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Risk factors for substance use in Swedish adolescents: A study across substances and time points.

Birger C ForsbergPatrik KarlssonMats Ekendahl
Published in: Nordisk alkohol- & narkotikatidskrift : NAT (2022)
Aim: The public health model for prevention of disease and disorder has been influential in informing interventions regarding substance use. While a number of risk factors within this model have been found to predict substance use, few studies have explored the associations across substances, at different time points and in the same individuals. The aim of this study was to test this model across legal and illegal substance use among adolescents, and to identify potential changes in associations over time. Methods: Data from two waves of a nationally representative cohort study among Swedish adolescents were used. Baseline data were collected in 2017 (9th grade) with a follow-up in 2019 (11th grade). Using modified Poisson regression analyses, we explored cross-sectional associations between factors from different domains and prevalence of cigarette use, binge-drinking and illegal drug use at both baseline and follow-up. Results: The results in part supported the public health model. Substance use was predicted by factors within the family, school and the individual/peer domain, but several associations were not statistically significant. The only consistent risk factors across substances and time points were lack of parental monitoring, truancy and minor criminal activities. Conclusion: Despite widely different prevalence rates across substances, some risk factors were consistently associated with adolescent substance use. Nonetheless, the findings challenge the assumption that risk factors are stable over adolescence. They suggest a need for flexible prevention interventions spanning across substances and legal boundaries of substances, but also over domains to reflect the heterogenous needs of adolescents.
Keyphrases
  • risk factors
  • public health
  • physical activity
  • young adults
  • drinking water
  • cross sectional
  • mental health
  • depressive symptoms
  • electronic health record
  • big data
  • climate change
  • risk assessment
  • machine learning