Patterns of drug use among the community-dwelling old-old population in Israel

Isr Med Assoc J. 2003 May;5(5):346-51.

Abstract

Background: Due to multiple chronic illness and disability, the elderly consume a disproportionately large share of medications.

Objectives: To assess the patterns and determinants of drug use among the community dwelling old-old population.

Methods: The study population included 1,369 old-old persons from the baseline data of the Cross-Sectional and Longitudinal Aging Study (CALAS), which is based on a national random stratified sample of the Israeli Jewish population aged 75-94 years.

Results: The mean number of drugs used by the study population was 3.3, and only 12.5% did not consume any drugs, Multivariate linear regression analysis showed that women used significantly more drugs than men, and that those born in Europe took significantly more drugs than those born in Israel and Asia-Africa. The number of medical conditions was the strongest predictor of drug use. Hospitalizations during the last year and frequent visits to family physician were also significant factors related to drug use. All variables combined explained 40% of the variance in drug use by the old-old. The most commonly used therapeutic groups were cardiovascular drugs (53%), psychotropic drugs (31%), analgesics (30%), and gastrointestinal drugs (28%).

Conclusions: Our data indicate that in addition to the association of drug use with health status and healthcare utilization, the number and type of drugs taken vary with gender and place of birth.

Publication types

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

MeSH terms

  • Africa / ethnology
  • Age Factors
  • Aged
  • Aged, 80 and over
  • Asia / ethnology
  • Drug Utilization / classification
  • Drug Utilization / statistics & numerical data*
  • Europe / ethnology
  • Female
  • Health Care Surveys / classification
  • Health Care Surveys / statistics & numerical data
  • Health Status
  • Humans
  • Israel
  • Male
  • Residence Characteristics / classification
  • Residence Characteristics / statistics & numerical data*
  • Sex Factors
  • Socioeconomic Factors