Development and validation of a drug adherence index for COPD

BACKGROUND: Inhaled medications are the mainstay of treatment for chronic obstructive pulmonary disease (COPD). Despite their importance, adherence to these medications is low. Low adherence is linked to increased exacerbation rates, mortality rates, health care utilization, and, ultimately, increased costs. A drug adherence index (DAI) is a predictive modeling tool that identifies patients most likely to change adherence status so that they can be targeted for support programs. Optum has previously developed DAI tools for diabetes, hypertension, and high cholesterol. In this study, a COPD-specific DAI was developed. This DAI tool could be used to better target medication adherence support in patients with COPD, aiming to increase adherence. OBJECTIVES: To develop a COPD-specific DAI using (a) enrollment, medical, and pharmacy variables and (b) only enrollment and pharmacy variables for potential application to pharmacy benefit managers and pharmacy plans. METHODS: This was a retrospective observational study using health care claims among Medicare Advantage with Part D beneficiaries with COPD in the United States. Potential predictors of adherence were measured during a 1-year baseline period. The adherence outcome was measured during a subsequent 1-year at-risk period. Adherence to long-acting bronchodilators was defined as a proportion of days covered (PDC) ≥80%. Nonadherence was defined as a PDC of <80%. Patients were stratified according to their adherence status at baseline, and logistic regression models were developed separately for each set of patients. Separate models were also developed using enrollment, medical, and pharmacy variables (primary objective) or using enrollment and pharmacy variables only (secondary objective). RESULTS: A total of 61,507 patients met all inclusion and exclusion criteria. For the primary objective, at baseline, 31,142 patients were adherent and 30,365 patients were nonadherent. The final DAI model used to predict future nonadherence included 30 covariates, with 7 predictors from medical claims. The validated model c-statistic was 0.752. The final DAI model used to predict future adherence included 29 covariates; only 4 predictors were from medical claims. The validated model c-statistic was 0.691. Findings were similar for the secondary objective using only enrollment and pharmacy variables. CONCLUSIONS: This DAI was developed and validated specifically to predict future adherence status to long-acting bronchodilator medications among patients with COPD. The DAI models performed better for predicting nonadherence than predicting adherence. Both organizations with medical and pharmacy data and organizations with only pharmacy data could utilize the DAI tool to target patients for adherence programs, as results were similar with and without the use of medical variables.