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

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.

The influencing factors of the smoking behavior of online ride-hailing drivers in China: A cross-sectional analysis

2021 ◽  
Author(s):  
Xinlin Chen ◽  
Xuefei Gu ◽  
Tingting Li ◽  
Lirong Xu ◽  
Qiaoyan Liu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Health Literacy ◽  
Tobacco Control ◽  
Influencing Factors ◽  
Smoking Status ◽  
Smoking Behavior ◽  
Cross Sectional ◽  
Current Smoking ◽  
Work Related ◽  
Tobacco Control Policies

Abstract Background：Online ride-hailing is a fast-developing new travel mode, and tobacco control policies on it have not yet been improved. This study aims to reveal the smoking status and influencing factors of ride-hailing drivers, so as to provide a basis for the formulation of tobacco control policies.Methods：The cross-sectional data used in this study were derived from an online survey of full-time ride-hailing drivers in China. Questionnaires were employed to collect variables including sociodemographic and work-related characteristics, health status, health behavior, health literacy, and smoking status. Chi-Square test and multivariate logistic regression were used to analyze the influencing factors of current smoking.Results：A total of 8990 ride-hailing drivers were investigated, in which 5024 were current smokers, accounted to 55.9%. Current smokers (53.7% (2696/5024) v 44.2% (1752/3966); P<0.001) and drivers who smoked on the car (85.8% (1389/1618) v 38.4 (1307/3406); P<0.001) were more likely to allow passengers to smoke. Logistic regression analysis showed that men (OR=0.519, 95%CI (0.416, 0.647)), central regions (OR=1.172, 95%CI (1.049, 1.309)), 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 working at non-fixed time (OR=0.847, 95%CI (0.718, 0.999)), 35-54 years old (OR=0.585, 95%CI (0.408, 0.829)), current drinker (OR=1.663, 95%CI (1.526, 1.813)), eating very irregularly (OR=1.370, 95%CI (1.233, 1.523)), the number of days a week of engaging in at least 10 minutes 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)) , underweight (OR=1.249, 95%CI (1.001, 1.559)) and overweight (OR=0.846, 95%CI (0.775, 0.924)) were associated with the prevalence of current smoking among online ride-hailing drivers (P<0.05). Conclusions：The smoking rate of ride-hailing drivers was high, and the social demographic and work-related characteristics, and health-related factors all affected their smoking behavior. Tobacco control measures targeted at online-hailing drivers should correct their cultural beliefs about smoking, increase their health literacy, guide them to exercise more and keep a regular schedule, as well as combine with drinking intervention and weight intervention.

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

2019 ◽  
Vol 73 (8) ◽  
pp. 759-767 ◽  
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.

Association between tobacco control policies and smoking behaviour among adolescents in 29 European countries

Addiction ◽  
2009 ◽  
Vol 104 (11) ◽  
pp. 1918-1926 ◽  
Author(s):  
Anne Hublet ◽  
Holger Schmid ◽  
Els Clays ◽  
Emmanuelle Godeau ◽  
Saoirse Nic Gabhainn ◽  
...  
Keyword(s):  
Tobacco Control ◽  
Smoking Behaviour ◽  
European Countries ◽  
Control Policies ◽  
Tobacco Control Policies

scholarly journals Smoking behaviour and attitudes of Hungarian Roma and non-Roma population towards tobacco control policies

2011 ◽  
Vol 56 (5) ◽  
pp. 485-491 ◽  
Author(s):  
Edit Paulik ◽  
László Nagymajtényi ◽  
Douglas Easterling ◽  
Todd Rogers
Keyword(s):  
Tobacco Control ◽  
Smoking Behaviour ◽  
Control Policies ◽  
Roma Population ◽  
Tobacco Control Policies

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

2019 ◽  
Vol 16 (21) ◽  
pp. 4130 ◽  
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.

scholarly journals Adolescent Support for Tobacco Control Policies and Associations with Tobacco Denormalization Beliefs and Harm Perceptions

2019 ◽  
Vol 16 (1) ◽  
pp. 147 ◽  
Author(s):  
Jianjiu Chen ◽  
Sai Yin Ho ◽  
Lok Tung Leung ◽  
Man Ping Wang ◽  
Tai Hing Lam
Keyword(s):  
Hong Kong ◽  
Tobacco Control ◽  
Smoking Status ◽  
Policy Support ◽  
Cross Sectional Survey ◽  
School Students ◽  
Cross Sectional ◽  
Control Policies ◽  
Tobacco Control Policies ◽  
Tobacco Tax

Public support is crucial for advancing tobacco control policies. We investigated adolescent support for such policies, and its association with potential factors of social denormalization (SD) beliefs of smoking, tobacco industry denormalization (TID) beliefs (negative perceptions of the industry), and harm perceptions of smoking. In a cross-sectional survey in Hong Kong, 13,964 secondary school students (mean age 15.0 years, 51.3% boys) reported their support (yes/no) for each of 14 tobacco control policies (e.g., further increase tobacco tax). Tobacco-related beliefs and perceptions, and smoking status were also measured. Support for the 14 tobacco control policies ranged from 17.6% to 54.1%. In current non-smokers, SD beliefs, TID beliefs, and harm perceptions were all associated with support for all tobacco control policies. In current smokers, the study factors were each associated with support for two to three policies. To conclude, support for tobacco control policies was weak to moderate in Hong Kong adolescents. SD beliefs, TID beliefs, and harm perceptions of smoking were associated with policy support in current non-smokers. In current smokers, the corresponding associations were less consistent or weaker.

Tool to monitor tobacco control policy implementation: the Tobacco Control Scale in Latin America. Adaptation process and pilot study

2021 ◽  
pp. tobaccocontrol-2020-056395
Author(s):  
Ariadna Feliu ◽  
Cristina Martinez ◽  
Armando Peruga ◽  
Luk Joossens ◽  
Eduardo Bianco ◽  
...  
Keyword(s):  
Latin America ◽  
Policy Implementation ◽  
Tobacco Control ◽  
Control Policy ◽  
Tobacco Control Policy ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Control Scale ◽  
Control Policies ◽  
Tobacco Control Policies

BackgroundMonitoring tobacco control policy implementation is one of the keys for tobacco consumption reduction in Latin America (LA). This study reports on the adaptation of the Tobacco Control Scale (TCS) for use in LA countries and the level of tobacco control policy implementation in this region according to the scale.MethodsEcological cross-sectional survey. The questionnaire to measure tobacco control policies was a translated (into Spanish and Portuguese) and adapted version of the last TCS as used in Europe. The resulting TCS-LA maintains the same structure as the original TCS, with eight policy domains and 100 points (pts) as maximum score; however, four policy domains were adapted because the exact same rationale could not be applied. At least two non-governmental tobacco control experts were contacted per country to answer the TCS-LA.ResultsInformants from 17 out of 18 countries completed the questionnaire. Using the TCS-LA, Panama (70 pts), Uruguay (68 pts) and Ecuador (61 pts) exhibited the strongest tobacco control policies, while Guatemala (32 pts), Bolivia (30 pts) and Dominican Republic (29 pts) have implemented a lower number of tobacco control policies. Eight countries reached 50% of the TCS-LA total possible score, indicating a relatively good implementation level of tobacco control policies.ConclusionsPanama, Uruguay and Ecuador are the tobacco control policy leaders in LA; however, tobacco control in the region has room for improvement since nine countries have a total score under 50 pts. The TCS is a feasible and adaptable tool to monitor tobacco control in other WHO regions beyond Europe.

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