title: Multiple medication use in the elderly. Use of prescription and non-prescription drugs in an Australian community setting.
abstract: OBJECTIVE
  To document the extent of polypharmacy or multiple medication use in the elderly.
  
  
  METHODS
  Cross-sectional examination of an age cohort of a community.
  
  
  METHODS
  Community-based study in Dubbo, NSW, in 1988-1989.
  
  
  METHODS
  All non-institutionalised residents aged 60 years and over, numbering 1237 men and 1568 women.
  
  
  METHODS
  Assessment of use of prescription and non-prescription drugs, recent hospitalisation, years of education, psychosocial variables.
  
  
  RESULTS
  18% of men and 25% of women were currently using three or more classes of prescription drugs. The corresponding values for two or more classes of non-prescription drugs were 29% and 44%. Of those who were using multiple prescription drugs 56% of men and 76% of women were also using multiple non-prescription drugs. In a multiple logistic model, the following possible predictors of multiple drug use were included: hospitalisation in the last six months, age, sex, depression, life satisfaction and education. Multiple prescription drug use was significantly predicted by recent hospitalisation (odds ratio [OR] = 2.40; 95% confidence interval [CI], 1.63-3.56), increasing age (e.g. 70-79 years versus 60-69 years; OR = 2.54; CI, 1.97-3.25), female sex (OR = 1.59; CI, 1.25-2.01) and increasing depression (e.g. highest tertile of depression scale versus lowest; OR = 2.52; CI, 1.84-3.42). Multiple non-prescription drug use was significantly predicted by female sex (OR = 2.38; CI, 1.95-2.92) and increasing depression (OR = 2.77; CI, 2.16-3.56). For prescription items, non-prescription items, and both categories in combination levels of use 20% above the population average have been documented.
  
  
  CONCLUSIONS
  Polypharmacy in the elderly population appears to be predicted by recent hospitalisation, increasing age, female sex and increasing depression. There is potential for drug-drug interaction to occur, but the findings suggest target areas for preventive action.
authors.affiliation: University of NSW School of Medicine, Darlinghurst.
authors.forename: L A
authors.initials: LA
authors.lastname: Simons
authors.forename: S
authors.initials: S
authors.lastname: Tett
authors.forename: J
authors.initials: J
authors.lastname: Simons
authors.forename: R
authors.initials: R
authors.lastname: Lauchlan
authors.forename: J
authors.initials: J
authors.lastname: McCallum
authors.forename: Y
authors.initials: Y
authors.lastname: Friedlander
authors.forename: I
authors.initials: I
authors.lastname: Powell
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D003430:Cross-Sectional Studies; D003863:Depression; D011307:Drug Prescriptions; D004359:Drug Therapy, Combination; D004363:Drug Utilization / Q000706:statistics & numerical data; D004522:Educational Status; D005260:Female; D006760:Hospitalization; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009517:New South Wales; D004366:Nonprescription Drugs; D010549:Personal Satisfaction; D012651:Self Medication / Q000706:statistics & numerical data; D011795:Surveys and Questionnaires
chemical_list: D004366:Nonprescription Drugs