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

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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Impact of nations meeting the MPOWER targets between 2014 and 2016: an update

Tobacco Control ◽

10.1136/tobaccocontrol-2018-054837 ◽

2019 ◽

pp. tobaccocontrol-2018-054837 ◽

Cited By ~ 4

Author(s):

David T Levy ◽

Yameng Li ◽

Zhe Yuan

Keyword(s):

Tobacco Control ◽

Tobacco Use ◽

Health Impact ◽

Health Warnings ◽

Control Policies ◽

The Public ◽

Policy Effect ◽

Tobacco Taxes ◽

Tobacco Control Policies ◽

Free Air

ObjectiveSince the WHO released the Monitoring tobacco use and tobacco control policies; Protecting from the dangers of tobacco smoke; Offering help to quit tobacco; Warning the public about the dangers; Enforcing bans on advertising, promotion and sponsorship; and Raising tobacco taxes (MPOWER) policy package to assist nations with implementing the Framework Convention on Tobacco Control (FCTC), 88 countries have adopted at least one MPOWER policy at the highest level as of 2014. Building on previous evaluations, we estimated the reduction in smoking-attributable deaths (SADs) from all policies newly adopted at the highest level between 2014 and 2016.MethodsFor each nation that implemented highest level policies, the difference in policy effect sizes from previously validated SimSmoke models for the policies in effect in 2014 and 2016 were multiplied by the number of smokers in that nation to derive the reduction in the number of smokers. Based on research that half of all smokers die from smoking, we derived SADs averted.FindingsIn total, 43 nations adopted at least one highest-level MPOWER policy between 2014 and 2016, resulting in 14.6 million fewer SADs. The largest number of SADs averted were due to stronger health warnings (13.3 million), followed by raising taxes (0.6 million), increased marketing bans (0.4 million), smoke-free air laws (0.3 million) and cessation interventions (2500).ConclusionThese findings demonstrate the continuing public health impact of tobacco control policies adopted globally since the FCTC, and highlight the importance of more countries adopting MPOWER policies at the highest level to reduce the global burden of tobacco use.

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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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Assessing the Relationship between Retail Store Tobacco Advertising and Local Tobacco Control Policies: A Massachusetts Case Study

Journal of Environmental and Public Health ◽

10.1155/2019/1823636 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11

Author(s):

Bukola Usidame ◽

Edward Alan Miller ◽

Joanna E. Cohen

Keyword(s):

Tobacco Control ◽

Sampling Method ◽

Control Policy ◽

Retail Store ◽

Cluster Sampling ◽

Retail Stores ◽

Control Policies ◽

Tobacco Control Policies ◽

The Relationship

Objective. This study documents the extent of tobacco ads in retail stores and evaluates its association with the comprehensiveness of local tobacco control policies in the state of Massachusetts, US. Methods. Using a two-stage cluster sampling method, we sampled 419 retail stores across 42 municipalities to assess the presence and count of nine mutually exclusive tobacco ad categories. Tobacco ads by store type and municipality were analyzed using summary statistics and contingency tables. Regression models tested the association between the extent of tobacco ads and local tobacco control policy comprehensiveness. Results. Overall, 86.6% (n = 363) of all the retail stores had tobacco ads. On average, there were 6.7 ads per retail store (SD = 6.61) and 2804 ads across all the retail stores (range = 0 : 32). Retail stores had an average of three different categories of tobacco ads (mean = 2.98, SD = 1.84). Across all retail stores, the most frequent ad categories were power walls (80.0%) and e-cigarette ads (55.8%). Retail stores in municipalities with more comprehensive local tobacco control policies were more likely to have fewer tobacco ads (IRR = 0.92, p<0.01) and a lower number of tobacco ad categories (OR = 0.88, p<0.05). Conclusion. Municipalities can adopt more comprehensive tobacco control policies to help limit the extent of tobacco retail advertising. This can ultimately reduce smoking in their jurisdiction.

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The relationship of smoking cessation to sociodemographic characteristics, smoking intensity, and tobacco control policies

Nicotine & Tobacco Research ◽

10.1080/14622200500125443 ◽

2005 ◽

Vol 7 (3) ◽

pp. 387-396 ◽

Cited By ~ 78

Author(s):

David Levy ◽

Eduardo Romano ◽

Elizabeth Mumford

Keyword(s):

Smoking Cessation ◽

Tobacco Control ◽

Sociodemographic Characteristics ◽

Control Policies ◽

Smoking Intensity ◽

Tobacco Control Policies ◽

Relationship Of ◽

The Relationship

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

Journal of Epidemiology & Community Health ◽

10.1136/jech-2018-211935 ◽

2019 ◽

Vol 73 (8) ◽

pp. 759-767 ◽

Cited By ~ 1

Author(s):

Vivian HT So ◽

Catherine Best ◽

Dorothy Currie ◽

Sally Haw

Keyword(s):

Tobacco Control ◽

Current Smoker ◽

Multilevel Regression ◽

Occupational Groups ◽

Control Policies ◽

Current Smoking ◽

Tobacco Control Policies ◽

The Cross ◽

The Eu ◽

Cross National

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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The effect of cigarette smoking on subjective well-being in South Africa and its implications for tobacco control

Journal of Economic and Financial Sciences ◽

10.4102/jef.v13i1.451 ◽

2020 ◽

Vol 13 (1) ◽

Author(s):

Gorton Wilmans ◽

Naiefa Rashied

Keyword(s):

South Africa ◽

Life Satisfaction ◽

Cigarette Smoking ◽

Tobacco Control ◽

Well Being ◽

Psychological Effects ◽

Subjective Well Being ◽

Control Policies ◽

Tobacco Control Policies ◽

The Relationship

Orientation: The effect of cigarette smoking on health and economic well-being has been widely studied. Its effect on subjective well-being measures, such as life satisfaction, has received less scholarly attention.Research purpose: This study tested the effect of cigarette smoking on life satisfaction amongst smokers in South Africa as a precursor to assessing the effectiveness of traditional tobacco control methods.Motivation for the study: Taxation has long been the primary tool for tobacco control in South Africa; however, the psychological effects of cigarette smoking are not considered when selecting tobacco control tools.Research approach/design and method: The study applied an ordered probit regression to a panel of five waves of the National Income Dynamics Study (NIDS) data to test the relationship between cigarette smoking and life satisfaction in South Africa.Main findings: Smoking was found to negatively affect an individual’s likelihood of reporting higher satisfaction with life relative to non-smokers, a finding that is in line with the limited literature on the subject and with the findings of similar studies that used objective measures of well-being. Furthermore, the current tobacco control framework is not as effective as expected as smoking prevalence is fairly constant, notably amongst the poor, despite large increases in excise duties on cigarettes over time.Practical/managerial implications: The study’s main finding promotes the case for reassessing the approach taken to formulating tobacco control policies and for implementing alternative tobacco control policies that consider the psychological effects of cigarette smoking. As smoking cessation is shown to increase the likelihood of reporting higher life satisfaction, measures aimed at cessation (such as broad-scale smoking bans) could prove more successful than taxation.Contribution/value-add: This study contributes to the limited literature regarding the relationship between subjective well-being and cigarette smoking in the developing world. The study provides insight to whether standard tobacco control policies should be applied generically without accounting for the relationship between cigarette smoking and subjective well-being.

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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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