Over 2 million subjects were identified (). After the exclusion criteria were applied, the antihypertensives class had the most subjects (1 294 521) followed by antihyperlipidemics (790 883) and oral hypoglycemics (278 029). The antihypertensives class contained more than four times as many drugs (74) as the two other classes (17 and 16 for antihyperlipidemics and oral hypoglycemic, respectively) (online supplementary appendix table A1). In all three classes, between 50% and 60% of subjects filled prescriptions for more than one drug in the same class, resulting in 1.4 to 1.9 times as many drug eras as unique subjects in each class. The final cohort had slightly more females than males in all three classes, and the average age on the date of the first prescription was between 62 and 67 years ().
Cohort characteristics on date of first prescription fill
A two-phase adherence monitor was applied. First, a large-gap detector, identifying periods of more than 30 days between expected fills, was applied. For antihypertensives, antihyperlipidemics, and oral hypoglycemics, respectively, 66.5%, 64.5%, and 61.3% of drug eras contained medication gaps greater than 30 days. These include long-term discontinuations, where the subject never again filled a prescription for that drug in the dataset, which represent 29.0%, 29.1%, and 23.7% of all the drug eras and 59.5%, 68.6%, and 51.8% of the drug eras containing large medication gaps. Among the long-term discontinuations, 50.4%, 50.3%, and 36.6% had only one prescription fill. On average, long-term discontinuation drug eras had 1.7, 1.6, and 2.2 prescription fills, and 71.3, 59.6, and 59.5 dispensed supply days. The drug eras defined as short-term discontinuations—containing large gaps followed by prescription fills (short-term discontinuations)—had average gap lengths of 105.6, 120.0, and 107.6 days for antihyperlipidemics, antihypertensives, and oral hypoglycemic, respectively.
After applying the large-gap detector, we applied an algorithm to detect non-adherence defined as an MPR of less than 0.80 at 1 year. The MPR, a standard measure of adherence, is defined as the days supply of medication divided by the days between refills. Among drug eras not containing large gaps between fills (‘No gap, no switch’ category in ), those which had MPRs less than 0.80 at the 1-year outcome date (‘Poor adherence’ category in ) met our definition of long-term poor adherence. For antihyperlipidemics, antihypertensives, and oral hypoglycemic, respectively, these drug eras represented 1.0%, 0.8%, and 1.3% of all the drug eras, and 3.1%, 2.6%, and 3.5% of the drug eras in the ‘No gap, no switch’ category. The distributions of the 1-year MPR values for the drug eras in this group are similar for the three drug categories (). These drug eras were used to build logistic regression models for predicting long-term poor adherence. Models were built for making predictions at three early time-points in the drug era: 60, 90, and 120 days after the first prescription fill.
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 (more ...)
The logistic regression models are in the form:
is the latest available MPR value and x2
is the age of the subject at the beginning of the drug era. The intercepts (β0
) and coefficients (β1
) for the nine models are given in .
Coefficients for the logistical regression models where the risk of poor adherence is calculated using the latest available MPR value and the age of the subject at the beginning of the drug era
The performance of the prediction models developed for each drug category and each time point to detect drug eras with poor adherence is shown in . The models performed similarly for the three drug classes, and performance improved across the three time points of prediction when starting at a later point. For antihyperlipidemics, antihypertensives, and oral hypoglycemics, respectively, for predictions made at 120 days after the first fill, compared with 60 days, the areas under the curve (AUC) improved from 0.83, 0.81, and 0.83 to 0.93, 0.92, and 0.93 ().
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 (more ...)
With sensitivity held constant at 90%, shows the performance statistics for each model, which like the AUC, improved when making predictions at later time points. Specificity, accuracy, positive predictive value, and negative predictive value all increased from below 50% to above 80% between days 60 and 120. The negative predictive value remained constant at 99% for all three drug categories and the three time points.
Performance statistics (with sensitivity set to 90%) of models for detecting drug eras at high risk of future poor adherence among the drug eras remaining after the large medication gap filter was applied