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Socio-economic differences in smoking among adolescents in a school-based smoking intervention: The X:IT II study.

Lotus Sofie BastLisbeth LundStine G LauemØllerSimone G KjeldPernille DueAnette Andersen
Published in: Scandinavian journal of public health (2021)
Aims: Socio-economic inequalities in health behaviour may be influenced by health interventions. We examined whether the X:IT II intervention, aiming at preventing smoking in adolescence, was equally effective among students from different occupational social classes (OSC). Methods: We used data from the multi-component school-based smoking preventive intervention X:IT II, targeting 13- to 15-year-olds in Denmark. The intervention was tested in 46 schools with 2307 eligible students at baseline (response rate=86.6%) and had three main intervention components: smoke-free school time, smoke-free curriculum and parental involvement. We used a difference-in-difference design and estimated the change in current smoking after the first year of implementation in high versus low OSC. Analyses were based on available cases (N=1190) and imputation of missing data at follow-up (N=1967). Results: We found that 1% of the students from high OSC and 4.9% from low OSC were smokers at baseline (imputed data), and 8.2% of the students from high OSC and 12.2% from low OSC were smokers at follow-up. Difference-in-difference estimates were close to zero, indicating no differential trajectory. Conclusions: As intended, the X:IT II intervention, designed to apply equally to students from all socio-economic groups, did not seem to create different trajectories in current smoking among adolescents in high and low socio-economic groups. To diminish social inequality in health, future studies should carefully consider the ability to affect all socio-economic groups equally, or even to appeal mainly to participants from lower socio-economic groups, as they are often the ones most in need of intervention.
Keyphrases
  • randomized controlled trial
  • smoking cessation
  • healthcare
  • mental health
  • high school
  • public health
  • physical activity
  • risk assessment
  • big data
  • machine learning
  • medical students