Abstract
Introduction
Complex or personalized insulin regimens challenge traditional adherence measures. Our objective was to develop an improved basal insulin (BI) adherence measure using both patient-reported and administrative claims data, resulting in a more complete measure.
Methods
Patients' self-reported BI utilization over the previous 12 months was linked with their claims data for the same period. Hybrid medication possession ratio (MPR) was derived by calculating expected days of insulin supply [total dispensed insulin units from claims over 12 months divided by self-reported total daily dose (TDD)]. The hybrid MPR was compared against traditional claims-based MPR, adjusted claims-based MPR, and patient-reported MPR. For all MPR measures, the adherence threshold was ≥ 0.8. A logistic model was used to predict non-adherence per hybrid MPR. The predicted model-based MPR was compared with existing measures in a larger cohort.
Results
The study sample consisted of 296 patients. TDD derived from claims was higher than self-reported TDD [77.9 (71.8) vs. 57.7 (38.3)], implying average dispensed insulin would last longer than claims-based days supply. Correspondingly, hybrid and MPRs adjusted for package size (56% and 71%, respectively) were higher than claims-based MPR (50%). Age, total claims-based days supply, retinopathy, adjusted MPR-based adherence, and non-insulin injectable use were key predictors of hybrid MPR-based adherence. Applying the claims-based prediction model to a larger cohort to test validity showed high correlations with predicted and adjusted MPR-based adherence.
Conclusions
Traditional claims-based MPR underestimated adherence while adjusted MPR overestimated adherence when self-reported total daily dose was taken as benchmark insulin dose. The predicted model may help identify patients with poor basal insulin adherence. More research is needed to further confirm the findings.
Funding
Eli Lilly and Company, Indianapolis, IN, USA.
https://ift.tt/2FZOW4a
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