title: Patient, physician, and payment predictors of statin adherence.
abstract: BACKGROUND
  Although many patient, physician, and payment predictors of adherence have been described, knowledge of their relative strength and overall ability to explain adherence is limited.
  
  
  OBJECTIVE
  To measure the contributions of patient, physician, and payment predictors in explaining adherence to statins.
  
  
  METHODS
  Retrospective cohort study using administrative data.
  
  
  METHODS
  A total of 14,257 patients insured by Horizon Blue Cross Blue Shield of New Jersey who were newly prescribed a statin cholesterol-lowering medication.
  
  
  METHODS
  Adherence to statin medication was measured during the year after the initial prescription, based on proportion of days covered. The impact of patient, physician, and payment predictors of adherence were evaluated using multivariate logistic regression. The explanatory power of these models was evaluated with C statistics, a measure of the goodness of fit.
  
  
  RESULTS
  Overall, 36.4% of patients were fully adherent. Older patient age, male gender, lower neighborhood percent black composition, higher median income, and fewer number of emergency department visits were significant patient predictors of adherence. Having a statin prescribed by a cardiologist, a patient's primary care physician, or a US medical graduate were significant physician predictors of adherence. Lower copayments also predicted adherence. All of our models had low explanatory power. Multivariate models including patient covariates only had greater explanatory power (C = 0.613) than models with physician variables only (C = 0.566) or copayments only (C = 0.543). A fully specified model had only slightly more explanatory power (C = 0.633) than the model with patient characteristics alone.
  
  
  CONCLUSIONS
  Despite relatively comprehensive claims data on patients, physicians, and out-of-pocket costs, our overall ability to explain adherence remains poor. Administrative data likely do not capture many complex mechanisms underlying adherence.
authors.affiliation: Department of Medicine, Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA.
authors.forename: David C
authors.initials: DC
authors.lastname: Chan
authors.forename: William H
authors.initials: WH
authors.lastname: Shrank
authors.forename: David
authors.initials: D
authors.lastname: Cutler
authors.forename: Saira
authors.initials: S
authors.lastname: Jan
authors.forename: Michael A
authors.initials: MA
authors.lastname: Fischer
authors.forename: Jun
authors.initials: J
authors.lastname: Liu
authors.forename: Jerry
authors.initials: J
authors.lastname: Avorn
authors.forename: Daniel
authors.initials: D
authors.lastname: Solomon
authors.forename: M Alan
authors.initials: MA
authors.lastname: Brookhart
authors.forename: Niteesh K
authors.initials: NK
authors.lastname: Choudhry
mesh_terms: D000328:Adult; D000367:Age Factors; D002318:Cardiovascular Diseases / Q000188:drug therapy / Q000453:epidemiology; D015331:Cohort Studies; D017047:Cost Sharing / Q000706:statistics & numerical data*; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors / Q000008:administration & dosage* / Q000627:therapeutic use; D007345:Insurance Claim Review / Q000706:statistics & numerical data; D008297:Male; D008511:Medicine / Q000706:statistics & numerical data; D008875:Middle Aged; D010361:Patients / Q000706:statistics & numerical data*; D010820:Physicians / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D012737:Sex Factors; D012959:Socioeconomic Factors; D016903:Drug Monitoring
chemical_list: D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors