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Investigating gateway effects using the PATH study

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 th...

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Autores principales: Lee, Peter, Fry, John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6950312/
https://www.ncbi.nlm.nih.gov/pubmed/31956397
http://dx.doi.org/10.12688/f1000research.18354.2
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author Lee, Peter
Fry, John
author_facet Lee, Peter
Fry, John
author_sort Lee, Peter
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description 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.
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spelling pubmed-69503122020-01-16 Investigating gateway effects using the PATH study Lee, Peter Fry, John F1000Res Research Article 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. F1000 Research Limited 2019-12-04 /pmc/articles/PMC6950312/ /pubmed/31956397 http://dx.doi.org/10.12688/f1000research.18354.2 Text en Copyright: © 2019 Lee P and Fry J http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Lee, Peter
Fry, John
Investigating gateway effects using the PATH study
title Investigating gateway effects using the PATH study
title_full Investigating gateway effects using the PATH study
title_fullStr Investigating gateway effects using the PATH study
title_full_unstemmed Investigating gateway effects using the PATH study
title_short Investigating gateway effects using the PATH study
title_sort investigating gateway effects using the path study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6950312/
https://www.ncbi.nlm.nih.gov/pubmed/31956397
http://dx.doi.org/10.12688/f1000research.18354.2
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