Association between tobacco control policies and current smoking across different occupational groups in the EU between 2009 and 2017

BackgroundThis study investigated the cross-national and longitudinal associations between national tobacco control policies and current smoking in 28 European Union (EU) member states between 2009 and 2017. It also examined the interaction between tobacco control policies and occupational status.MethodsWe used data from four waves of Eurobarometer (2009, 2012, 2014 and 2017). The total sample size was 105 231 individuals aged ≥15 years. Tobacco Control Scale (TCS) scores (range 0 to 100) for years 2005, 2007, 2012 and 2014 measured the strength of country-level tobacco control policies. Logistic multilevel regression analyses with three levels (the individual, the country-year and the country) were performed with current smoker as the dependent variable.ResultsAcross the EU, average smoking prevalence fell from 29.4% (95% CI 28.5% to 30.2%) in 2009 to 26.3% (95% CI 25.4% to 27.1%) in 2017. We confirmed that cross-nationally, strong national tobacco control policies are significantly associated with a low probability of smoking. A one-point increase in TCS score was associated with lower odds of smoking (OR=0.990; 95% CI 0.983 to 0.998), but longitudinally (within-country) increases in TCS were not associated with current smoking (OR=0.999; 95% CI 0.994 to 1.005). Compared with those in manual occupations, the cross-national association was stronger in the upper occupational group (conditional OR for the interaction=0.985; 95% CI 0.978 to 0.992) and weaker in the economically inactive group (conditional OR for the interaction=1.009; 95% CI 1.005 to 1.013).ConclusionDifferences in tobacco control policies between countries were associated with the probability of smoking but the changes in TCS within countries over time were not. Differences between countries in tobacco control policies were found to be most strongly associated with the likelihood of smoking in the highest occupational groups and were found to have only a weak association with smoking among the economically inactive in this sample.

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Examining the impact of changes in school tobacco control policies and programs on current smoking and susceptibility to future smoking among youth in the first two years of the COMPASS study: looking back to move forward

Tobacco Induced Diseases ◽

10.1186/s12971-015-0031-1 ◽

2015 ◽

Vol 13 (1) ◽

Cited By ~ 14

Author(s):

Scott T Leatherdale ◽

Adam Cole

Keyword(s):

Tobacco Control ◽

Control Policies ◽

Current Smoking ◽

Tobacco Control Policies ◽

Policies And Programs ◽

The Impact ◽

Looking Back

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Are smokers “hardening” or rather “softening”? An ecological and multilevel analysis across 28 European Union countries

European Respiratory Journal ◽

10.1183/13993003.00596-2019 ◽

2019 ◽

Vol 54 (3) ◽

pp. 1900596 ◽

Cited By ~ 5

Author(s):

Ariadna Feliu ◽

Esteve Fernandez ◽

Cristina Martinez ◽

Filippos T. Filippidis

Keyword(s):

European Union ◽

Tobacco Control ◽

Fixed Effects ◽

Smoking Prevalence ◽

The European Union ◽

Control Policies ◽

Tobacco Control Policies ◽

Hardcore Smokers ◽

Over Time ◽

The Eu

BackgroundTobacco control policies can reduce smoking prevalence. These measures may be less effective where smoking prevalence has significantly declined, as the remaining smokers have “hardened”. Our aim was to empirically evaluate the “hardening hypothesis” at the population level in the European Union (EU) and explore factors associated with hardcore smoking.MethodsWe conducted two separate analyses in the EU using data on smoking from the Eurobarometer surveys (2009–2017, n=112 745). 1) A panel-data fixed-effects linear regression to investigate changes over time in the percentage of hardcore smokers in relation to standardised smoking prevalence at the country level. 2) A multilevel logistic regression analysis with hardcore (daily smokers, ≥15 cigarettes per day who have not attempted to quit in the last 12 months) or light (<5 cigarettes per day) smoking as the dependent variable and time as the main independent variable, controlling for individual and ecological variables.ResultsWe studied 29 010 current smokers (43.8% hardcore smokers and 14.7% light smokers). The prevalence of hardcore smoking among adult smokers increased by 0.55 (95% CI 0.14–0.96) percentage points per each additional percentage point in the overall smoking prevalence. The odds of being a hardcore smoker increased over time and were higher in middle-aged males and people with financial difficulties, while the odds of being a light smoker significantly declined among females.ConclusionThis study does not support the “hardening hypothesis” in the EU between 2009 and 2017, but suggests a softening of the smoking population. Existing tobacco control policies are likely to be suitable to further decrease smoking prevalence in Europe.

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Differential Relationship between Tobacco Control Policies and U.S. Adult Current Smoking by Poverty

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16214130 ◽

2019 ◽

Vol 16 (21) ◽

pp. 4130 ◽

Cited By ~ 1

Author(s):

Lauren M. Dutra ◽

Matthew C. Farrelly ◽

James Nonnemaker ◽

Brian Bradfield ◽

Jennifer Gaber ◽

...

Keyword(s):

Tobacco Control ◽

Poverty Line ◽

Odds Ratios ◽

Control Policies ◽

Current Smoking ◽

Cigarette Taxes ◽

Tobacco Control Policies ◽

Free Air ◽

The Relationship ◽

Differential Relationship

The study’s purpose was to identify differences in the relationship between tobacco control policies and smoking by poverty. We matched state smoke-free air law coverage (SFALs), tobacco control funding (TCF), and cigarette taxes with individual current smoking and demographics from supplements to the Current Population Survey (1985–2015). We regressed (logistic) smoking on policy variables, poverty (<138% of poverty line versus ≥138% of poverty line), interactions of policy and poverty, and covariates, presenting beta coefficients instead of odds ratios because it is difficult to interpret interactions using odds ratios (they are ratios of odds ratios). We coded SFALs as (1) proportion of state covered by 100% workplace, restaurant and bar laws (SFAL-All) or (2) proportion of state covered by workplace laws (SFAL-WP) and proportion covered by restaurant or bar laws (SFAL-RB). In the SFAL-All model, SFAL-All (Beta coeff: −0.03, 95% CI: −0.06, −0.002), tax (Coeff: −0.06, 95% CI: −0.07, −0.05), and TCF (Coeff: −0.01, 95% CI: −0.01, −0.001) were associated with less smoking. In this model, the interaction of SFAL-All by poverty was significant (Coeff: 0.08, 95% CI: 0.02, 0.13). In the SFAL-WP/RB model, SFAL-RB (Coeff: −0.05, 95% CI: −0.08, −0.02), tax (Coeff: −0.05, 95% CI: −0.06, −0.04), and TCF (Coeff: −0.01, 95% CI: −0.01, −0.00) were significant. In the same model, SFAL-WP (Coeff: 0.09, 95% CI: 0.03, 0.15), SFAL-RB (Coeff: −0.14, 95% CI: −0.19, −0.09), and TCF (Coeff: 0.01, 95% CI: 0.00, 0.02) interacted with poverty. Tax by poverty was of borderline significance in this model (Coeff = 0.02, 95% CI: −0.00, 0.04, p = 0.050). Among adults, SFALs, TCF, and tax were associated with less current smoking, and SFALs and TCF had differential relationships with smoking by poverty.

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Factors influencing smoking behaviour of online ride-hailing drivers in China: a cross-sectional analysis

BMC Public Health ◽

10.1186/s12889-021-11366-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Xinlin Chen ◽

Xuefei Gu ◽

Tingting Li ◽

Qiaoyan Liu ◽

Lirong Xu ◽

...

Keyword(s):

Logistic Regression ◽

Tobacco Control ◽

Influencing Factors ◽

Smoking Behaviour ◽

Full Time ◽

Cross Sectional ◽

Control Policies ◽

Current Smoking ◽

Work Related ◽

Tobacco Control Policies

Abstract Background Online ride-hailing is a fast-developing new travel mode. However, tobacco control policies on its drivers remain underdeveloped. This study aims to reveal the status and determine the influencing factors of ride-hailing drivers’ smoking behaviour to provide a basis for the formulation of tobacco control policies. Methods We derived our cross-sectional data from an online survey of full-time ride-hailing drivers in China. We used a survey questionnaire to collect variables, including sociodemographic and work-related characteristics, health status, health behaviour, health literacy and smoking status. Finally, we analysed the influencing factors of current smoking by conducting chi-square test and multivariate logistic regression. Results A total of 8990 ride-hailing drivers have participated in the survey, in which 5024 were current smokers, accounting to 55.9%. Nearly one-third of smokers smoked in their cars (32.2%). The logistic regression analysis results were as follows: male drivers (OR = 0.519, 95% CI [0.416, 0.647]), central regions (OR = 1.172, 95% CI [1.049, 1.309]) and eastern regions (OR = 1.330, 95% CI [1.194, 1.480]), working at both daytime and night (OR = 1.287, 95% CI [1.164, 1.424]) and non-fixed time (OR = 0.847, 95% CI [0.718, 0.999]), ages of 35–54 years (OR = 0.585, 95% CI [0.408, 0.829]), current drinker (OR = 1.663, 95% CI [1.526, 1.813]), irregular eating habits (OR = 1.370, 95% CI [1.233, 1.523]), the number of days in a week of engaging in at least 10 min of moderate or vigorous exercise ≥3 (OR = 0.752, 95% CI [0.646, 0.875]), taking the initiative to acquire health knowledge occasionally (OR = 0.882, 95% CI [0.783, 0.992]) or frequently (OR = 0.675, 95% CI [0.591, 0.770]) and underweight (OR = 1.249, 95% CI [1.001, 1.559]) and overweight (OR = 0.846, 95% CI [0.775, 0.924]) have association with the prevalence of current smoking amongst online ride-hailing drivers. Conclusion The smoking rate of ride-hailing drivers was high. Sociodemographic and work-related characteristics and health-related factors affected their smoking behaviour. Psychological and behavioural interventions can promote smoking control management and encourage drivers to quit or limit smoking. Online car-hailing companies can also establish a complaint mechanism combined with personal credit.

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