Adolescent multiple risk behaviours cluster by number of risks rather than distinct risk profiles in the ALSPAC cohort

Research output: Contribution to journalArticle (Academic Journal)peer-review

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Background: Experimentation with new behaviours during adolescence is normal. However, engagement in two or more risk behaviours, termed multiple risk behaviours is associated with socioeconomic disadvantage and poor health and social outcomes. Evidence of how adolescents cluster based on their risk behaviours is mixed.  Methods: Latent Class Analysis was used to study patterns of engagement in 10 self-reported risk behaviours (including substance use, self-harm and sexual health) from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort at ages 15-16 years. Data was available for 6,556 adolescents. Associations between risk profile and sex were explored. Results:  A 3-class model for both females and males was deemed to have acceptable fit. Whilst we found evidence of a sex difference in the risk behaviours reported within each class, the sex-specific results were very similar in many respects. For instance, the prevalence of membership of the high-risk class was 8.5% for males and 8.7% for females and both groups had an average of 5.9 behaviours. However, the classes were both statistically dubious, with class separation (entropy) being poor as well as conceptually problematic, because the resulting classes did not provide distinct profiles and varied only by quantity of risk-behaviours.  Conclusion: Clusters of adolescents were not characterised by distinct risk behaviour profiles, and provide no additional insight for intervention strategies. Given this is a more complicated, software-specific method, we conclude that an equally effective, but more readily replicable approach is to use a count of the number of risk behaviours.  
Original languageEnglish
JournalBMC Public Health
Publication statusPublished - 4 Mar 2020


  • multiple risk behaviours
  • Public health intervention
  • latent class analysis
  • Clustering


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