title: Factors affecting patient compliance with antihyperlipidemic medications in an HMO population.
abstract: OBJECTIVE
  To identify factors that influence compliance in patients taking antihyperlipidemic medications.
  
  
  METHODS
  This was a retrospective cohort study in which computerized pharmacy records were used to estimate medication compliance in patients in a Health Maintenance Organization from 1993 to 1995.
  
  
  METHODS
  Data on 772 patients on antihyperlipidemic medications were obtained from pharmacy and healthcare utilization claims and from a cross-sectional survey. The medication compliance ratio for each patient was calculated from the prescription profile. Patient compliance was modeled as a function of four clusters of determinants: patient characteristics, complexity of drug regimen, health status, and patient-provider interaction. Correlation between specific characteristics and compliance was estimated by logistic regressions.
  
  
  RESULTS
  Approximately 37% of patients complied with at least 90% or more of their antihyperlipidemic medications. The following variables had a significant influence on compliance: female gender (odds ratio [OR], 0.64), baseline compliance (high: OR, 3.42; medium: OR, 1.86), perceived health status (SF-36 bodily pain score: OR, 1.02; SF-36 vitality score: OR, 0.97), comorbidity (OR, 0.90), and number of daily doses of antihyperlipidemic medications (OR, 0.60).
  
  
  CONCLUSIONS
  The findings suggest that women, patients with comorbidities, patients reporting high SF-36 vitality scores, and patients with multiple doses of antihyperlipidemic medications are less likely to be compliant. Patients who self-report good compliance with previous medications are more likely to comply. This information may be used to target interventions at patients who are likely to be noncompliant with their medication regimens.
authors.affiliation: Department of Pharmaceutical Economics and Policy, School of Pharmacy, University of Southern California, Los Angeles 90033, USA.
authors.forename: J C
authors.initials: JC
authors.lastname: Sung
authors.forename: M B
authors.initials: MB
authors.lastname: Nichol
authors.forename: F
authors.initials: F
authors.lastname: Venturini
authors.forename: K L
authors.initials: KL
authors.lastname: Bailey
authors.forename: J S
authors.initials: JS
authors.lastname: McCombs
authors.forename: M
authors.initials: M
authors.lastname: Cody
mesh_terms: D000368:Aged; D002140:California / Q000453:epidemiology; D016000:Cluster Analysis; D015331:Cohort Studies; D003710:Demography; D004334:Drug Administration Schedule; D005260:Female; D006279:Health Maintenance Organizations / Q000706:statistics & numerical data*; D006305:Health Status Indicators; D006801:Humans; D000960:Hypolipidemic Agents / Q000008:administration & dosage*; D008297:Male; D008875:Middle Aged; D010349:Patient Compliance; D012189:Retrospective Studies
chemical_list: D000960:Hypolipidemic Agents