Dataset Information

Association of prevalence of electronic cigarette use with smoking cessation and cigarette consumption in England: a time-series analysis between 2006 and 2017.

ABSTRACT: AIMS:To provide up-to-date estimates of how changes in the prevalence of electronic cigarette (e-cigarette) use in England have been associated with changes in smoking cessation activities and daily cigarette consumption among smokers in England. DESIGN:Time-series analysis of population trends. SETTING:England. PARTICIPANTS:Participants came from the Smoking Toolkit Study, which involves repeated, cross-sectional household surveys of individuals aged 16 years and older in England. Data were aggregated on approximately 1200 past-year smokers each quarter (total n = 50?498) between 2007 and 2017. MEASUREMENTS:Prevalence of e-cigarette use in current smokers was used to predict (a) prevalence of quit attempts among last-year smokers, (b) overall quit rates among last-year smokers and (c) mean cigarette consumption per day among current smokers. Prevalence of e-cigarette use during a quit attempt among last-year smokers was used to predict (a) quit success rate among last-year smokers and (b) overall quit rates among last-year smokers. FINDINGS:Overall quit rates increased by 0.054% [95% confidence interval (CI) = 0.032-0.076, P < 0.001] and 0.050% (95% CI = 0.031-0.069, P < 0.001) respectively for every 1% increase in the prevalence of e-cigarette use by smokers and e-cigarette use during a quit attempt. Quit success rates increased by 0.060% (95% CI = 0.043-0.078, P < 0.001) for every 1% increase in the prevalence of e-cigarette use during a quit attempt. No clear evidence was found for an association between e-cigarette use and either prevalence of quit attempt (BAdj = 0.011, 95% CI = -0.046 to 0.069, P = 0.698) or cigarette consumption (BAdj = 0.019, 95% CI = -0.043 to 0.082, P = 0.542). CONCLUSION:Changes in prevalence of e-cigarette use in England have been positively associated with the overall quit rates and quit success rates but not clearly associated with the prevalence of quit attempts and mean cigarette consumption.

SUBMITTER: Beard E

PROVIDER: S-EPMC7187187 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Association of prevalence of electronic cigarette use with smoking cessation and cigarette consumption in England: a time-series analysis between 2006 and 2017.

Beard Emma E West Robert R Michie Susan S Brown Jamie J

Addiction (Abingdon, England) 20191204 5

<h4>Aims</h4>To provide up-to-date estimates of how changes in the prevalence of electronic cigarette (e-cigarette) use in England have been associated with changes in smoking cessation activities and daily cigarette consumption among smokers in England.<h4>Design</h4>Time-series analysis of population trends.<h4>Setting</h4>England.<h4>Participants</h4>Participants came from the Smoking Toolkit Study, which involves repeated, cross-sectional household surveys of individuals aged 16 years and ol ...[more]

PMID: 31621131

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Project description:Background: The microbiome is increasingly being linked to cancer risk. Little is known about the lung and oral cavity microbiomes in healthy smokers (SM), and even less for electronic cigarette (EC) users, compared healthy never-smokers (NS). Methods: In a cross-sectional pilot study of SM (N=8), EC users (N=10) and NS (N=10) saliva and bronchoscopy-collected bronchoalveolar lavage samples were collected. Bacteria species were identified through metatranscriptome profiling by RNA-sequencing to study associations with the lung and oral microbiome. Pairwise comparisons and linear modeling was assessed with false discovery rates <0.1. Results: Total bacterial load was similar for the SM, EC users and NS, and there was no differences in the bacterial diversity across groups. In the lung, there were 44 bacterial species that were statistically significantly different for SM/NS, 80% of which were decreased in the SM. There were 12 bacterial species that were different for SM/EC users, all of which were decreased, 10 of which were also identified in the SM/NS comparison. The 2 bacterial species unique to SM/EC comparison were Neisseria sp. KEM232 and Curvibacter sp. AEP1-3. From the top 5 decreased species in SM/EC, 3 were also identified in the SM/NS comparison (Neisseria elongata, Neisseria sicca, and Haemophilus parainfluenzae) and 2 of these were unique to the SM/EC comparison (Neisseria zoodegmatis and Ottowia sp. oral taxon 894). There were 8 species increased in SM compared to NS, none of which are known to be clinically significant. In the oral microbiome, 152 bacteria species were differentially abundant for the SM/NS analysis, and only 17 for the EC/NS comparison, all which were also present in SM/NS comparisons. There were 21 bacteria that were differentially abundant in both the lung and oral cavity for SM and NS, 95% also were decreased in the SM. Conclusion: Smoking and EC use do not appear to materially affect the lung microbiome, although differences are noted of unclear clinical significance. Most differentially abundant bacteria decreased, which may be due to a toxic effect of cigarette smoke, including a change in humidity or heating. Given the low number of overlapping oral and lung microbes, the oral microbiome does not appear to be a good surrogate for smoking-related effects in the lung.

Dataset Information

Association of prevalence of electronic cigarette use with smoking cessation and cigarette consumption in England: a time-series analysis between 2006 and 2017.

Publications

Association of prevalence of electronic cigarette use with smoking cessation and cigarette consumption in England: a time-series analysis between 2006 and 2017.

Similar Datasets

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