSIST trial participants comprised 8756 students aged 12–13 years in 59 schools assigned using stratified block randomization to the control (29 schools, 4193 students) or intervention (30 schools, 4563 students) condition. The target population was represented by 12–13-year-old participants in the Smoking, Drinking and Drug Use Among Young People in England Survey (SDDU) in 2004 (&lt;i&gt;n&lt;/i&gt; = 3958), 2006 (&lt;i&gt;n&lt;/i&gt; = 3377), 2014 (&lt;i&gt;n&lt;/i&gt; = 3145), 2016 (&lt;i&gt;n&lt;/i&gt; = 4874) and 2021 (&lt;i&gt;n&lt;/i&gt; = 3587), which are randomly sampled school-based surveys with student response rates varying between 85% and 93%.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Intervention and comparator&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The ASSIST intervention involved 2 days of off-site training of influential students to encourage their peers not to smoke over a 10-week period. The control group continued with their usual education.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Measurements&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The outcome was the proportion of students who self-reported weekly smoking 2 years post-intervention.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Findings&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The prevalence of weekly smoking at the 2-year follow-up in the ASSIST trial in 2004 was 4.1%, 49.5% of students were girls, and 7.8% ethnic minorities. In the SDDU in 2004, the prevalence of weekly smoking was 3.6%, 47.6% students were girls and 14.4% ethnic minorities and in 2021 0.2% were weekly smokers, 48.6% girls and 27.8% ethnic minorities. The odds ratio of weekly smoking in the ASSIST trial in 2004 was 0.85 [95% confidence interval (95% CI) = 0.71–1.02]. The estimated odds ratio in the SDDU target population in 2004 was 0.90 (95% CI = 0.72–1.13), in 2014 was 0.89 (95% CI = 0.70–1.14), and by 2021 was 0.88 (95% CI = 0.60–1.28). The confidence interval ratio was used to estimate precision in the transported estimates in the t","PeriodicalId":109,"journal":{"name":"Addiction","volume":"120 11","pages":"2223-2230"},"PeriodicalIF":5.3,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/add.70141","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144582705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unreliable evidence from problematic risk of bias assessments: Comment on Begh et al., ‘Electronic cigarettes and subsequent cigarette smoking in young people: A systematic review’","authors":"Sam Egger,&nbsp;Martin McKee","doi":"10.1111/add.70143","DOIUrl":"10.1111/add.70143","url":null,"abstract":"&lt;p&gt;It is widely acknowledged that cohort studies consistently find young people who use e-cigarettes are more likely to start smoking compared with non-users [&lt;span&gt;1&lt;/span&gt;]. It is also recognised that in many countries, youth smoking prevalence has declined for decades and continues to decline [&lt;span&gt;2&lt;/span&gt;]. However, these findings, while not necessarily contradictory, have been portrayed as inconsistent by e-cigarette and tobacco manufacturers, to emphasise uncertainty and doubt. Manufacturers often focus on results from ecological studies to minimise perceived risks to youth and argue against precautionary regulations [&lt;span&gt;3, 4&lt;/span&gt;]. Given these complexities, high-quality systematic reviews integrating evidence from cohort and ecological studies are welcome, but they must assess both study types fairly and appropriately. Unfortunately, the recent review ‘Electronic cigarettes and subsequent cigarette smoking in young people: A systematic review’ [&lt;span&gt;5&lt;/span&gt;] fails to do so.&lt;/p&gt;&lt;p&gt;Before addressing specific issues with the review, we must note that there is no established instrument for assessing the risk of bias (ROB) in ecological studies, at least not one recommended by Cochrane, the leading authority on systematic review methodology. The ROB assessment tools endorsed by Cochrane for non-randomised studies, including those by Morgan &lt;i&gt;et al&lt;/i&gt;. [&lt;span&gt;6&lt;/span&gt;], ROBINS-I (risk of bias in non-randomised studies of interventions) [&lt;span&gt;7&lt;/span&gt;] or ROBINS-E (risk of bias in non-randomised studies of exposures) [&lt;span&gt;8&lt;/span&gt;], are primarily designed for prospective cohort studies. While these include suggestions for possible adaptations for other individual-level studies, such as case–control studies, they lack guidance for ecological studies. Consequently, the ROB instrument used in this review to assess ecological studies appears largely self-designed and does not align with those tools, despite the authors’ claim that their instrument was adapted from Morgan &lt;i&gt;et al&lt;/i&gt;.&lt;/p&gt;&lt;p&gt;A close examination of the review’s ROB assessment tool (https://osf.io/svgud) reveals a recurring pattern of ‘problematic standards’ in the design and application of ROB criteria. We consider them ‘problematic’ because they lead to unduly harsh ROB assessments of prospective cohort studies (individual-level studies) and/or unduly lenient ROB assessments of ecological studies (population-level studies). In many cases, they take the form of ‘double standards’, as the same or similar criteria could have been applied to both study types but were not. In Appendix S1, we detail 17 examples of ‘problematic standards’. The first two, relating to the ROB domain of ‘bias due to confounding’, are described as follows.&lt;/p&gt;&lt;p&gt;The first problematic standard concerns the requirement of instrumental variables (IVs) in cohort studies. The ROB criteria used in the review specify that for cohort studies to be classified as being at ‘low’ or ‘moderate’ risk of ‘bias du","PeriodicalId":109,"journal":{"name":"Addiction","volume":"120 11","pages":"2355-2358"},"PeriodicalIF":5.3,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/add.70143","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144566855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “Alcohol-specific inhibition training in patients with alcohol use disorder: A multi-centre, double-blind randomized clinical trial examining drinking outcome and working mechanisms”","authors":"","doi":"10.1111/add.70124","DOIUrl":"10.1111/add.70124","url":null,"abstract":"<p>\u0000 <span>Stein, M</span>, <span>Soravia, LM</span>, <span>Tschuemperlin, RM</span>, <span>Batschelet, HM</span>, <span>Jaeger, J</span>, <span>Roesner, S</span>, et al. <span>Alcohol-specific inhibition training in patients with alcohol use disorder: A multi-centre, double-blind randomized clinical trial examining drinking outcome and working mechanisms</span>. <i>Addiction</i>. <span>2023</span>; <span>118</span>(<span>4</span>): <span>646</span>–<span>657</span>. https://doi.org/10.1111/add.16104</p><p>In the context of secondary analyses [<span>1</span>] of this study's data, information relevant to the statistics presented in the original publication [<span>2</span>] came to our attention.</p><p>First, the multiple imputations used in the original publication in the regression analysis were distorted and do not replicate with newer versions of <i>R</i> and of the <i>mice</i> package [<span>3</span>]. Recomputing the regression analyses using imputations generated with the newer versions did not replicate the effects reported in the original publication; while similar on a descriptive level [with estimates for improved alcohol-specific inhibition training (Alc-IT) being superior to standard Alc-IT and control], no indicators for a significant effect of improved (β = 8.06, SE = 5.49, <i>P</i> = 0.145, 95% CI = −2.84 to 19.00) or standard Alc-IT (β = −2.22, SE = 5.66, <i>P</i> = 0.695, CI = −13.5 to 9.05) were yielded. We, therefore, must correct our statement that a significant effect of improved Alc-IT can be observed with a linear regression based on multiple imputations.</p><p>Importantly, the hierarchical linear model (HLM) results, which are not based on the multiple imputations and are, therefore, not affected by these corrections, still yield a significant effect of improved Alc-IT, as described in the original publication.</p><p>In such a case, maximum likelihood methods, like the HLM analyses, which are presented in section 2.3 of the Supporting information, are more appropriate [<span>7, 8, 10-12</span>]. We regret not addressing these issues more thoroughly before the original publication, as this would have led us to stick more tenaciously to the HLMs. These HLMs—originally presented as the main analyses by us—were moved from the main text to the Supporting information on intervention during the review process with the aim to enhance comparability with earlier studies. However, given the reasons above, it seems more important to analyze the data with the most appropriate approach, which is represented by the HLMs.</p><p>Second, in the sensitivity analyses, an error in the condition labels occurred, leading to control and improved Alc-IT being compared against standard Alc-IT as a baseline. Correct labels in eTable 2 would, therefore, have been as follows:</p><p>eTable 2(a): Analyses of PDA at 3-month follow-up under a MCAR and MNAR assumption (comparing control and improved Alc-IT against standard Alc-IT)\u0000\u0000 ","PeriodicalId":109,"journal":{"name":"Addiction","volume":"120 9","pages":"1905-1907"},"PeriodicalIF":5.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/add.70124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144551472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"United States trends in non-prescribed use of Adderall and Ritalin: Population Assessment of Tobacco and Health (PATH) Study estimates from 2013 to 2022","authors":"Joshua P. Rising,&nbsp;Michael J. Parks,&nbsp;Beth Han,&nbsp;Rose Radin,&nbsp;Celeste Mallama,&nbsp;Heather L. Kimmel,&nbsp;MeLisa R. Creamer,&nbsp;Wilson M. Compton","doi":"10.1111/add.70123","DOIUrl":"10.1111/add.70123","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background and Aims&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Dispensing of prescription stimulants to adults has risen dramatically over the past decade. Examining trends in nonprescribed use of prescription stimulants can inform public health responses. Most studies in the United States (U.S.) have faced challenges in assessing trends over time due to changes in survey methodologies and variation in populations assessed. We examined data from the Population Assessment of Tobacco and Health (PATH) Study to assess changes in nonprescribed use of prescription stimulants in the U.S. from 2013 to 2022.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Design&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The PATH Study is an ongoing longitudinal study of U.S. youth and adults, representative of the civilian noninstitutionalized population. Repeated cross-sectional estimates at each wave were used (8 total waves). Trends from Wave (W) 1 (September 2013–December 2014) to W7 (January 2022–April 2023) were assessed. Full-sample and replicate weights were used; joinpoint analyses and wave-to-wave comparisons were applied to test trends.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Setting&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Civilian noninstitutionalized U.S. youth and adults.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Participants/Cases&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Youth aged 12–17 and adults aged 18 + were assessed, with a total of 45 727 participants at wave 1 (Ns vary by wave).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Measurements&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Past 12-month (P12M) prevalence of nonprescribed use of Ritalin or Adderall was assessed. Nonprescribed use of stimulants was assessed across subgroups according to age (12–17, 18–24, 25–39, ≥40) and sex (male, female).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Findings&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;While wave-to-wave comparisons showed fluctuations across certain waves, overall, there were no statistically significant changes in P12M prevalence of Ritalin or Adderall nonprescribed use (1.3% at W1 and 1.5% at W7) across the study period. However, statistically significant differences in trends existed across age groups. Among 12–17 year-olds, nonprescribed use prevalence remained stable (1.4% in W1 and 1.5% in W7). Nonprescribed use prevalence also remained stable for 18–24-year-olds from W1 to W3, but then significantly declined (p = 0.016) from W3 (5.3%) to W7 (2.6%). There were no significant changes in nonprescribed use prevalence among 25–39-year-olds (1.7% in W1 and 2.4% in W7) and those aged ≥40 (0.3% ","PeriodicalId":109,"journal":{"name":"Addiction","volume":"120 11","pages":"2348-2354"},"PeriodicalIF":5.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/add.70123","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144537516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}