Potentially inappropriate medications and risk of hospitalization in retirees: analysis of a US retiree health claims database

Drugs Aging. 2010 May;27(5):407-15. doi: 10.2165/11315990-000000000-00000.

Abstract

One important health outcome of inappropriate medication use in the elderly is risk of hospitalization. We examined this relationship over 3 years in a retiree health claims database to determine the strength of this association using alternative definitions of potentially inappropriate medications. Prescription and hospitalization claims for US retirees from a single large corporation were examined over the 3-year period, 2003-5. Purging the database of non-employees (dependents, spouses), employees aged <65 years (who were not Medicare-eligible) and retirees not covered for the full 3-year period left a sample of 7459 retirees. Respondents' medications were categorized according to two lists of 'drugs to avoid': Beers (2003 update) and the National Committee for Quality Assurance (NCQA). Logistic regression models were developed to examine risk of hospitalization in 2005 relative to use of potentially inappropriate medications across different periods of follow-up. Retirees taking one or more of the potentially inappropriate medications on the Beers or NCQA lists were 1.8-1.9 times more likely to have a hospital admission in models that adjusted for age, gender, number of prescriptions overall and aggregate disease severity. Risk of hospitalization increased in a dose-response relationship according to number of potentially inappropriate medications. Consistency in the strength of the association between 'drugs to avoid' and hospital admission across different definitions of inappropriate medication use suggests the finding is robust. Findings from the retiree cohort provide further evidence for the inappropriateness of these medications among the elderly.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Aged
  • Aged, 80 and over
  • Databases, Factual*
  • Disease Progression
  • Female
  • Hospitalization / statistics & numerical data*
  • Humans
  • Insurance, Health*
  • Male
  • Medication Errors / statistics & numerical data*
  • Retirement*
  • Risk Factors
  • Time Factors
  • United States