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Introduction: This study estimates the extent to which individuals' smoking cessation and relapse patterns are associated with the smoking behaviour of their household members.Methods: Longitudinal data on household members' smoking behaviour was sourced from a representative sample of 12,723 Australians who ever reported smoking between 2001 and 2019. Controlling for a rich set of confounders, multivariate regression analyses were used to predict the likelihood of smoking cessation and relapse given other household members' smoking status and their relationship type. The models were then used to forecast smoking prevalence over ten years across different household types.Results: Individuals living with a smoking spouse were less likely to quit [OR0.77 (95%CI 0.72;0.83)] and more likely to relapse [OR1.47 (95%CI 1.28;1.69)] compared to those living with non-smoking spouses. Subsequently, the proportion of smokers living with other smoking household members increased by 15% between 2011 and 2019. A ten-year forecast using the smoking cessation and relapse models predicts that, on average, smokers living with non-smokers will reduce by 43%, while those living alone or with a smoking partner will only reduce by 26% and 28% respectively.Conclusions: Over time, those who are still smoking are more likely to live with other smokers. Therefore, the current cohort of smokers are increasingly less likely to quit and more likely to relapse. Smoking projection models that fail to account for this dynamic risk may overstate the downstream health benefits and health cost savings. Interventions which encourage smoking cessation at the household level, particularly for spouses, may assist individuals to quit and abstain from smoking.Implications: The current and future paradigm shift in the smoking environment suggests that smoking cessation and relapse prevention policies should consider household structure. Policies designed to affect smoking at the household level are likely to be particularly effective. When estimating the long-term benefits of current smoking policies intrahousehold smoking behaviour needs to be considered. [ABSTRACT FROM AUTHOR]

Aims: To assess the methodological quality and effectiveness of technology-based smoking cessation interventions in disadvantaged groups.Method: Four databases (EMBASE, Cochrane, Medline, and PsycInfo) were searched for studies conducted from 1980 to May 2016. Randomized controlled trials that compared a behavioral smoking cessation intervention delivered primarily through a technology-based platform (eg, mobile phone) with a no-intervention comparison group among disadvantaged smokers were included. Three reviewers assessed all relevant studies for inclusion, and one reviewer extracted study, participant and intervention-level data, with a subset crosschecked by a second reviewer.Results: Thirteen studies targeting disadvantaged smokers (n =4820) were included. Only one study scored highly in terms of methodological rigor on EPOC criteria for judging risk of bias. Of the 13 studies using a technology-based platform, most utilized websites (n = 5) or computer programs (n = 5), and seven additionally offered nicotine replacement therapy. Technology-based interventions increased the odds of smoking cessation for disadvantaged groups at 1 month (odds ratio [OR] 1.70, 95% confidence interval [CI] 1.10, 2.63), 3 months (OR 1.30, 95% CI 1.07, 1.59), 6 months (OR 1.29, 95% CI 1.03, 1.62), and 18 months post-intervention (OR 1.83, 95% CI 1.11, 3.01).Conclusion: Few methodologically rigorous studies were identified. Mobile phone text-messaging, computer- and website-delivered quit support showed promise at increasing quit rates among Indigenous, psychiatric and inpatient substance use disorder patients. Further research is needed to address the role technology-based interventions have on overcoming health inequalities to meet the needs of disadvantaged groups.Implications: This review provides the first quantitative evidence of the effectiveness of a range of technology-based smoking cessation interventions among disadvantaged smokers, with separate estimates on the basis of intervention type, and cessation outcome measure. Providing cost-effective, easily accessible and real-time smoking cessation treatment is needed, and innovative technology-based platforms will help reach this endpoint. These interventions need to be tested in larger scale randomized controlled trial designs and target broader disadvantaged groups. Data collection beyond 6 months is also needed in order to establish the efficacy of these intervention approaches on long-term cessation rates among disadvantaged population groups. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this research was to examine the use of quit smoking services and support options in Australia with national survey data to determine whether use of quit smoking services and pharmacotherapy contributes to socioeconomic status (SES) differences in smoking. Methods: Analyses were performed with data from 4 waves of the Australian National Drug Strategy Household Survey between 2001 and 2010. The primary outcomes were the use of quit smoking services or pharmacotherapy. The main predictor variables used were measures of SES including the Socio-Economic Indexes for Areas, income, and education. Other sociodemographic variables were also examined. Results: There was no systematic SES difference in the use of services or support options, with some used more by high SES and some used more by low SES. Those with university education (odds ratio [OR] = 0.72; 95% CI = 0.56–0.93) were less likely to use prescription medication. Conversely, those with higher incomes were more likely to use patches, gum, or an inhaler (OR = 1.13; 95% CI = 1.01–1.27). Prescription medication was significantly associated with increased odds for recent cessation, although only among those with lower levels of education (OR = 2.50; 95% CI = 1.94–3.23). Those with higher SES had the greatest odds for recent cessation. Conclusions: The evidence suggests that low-SES smokers use quit services and support options equally compared with high-SES smokers; thus, access does not drive the SES differential in smoking. However, the findings are positive, suggesting that increasing the uptake of prescription medication may help increase cessation rates among low-SES smokers. [ABSTRACT FROM PUBLISHER]