Surveillance of medication use: early identification of poor adherence

Abstract

Background: We sought to measure population-level adherence to antihyperlipidemics, antihypertensives, and oral hypoglycemics, and to develop a model for early identification of subjects at high risk of long-term poor adherence.

Methods: Prescription-filling data for 2 million subjects derived from a payor's insurance claims were used to evaluate adherence to three chronic drugs over 1 year. We relied on patterns of prescription fills, including the length of gaps in medication possession, to measure adherence among subjects and to build models for predicting poor long-term adherence.

Results: All prescription fills for a specific drug were sequenced chronologically into drug eras. 61.3% to 66.5% of the prescription patterns contained medication gaps >30 days during the first year of drug use. These interrupted drug eras include long-term discontinuations, where the subject never again filled a prescription for any drug in that category in the dataset, which represent 23.7% to 29.1% of all drug eras. Among the prescription-filling patterns without large medication gaps, 0.8% to 1.3% exhibited long-term poor adherence. Our models identified these subjects as early as 60 days after the first prescription fill, with an area under the curve (AUC) of 0.81. Model performance improved as the predictions were made at later time-points, with AUC values increasing to 0.93 at the 120-day time-point.

Conclusions: Dispensed medication histories (widely available in real time) are useful for alerting providers about poorly adherent patients and those who will be non-adherent several months later. Efforts to use these data in point of care and decision support facilitating patient are warranted.

Figure 1

Cohort construction. Analysis of prescription fills for three chronic drug categories: antihyperlipidemics, antihypertensives, and oral hypoglycemic. For each subject, prescription fills for the same drug were sequenced temporally into drug eras. Drug eras were excluded from analysis if there were: (1) gaps in membership in the insurance plan during the first year of prescriptions; (2) drug eras that began before a 6-month wash-out period, or in the last 13 months of the dataset (left and right censoring); (3) hospitalizations during the first year of prescriptions; and (4) unusual age, gender, and fill quantities likely due to human errors during data entry.

Figure 2

Classification of drug eras by adherence during the first year of prescription fills. The drug eras were initially separated into three categories: (1) subject switched to another drug in the same category; (2) flagged by the large-gap detector because they accumulated a medication gap greater than 30 days; and (3) neither switch to another drug nor accumulation of a large medication gap. The drug eras that accumulated a large medication gap were further categorized into those with a switch to another drug in the same category within 90 days after the flagging date, long-term discontinuers who never filled a prescription for that same drug again in the dataset, and short-term discontinuers who did fill a prescription for the same drug sometime after the flagging date. The drug eras that exhibited consistent prescription filling for the entire year without any large gaps (‘No gap, no switch’) were further categorized into those that had a medication possession ratio greater than 0.80 (‘Good adherence’) and those who fell below the threshold (‘Poor adherence’).

Figure 3

Distribution of medication possession ratios (MPRs) measured 1 year after the first prescription fill. Data include drug eras remaining after the removal of those containing medication gaps greater than 30 days during that time period and those that switched to another drug in the same category (‘No gap, no switch’ category in figure 2).

Figure 4

Model performance for prediction of drug eras with medication possession ratios of less than 0.80 (poor adherence) at 1 year from the vantage point of 60, 90, and 120 days after beginning the first fill. The data represent drug eras remaining after the large medication gap filter was applied (‘No gap, no switch’ category). The areas under the receiver-operator characteristic curve (AUC) show the improving performance of the models with increasing number of days since the first prescription fill.