Predicting Adherence to Chronic Disease Medications in Patients with Long-term Initial Medication Fills Using Indicators of Clinical Events and Health Behaviors.

BACKGROUND: Efforts at predicting long-term adherence to medications have been focused on patients filling typical month-long supplies of medication. However, prediction remains difficult for patients filling longer initial supplies, a practice that is becoming increasingly common as a method to enhance medication adherence.

OBJECTIVES: To (a) extend methods involving short-term filling behaviors and (b) develop novel variables to predict adherence in a cohort of patients receiving longer initial prescriptions.

METHODS: In this retrospective cohort study, we used claims from a large national insurer to identify patients initiating a 90-day supply of oral medications for diabetes, hypertension, and hyperlipidemia (i.e., statins). Patients were included in the cohort if they had continuous database enrollment in the 180 days before and 365 days after medication initiation. Adherence was measured in the subsequent 12 months using the proportion of days covered metric. In total, 125 demographic, clinical, and medication characteristics at baseline and in the first 30-120 days after initiation were used to predict adherence using logistic regression models. We used 10-fold cross-validation to assess predictive accuracy by discrimination (c-statistic) measures.

RESULTS: In total, 32,249 patients met the inclusion criteria, including 14,930 patients initiating statins, 12,887 patients initiating antihypertensives, and 4,432 patients initiating oral hypoglycemics. Prediction using only baseline variables was relatively poor (cross-validated c-statistic = 0.644). Including indicators of acute clinical conditions, health resource utilization, and short-term medication filling in the first 120 days greatly improved predictive ability (0.823). A model that incorporated all baseline characteristics and predictors within the first 120 days after medication initiation more accurately predicted future adherence (0.832). The best performing model that included all 125 baseline and postbaseline characteristics had strong predictive ability (0.837), suggesting the utility of measuring these novel postbaseline variables in this population.

CONCLUSIONS: We demonstrate that long-term, 12-month adherence in patients filling longer supplies of medication can be strongly predicted using a combination of clinical, health resource utilization, and medication filling characteristics before and after treatment initiation.

DISCLOSURES: This work was supported by an unrestricted grant from CVS Health to Brigham and Women's Hospital. Shrank and Matlin were employees and shareholders at CVS Health at the time of this study; they report no financial interests in products or services that are related to this subject. Spettell is an employee of, and shareholder in, Aetna. This research was previously presented at the 2016 Annual Conference of the International Society for Pharmacoepidemiology; August 25-28, 2016; Dublin, Ireland.