Dataset Information

Electronic cigarette use in Greece: an analysis of a representative population sample in Attica prefecture.

ABSTRACT:

Background

The purpose was to assess prevalence and correlates of electronic cigarette (e-cigarette) use in Greece in 2017.

Methods

A cross-sectional survey of a representative sample of 4058 adults living in Attica prefecture (35% of the Greek adult population) was performed in May 2017 through telephone interviews. Prevalence and frequency of e-cigarette use were assessed according to the smoking status, and logistic regression analysis was performed to identify correlates of use.

Results

Current smoking was reported by 32.6% of participants. Ever e-cigarette use was reported by 54.1% (51.4-56.8%) of current smokers, 24.1% (21.7-26.5%) of former smokers and 6.5% (5.3-7.7%) of never smokers. Past experimentation was the most prevalent pattern of e-cigarette use among ever users (P?ConclusionsE-cigarette use in Greece is largely confined to current or former smokers, while current use and nicotine use by never smokers is extremely rare. The majority of current e-cigarette users were former smokers. Most participants overestimate the harmfulness of e-cigarettes relative to smoking.

SUBMITTER: Farsalinos KE

PROVIDER: S-EPMC5899338 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Electronic cigarette use in Greece: an analysis of a representative population sample in Attica prefecture.

Farsalinos Konstantinos E KE Siakas Georgios G Poulas Konstantinos K Voudris Vassilis V Merakou Kyriakoula K Barbouni Anastasia A

Harm reduction journal 20180413 1

<h4>Background</h4>The purpose was to assess prevalence and correlates of electronic cigarette (e-cigarette) use in Greece in 2017.<h4>Methods</h4>A cross-sectional survey of a representative sample of 4058 adults living in Attica prefecture (35% of the Greek adult population) was performed in May 2017 through telephone interviews. Prevalence and frequency of e-cigarette use were assessed according to the smoking status, and logistic regression analysis was performed to identify correlates of us ...[more]

PMID: 29653578

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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.

Project description:ImportanceCigarettes are still a commonly used tobacco product among youth despite recent declines in cigarette use.ObjectiveThe aim of this study was to prospectively estimate the age of cigarette use initiation among youth (aged 12-17 years) overall, by sex, and by race/ethnicity.Design, setting, and participantsThis cohort study used data from waves 1 through 4 of the nationally representative Population Assessment of Tobacco and Health (PATH) study, conducted from September 12, 2013, to January 3, 2018. Two subpopulations of youth were assessed: (1) those nonsusceptible to cigarette use and (2) never users of cigarettes at their first wave of PATH participation. Weighted interval-censoring survival analyses were used to prospectively estimate the age of initiation of cigarette use outcomes. Weighted interval-censoring Cox proportional hazard models were used to estimate differences in the age of initiation by sex and by race/ethnicity. Statistical analyses were performed from October 7, 2019, to May 1, 2020.ExposuresDifferences in the age of initiation by sex and race/ethnicity.Main outcomes and measuresAge of initiation of susceptibility to cigarette use, ever use, past 30-day use, and fairly regular cigarette use overall, by sex, and by race/ethnicity.ResultsA total of 15 776 youth never users and, among them, 11 022 youth who were nonsusceptible to cigarette use, were included in this study (weighted mean [SE] age, 13.5 [0.01] years; 58.6% [SE, 0.46%] non-Hispanic White; and 51.0% [SE, 0.32%] boys) and 15 776 were never users of cigarettes at their first wave of PATH participation (weighted mean [SE] age, 13.7 [0.01] years; 55.0% [SE, 0.29%] non-Hispanic White; and 51.0% [SE, 0.15%] boys). By age 18 years, among those who were nonsusceptible, 46.2% (95% CI, 44.3%-48.2%) became susceptible to cigarette use. Among never users, 24.4% (95% CI, 22.9%-25.9%) initiated ever cigarette use, 16.4% (95% CI, 15.2%-17.6%) initiated past 30-day cigarette use, and 4.3% (95% CI, 3.9%-4.8%) initiated fairly regular cigarette use. Boys had a higher risk of initiating ever (hazard ratio [HR], 1.21; 95% CI, 1.08-1.36) and past 30-day cigarette use (HR, 1.27; 95% CI, 1.10-1.47) at earlier ages compared with girls. Non-Hispanic White youth had a higher risk of an earlier age of initiation of susceptibility to cigarette use (HR, 0.77; 95% CI, 0.68-0.88), ever use (HR, 0.59; 95% CI, 0.49-0.71), past 30-day use (HR, 0.64; 95% CI, 0.52-0.77), and fairly regular cigarette use (HR, 0.25; 95% CI, 0.14-0.43) compared with non-Hispanic Black youth.Conclusions and relevanceThe results of this cohort study suggest that, despite current interventions and existing laws, a large number of youth initiated cigarette use before the legal age to purchase tobacco products.

Project description:BACKGROUND:Electronic health (eHealth) services may help people obtain information and manage their health, and they are gaining attention as technology improves, and as traditional health services are placed under increasing strain. We present findings from the first representative, large-scale, population-based study of eHealth use in Norway. OBJECTIVE:The objectives of this study were to examine the use of eHealth in a population above 40 years of age, the predictors of eHealth use, and the predictors of taking action following the use of these eHealth services. METHODS:Data were collected through a questionnaire given to participants in the seventh survey of the Tromsø Study (Tromsø 7). The study involved a representative sample of the Norwegian population aged above 40 years old. A subset of the more extensive questionnaire was explicitly related to eHealth use. Data were analyzed using logistic regression analyses. RESULTS:Approximately half (52.7%; 9752/18,497) of the respondents had used some form of eHealth services during the last year. About 58% (5624/9698) of the participants who had responded to a question about taking some type of action based on information gained from using eHealth services had done so. The variables of being a woman (OR 1.58; 95% CI 1.47-1.68), of younger age (40-49 year age group: OR 4.28, 95% CI 3.63-5.04), with a higher education (tertiary/long: OR 3.77, 95% CI 3.40-4.19), and a higher income (>1 million kr [US $100,000]: OR 2.19, 95% CI 1.77-2.70) all positively predicted the use of eHealth services. Not living with a spouse (OR 1.14, 95% CI 1.04-1.25), having seen a general practitioner (GP) in the last year (OR 1.66, 95% CI 1.53-1.80), and having had some disease (such as heart disease, cancer, asthma, etc; OR 1.29, 95% CI 1.18-1.41) also positively predicted eHealth use. Self-rated health status did not significantly influence eHealth use. Taking some action following eHealth use was predicted with the variables of being a woman (OR 1.16, 95% CI 1.07-1.27), being younger (40-49 year age group: OR 1.72, 95% CI 1.34-2.22), having a higher education (tertiary/long: OR 1.65, 95% CI 1.42-1.92), having seen a GP in the last year (OR 1.58, 95% CI 1.41-1.77), and having ever had a disease (such as heart disease, cancer or asthma; OR 1.26, 95% CI 1.14-1.39). CONCLUSIONS:eHealth appears to be an essential supplement to traditional health services for those aged above 40 years old, and especially so for the more resourceful. Being a woman, being younger, having higher education, having had a disease, and having seen a GP in the last year all positively predicted using the internet to get health information and taking some action based on this information.

Dataset Information

Electronic cigarette use in Greece: an analysis of a representative population sample in Attica prefecture.

Background

Methods

Results

Publications

Electronic cigarette use in Greece: an analysis of a representative population sample in Attica prefecture.

Similar Datasets

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