scholarly journals Youth vaping and smoking and parental vaping: a cross-sectional survey

2020 ◽  
Author(s):  
Michael Green ◽  
Linsay Gray ◽  
Helen Sweeting
Keyword(s):  
Smoking Status ◽  
Smoking Initiation ◽  
Smoking Behaviour ◽  
Youth Smoking ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Unmeasured Confounding ◽  
Increase Risk ◽  
Alternative Scenarios ◽  
Negative Impacts

Abstract Background: Concerns remain about potential negative impacts of e-cigarettes including possibilities that: youth e-cigarette use (vaping) increases risk of youth smoking; and vaping by parents may have impacts on their children’s vaping and smoking behaviour. Methods: With cross-sectional data from 3291 youth aged 10-15 years from the Understanding Society Survey, we estimated effects of youth vaping on youth smoking (ever, current and initiation in the past year), and of parental vaping on youth smoking and vaping, and examined whether the latter differed by parental smoking status. Propensity weighting was used to adjust for measured confounders and estimate effects of vaping under alternative scenarios of no vaping vs universal adoption, and vs observed vaping levels. E-values were calculated to assess the strength of unmeasured confounding influences needed to negate our estimates. Results: Associations between youth vaping and youth smoking were attenuated considerably by adjustment for measured confounders. Estimated effects of youth vaping on youth smoking were stronger comparing no use to universal adoption (e.g. OR for smoking initiation: 32.5; 95% CI: 9.8-107.1) than to observed levels of youth vaping (OR: 4.4; 0.6-30.9). Relatively strong unmeasured confounding would be needed to explain these effects. Associations between parental vaping and youth vaping were explained by measured confounders. However, estimates for parental vaping on youth smoking indicated effects, especially for youth with ex-smoking parents (e.g. OR for smoking initiation: 11.3; 2.7-46.4) rather than youth with currently smoking parents (OR: 1.0; 0.2-6.4). Relatively weak unmeasured confounding could explain these parental vaping effects. Conclusions: While results for youth vaping and youth smoking associations indicated support for underlying propensities, estimated effects still required considerable unmeasured confounding to be explained fully. However, these estimates from cross-sectional data could also be explained by smoking leading to vaping. Stronger estimates for universal vaping adoption vs observed usage, indicated that if youth vaping does increase risk of youth smoking, this effect may be stronger in the general population of youth, than among those youth who typically vape. Associations of parental vaping with youth smoking and vaping were either explained by measured confounding or could be relatively easily explained by unmeasured confounding.

scholarly journals Youth vaping and smoking and parental vaping: a panel survey

2020 ◽  
Author(s):  
Michael Green ◽  
Linsay Gray ◽  
Helen Sweeting
Keyword(s):  
Smoking Status ◽  
Smoking Initiation ◽  
Smoking Behaviour ◽  
Residual Effects ◽  
Cigarette Use ◽  
Youth Smoking ◽  
Unmeasured Confounding ◽  
Youth Population ◽  
Negative Impacts ◽  
The Uk

Abstract Background: Concerns remain about potential negative impacts of e-cigarettes including possibilities that: youth e-cigarette use (vaping) increases risk of youth smoking; and vaping by parents may have impacts on their children’s vaping and smoking behaviour.Methods: With panel data from 3291 youth aged 10-15 years from the 7th wave of the UK Understanding Society Survey (2015-2017), we estimated effects of youth vaping on youth smoking (ever, current and past year initiation), and of parental vaping on youth smoking and vaping, and examined whether the latter differed by parental smoking status. Propensity weighting was used to adjust for measured confounders and estimate average effects of vaping for all youth, and among youth who vaped. E-values were calculated to assess the strength of unmeasured confounding influences needed to negate our estimates.Results: Associations between youth vaping and youth smoking were attenuated considerably by adjustment for measured confounders. Estimated average effects of youth vaping on youth smoking were stronger for all youth (e.g. OR for smoking initiation: 32.5; 95% CI: 9.8-107.1) than among youth who vaped (OR: 4.4; 0.6-30.9). Relatively strong unmeasured confounding would be needed to explain these effects. Associations between parental vaping and youth vaping were explained by measured confounders. Estimates indicated effects of parental vaping on youth smoking, especially for youth with ex-smoking parents (e.g. OR for smoking initiation: 11.3; 2.7-46.4) rather than youth with currently smoking parents (OR: 1.0; 0.2-6.4), but these could be explained by relatively weak unmeasured confounding.Conclusions: While measured confounding accounted for much of the associations between youth vaping and youth smoking, indicating support for underlying propensities, our estimates suggested residual effects that could only be explained away by considerable unmeasured confounding or by smoking leading to vaping. Estimated effects of youth vaping on youth smoking were stronger among the general youth population than among the small group of youth who actually vaped. Associations of parental vaping with youth smoking and vaping were either explained by measured confounding or could be relatively easily explained by unmeasured confounding.

scholarly journals Youth vaping and smoking and parental vaping: a panel survey

2020 ◽  
Author(s):  
Michael Green ◽  
Linsay Gray ◽  
Helen Sweeting
Keyword(s):  
Smoking Status ◽  
Smoking Initiation ◽  
Smoking Behaviour ◽  
Residual Effects ◽  
Cigarette Use ◽  
Youth Smoking ◽  
Unmeasured Confounding ◽  
Youth Population ◽  
Negative Impacts ◽  
The Uk

Abstract Background: Concerns remain about potential negative impacts of e-cigarettes including possibilities that: youth e-cigarette use (vaping) increases risk of youth smoking; and vaping by parents may have impacts on their children’s vaping and smoking behaviour.Methods: With panel data from 3291 youth aged 10-15 years from the 7th wave of the UK Understanding Society Survey (2015-2017), we estimated effects of youth vaping on youth smoking (ever, current and past year initiation), and of parental vaping on youth smoking and vaping, and examined whether the latter differed by parental smoking status. Propensity weighting was used to adjust for measured confounders and estimate average effects of vaping for all youth, and among youth who vaped. E-values were calculated to assess the strength of unmeasured confounding influences needed to negate our estimates.Results: Associations between youth vaping and youth smoking were attenuated considerably by adjustment for measured confounders. Estimated average effects of youth vaping on youth smoking were stronger for all youth (e.g. OR for smoking initiation: 32.5; 95% CI: 9.8-107.1) than among youth who vaped (OR: 4.4; 0.6-30.9). Relatively strong unmeasured confounding would be needed to explain these effects. Associations between parental vaping and youth vaping were explained by measured confounders. Estimates indicated effects of parental vaping on youth smoking, especially for youth with ex-smoking parents (e.g. OR for smoking initiation: 11.3; 2.7-46.4) rather than youth with currently smoking parents (OR: 1.0; 0.2-6.4), but these could be explained by relatively weak unmeasured confounding.Conclusions: While measured confounding accounted for much of the associations between youth vaping and youth smoking, indicating support for underlying propensities, our estimates suggested residual effects that could only be explained away by considerable unmeasured confounding or by smoking leading to vaping. Estimated effects of youth vaping on youth smoking were stronger among the general youth population than among the small group of youth who actually vaped. Associations of parental vaping with youth smoking and vaping were either explained by measured confounding or could be relatively easily explained by unmeasured confounding.

scholarly journals Family Role towards Smoking Behaviour among Children in Jakarta

2014 ◽  
Vol 9 (1) ◽  
pp. 58 ◽  
Author(s):  
Wahyu Septiono ◽  
Dan Wolf Meyrowitsch
Keyword(s):  
Smoking Status ◽  
Parental Control ◽  
Smoking Habit ◽  
Total Sample ◽  
Smoking Behaviour ◽  
Intervention Programs ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
The Past ◽  
Parental Approval

In Indonesia, the prevalence of smoking among 5 – 9 years old children has increased from 0.4% in 2001 to 2% in 2007. Among present adults smokers (>20 years), 17% started to smoke before the age of 13 years. This study identified factors related to smoking behaviour among 8 – 12 years old children in Jakarta, Indonesia using a questionnaire based cross sectional survey to obtain smoking status and possible predictors towards smoking habit. The total sample size was 1,097 students among 3rd - 7th grade students from schools in Jakarta. Self-reported smoking status was defined as whether the child had smoked tobacco within the past two months prior to the interview. The prevalence of smoking was 13.4%. Logistic regression analysis showed that high parental approval on tobacco use (OR=13.4; CI 95%: 5.1 – 35.1) was the strongest predictor on children smoking status, followed by low parental control (OR=12.1; CI 95%: 6.9 – 21.2), being a male compared to a female (OR=10.7; CI 95%: 5.3 – 21.7), mother (OR=10.58; CI 95%: 3.96 – 28.28), father (OR=7.69; CI 95%: 3.59 – 16.47), sibling (OR=7.91; CI 95%: 4.41 – 14.17) smoking status. Smoking parents and siblings, low parental control, and high parental approval on smoking were related to higher odds of smoking among children. The results were used as a rationale for suggestions and recommendations of relevance for future intervention programs and tobacco related research with specific focus on children.Prevalensi anak perokok umur 5-9 tahun di Indonesia meningkat dari 0,4% di tahun 2001 menjadi 2% di tahun 2007. Tujuh belas persen perokok dewasa menyatakan mulai merokok ketika berumur di bawah 13 tahun. Penelitian ini bertujuan untuk menentukan faktor terkait perilaku merokok anak umur 8-12 tahun di Jakarta dengan menggunakan pendekatan potong lintang untuk menjaring perokok anak dan faktor yang mungkin menyebabkan perilaku tersebut. Kuesioner digunakan untuk menjaring status perilaku merokok anak dalam dua bulan terakhir sebelum survei. Total 1.097 murid kelas 3 sampai 7 di Jakarta menjadi sampel penelitian dengan 13,4% responden merokok dalam 2 bulan terakhir. Analisis regresi logistik menunjukkan bahwa pembolehan merokok di dalam rumah oleh orang tua (OR=13,4; CI 95%: 5,1 – 35,1) menjadi penyebab terkuat, diikuti dengan rendahnya kontrol orang tua (OR=12,1; CI 95%: 6,9 – 21,2), siswa laki-laki (OR=10,7; CI 95%: 5,3 – 21,7), ibu (OR=10.58; CI 95%: 3.96 – 28.28), ayah (OR=7,69; CI 95%: 3,59 – 16,47), dan saudara kandung yang perokok (OR=7,91; CI 95%: 4,41 – 14,17). Orang tua dan saudara kandung yang merokok, rendahnya pengawasan orang tua, dan tingginya pembolehan merokok di dalam rumah menjadi penyebab perilaku merokok anak umur 8- 12 tahun. Hasil penelitian dapat dimanfaatkan sebagai rekomendasi untuk program intervensi di masa depan dan penelitian terkait tembakau dengan fokus kepada anak-anak.

scholarly journals Smoking prevention in adolescents: a cross-sectional and qualitative evaluation of a newly implemented prevention program in Switzerland

BMJ Open ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. e048319
Author(s):  
Andrea Meienberg ◽  
Michael Mayr ◽  
Annina Vischer ◽  
Michael J Zellweger ◽  
Thilo Burkard
Keyword(s):  
Vascular Diseases ◽  
Qualitative Evaluation ◽  
Peer Pressure ◽  
Smoking Initiation ◽  
Smoking Behaviour ◽  
Smoking Prevention ◽  
Cross Sectional Survey ◽  
Related Factors ◽  
Cross Sectional ◽  
Pressure Stress

ObjectivesIn 2014, a hospital-based smoking prevention programme ‘Nichtrauchen ist clever!’ (NIC!) for adolescents aged 12–14 was initiated. The aim of the study was to evaluate participants’ smoking behaviour and to explore the acceptance of the programme, and participants’ awareness on smoking-related diseases and factors that promote smoking initiation.DesignWe performed a cross-sectional survey to evaluate participant’s acceptance of the NIC! program and their smoking habits. A total of 1658 participants completed the survey between January 2016 and December 2019. A qualitative approach, including analysing feedback from students gathered at 77 prevention events between June 2014 and December 2019, was used to assess their view on reasons for smoking initiation and their knowledge about smoking-related diseases.ResultsTwenty-six per cent (429/1658) have already tried tobacco products (so called triers), specifically cigarettes, electronic (e)-cigarettes and shisha. The use of e-cigarettes was most popular among triers 58% (252/429). Eighty-eight per cent of participants (1408/1604) reported they had acquired good or excellent knowledge about smoking, and 89% (1439/1617) of participants stated that they intend not to smoke in the future. Particularly lung cancer and cancer in general, heart—and vascular diseases were frequently mentioned to be smoking related; where, on the other hand, a large number of relevant smoking-associated diseases were mentioned irregularly. Peer pressure, stress and coolness were identified as reasons for smoking initiation, whereas the influence of marketing and multimedia, as well as socioeconomic—and lifestyle-related factors on smoking behaviour was barely noticed.ConclusionNIC! had a high acceptance among the participants and a large number of students reported relevant gain of knowledge. We identified important knowledge-gaps relating to smoking initiation and smoking-related diseases, helping to improve further smoking prevention approaches.

scholarly journals Prevalence of and factors associated with violations of a campus smoke-free policy: a cross-sectional survey of undergraduate students on a university campus in the USA

BMJ Open ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. e030504
Author(s):  
Sujith Ramachandran ◽  
Sandra Bentley ◽  
Ethan Casey ◽  
John P Bentley
Keyword(s):  
Undergraduate Students ◽  
Smoking Status ◽  
Smoking Behaviour ◽  
Policy Support ◽  
Policy Enforcement ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Policy Success ◽  
Smoke Free Policy ◽  
The Usa

ObjectiveThe aim of this study is to estimate the prevalence of smoking behaviour on campus and to identify the key factors that influence adherence to a campus smoke-free policy.Design & participantsThis study employed a cross-sectional, self-administered survey of undergraduate students at the University of Mississippi. A random sample of all available undergraduate classes was recruited for data collection. Students were provided a survey that included questions on demographics, alcohol use, smoking status, policy awareness, policy attitudes, smoking attitudes, policy support, barriers to policy success and policy violations.ResultsThe prevalence of past 30-day smoking was 23%. More than 63% of current smokers report ever smoking on campus, but less than 10% ever received a warning or a ticket for their violation. Nearly all respondents (92.5%) reported witnessing someone smoking on campus, and 22% reported witnessing someone receiving a ticket. Barriers to policy success include lack of reminders about the policy, lack of support from students and University administrators, and insufficient fines. Smoking behaviour (OR: 7.96; 95% CI: 5.13 to 12.36), beliefs about policy adherence (OR: 0.52; 95% CI: 0.40 to 0.69), support for the policy (OR: 0.71; 95% CI: 0.55 to 0.91) and attitudes against smoking behaviour (OR: 0.35; 95% CI: 0.25 to 0.49) were all significantly associated with self-reported policy violations.ConclusionsThis study found that violations of the campus smoke-free policy were fairly frequent and the policy has been largely ineffective, indicating a need for other interventions. Approaches to improve adherence to the policy should address barriers such as reminders about the policy, better policy enforcement and support from the administration.

scholarly journals Psychosocial Factors, Smoke-Free Restrictions, and Media Exposure in Relation to Smoking-Related Attitudes and Behaviors among Adults in Armenia and Georgia

2021 ◽  
Vol 18 (8) ◽  
pp. 4013
Author(s):  
Christina N. Wysota ◽  
Marina Topuridze ◽  
Zhanna Sargsyan ◽  
Ana Dekanosidze ◽  
Lela Sturua ◽  
...  
Keyword(s):  
Smoking Status ◽  
Media Exposure ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Middle Income ◽  
Middle Income Countries ◽  
Quit Attempts ◽  
Multivariable Regression ◽  
Readiness To Quit ◽  
Perceived Harm

Background: Perceived harm, social influences, smoke-free policies, and media exposure have been understudied in relation to tobacco-related attitudes/behaviors in aggregate or in low and middle-income countries; thus, this study examined these factors collectively in relation to smoking-related outcomes among Armenian and Georgian adults. Methods: Using 2018 cross-sectional survey data (n = 1456), multivariable regression analyses examined these factors in relation to smoking status, perceived harm among nonsmokers, and readiness to quit and past-year quit attempts among smokers. Results: Significant predictors (p < 0.05) of current smoking (27.3%) included lower perceived harm, more smoking friends, and fewer home and vehicle restrictions. Among nonsmokers, more home and restaurant/bar restrictions, fewer vehicle restrictions, greater anti-tobacco media exposure, and less pro-tobacco media exposure predicted greater perceived harm. Among smokers, greater perceived social acceptability of smoking, less anti-tobacco media exposure, and greater pro-tobacco media exposure predicted readiness to quit (12.7% of smokers). More smoking friends, more home restrictions, less anti-tobacco media exposure, and greater pro-tobacco media exposure predicted past-year quit attempts (19.2%). Conclusions: Findings support the importance of smoke-free policies but were counterintuitive regarding the roles of social and media influences, underscoring the need to better understand how to address these influences, particularly in countries with high smoking rates.

scholarly journals Electronic cigarette marketing and smoking behaviour in adolescence: a cross-sectional study

2018 ◽  
Vol 4 (4) ◽  
pp. 00155-2018 ◽  
Author(s):  
Julia Hansen ◽  
Reiner Hanewinkel ◽  
Matthis Morgenstern
Keyword(s):  
Cross Sectional Study ◽  
Smoking Behaviour ◽  
Electronic Cigarette ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Mixed Effect ◽  
Logistic Regression Models ◽  
Tobacco Control Policies ◽  
Contact Frequency ◽  
Cross Sectional Design

The aim of this study was to investigate the association between exposure to electronic cigarette (e-cigarette) advertisements and use of e-cigarettes, combustible cigarettes and hookahs.A cross-sectional survey of 6902 German students (mean age 13.1 years, 51.3% male) recruited in six German states was performed. Exposure to e-cigarette advertisements was measured with self-rated contact frequency to three advertising images. Multilevel mixed-effect logistic regression models were used to assess associations between exposure to e-cigarette advertisement and use of e-cigarettes, combustible cigarettes and hookahs (ever and past 30 days).Overall, 38.8% of the students were exposed to e-cigarette advertisements; ever-use of e-cigarettes was 21.7%, of combustible cigarettes was 21.8% and of hookahs was 23.2%, and poly-use of all three products was 12.4%. Exposure to e-cigarette advertisements was positively related to ever and past 30-day use of e-cigarettes, combustible cigarettes, hookahs and combined use.We concluded that a considerable number of German teenagers are exposed to e-cigarette advertisement. There was a clear exposure–behaviour link, indicating that advertising contact was associated with different kinds of “vaping” and also smoking behaviour. Although causal interpretation is not possible due to the cross-sectional design, findings raise concerns about the current tobacco control policies.

scholarly journals Prostate specific antigen (PSA) screening rates and factors associated with screening in Eastern Canadian men: Findings from cross-sectional survey data

2020 ◽  
Vol 14 (7) ◽  
Author(s):  
Devan Tchir ◽  
Marwa Farag ◽  
Michael Szafron
Keyword(s):  
Prostate Specific Antigen ◽  
Smoking Status ◽  
Specific Antigen ◽  
Age Groups ◽  
Canadian Community Health Survey ◽  
Cross Sectional Survey ◽  
Related Factors ◽  
Cross Sectional ◽  
Logistic Regression Models ◽  
Psa Screening

Introduction: The prostate-specific antigen (PSA) test is used in Canada to detect prostate cancer (PCa) despite mixed recommendations. Complications arising from false-positives are common, posing as a cancer-screening concern. This work estimates some Canadian rates of PSA screening and identifies men at increased odds for PSA screening. Methods: The Canadian Community Health Survey (CCHS) from 2009/10 (Atlantic Canada; ATL), 2011/2012 (Ontario; ON), and 2013/2014 (Quebec; QC) were used. Lifetime and recent PSA screening with confidence intervals were constructed to estimate PSA screening in ATL, ON, and QC. Two logistic regression models (for men <50 and ≥50 years of age) were used to determine associations between factors and lifetime PSA screening. Results: PSA screening rates have increased in most age groups for ATL, ON, and QC since 2000/2001. Factors positively associated with lifetime PSA screening in men of all ages were: having a digital rectal exam, having a regular doctor, and having a colorectal exam. Fruit and vegetables consumption and non-smoking status were positively associated with lifetime PSA screening in men <50 years of age. High income and the presence of chronic health conditions were positively associated with lifetime PSA screening in men ≥50 years of age. Conclusions: PSA screening rates have generally increased since 2000/2001 in Canada. Physician-related factors play a role in men at all ages, while different factors are associated in men <50 years of age and men ≥50 years of age. Limitations include the generalizability to all of Canada and the potential for recall bias.

scholarly journals Associations of tobacco smoking with body mass distribution; a population-based study of 65,875 men and women in midlife

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Sidsel Graff-Iversen ◽  
Stephen Hewitt ◽  
Lisa Forsén ◽  
Liv Grøtvedt ◽  
Inger Ariansen
Keyword(s):  
Cardiovascular Risk ◽  
Smoking Status ◽  
Health Indicators ◽  
Population Based ◽  
Lower Percentage ◽  
Body Region ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Men And Women ◽  
Never Smokers

Abstract Background Studies indicate an effect of smoking toward abdominal obesity, but few assess hip and waist circumferences (HC and WC) independently. The present study aimed to assess the associations of smoking status and volume smoked with HC and WC and their ratio in a population with low prevalence of obesity together with high prevalence of smoking. Methods We used cross-sectional survey data from 11 of a total 19 Norwegian counties examined in 1997–99 including 65,875 men and women aged 39–44 years. Analysis of associations were adjusted for confounding by socioeconomic position, health indicators, and additionally for BMI. Results Compared with never-smokers, when adjusting for confounders and in addition for BMI, mean HC remained lower while mean WC and waist-hip-ratio (WHR) were higher in current smokers. The finding of a lower HC and higher WHR level among smokers was consistent by sex and in strata by levels of education and physical activity, while the finding of higher WC by smoking was less consistent. Among current smokers, BMI-adjusted mean HC decreased whereas WC and WHR increased by volume smoked. Compared with current smokers, former smokers had higher BMI-adjusted HC, lower WHR and among women WC was lower. Conclusions The main finding in this study was the consistent negative associations of smoking with HC. In line with the hypothesis that lower percentage gluteofemoral fat is linked with higher cardiovascular risk, our results suggest that smoking impacts cardiovascular risk through mechanisms that reduce the capacity of fat storage in the lower body region.

scholarly journals Associations between Macronutrient Intakes and Obesity/Metabolic Risk Phenotypes: Findings of the Korean National Health and Nutrition Examination Survey

Nutrients ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 628 ◽  
Author(s):  
Ha-Na Kim ◽  
Sang-Wook Song
Keyword(s):  
National Health ◽  
Smoking Status ◽  
Positive Association ◽  
Normal Weight ◽  
Metabolic Risk ◽  
Metabolic Abnormalities ◽  
Nutrition Examination Survey ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Health And Nutrition

Obesity is a risk factor for many health issues, as are metabolic abnormalities. However, few studies have addressed the associations between obesity/metabolic risk phenotypes and dietary macronutrient intakes (carbohydrate, protein, and fat). Therefore, this study examined the associations between macronutrient intakes and obesity/metabolic risk phenotypes in a Korean population. We used data from the Korean National Health and Nutrition Examination Survey, a cross-sectional survey of Korean civilians, conducted in 2014 and 2016, and data on a total of 7374 participants were analyzed. Macronutrient intakes were defined as the proportions of energy derived from carbohydrate, protein, and fat. Those exhibiting obesity/metabolic risk phenotypes (or not) were divided into four groups: normal weight without metabolic abnormalities; obese without metabolic abnormalities; normal weight with metabolic abnormalities; and obese with metabolic abnormalities. After adjusting for age, smoking status, alcohol consumption, extent of physical activity, household income, and daily fiber intake, no association was found between the proportions of carbohydrate, protein, or fat intakes and obesity/metabolic risk phenotypes except for a positive association between metabolically healthy but obese status and low protein intake in females. Further studies are required to evaluate the effects of macronutrient intakes on obesity/metabolic risk phenotypes and associated health outcomes.

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