title: Prescription co-pay reduction program for diabetic employees.
abstract: The objective of this study was to examine the impact of reducing the prescription co-pay for diabetes medications on pharmacy utilization, medication adherence, medical utilization, and expenditures. The co-pay reduction involved placing all diabetic drugs and testing supplies on the lowest co-pay tier for one employer group. The sample comprised members with diabetes who were both continuously enrolled in the 12-month pre period and the 2 years following co-pay reduction. Measured outcomes included diabetic prescription utilization, medication adherence, medical utilization, and expenditures. Generalized estimating equations for repeated measures were used to estimate differences between the pre period and years 1 and 2, while adjusting for age, sex, and comorbidity risk. Diabetic prescription utilization and medication adherence increased by approximately 3.0% in year 1 and dropped in year 2. The increases were primarily in brand name diabetes medications, which increased by approximately 5%, while generic use decreased in both years. Decreases in emergency room visits and hospitalizations were also observed in both years, followed by a decrease in health care expenditures in year 2. Adherent members experienced greater decreases in emergency room visits following the co-pay reduction compared to nonadherent members. After the implementation of a co-pay reduction, a modest increase in adherence and use of diabetes medications was observed. There were some compensatory cost savings for the employer from lower medical expenditures in year 1. In addition to financial strategies, additional strategies to reinforce medication adherence are needed to gain and sustain more meaningful increases in prescription utilization.
authors.affiliation: School of Pharmacy, University of Colorado Denver , Denver, CO 80045, USA. Kavita.Nair@ucdenver.edu
authors.forename: Kavita V
authors.initials: KV
authors.lastname: Nair
authors.forename: Kerri
authors.initials: K
authors.lastname: Miller
authors.forename: Jinhee
authors.initials: J
authors.lastname: Park
authors.forename: Richard R
authors.initials: RR
authors.lastname: Allen
authors.forename: Joseph J
authors.initials: JJ
authors.lastname: Saseen
authors.forename: Vinita
authors.initials: V
authors.lastname: Biddle
mesh_terms: D000328:Adult; D000368:Aged; D003920:Diabetes Mellitus / Q000188:drug therapy / Q000191:economics*; D005260:Female; D005102:Health Expenditures / Q000706:statistics & numerical data; D006801:Humans; D007004:Hypoglycemic Agents / Q000191:economics / Q000627:therapeutic use; D007342:Insurance Benefits / Q000191:economics*; D007356:Insurance, Pharmaceutical Services / Q000191:economics*; D016013:Likelihood Functions; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D016012:Poisson Distribution; D055553:Prescription Drugs / Q000191:economics*; D016730:Program Development / Q000191:economics*; D015397:Program Evaluation / Q000191:economics; D018570:Risk Assessment; D014481:United States; D016903:Drug Monitoring
chemical_list: D007004:Hypoglycemic Agents; D055553:Prescription Drugs