Why Are National Estimates So Different? A Comparison of Youth E-Cigarette Use and Cigarette Smoking in the MTF and PATH Surveys
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Updated: Dec 11, 2025

Comparing the Effects of Electronic Cigarette Vapor and Cigarette Smoke in a Novel In Vivo Exposure System
Published on: May 24, 2017
Carol J Boyd1,2,3, Philip Veliz1,2, Rebecca J Evans-Polce1,2
1Center for the Study of Drugs, Alcohol, Smoking and Health, School of Nursing, University of Michigan, Ann Arbor, Michigan.
Objective:
We compared estimates of adolescents' nicotine product use and perceptions of harm from two national surveys: Monitoring the Future (MTF) and Population Assessment of Tobacco and Health (PATH). We explored one explanation for the different estimates for nicotine product use and adolescents' perceptions of harm.
Method:
We used data source triangulation examining 30-day e-cigarette use and cigarette smoking, beliefs about harm, and friends' use of these products in two samples of adolescents from the 2015-2016 MTF and PATH samples.
Results:
Differences were found, with MTF reporting higher prevalence rates in both past-30-day e-cigarette use (12.4% vs. 6.7%) and cigarette smoking (8.6% vs. 5.1%) when compared with PATH. Differences were significant at the .001 alpha level. MTF respondents were less likely than PATH respondents to view both e-cigarettes (17.7% vs. 48.6%) and cigarettes (75.6% vs. 82.4%) as harmful. The unadjusted odds ratio (OR) shows that PATH respondents had significantly lower odds of indicating either e-cigarette (OR = 0.509, 95% confidence interval [CI] [0.400, 0.648]) or cigarette smoking (OR = 0.571, 95% CI [0.433, 0.753]) when compared with MTF respondents. However, these differences in e-cigarette use (adjusted odds ratio [AOR] = 0.849, 95% CI [0.630, 1.144]) and cigarette smoking (AOR = 0.829, 95% CI = [0.578, 1.189]) were mediated when additional predictors were included in the model (i.e., friends use, risk of harm).
Conclusions:
Substantial differences were found between national surveys estimating population rates of e-cigarette use and cigarette smoking. Data source triangulation allowed for new explanations for several of the disparate nicotine use estimates between MTF and PATH.
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