title: Predicting 1-Year Statin Adherence Among Prevalent Users: A Retrospective Cohort Study.
abstract: BACKGROUND
  Attempts to predict who is at risk of future nonadherence have largely focused on predictions at the time of therapy initiation; however, these users are only a small proportion of all patients on therapy at any point in time. Methods to predict nonadherence for established medication users, which have not been previously described in the literature, would be helpful to guide efforts to enhance the use of evidence-based therapies.
  
  
  OBJECTIVE
  To test approaches for adherence prediction among prevalent statin users, namely the use of short-term filling behavior, investigator-specified predictors from medical and pharmacy administrative claims, and the empirical selection of potential predictors using the high-dimensional propensity score variable selection algorithm.
  
  
  METHODS
  Medical and prescription claims data from a large national health insurer were used to create a cohort of patients who filled statin medication prescriptions in January 2012. We defined 6 groups of adherence predictors and estimated 10 main models to predict medication adherence in the full cohort. The same was done for the population stratified based on the days supply of the index statin prescription (≤ 30 days vs. > 30 days).
  
  
  RESULTS
  The study cohort consisted of 93,777 individuals, 58.4% of which were adherent to statins during follow-up. The use of 3 pre-index adherence predictors alone achieved a c-statistic of 0.70. Investigator-specified and empirically selected pharmacy, medical, and demographic variables did substantially worse (0.57-0.60). The use of 3 indicators of post-index adherence achieved a higher c-statistic than the best-performing model using pre-index information (0.74 vs. 0.72). The addition of 3 pre-index adherence predictors further improved discrimination (0.78).
  
  
  CONCLUSIONS
  This analysis demonstrated the ability to predict adherence among medication users using filling behavior before and immediately after an index prescription fill.
  
  
  BACKGROUND
  This work was supported by an unrestricted grant from CVS Health to Brigham and Women's Hospital. Shrank, Brennan, and Matlin were employees and shareholders at CVS Health at the time of this manuscript preparation; they report no financial interests in products or services that are related to the subject of the manuscript. Franklin has received consulting fees from Aetion. Chourdry has received grants from the National Heart, Lung, and Blood Institute, PhRMA Foundation, Merck, Sanofi, AstraZeneca, and MediSafe. Spettell is an employee of, and shareholder in, Aetna. The other authors have nothing to disclose. Krumme, Choudhry, Tong, and Franklin contributed to the study design, interpretation of results, and manuscript drafting. Tong prepared and analyzed the data. Isaman, Spettell, Shrank, Brennan, and Matlin provided interpretation of results and critical manuscript revisions.
authors.affiliation: 1 Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
authors.forename: Alexis A
authors.initials: AA
authors.lastname: Krumme
authors.affiliation: 1 Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
authors.forename: Jessica M
authors.initials: JM
authors.lastname: Franklin
authors.affiliation: 1 Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
authors.forename: Danielle L
authors.initials: DL
authors.lastname: Isaman
authors.affiliation: 2 CVS Health, Woonsocket, Rhode Island.
authors.forename: Olga S
authors.initials: OS
authors.lastname: Matlin
authors.affiliation: 1 Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
authors.forename: Angela Y
authors.initials: AY
authors.lastname: Tong
authors.affiliation: 3 Data Science, Aetna, Hartford, Connecticut.
authors.forename: Claire M
authors.initials: CM
authors.lastname: Spettell
authors.affiliation: 2 CVS Health, Woonsocket, Rhode Island.
authors.forename: Troyen A
authors.initials: TA
authors.lastname: Brennan
authors.affiliation: 2 CVS Health, Woonsocket, Rhode Island.
authors.forename: William H
authors.initials: WH
authors.lastname: Shrank
authors.affiliation: 1 Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
authors.forename: Niteesh K
authors.initials: NK
authors.lastname: Choudhry
mesh_terms: D015331:Cohort Studies; D005260:Female; D005544:Forecasting; D006801:Humans; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors / Q000627:therapeutic use*; D007348:Insurance, Health / Q000706:statistics & numerical data; D008297:Male; D055118:Medication Adherence; D008875:Middle Aged; D015233:Models, Statistical; D010349:Patient Compliance / Q000706:statistics & numerical data*; D057216:Propensity Score; D012189:Retrospective Studies; D012959:Socioeconomic Factors
chemical_list: D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors