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Increasing Prevalence of Electronic Cigarette Use among Medical Students. Repeated Cross-Sectional Multicenter Surveys in Germany and Hungary, 2016-2018.

Erika BaloghZoltán WagnerNóra FaublHenna RiemenschneiderKaren VoigtAndrás TerebessyFerenc HorváthZsuzsanna FüzesiIstván Kiss
Published in: Substance use & misuse (2020)
Use of electronic cigarettes (e-cigarettes) is gaining popularity among young adults. Medical students' nicotine use behavior is of particular interest because of their impending role in health promotion. Objectives: Aim of our study is to assess changes that occurred between 2016 and 2018 in the prevalence of e-cigarette use among medical students and to explore associations between e-cigarette use, demographic characteristics, and cigarette smoking. Self-administered questionnaire surveys were used to obtain cross-sectional data of medical students in Budapest and Pécs, Hungary, and Dresden, Germany. Results: Sample sizes for 2016 and 2018 were 2297 and 1514, respectively. In the whole sample, past-30-day use of e-cigarettes increased from 4.5% to 8.0% (p < 0.001). The increase in e-cigarette use was significant in both genders (from 3.6% to 5.6% among females, p = 0.028, and from 5.9 to 11.4% among males, p < 0.001). Prevalence of e-cigarette use was higher among Hungarian students than among German students (2.2% versus 5.7% in 2016, and 4.1% versus 10.5% in 2018, p < 0.05 for both years). There was no significant difference in e-cigarette use among different academic years. The ratio of e-cigarette users increased significantly among current cigarette smokers but not among nonsmokers. We could not detect a decrease in cigarette smoking. Conclusions: Prevalence of e-cigarette use increased significantly among medical students without a reduction in cigarette smoking. Medical schools should add the topic of e-cigarettes to their curricula and need to develop cessation programs to help their students quit both cigarettes and e-cigarettes.
Keyphrases
  • smoking cessation
  • medical students
  • cross sectional
  • replacement therapy
  • risk factors
  • young adults
  • health promotion
  • public health
  • clinical trial
  • deep learning
  • artificial intelligence