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Investigating gateway effects using the PATH study
- Source :
- F1000Research
- Publication Year :
- 2019
- Publisher :
- F1000 Research Ltd, 2019.
-
Abstract
- 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.
- Subjects :
- Male
Propensity score
Gateway effects
030508 substance abuse
Electronic Nicotine Delivery Systems
Modelling
General Biochemistry, Genetics and Molecular Biology
Cigarette Smoking
Continuous variable
03 medical and health sciences
0302 clinical medicine
Cigarette smoking
030225 pediatrics
Humans
Medicine
Confounding
030212 general & internal medicine
General Pharmacology, Toxicology and Pharmaceutics
Cigarettes
General Immunology and Microbiology
business.industry
Smoking
Tobacco Products
Tobacco Use Disorder
Articles
General Medicine
Odds ratio
Gateway (computer program)
Confidence interval
E-cigarettes
Propensity score matching
Female
0305 other medical science
business
Research Article
Demography
Cohort study
Subjects
Details
- ISSN :
- 20461402
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- F1000Research
- Accession number :
- edsair.doi.dedup.....d3c64047bb1ae9756997979908988457
- Full Text :
- https://doi.org/10.12688/f1000research.18354.2