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Electronic Cigarettes to Vaporize Cannabis: Prevalence of Use and Associated Factors among Current Electronic Cigarette Users in Germany (DEBRA Study).

Sabrina KastaunJaqueline HildebrandtDaniel Kotz
Published in: Substance use & misuse (2020)
Background: In Germany, cannabis is the most widely used illicit drug, and inhalation together with tobacco is most popular. However, it has been described that electronic cigarettes (ECs) are being used to vaporize cannabis (extract). No current data on EC cannabis use in the German population are yet available. Objectives: This study examines the prevalence of EC cannabis consumption for mood changing effects among current EC users, and associated consumer characteristics in Germany. Methods: We used data from the German Study on Tobacco Use (period: 8/2016-01/2019, DEBRA, www.debra-study.info), a nationally representative household survey. EC cannabis use for mood-changing effects was assessed in 504 current EC users (aged ≥ 18 years) of the total sample (N = 32,678). Ever use was defined by: (1) occasional or regular use, or (2) experimental consumption. Associations with socio-demographic consumer characteristics and tobacco smoking were analyzed using multivariable regression analyses. Results: Amongst current EC users, 7.2% had ever vaporized cannabis: 2.3% (95%CI = 1.2-3.9) reported occasional or regular use (1) and 4.8% (95%CI = 3.2-7.1) reported experimental use (2). Age was associated with ever EC cannabis use: highest prevalence rates were found among 18-24-year-olds: 6.5% (95%CI = 2.3-13.1) (1) and 8.0% (95%CI = 3.7-15.8) (2), respectively. The majority (90.2%) of ever EC cannabis users were current tobacco smokers. Conclusions: One in 14 current EC users in Germany has ever vaporized cannabis for mood-changing reasons, and almost all EC cannabis consumers also smoke tobacco. Highest usage rates can be observed among young adults. Hence, trends of EC drug misuse need to be monitored consequently, particularly in young people.
Keyphrases
  • young adults
  • smoking cessation
  • risk factors
  • oxidative stress
  • healthcare
  • machine learning
  • depressive symptoms
  • deep learning
  • cross sectional
  • drug induced
  • replacement therapy