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Deciphering Usage and Expenditures of Complementary Medicine: A Five-Year Longitudinal Data Analysis of 205,423
David De Ridder1, Christophe Bagnoud2, Stéphane Joost3
1Geographic Information Research and Analysis in Population Health (GIRAPH) Lab, Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland; Geospatial Molecular Epidemiology (GEOME), Laboratory of Biologic Geochemistry, School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland.
Objective:
To identify factors associated with complementary and alternative medicine (CAM) use, investigate regional patterns, and examine whether CAM usage is associated with decreased expenditures in conventional medicine (CM).
Participants And Methods:
We conducted a retrospective analysis of Swiss health insurance claims data from January 1, 2017, to December 31, 2021, including mandatory health insurance (MHI) and supplementary insurance (SI) schemes. We analyzed 816,080 person-years from 205,423 Swiss residents with dual coverage using 2-part multilevel models and geographic clustering analyses.
Results:
CAM utilization differed markedly between schemes: 59% (481,176 of 816,080) used CAM (SI) while 2.2% (18,023) used CAM (MHI). Women had nearly double the odds of CAM (MHI) usage (adjusted odds ratio, 1.97; P<.001) and 56% higher odds of CAM (SI) usage (adjusted odds ratio, 1.56; P<.001). Higher socioeconomic status was associated with dose-response relationships, with increased usage across both schemes. Substantial geographic variations emerged, with French-speaking regions having 33% lower odds of CAM (MHI) usage (adjusted odds ratio, 0.67; P<.001) yet 26.6% higher expenditures (P<.001) among users. CAM users initially incurred 49.1% higher CM expenditures, but this gap narrowed to 34.2% by year 5, representing a 14.9 percentage point convergence. This pattern was most pronounced among individuals without chronic conditions based on medication patterns (CAM MHI: 139.7% to 20.1% difference) and patients with cancer (CAM SI: expenditure differences shifted from +13.9% to -5.9%).
Conclusion:
CAM serves distinct populations through different insurance schemes, with initial higher CM costs but slower expenditure growth over time. These findings suggest expenditure patterns that warrant further mechanistic investigation to optimize integrative health care delivery.
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