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Investigating gateway effects using the PATH study.

Peter Lee¹, John Fry²

¹P.N.Lee Statistics and Computing Ltd, Sutton, Surrey, SM2 5DA, UK.

|January 21, 2020

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Confounding factors significantly explain the observed "gateway effect" of e-cigarette use leading to cigarette smoking in youth. Further research is needed to fully understand this association.

Area of Science:

Public Health
Tobacco Control
Adolescent Health

Background:

Previous meta-analysis indicated e-cigarette use predicts future cigarette smoking initiation in youth.
The
gateway effect
hypothesis suggests e-cigarette use leads to traditional smoking.
Limited consideration of confounding factors in prior studies necessitates further investigation.

Purpose of the Study:

To investigate the potential for confounding in the association between e-cigarette use and subsequent cigarette smoking initiation.
To re-evaluate the
gateway effect
using a more comprehensive adjustment for confounders.
To utilize data from the Population Assessment of Tobacco and Health (PATH) study, Waves 1 and 2.

Keywords:

Cigarettes Confounding E-cigarettes Gateway effects Modelling Propensity score

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Main Methods:

Propensity score analysis was used to adjust for baseline confounders in Wave 1 never cigarette smokers.
Sensitivity analyses included adjustment for other tobacco product use and residual confounding.
Analyses examined Wave 1 e-cigarette use predicting Wave 2 cigarette smoking initiation.

Main Results:

Adjustment for propensity scores significantly reduced the odds ratio for e-cigarette use predicting smoking initiation.
Further adjustment for other tobacco products further diminished the observed association.
Sensitivity analyses confirmed that confounding accounted for the majority of the apparent gateway effect.

Conclusions:

Confounding is a substantial contributor to the observed association between adolescent e-cigarette use and subsequent cigarette smoking.
The
gateway effect
is largely explained by unmeasured or inadequately measured confounding variables.
Further research with larger sample sizes and longitudinal data is recommended to clarify residual associations.