title: Impact of pharmacy benefit design on prescription drug utilization: a fixed effects analysis of plan sponsor data.
abstract: OBJECTIVE
  To study the impact of various elements of pharmacy benefit design on both the absolute and relative utilization of generics, brands, retail pharmacy, and mail service.
  
  
  METHODS
  Panel data on 1,074 plan sponsors covering 21.6 million individuals over 12 calendar quarters (2005-2007).
  
  
  METHODS
  A retrospective analysis of pharmacy claims.
  
  
  METHODS
  To control for potential endogeneity, linear fixed effects models were estimated for each of six dependent variables: the generic utilization rate, the brand utilization rate, the generic dispensing rate (GDR), the retail pharmacy utilization rate, the mail service utilization rate, and the mail distribution rate.
  
  
  RESULTS
  Most member cost-share variables were nonlinearly associated with changes in prescription drug utilization. Marginal effects were generally greater in magnitude for brand out-of-pocket costs than for generic out-of-pocket costs. Time dummies, as well as other pharmacy benefit design elements, also yielded significant results.
  
  
  CONCLUSIONS
  Prior estimates of the effect of member cost sharing on prescription drug utilization may be biased if complex benefit designs, mail service fulfillment, and unmeasured factors such as pharmaceutical pipelines are not accounted for. Commonly cited relative utilization metrics, such as GDR, may be misleading if not examined alongside absolute prescription drug utilization.
authors.affiliation: CVS Caremark, Hunt Valley, MD 21031, USA. chris.roebuck@caremark.com
authors.forename: M Christopher
authors.initials: MC
authors.lastname: Roebuck
authors.forename: Joshua N
authors.initials: JN
authors.lastname: Liberman
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000704:Analysis of Variance; D015982:Bias; D002648:Child; D002675:Child, Preschool; D017047:Cost Sharing / Q000191:economics; D016527:Drug Costs / Q000706:statistics & numerical data; D004363:Drug Utilization / Q000706:statistics & numerical data; D016568:Drugs, Generic / Q000191:economics / Q000627:therapeutic use; D005260:Female; D005569:Formularies as Topic; D019538:Health Care Surveys; D006801:Humans; D007223:Infant; D007344:Insurance Claim Reporting / Q000706:statistics & numerical data*; D019458:Insurance Coverage / Q000191:economics; D007356:Insurance, Pharmaceutical Services / Q000191:economics; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D017059:Models, Econometric; D011177:Postal Service / Q000706:statistics & numerical data; D055553:Prescription Drugs / Q000191:economics* / Q000627:therapeutic use; D012052:Reimbursement, Incentive / Q000191:economics; D012189:Retrospective Studies
chemical_list: D016568:Drugs, Generic; D055553:Prescription Drugs