Investigating gateway effects using the PATH study
| Autor: | Peter N. Lee, John S. Fry |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
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 |
| Zdroj: | F1000Research |
| ISSN: | 2046-1402 |
| DOI: | 10.12688/f1000research.18354.2 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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