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Background and Aims If socio-economic disadvantage is associated with more adolescent smoking, but less participation in tertiary education, and smoking and tertiary education are both associated with heavier drinking, these may represent opposing pathways to heavy drinking. This paper examines contextual variation in the magnitude and direction of these associations. Design Comparing cohort studies. Setting United Kingdom. Participants Participants were from the 1958 National Child Development Study (NCDS58; n = 15 672), the British birth cohort study (BCS70; n = 12 735) and the West of Scotland Twenty-07 1970s cohort (T07; n = 1515). Measurements Participants self-reported daily smoking and weekly drinking in adolescence (age 16 years) and heavy drinking (> 14/21 units in past week) in early adulthood (ages 22-26 years). Parental occupational class (manual versus non-manual) indicated socio-economic background. Education beyond age 18 was coded as tertiary. Models were adjusted for parental smoking and drinking, family structure and adolescent psychiatric distress. Findings Respondents from a manual class were more likely to smoke and less likely to enter tertiary education (e.g. in NCDS58, probit coefficients were 0.201 and -0.765, respectively; P < 0.001 for both) than respondents from a non-manual class. Adolescent smokers were more likely to drink weekly in adolescence (0.346; P < 0.001) and more likely to drink heavily in early adulthood (0.178; P < 0.001) than adolescent non-smokers. Respondents who participated in tertiary education were more likely to drink heavily in early adulthood (0.110 for males, 0.182 for females; P < 0.001 for both) than respondents with no tertiary education. With some variation in magnitude, these associations were consistent across all three cohorts. Conclusions In Britain, young adults are more likely to drink heavily both if they smoke and participate in tertiary education (college and university) despite socio-economic background being associated in opposite directions with these risk factors. [ABSTRACT FROM AUTHOR]

Aims: To measure the association between problem gambling severity and 19 different gambling activities among emerging adults (aged 16–26). Design: An online non‐probability longitudinal survey collecting data in two waves: wave 1, July/August 2019; wave 2, July/October 2020. Setting: Great Britain Participants: A total of 2080 young adults participating in both waves. Measurements Problem gambling scores were collected using the Problem Gambling Severity Index (PGSI). Binary variables recorded past year participation in 19 different gambling forms, ranging from lotteries to online casino and gambling‐like practices within digital games (e.g. loot box purchase, skin betting). Controls included socio‐demographic/economic characteristics, the Eysenck Impulsivity Scale and the number of gambling activities undertaken. Findings Zero inflated negative binomial model lacked evidence of an effect between past year participation in any individual activities and subsequent PGSI scores. However, negative binomial random effects models for current gamblers (n = 497) showed that skin betting (incidence‐rate ratio [IRR] = 2.32; 95% CI = 1.69–3.19), fixed odd betting terminals (IRR = 2.21, 95% CI = 1.61–3.05), slot/fruit machines (IRR = 1.43, 95% CI = 1.07–1.91), online betting on horse/dog races (IRR = 1.53, 95% CI = 1.17–2.00) and online betting on non‐sports events (IRR = 1.44, 95% CI = 1.11–1.89) were associated with increased PGSI scores. Online casino gambling had a significant interaction by wave; the impact of online casino betting in wave 2 on PGSI scores increased by a factor of 1.61. Conclusions: Past year participation of young adults (aged 16–26) in certain forms of gambling does not appear to be associated with future Problem Gambling Severity Index scores. Among young adults who are current gamblers, past year participation in certain land‐based (e.g. electronic gaming machines) and online forms (e.g. skin betting) of gambling appears to be strongly associated with elevated Problem Gambling Severity Index scores. [ABSTRACT FROM AUTHOR]

Background and Aims: The three-question Alcohol Use Disorders Identification Test (AUDIT-C) is frequently used in healthcare for screening and brief advice about levels of alcohol consumption. AUDIT-C scores (0-12) provide feedback as categories of risk rather than estimates of actual alcohol intake, an important metric for behaviour change. The study aimed to (i) develop a continuous metric from the Extended AUDIT-C expressed in United Kingdom (UK) units (8 g pure ethanol), offering equivalent accuracy, and providing a direct estimator of weekly alcohol consumption (EWAC) and (ii) evaluate the EWAC's bias and error using the graduated-frequency (GF) questionnaire as a reference standard of alcohol consumption. Design: Cross-sectional diagnostic study based on a nationally-representative survey. Settings: Community dwelling households in England. Participants: A total of 22 404 household residents aged =16 years reporting drinking alcohol at least occasionally. Measurements: Computer-assisted personal interviews consisting of (i) AUDIT questionnaire with extended response items (the 'Extended AUDIT') and (ii) GF. Primary outcomes were: mean deviation <1 UK unit (metric of bias); root-mean-square deviation <2 UK units (metric of total error) between EWAC and GF. The secondary outcome was the receiver operating characteristic area under the curve for predicting alcohol consumption in excess of 14 and 35 UK units. Findings: EWAC had a positive bias of 0.2 UK units (95% CI = 0.08, 0.4) compared with GF. Deviations were skewed: whereas the mean error was 11 UK units/week [9.5, 11.9], in half of participants the deviation between EWAC and GF was between 0 and 2.1 UK units/week. EWAC predicted consumption in excess of 14 UK units/week with a significantly greater area under the curve (0.918 [0.914, 0.923]) than AUDIT-C (0.870 [0.864, 0.876]) or the full AUDIT (0.854 [0.847, 0.860]). Conclusions: A new estimator of weekly alcohol consumption, which uses answers to the Extended AUDIT-C, meets the targeted bias tolerance. It is superior in accuracy to AUDIT-C and the full 10-item AUDIT when predicting consumption thresholds, making it a reliable complement to the Extended AUDIT-C for health promotion interventions. [ABSTRACT FROM AUTHOR]

Aims To determine whether abstinence or relapse on a quit attempt in the previous year is associated with current depressive symptoms. Design Prospective cohort with approximately annual waves. Mixed-effect logistic regressions tested whether time 2 (T2) quitting status was associated with reporting symptoms at T2, and whether time 1 (T1) symptoms moderated this relationship. Setting Waves 5-8 of the Four-Country International Tobacco Control Study: a quasi-experimental cohort study of smokers from Canada, the United States, the United Kingdom and Australia. Participants A total of 6978 smokers who participated in telephone surveys. Measurements T1 and T2 depressive symptoms in the last 4 weeks were assessed with two screening items from the PRIME-MD questionnaire. Quitting status at T2: (1) no attempt since T1; (2) attempted and relapsed; and (3) attempted and abstinent at T2. Findings Compared with no attempt, relapse was associated with reporting T2 symptoms [odds ratio (OR) = 1.46, 95% confidence interval (CI) = 1.33, 1.59]). Associations between T2 quitting status and T2 symptoms were moderated by T1 symptoms. Relapse was associated positively with T2 symptoms for those without T1 symptoms (OR = 1.71, 95% CI = 1.45, 2.03) and those with T1 symptoms (OR = 1.45, 95% CI = 1.23, 1.70). Abstinence was associated positively for those without T1 symptoms (OR = 1.37, 95% CI = 1.10, 1.71) and negatively for those with T1 symptoms (OR = 0.74, 95% CI = 0.59, 0.94). Age moderated these associations significantly. Relapse did not predict T2 symptoms for those aged 18-39 irrespective of T1 symptoms. The negative effect of abstinence on T2 symptoms for those with T1 symptoms was significant only for those aged 18-39 (OR = 0.61, 95% CI = 0.40, 0.94) and 40-55 (OR = 0.58, 95% CI = 0.40, 0.84). The positive effect of abstinence on T2 symptoms for those without T1 symptoms was significant only for those aged more than 55 (OR =1.97, 95% CI = 1.35, 2.87). Conclusions Most people who stop smoking appear to be at no greater risk of developing symptoms of depression than if they had continued smoking. However, people aged more than 55 who stop smoking may be at greater risk of developing symptoms of depression than if they had continued smoking. [ABSTRACT FROM AUTHOR]