The prevalence and pattern of complementary and alternative medicine use in individuals with diabetes

Diabetes Care. 2002 Feb;25(2):324-9. doi: 10.2337/diacare.25.2.324.

Abstract

Objective: This study compared the prevalence and pattern of use of complementary and alternative medicine (CAM) in individuals with and without diabetes and identified factors associated with CAM use.

Research design and methods: The 1996 Medical Expenditure Panel Survey, a nationally representative sample of the U.S. noninstitutionalized civilian population, was analyzed. Estimates of CAM use in individuals with common chronic conditions were determined, and estimates of CAM use in patients with diabetes were compared with that in individuals with chronic medical conditions. Patterns of use and costs of CAM use in patients with diabetes were compared with those in nondiabetic individuals. Multiple logistic regression was used to determine independent predictors of CAM use in individuals with diabetes, controlling for age, sex, race/ethnicity, household income, educational level, and comorbidity.

Results: Individuals with diabetes were 1.6 times more likely to use CAM than individuals without diabetes (8 vs. 5%, P < 0.0001). In the general population, estimates of CAM use were not significantly different across selected chronic medical conditions, but diabetes was an independent predictor of CAM use. Among individuals with diabetes, older age (> or =65 years) and higher educational attainment (high school education or higher) were independently associated with CAM use.

Conclusions: Diabetes is an independent predictor of CAM use in the general population and in individuals with diabetes. CAM use is more common in individuals aged > or =65 years and those with more than high school education.

Publication types

  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adult
  • Aged
  • Chronic Disease
  • Complementary Therapies / statistics & numerical data*
  • Diabetes Mellitus / epidemiology*
  • Diabetes Mellitus / therapy*
  • Educational Status
  • Female
  • Humans
  • Logistic Models
  • Male
  • Middle Aged
  • Prevalence
  • Socioeconomic Factors