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Evaluating the One-Year Impact of School e-Cigarette Use Interventions among Current Youth e-Cigarette Users in the COMPASS Study, 2017/18-2018/19.

Adam Geoffrey ColeMahmood R GohariScott T Leatherdale
Published in: International journal of environmental research and public health (2023)
There is a lack of evidence for the impact of school-based e-cigarette interventions among current e-cigarette users. This natural experimental evaluation study evaluated the one-year impact of various school-based e-cigarette prevention/cessation programs among a sample of current youth e-cigarette users. The COMPASS study sample included n = 3586 current e-cigarette users from n = 90 schools with data collected between 2017 and 2019. Student e-cigarette use patterns were categorized as "escalated", "maintained", and "reduced" based on the change in past 30-day e-cigarette use between baseline and follow-up. Intervention schools added e-cigarette use "prevention", "cessation", or "protection" programs, while control schools did not make any changes. Logistic regression models identified how each category of added programs was associated with e-cigarette use patterns. About one quarter of schools added an e-cigarette use prevention/cessation program over one year. Student e-cigarette use patterns between control and intervention groups differed in proportion ranging from a decrease of 3.35% to an increase of 5.80%. Regression models did not identify any significant differences in the odds of escalating or reducing e-cigarette use in intervention relative to control schools. While many schools implemented new e-cigarette programs over one year, none of the interventions led to significant changes in e-cigarette escalation or reduction among current youth e-cigarette users. Additional studies are needed to evaluate the impact of e-cigarette interventions among current e-cigarette users.
Keyphrases
  • smoking cessation
  • physical activity
  • randomized controlled trial
  • public health
  • mental health
  • young adults
  • clinical trial
  • machine learning
  • open label
  • study protocol
  • medical students