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Who self-medicates? Results from structural equation modeling in the Greater Paris area, France

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  • A Vanhaesebrouck
  • C Vuillermoz
  • S Robert
  • I Parizot
  • P Chauvin

Abstract

Objectives: Our study aimed to describe the prevalence of self-medication among the Paris adult population and to identify the factors associated with self-medication. Materials and methods: This cross-sectional study was based on data collected from the SIRS cohort (a French acronym for “Health, inequalities and social ruptures”) in 2005 in the Paris metropolitan area using a face-to-face administration questionnaire among a representative sample of 3,023 French-speaking adults. Structural equation models were used to investigate the factors associated with self-medication in the overall population and according to income. Results: The prevalence of self-medication in the past four weeks was 53.5% in the Paris metropolitan area. Seven factors were directly associated with self-medication in the structural equation model. Self-medication was found more common among women, young people, in active employment or student, with a high income, but also among people with a health information seeking behavior, with a high daily mobility, and/or with a history of unmet healthcare needs due to economic reasons. When looking at these coefficients according to income, the association between self-medication and daily mobility appeared stronger in the bottom quartile of income whereas it was no longer significant in the rest of the survey population. Conclusion: Self-medication is a frequent practice in the Paris metropolitan area. This study confirms the role of some factors found to be associated with self-medication in the literature such as age or gender and draws attention to other factors rarely explored such as daily mobility, especially among people with a low income, or health information seeking behavior.

Suggested Citation

  • A Vanhaesebrouck & C Vuillermoz & S Robert & I Parizot & P Chauvin, 2018. "Who self-medicates? Results from structural equation modeling in the Greater Paris area, France," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-18, December.
  • Handle: RePEc:plo:pone00:0208632
    DOI: 10.1371/journal.pone.0208632
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    References listed on IDEAS

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    1. Thomas Lefèvre & Claire Rondet & Isabelle Parizot & Pierre Chauvin, 2014. "Applying Multivariate Clustering Techniques to Health Data: The 4 Types of Healthcare Utilization in the Paris Metropolitan Area," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-20, December.
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