Predictors for adolescent visits to practitioners of complementary and alternative medicine in a total population (the Young-HUNT Studies)

PLoS One. 2011;6(10):e25719. doi: 10.1371/journal.pone.0025719. Epub 2011 Oct 7.

Abstract

Aim: To investigate the factors predicting adolescent visits to practitioners of complementary and alternative medicine (CAM).

Methods: A longitudinal cohort study conducted in an adolescent total population in Central Norway (The Nord-Trøndelag Health Studies (HUNT)). In Young-HUNT 1, all inhabitants aged 13 to 19 years (N = 8944, 89% response rate) were invited to participate, and the youngest group (13 to 15 year olds) was surveyed again 4 years later (Young-HUNT 2, N = 2429, 82% response rate). The participants completed a comprehensive questionnaire on health and life style which included a question regarding visits to a CAM practitioner in the last 12 months.

Results: One in eleven (8.7%, 95%CI 7.6-9.8%) had visited a CAM practitioner, an increase of 26% in 4 years (1.8% points). The final multivariable analysis predicted increased odds of an adolescent becoming a CAM visitor four years later (p<0.05) if she or he had previously visited a CAM practitioner (adjOR 3.4), had musculoskeletal pain (adjOR 1.5), had migraine (adjOR 2.3), used asthma medicines (adjOR 1.8) or suffered from another disease lasting more than three months (adjOR 2.1). Being male predicted reduced odds of visiting a CAM practitioner in the future (adjOR 0.6).

Conclusion: We can conclude from this study that future visits to a CAM practitioner are predicted by both predisposing factors (being female, having visited a CAM practitioner previously) and medical need factors (having had musculoskeletal pain, migraine, used asthma medicines or experienced another disease lasting more than three months). None of the specific variables associated with CAM visits were predictive for CAM visits four years later.

MeSH terms

  • Adolescent
  • Analysis of Variance
  • Complementary Therapies / statistics & numerical data*
  • Cross-Sectional Studies
  • Female
  • Humans
  • Logistic Models
  • Longitudinal Studies
  • Male
  • Patient Acceptance of Health Care / statistics & numerical data*
  • Young Adult