Delta T, a Useful Indicator for Pharmacy Dispensing Data to Monitor Medication Adherence

Abstract

Introduction: Calculating patients' medication availability from dispensing or refill data is a common method to estimate adherence. The most often used measures, such as the medication possession ratio (MPR), average medication supplies over an arbitrary period. Averaging masks the variability of refill behavior over time.

Goal: To derive a new absolute adherence estimate from dispensing data.

Method: Dispensing histories of patients with 19 refills of direct oral anticoagulants (DOAC) between 1 January 2008 and 31 December 2017 were extracted from 39 community pharmacies in Switzerland. The difference between the calculated and effective refill day (ΔT) was determined for each refill event. We graphed ΔT and its dichotomized version (dΔT) against the MPR, calculated mean ΔT and mean dΔT per refill, and applied cluster analysis.

Results: We characterized 2204 refill events from 116 DOAC patients. MPR was high (0.975 ± 0.129) and showed a positive correlation with mean ΔT. Refills occurred on average 17.8 ± 27.9 days "too early", with a mean of 75.8 ± 20.2 refills being "on time". Four refill behavior patterns were identified including constant gaps within or at the end of the observation period, which were critical.

Conclusion: We introduce a new absolute adherence estimate ΔT that characterizes every refill event and shows that the refill behavior of DOAC patients is dynamic.

Keywords: cluster analysis; compliance; measures; medication adherence; pharmacy claims.

Figure 1

Visualization of a refill history of a fictitious patient with the phases of medication adherence according to the ABC taxonomy [11] (Adapted from Frontiers, 2018), the defined timelines and events according to the TEOS framework adapted from [15], and the characterization of the refill events with ΔT. Dots represent the dosing history with dosing event (blue) and without dosing event (grey). The backline of the triangles indicates the refill events and must not correspond to the dosing events (T0-T3). Blue bars represent the duration of the supply.

Figure 2

Upper panel: (A) distribution of ΔT around the mean of 17.8 ± 27.9; (B), distribution of dΔT around the mean of 75.8 ± 20.2%; (C) distribution of MPR around the mean of 0.975 ± 0.129 lower panel: (D) ΔT graphed against the corresponding MPR; (E) mean dΔT graphed against the corresponding MPR. MPR, medication possession ratio; ΔT, mean ΔT; dΔT, mean dichotomized ΔT. Refer to text for details.

Figure 3

Unique refill patterns with ΔT from three illustrative patients with MPR of 1 (square), of 0.95 (triangle), and of 0.78 (dots) over the period of 19 refills. The green area above the x-axis indicates positive ΔT that is, refilling too early and oversupply; the red area below the x-axis indicates negative ΔT that is, refilling too late and gaps in DOAC supply.

Figure 4

Scatter plots of ΔT (triangle) and dΔT (square) against the refill number over the period of 19 refills; y-axis in days (left) and percent (right).

Figure 5

Heat maps replicating all 2204 ΔT (19 refills × 116 patients) per refill number with green color indicating refill events “on time” (ΔT ≥ 0) and red color indicating events “too late” (ΔT < 0); (A) with a color gradient from +150 days (green) to −150 days (red); (B) after dichotomization into green (“on time”) and red (“too late”); and (C) after clustering into the 4 refill groups: (1) refills are mostly “on time”, (2) erratic refills, (3) gaps at the end of the refill period, and (4) gaps in the middle of the refill period.