scholarly journals Further investigations of gateway effects using the PATH study

2020 ◽  
Author(s):  
Peter N Lee ◽  
John S Fry
Keyword(s):  
Tobacco Use ◽  
Causal Effect ◽  
Smoking Initiation ◽  
Continuous Variable ◽  
Tobacco Product ◽  
Sensitivity Analyses ◽  
Health Study ◽  
Using Data ◽  
Confounder Adjustment ◽  
Confounding Adjustment

Abstract Background: Considerable interest exists in whether e-cigarette use (“vaping”) by youths increases the risk of initiating cigarette smoking. Based on Waves 1 and 2 of the Population Assessment of Tobacco and Health study we reported that adjustment for propensity for vaping using Wave 1 variables explained about 80% of the unadjusted relationship. This analysis may be over-adjusted had vaping at Wave 1 affected some variables recorded then. Here we present analyses using Waves 1 to 3 to avoid this possibility. Methods: Our main analysis M1 concerned those who had never smoked by Wave 2 and never vaped by Wave 1. Wave 2 vaping was linked to smoking initiation by Wave 3, adjusting for Wave 1 predictors. Sensitivity analyses excluded other tobacco product users at Wave 1, included other tobacco product use as an additional predictor, or were based on propensity for ever smoking or ever any tobacco use, rather than ever vaping. Other analyses adjusted for propensity as derived originally, or ignored Wave 1 data. Other analyses used grouped age (only available originally) or exact age (available now) as a confounder variable, attempted residual confounding adjustment by modifying values of predictor variables using data later recorded, or considered interactions with age. Results: In M1, propensity adjustment removed about 50% of the excess odds ratio (i.e. OR–1), the unadjusted OR, 5.60 (95% CI 4.52-6.93) becoming 3.37 (2.65-4.28), 3.11 (2.47-3.92) or 3.27 (2.57-4.16) depending whether adjustment was for propensity as a continuous variable, as quintiles, or for the 16 variables making up the propensity score. Many factors studied hardly affected the results, including using grouped or exact age, consideration of other tobacco products, including interactions, or using predictors of smoking or tobacco use rather than vaping. The clearest conclusion was that analyses avoiding over-adjustment only explained about 50% of the excess OR whereas analyses subject to over-adjustment explained about 80%. Conclusions: Although much of the unadjusted gateway effect results from uncontrolled confounding, our current analysis provides stronger evidence of a causal effect of vaping than did our earlier analysis. However, some doubts remain about the completeness of confounder adjustment.

scholarly journals Further investigation of gateway effects using the PATH study

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 607
Author(s):  
Peter N Lee ◽  
John S Fry
Keyword(s):  
Tobacco Use ◽  
Causal Effect ◽  
Smoking Initiation ◽  
Continuous Variable ◽  
Tobacco Product ◽  
Sensitivity Analyses ◽  
The Us ◽  
Never Smokers ◽  
Using Data ◽  
Confounding Adjustment

Background: Interest exists in whether youth e-cigarette use (“vaping”) increases risk of initiating cigarette smoking. Using Waves 1 and 2 of the US PATH study we reported that adjustment for vaping propensity using Wave 1 variables explained about 80% of the unadjusted relationship. Here we use data from Waves 1 to 3 to avoid over-adjustment if Wave 1 vaping affected variables recorded then. Methods: Our main analysis M1 concerned Wave 2 never smokers who never vaped by Wave 1, linking Wave 2 vaping to Wave 3 smoking initiation, adjusting for Wave 1 predictors. We conducted sensitivity analyses that: excluded Wave 1 other tobacco product users; included other product use as an extra predictor; or considered propensity for smoking or any tobacco use, rather than vaping. We also conducted analyses that: adjusted for propensity as derived originally; ignored Wave 1 data; used exact age (not previously available) as a confounder rather than grouped age; attempted residual confounding adjustment by modifying predictor values using data recorded later; or considered interactions with age. Results: In M1, adjustment removed about half the excess OR (i.e. OR–1), the unadjusted OR, 5.60 (95% CI 4.52-6.93), becoming 3.37 (2.65-4.28), 3.11 (2.47-3.92) or 3.27 (2.57-4.16), depending whether adjustment was for propensity as a continuous variable, as quintiles, or for the variables making up the propensity score. Many factors had little effect: using grouped or exact age; considering other products; including interactions; or using predictors of smoking or tobacco use rather than vaping. The clearest conclusion was that analyses avoiding over-adjustment explained about half the excess OR, whereas analyses subject to over-adjustment explained about 80%. Conclusions: Although much of the unadjusted gateway effect results from confounding, we provide stronger evidence than previously of some causal effect of vaping, though some doubts still remain about the completeness of adjustment.

scholarly journals Further investigation of gateway effects using the PATH study

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 607
Author(s):  
Peter N Lee ◽  
John S Fry
Keyword(s):  
Tobacco Use ◽  
Causal Effect ◽  
Smoking Initiation ◽  
Continuous Variable ◽  
Tobacco Product ◽  
Sensitivity Analyses ◽  
The Us ◽  
Never Smokers ◽  
Using Data ◽  
Confounding Adjustment

Background: Interest exists in whether youth e-cigarette use (“vaping”) increases risk of initiating cigarette smoking. Using Waves 1 and 2 of the US PATH study we previously reported adjustment for vaping propensity using Wave 1 variables explained about 80% of the unadjusted relationship. Here data from Waves 1 to 3 are used to avoid over-adjustment if Wave 1 vaping affected variables recorded then. Methods: Main analyses M1 and M2 concerned Wave 2 never smokers who never vaped by Wave 1, linking Wave 2 vaping to Wave 3 smoking initiation, adjusting for predictors of vaping based on Wave 1 data using differing  propensity indices.  M3 was similar but derived the index from Wave 2 data.  Sensitivity analyses excluded Wave 1 other tobacco product users, included other product use as another predictor, or considered propensity for smoking or any tobacco use, not vaping. Alternative analyses used exact age (not previously available) as a confounder not grouped age, attempted residual confounding adjustment by modifying predictor values using data recorded later, or considered interactions with age. Results: In M1, adjustment removed about half the excess OR (i.e. OR–1), the unadjusted OR, 5.60 (95% CI 4.52-6.93), becoming 3.37 (2.65-4.28), 3.11 (2.47-3.92) or 3.27 (2.57-4.16), depending whether adjustment was for propensity as a continuous variable, as quintiles, or the variables making up the propensity score. Many factors had little effect: using grouped or exact age; considering other products; including interactions; or using predictors of smoking or tobacco use rather than vaping. The clearest conclusion was that analyses avoiding over-adjustment explained about half the excess OR, whereas analyses subject to over-adjustment explained about 80%. Conclusions: Although much of the unadjusted gateway effect results from confounding, we provide stronger evidence than previously of some causal effect of vaping, though doubts still remain about the completeness of adjustment.

scholarly journals Investigating gateway effects using the PATH study

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 264 ◽  
Author(s):  
Peter Lee ◽  
John Fry
Keyword(s):  
Cigarette Smoking ◽  
Odds Ratio ◽  
Meta Analysis ◽  
Smoking Initiation ◽  
Continuous Variable ◽  
Sensitivity Analyses ◽  
Health Study ◽  
Cigarette Use ◽  
Tobacco Products ◽  
Residual Confounding

Background: A recent meta-analysis of nine cohort studies in youths reported that baseline ever e-cigarette use strongly predicted cigarette smoking initiation in the next 6-18 months, with an adjusted odds ratio of 3.62 (95% confidence interval 2.42-5.41).  A recent review of e-cigarettes agreed there was substantial evidence for this “gateway effect”.  However, the number of confounders considered in the studies was limited, so we investigated whether the effect might have resulted from inadequate adjustment, using Waves 1 and 2 of the Population Assessment of Tobacco and Health study. Methods: Our main analyses considered Wave 1 never cigarette smokers who, at Wave 2, had information available on smoking initiation.  We constructed a propensity score for ever e-cigarette use from Wave 1 variables, using this to predict ever cigarette smoking.  Sensitivity analyses accounted for use of other tobacco products, linked current e-cigarette use to subsequent current smoking, or used propensity scores for ever smoking or ever tobacco product use as predictors.  We also considered predictors using data from both waves to attempt to control for residual confounding from misclassified responses. Results: Adjustment for propensity dramatically reduced the unadjusted odds ratio (OR) of 5.70 (4.33-7.50) to 2.48 (1.85-3.31), 2.47 (1.79-3.42) or 1.85 (1.35-2.53), whether adjustment was made as quintiles, as a continuous variable or for the individual variables.  Additional adjustment for other tobacco products reduced this last OR to 1.59 (1.14-2.20).  Sensitivity analyses confirmed adjustment removed most of the gateway effect.  Control for residual confounding also reduced the association. Conclusions: We found that confounding is a major factor, explaining most of the observed gateway effect.  However, our analyses are limited by small numbers of new smokers considered and the possibility of over-adjustment if taking up e-cigarettes affects some predictor variables.  Further analyses are intended using Wave 3 data which should avoid these problems.

scholarly journals Investigating gateway effects using the PATH study

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 264 ◽  
Author(s):  
Peter Lee ◽  
John Fry
Keyword(s):  
Cigarette Smoking ◽  
Propensity Scores ◽  
Meta Analysis ◽  
Smoking Initiation ◽  
Continuous Variable ◽  
Tobacco Product ◽  
Sensitivity Analyses ◽  
Cigarette Use ◽  
Product Use ◽  
Residual Confounding

Background: A recent meta-analysis of nine cohort studies in youths reported that baseline ever e-cigarette use strongly predicted cigarette smoking initiation in the next 6-18 months, with an adjusted odds ratio (OR) of 3.62 (95% confidence interval 2.42-5.41). A recent e-cigarette review agreed there was substantial evidence for this “gateway effect”. As the number of confounders considered in the studies was limited we investigated whether the effect might have resulted from inadequate adjustment, using Waves 1 and 2 of the US PATH study. Methods: Our main analyses considered Wave 1 never cigarette smokers who, at Wave 2, had data on smoking initiation.We constructed a propensity score for ever e-cigarette use from Wave 1 variables, using this to predict ever cigarette smoking. Sensitivity analyses accounted for other tobacco product use, linked current e-cigarette use to subsequent current smoking, or used propensity scores for ever smoking or ever tobacco product use as predictors. We also considered predictors using data from both waves, attempting to reduce residual confounding from misclassified responses. Results: Adjustment for propensity dramatically reduced the unadjusted OR of 5.70 (4.33-7.50) to 2.48 (1.85-3.31), 2.47 (1.79-3.42) or 1.85 (1.35-2.53), whether adjustment was made as quintiles, as a continuous variable or for the individual variables. Additional adjustment for other tobacco products reduced this last OR to 1.59 (1.14-2.20). Sensitivity analyses confirmed adjustment removed most of the gateway effect. Control for residual confounding also reduced the association. Conclusions: We found that confounding is a major factor, explaining most of the observed gateway effect. However, our analyses are limited by small numbers of new smokers considered and the possibility of over-adjustment if taking up e-cigarettes affects some predictor variables. Further analyses are intended using Wave 3 data to try to minimize these problems, and clarify the extent of any true gateway effect.

scholarly journals Longitudinal pathways of exclusive and polytobacco cigar use among youth, young adults and adults in the USA: findings from the PATH Study Waves 1–3 (2013–2016)

2020 ◽  
Vol 29 (Suppl 3) ◽  
pp. s163-s169 ◽  
Author(s):  
Kathryn C Edwards ◽  
Eva Sharma ◽  
Michael J Halenar ◽  
Kristie A Taylor ◽  
Karin A Kasza ◽  
...  
Keyword(s):  
Young Adults ◽  
Tobacco Use ◽  
Age Groups ◽  
Health Impact ◽  
Tobacco Product ◽  
Health Study ◽  
Longitudinal Analyses ◽  
Cross Sectional ◽  
The Usa ◽  
Nationally Representative

ObjectiveThe goal of this study is to examine the cross-sectional prevalence of use and 3-year longitudinal pathways of cigar use in US youth (12-17 years), young adults (18-24 years), and adults 25+ (25 years or older).DesignData were drawn from the first three waves (2013–2016) of the Population Assessment of Tobacco and Health Study, a nationally representative, longitudinal cohort study of US youth and adults. Respondents with data at all three waves (youth, n=11 046; young adults, n=6478; adults 25+, n=17 188) were included in longitudinal analyses.ResultsWeighted cross-sectional prevalence of past 30-day (P30D) use was stable for adults 25+ (~6%), but decreased in youth (Wave 1 (W1) to Wave 3 (W3)=2.5% to 1.2%) and young adults (W1 to W3=15.7% to 14.0%). Among W1 P30D cigar users, over 50% discontinued cigar use (irrespective of other tobacco use) by Wave 2 (W2) or W3. Across age groups, over 70% of W1 P30D cigar users also indicated P30D use of another tobacco product, predominantly cigar polytobacco use with cigarettes. Discontinuing all tobacco use by W2 or W3 was greater in adult exclusive P30D cigar users compared with polytobacco cigar users.ConclusionsAlthough the majority of P30D cigar users discontinued use by W3, adult polytobacco users of cigars were less likely to discontinue all tobacco use than were exclusive cigar users. Tracking patterns of cigar use will allow further assessment of the population health impact of cigars.

scholarly journals Transitions to smokeless tobacco use among adult cigarette smokers in the Population Assessment of Tobacco and Health (PATH) Study, Waves 3–5 (2015–2019)

2021 ◽  
pp. tobaccocontrol-2021-056907
Author(s):  
Rebecca A Jackson ◽  
Chunfeng Ren ◽  
Blair Coleman ◽  
Hannah R Day ◽  
Cindy M Chang ◽  
...  
Keyword(s):  
Tobacco Use ◽  
Smokeless Tobacco ◽  
Health Study ◽  
Dual Use ◽  
Cigarette Smokers ◽  
Population Assessment ◽  
Current Cigarette ◽  
Smokeless Tobacco Use ◽  
Using Data

ObjectiveExamine patterns of dual use of cigarettes and smokeless tobacco and complete switching over time among adult current cigarette smokers using data from the Population Assessment of Tobacco and Health Study Wave 3 (2015–2016), Wave 4 (2016–2018) and Wave 5 (2018–2019).MethodsWe examined four tobacco use states among 6834 exclusive smokers and 372 dual users at Wave 3 with two waves of follow-up data: exclusive cigarette use, exclusive smokeless tobacco use, dual use and use of neither product.ResultsAmong exclusive smokers at Wave 3, only 1.6% (95% CI: 1.3% to 2.1%) transitioned to dual use at Wave 4, and 0.1% (95% CI: 0.07% to 0.2%) switched to exclusive smokeless tobacco use. Among exclusive smokers who switched to dual use, 53.1% (95% CI: 40.9% to 64.9%) returned to exclusive cigarette smoking, 34.3% (95% CI: 23.8% to 46.6%) maintained dual use and 12.6% (95% CI: 7.0% to 21.7%) did not smoke cigarettes after an additional wave of follow-up. Dual users at Wave 3 were likely to maintain their dual use status at Wave 4, 51.2% (95% CI: 46.1% to 56.3%) and Wave 5, 47.9% (95% CI: 40.1% to 55.8%).ConclusionsVery few cigarette smokers transition to smokeless tobacco use, and among those who do, dual use is more common than exclusive smokeless tobacco use. Further, the majority of exclusive cigarette smokers who transition to dual use at Wave 4 continue smoking cigarettes at Wave 5, either as dual users or as exclusive smokers.

scholarly journals Youth susceptibility to tobacco use: Is it general or specific?

2021 ◽  
Author(s):  
Hui Cheng ◽  
Pavel N. Lizhnyak ◽  
Natasha A. Knight ◽  
Andrea R. Vansickel ◽  
Edward G. Largo
Keyword(s):  
Tobacco Use ◽  
Holistic Approach ◽  
Risky Behaviors ◽  
Tobacco Product ◽  
Health Study ◽  
Tobacco Products ◽  
Nationally Representative ◽  
Institutionalized Youth ◽  
High Degree ◽  
The Relationship

Importance: Susceptibility to tobacco use can help identify youth that are at risk for tobacco use. Objective: To estimate the extent of overlap in susceptibilities across various tobacco products, investigate correlates with susceptibilities, and examine whether the relationship linking susceptibility with the onset of use is product specific or is accounted for by a general susceptibility-onset relationship. Design: Prospective cohort study. Setting: Analysis of data from the longitudinal Population Assessment of Tobacco and Health study wave 4 (December 2016 to January 2018) and wave 4.5 youth surveys (December 2017 to November 2018). Participants: A nationally representative sample of non-institutionalized youth 12-17 years old who had never used a tobacco product at baseline assessment. Main variable of interest: Susceptibility to the use of each type of tobacco product assessed at wave 4. Main outcomes: Onset of use of various tobacco products defined as the first use occurring between waves 4 and 4.5 assessments. Results: Cigarettes and e-cigarettes were the most common (~25%), while snus was the least common (<5%), tobacco product to which youth were susceptible. There was a high degree of overlap in susceptibilities across tobacco products (65% of tobacco-susceptible youth were susceptible to more than one tobacco product). Tobacco-susceptible youth were more likely to have used cannabis or consumed alcohol in the past 30 days or to have tobacco-using peers. Susceptibility to use predicted the onset of use (incidence ratio = 3.2 to 12.9). Estimates for the product-specific path were null, except for e-cigarettes (β=0.08, 95% CI=0.04 to 0.13) and filtered cigars (β= -0.09, 95% CI= -0.13 to -0.05), after accounting for the general susceptibility-to-tobacco-onset relationship (β=0.50, 95% CI=0.42 to 0.58). Conclusions and Relevance: Youth susceptibility to tobacco use overlaps widely across different tobacco products and other risky behaviors. Public health efforts may benefit from a holistic approach to risk behavior prevention planning.

scholarly journals Does smoking cause lower educational attainment and general cognitive ability? Triangulation of causal evidence using multiple study designs

2020 ◽  
pp. 1-9
Author(s):  
Suzanne H. Gage ◽  
Hannah M. Sallis ◽  
Glenda Lassi ◽  
Robyn E. Wootton ◽  
Claire Mokrysz ◽  
...  
Keyword(s):  
Educational Attainment ◽  
Cognitive Ability ◽  
Fluid Intelligence ◽  
Causal Effect ◽  
Smoking Initiation ◽  
Sensitivity Analyses ◽  
Mendelian Randomisation ◽  
General Cognitive Ability ◽  
Inverse Variance ◽  
Lower Educational Attainment

Abstract Background Observational studies have found associations between smoking and both poorer cognitive ability and lower educational attainment; however, evaluating causality is challenging. We used two complementary methods to explore this. Methods We conducted observational analyses of up to 12 004 participants in a cohort study (Study One) and Mendelian randomisation (MR) analyses using summary and cohort data (Study Two). Outcome measures were cognitive ability at age 15 and educational attainment at age 16 (Study One), and educational attainment and fluid intelligence (Study Two). Results Study One: heaviness of smoking at age 15 was associated with lower cognitive ability at age 15 and lower educational attainment at age 16. Adjustment for potential confounders partially attenuated findings (e.g. fully adjusted cognitive ability β −0.736, 95% CI −1.238 to −0.233, p = 0.004; fully adjusted educational attainment β −1.254, 95% CI −1.597 to −0.911, p < 0.001). Study Two: MR indicated that both smoking initiation and lifetime smoking predict lower educational attainment (e.g. smoking initiation to educational attainment inverse-variance weighted MR β −0.197, 95% CI −0.223 to −0.171, p = 1.78 × 10−49). Educational attainment results were robust to sensitivity analyses, while analyses of general cognitive ability were less so. Conclusion We find some evidence of a causal effect of smoking on lower educational attainment, but not cognitive ability. Triangulation of evidence across observational and MR methods is a strength, but the genetic variants associated with smoking initiation may be pleiotropic, suggesting caution in interpreting these results. The nature of this pleiotropy warrants further study.

scholarly journals Youth susceptibility to tobacco use: is it general or specific?

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hui G. Cheng ◽  
Pavel N. Lizhnyak ◽  
Natasha A. Knight ◽  
Andrea R. Vansickel ◽  
Edward G. Largo
Keyword(s):  
Tobacco Use ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Holistic Approach ◽  
Large Degree ◽  
Risky Behaviors ◽  
Tobacco Product ◽  
Health Study ◽  
Tobacco Products ◽  
Study Population

Abstract Background Susceptibility to tobacco use predicts tobacco use onset among youth. The current study aimed to estimate the extent of overlap in susceptibilities across various tobacco products, investigate sociopsychological correlates with susceptibilities, and examine whether the relationship linking susceptibility with the onset of use is product-specific or is accounted for by a general susceptibility-onset relationship. Methods The study population consisted of US youth 12–17 years old who had never used a tobacco product, sampled in the longitudinal Population Assessment of Tobacco and Health study wave 4 (Dec. 2016-Jan. 2018; n = 10,977). Tobacco product-specific susceptibility at wave 4 was assessed via questions about curiosity, likelihood to try, and likelihood of use if a best friend offered. The onset of use of various tobacco products was defined as first use occurring between the wave 4 and wave 4.5 (Dec. 2017-Dec. 2018) assessments (n = 8841). Generalized linear regression and structural equation models were used for data analysis. Results There is a large degree of overlap in susceptibilities across tobacco products (65% of tobacco-susceptible youth were susceptible to more than one tobacco product). Tobacco-susceptible youths were more likely to have recently used cannabis, consumed alcohol, or to have been associated with tobacco-using peers. Structural equation models suggest that the susceptibility-onset relationship largely operates in a non-product-specific manner after accounting for the general susceptibility-to-tobacco-onset relationship. Conclusions Youth susceptibility to tobacco use overlaps widely across different tobacco products and other risky behaviors. Findings from this study support a holistic approach towards the prevention of risk behaviors, supplemented by product-specific strategies when needed.

scholarly journals Associations between Black and Mild Cigar Pack Size and Demographics and Tobacco Use Behaviors among US Adults

2021 ◽  
Vol 18 (12) ◽  
pp. 6628
Author(s):  
Ollie Ganz ◽  
Jessica L. King ◽  
Daniel P. Giovenco ◽  
Mary Hrywna ◽  
Andrew A. Strasser ◽  
...  
Keyword(s):  
Tobacco Use ◽  
Health Study ◽  
Purchasing Behavior ◽  
Limited Data ◽  
User Characteristics ◽  
Chi Square ◽  
Cross Sectional ◽  
Younger Adults ◽  
Pack Size ◽  
Using Data

Pack size is an important pricing strategy for the tobacco industry, but there is limited data on how users differ based on preferred pack size for cigar products. Using data from Wave 4 of the Population Assessment of Tobacco and Health Study, this study identified differences in adult cigar user characteristics based on pack size purchasing behavior among users of a top cigar brand, Black and Mild. Weighted chi-square tests were used to examine the associations between Black and Mild pack size and sociodemographic, cigar and other substance use characteristics. Overall, our study found that users of Black and Mild cigars differ by demographic, cigar and other tobacco use characteristics based on preferred pack size, with smaller packs appealing to younger, female, less-experienced and less-established smokers, and larger packs appealing to older, male, more experienced, and more dependent cigar smokers. Dual use of cigarettes and cigars was also higher among users of smaller packs. While this study is cross-sectional, findings suggest that minimum packaging laws for cigars may impact younger adults who are purchasing smaller pack sizes and likely experimenting with new cigar products and styles.

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