title: How to use pharmacy claims data to measure patient nonadherence? The example of oral diabetics in therapy of type 2 diabetes mellitus.
abstract: BACKGROUND
  The purpose of this study was to describe the methodological framework underlying nonadherence (NA) measurement based on pharmacy claims data, its quantitative impact on the results of NA studies, and to identify those methodological categories most likely to explain diabetes-related clinical outcomes. We use the example of oral antidiabetics in the treatment of diabetes mellitus type 2; 113,108 patients derived from a German statutory health insurance fund were analyzed.
  
  
  METHODS
  We identified 12 methodological categories as pervasive features in pharmacy claims data based NA analyses. The influence of the different methodological categories and their parameters on analysis results was tested using sensitivity analysis. To validate alternative methodological framework options, we performed multivariate logistical regression estimates using diabetes-related hospitalization/clinical events as a combined dichotomized dependent variable.
  
  
  RESULTS
  The choice of parameters within the identified 12 methodological categories available has exceptional impact on the results of pharmacy data based claims NA analyses. When the full range of theoretically possible cases is considered in our sample, it can be seen that the resulting NA range is between 15.7% and 97.0%. The definition of the required daily dose, the decision to use either a prescription-/interval-based approach, and the classes of medication analyzed exert a notable influence on the study results. In our analysis, 69.4% of the 216 different study design options analyzed significantly explain the likelihood of diabetes-related clinical events.
  
  
  CONCLUSIONS
  We recommend strongly that methodological transparency is awarded a much more important role in the conduct of NA analyses made on the basis of pharmacy claims data.
authors: Institut für Pharmakoökonomie und Arzneimittellogistik, Hochschule Wismar, Wismar, Germany. Thomas.wilke@hs-wismar.de
authors: Thomas
authors: T
authors: Wilke
authors: Antje
authors: A
authors: Groth
authors: Sabrina
authors: S
authors: Mueller
authors: Dallas
authors: D
authors: Reese
authors: Roland
authors: R
authors: Linder
authors: Susanne
authors: S
authors: Ahrens
authors: Frank
authors: F
authors: Verheyen
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D003625:Data Collection / Q000379:methods*; D003924:Diabetes Mellitus, Type 2 / Q000188:drug therapy; D005260:Female; D006801:Humans; D007004:Hypoglycemic Agents / Q000008:administration & dosage; D007345:Insurance Claim Review / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D010594:Pharmacies / Q000706:statistics & numerical data*; D055815:Young Adult; D016903:Drug Monitoring
chemical_list: D007004:Hypoglycemic Agents