title: Predictors of Change in Adherence Status from 1 Year to the Next Among Patients with Type 2 Diabetes Mellitus on Oral Antidiabetes Drugs.
abstract: BACKGROUND
  Diabetes is a leading cause of morbidity, mortality, and medical resource utilization in the United States and worldwide. Treatment is aimed at keeping blood glucose levels close to normal and preventing or delaying medical complications. It has been estimated that only 50% of patients with diabetes in the United States achieve glycosylated hemoglobin A1c level < 7%. Nonadherence to antidiabetic medications has been identified as a major factor related to poor glycemic control.
  
  
  OBJECTIVE
  To (a) assess adult patients with type 2 diabetes mellitus (T2DM) whose adherence status to oral antidiabetic drugs (OADs) changed from 1 year to the next and (b) identify predictors of change in adherence status.
  
  
  METHODS
  This retrospective study of the Humana Medicare Advantage Database included patients with T2DM and continuous enrollment between 2010 and 2012. Proportion of days covered (PDC) by OADs was calculated for each of the 3 study years (2010, 2011, 2012). Patients were classified as adherent (PDC ≥ 80%) or nonadherent (PDC < 80%) during each year. Patient characteristics from the baseline period (2010) were used as covariates, and adherence status changes from baseline to follow-up year (2011) were used as response variables. Data from the subsequent study periods (2011 as baseline, 2012 as follow-up) were used to validate the model (final model).
  
  
  RESULTS
  A total of 238,402 patients met inclusion criteria. Among them, 144,216 (60.5%) were adherent, and 94,186 (39.5%) were nonadherent during the baseline period. Change in adherence status from baseline to follow-up year was observed in 31,320 (21.7%) patients that were adherent and 39,284 (41.7%) patients that were nonadherent during the baseline year. The final model for baseline adherent patients had a receiver-operating characteristic (ROC) index of 73% and a misclassification rate of 39%. The predictors of highest importance were identified as total number of prescriptions filled with 90-day supply, diabetes-related pill burden, longest gap in OADs, total number of antidiabetic classes filled, and copay for the last OAD filled. The final model had a sensitivity value of 76.4%. The final model for baseline nonadherent patients had a ROC index of 68%, a misclassification rate of 36.4%, and sensitivity value of 52.9%. The predictors of highest importance were diabetes-related pill burden, longest gap in OADs, month-wise patient oscillation from adherent to nonadherent during baseline year, total number of prescriptions filled with a 90-day supply, and total pill burden during the baseline year.
  
  
  CONCLUSIONS
  One third of the T2DM patients changed adherence status from 1 year to the next, and factors associated with adherence status changes were identified. Predictive models such as those used in this study can serve as useful and cost-effective tools for payers, helping to identify members that should be targeted for adherence enhancement programs and, ultimately, to improve patients' long-term outcomes.
  
  
  BACKGROUND
  Funding for this research was provided by Eli Lilly and Company. Comprehensive Health Insights, owned by Humana, completed this study. Peng, Fu, Ascher-Svanum, Ali, and Rodriguez are employees of Eli Lilly and Company. Saundankar and Louder are employed by Comprehensive Health Insights, and Slabaugh and Young are employed by Humana. Study concept and design were contributed by Peng, Ascher-Svanum, and Young. Saundankar and Louder took the lead in data collection, while Saundankar, Peng, Fu, and Louder interpreted the data. The manuscript was written by Saundankar, Peng, Fu, and Louder and revised by Saundankar, Rodriguez, Ali, and Louder.
authors.affiliation: 1 Comprehensive Health Insights, Louisville, Kentucky.
authors.forename: Vishal
authors.initials: V
authors.lastname: Saundankar
authors.affiliation: 2 Global Patient Outcomes and Real World Evidence.
authors.forename: Xiaomei
authors.initials: X
authors.lastname: Peng
authors.affiliation: 3 Statistics.
authors.forename: Haoda
authors.initials: H
authors.lastname: Fu
authors.affiliation: 4 Global Patient Outcomes and Real World Evidence.
authors.forename: Haya
authors.initials: H
authors.lastname: Ascher-Svanum
authors.affiliation: 5 Epidemiology-Global Product Safety, Eli Lilly and Company, Indianapolis, Indiana.
authors.forename: Angel
authors.initials: A
authors.lastname: Rodriguez
authors.affiliation: 6 Medical Diabetes, Eli Lilly, Alcobendas, Spain.
authors.forename: Ayad
authors.initials: A
authors.lastname: Ali
authors.affiliation: 7 Humana, Louisville, Kentucky.
authors.forename: Lane
authors.initials: L
authors.lastname: Slabaugh
authors.affiliation: 7 Humana, Louisville, Kentucky.
authors.forename: Peinie
authors.initials: P
authors.lastname: Young
authors.affiliation: 1 Comprehensive Health Insights, Louisville, Kentucky.
authors.forename: Anthony
authors.initials: A
authors.lastname: Louder
mesh_terms: D000368:Aged; D001786:Blood Glucose / Q000187:drug effects; D003362:Cost-Benefit Analysis; D003924:Diabetes Mellitus, Type 2 / Q000188:drug therapy*; D005260:Female; D006442:Glycated Hemoglobin / Q000378:metabolism; D006801:Humans; D007004:Hypoglycemic Agents / Q000627:therapeutic use*; D008137:Longitudinal Studies; D008297:Male; D012189:Retrospective Studies; D016903:Drug Monitoring
chemical_list: D001786:Blood Glucose; D006442:Glycated Hemoglobin; D007004:Hypoglycemic Agents