Introduction

Poor medication adherence costs the U.S. healthcare system up to nearly $300 billion[1-3] each year. Patients who adhere to prescribed medication have fewer hospitalizations, lower costs[4], and lower mortality[5], as compared to their non-adherent counterparts. Nonadherence is common; in studies of statin adherence, only around 50% of subjects remain fully adherent 6 months after initiation[6,7]. However, interventions to improve adherence are often cost- and time-intensive[8,9].

In 2012, as required by the Patient Protection and Affordable Care Act, the Centers for Medicare and Medicaid Services (CMS) began using star ratings to award substantive bonus payments to privately-run Medicare Advantage and stand-alone Medicare prescription drug plans[8,10,11]. Higher star ratings are also associated with higher plan enrollment[12]. An important component of assigning these ratings is the proportion of plan beneficiaries who achieve a proportion of days covered (PDC) of 0.8 or above[11], for three classes of medications: statins, renin angiotensin system antagonists, and oral hypoglycemic agents. The adherence measures for these classes are obtained from prescription fulfillment data and are measured over a one-year period. We sought to develop a predictive model of poor adherence to statins so that plan managers, early in the year, can efficiently implement and target programs to the right patients.

Methods

Baseline variables used to predict adherence

The primary independent measure was PDC for days 1-90, analyzed as a continuous variable (PDC1-90). Model building was similar to a prior analysis, on a different population, predicting medication possession ratio[13]. We considered 15 additional variables focusing on those previously shown to be associated with adherence[1,6,7,14]. Age, gender, and statin name were available from claims data. For statin name, simvastatin was designated as the comparator for all other categories. We calculated the following variables from the first 90 days only after the index date (Figure 1): proportion of statin fills at retail pharmacies, proportion of formulary statin fills, proportion of generic statin fills, average days supply per statin prescription, average statin reimbursement per prescription, average statin copayment per prescription, and average statin dose per prescription. Finally, we calculated the following variables to describe the period from the first non-statin prescription to the index date (Figure 1): average pills per day, average non-statin reimbursement per day, average non-statin copayment per day, presence of acute coronary syndrome within 30 days prior to index date (defined as hospitalization for unstable angina or myocardial infarction, ICD-9 codes 411.x or 410.x), and time from first eligibility to index date.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Figure 1. Timeline showing periods used to calculate adherence measures and baseline variables.

Outcome variable is proportion of days covered (PDC) by statin for days 91-365, respectively. Three baseline variables are calculated from the first non-statin prescription to the index date. Presence of ACS (acute coronary syndrome) as a baseline variable was determined in the 30 days prior to statin initiation. Nine baseline variables including early PDC are calculated from statin prescriptions in days 1-90. Time from eligibility to initiation is required to be 180 days (in both models) in order to include those truly initiating statins, and not those merely continuing statins after switching insurance plans.

https://doi.org/10.1371/journal.pone.0079611.g001

Results

A total of 624,781 members met inclusion criteria. After exclusion criteria were applied, 217,928 remained (Figure 2). Table 1 shows baseline characteristics of the members.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Figure 2. Study flow diagram showing exclusion criteria.

The sum of members who met each individual exclusion criterion does not equal the total number excluded because one member can meet more than one exclusion criterion.

https://doi.org/10.1371/journal.pone.0079611.g002

Table 2 shows the pairwise associations between baseline variables and PDC91-365. Early PDC had the strongest effect of all variables (odds ratio for poor adherence 19.9 per 2 standard deviation decrease in early PDC, 95% confidence interval 19.0-20.9).

In multivariable analysis, the model had an AUC of 0.80 for the training set and validation set both. Adjusted odds ratios and 95% confidence intervals for all variables are shown in Table 2. Early PDC had the strongest effect of all variables in the model. Specifically, patients with a lower PDC at 90 days had an odds ratio of 25.0 (95% confidence interval 23.7-26.5) for poor adherence from days 91 to 365. The positive predictive value for poor adherence is 87.7%, and negative predictive value is 53.4%. The model can predict the CMS definition of statin nonadherence with 69.5% sensitivity and 78.2% specificity. For every 1000 beneficiaries, the model predicts poor adherence for 562. Of those, 335 are true positives, 47 are false positives and 180 are censored (eligibility ends within a year of statin initiation). The model predicts good adherence for 438 beneficiaries, of whom 168 are true negatives, 147 are false negatives, and 123 are censored.

In the sensitivity analysis, models did not perform well if they were based on less than 30 days of early adherence monitoring (AUCs near 0.6). Model performance increases sharply from days 30 to 31 (from AUC 0.62 to 0.71) for those members whose first prescription was for a 30-day supply or less, and it improved steadily thereafter. For those members whose first prescription was for more than 30 days, (most of whom received 90-day supplies,) AUC did not increase until after day 90 (Figure 3). When members with 30-day supplies for their first statin fill had predictions made at 40 days, and members with 90-day supplies for their first fill had predictions made at 100 days, poor adherence could be predicted with 86% positive predictive value, 50% negative predictive value, 69% sensitivity, and 74% specificity.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Figure 3. Model performance versus time of prediction.

Separate curves are presented for members whose first statin fill was for 30 days or less (circles), and for members whose first statin fill was for more than 30 days (triangles). For the ≤30 day subset, performance improves sharply between days 30 and 31, and it improves steadily thereafter. However, for the >30 day subset, (most of whom had 90-day fills,) performance improves sharply only after day 90. Each Y coordinate expresses the performance of a model that uses prescription fills from days 1 to X to predict adherence from days X to 365.

https://doi.org/10.1371/journal.pone.0079611.g003

Discussion

Early PDC is a strong indicator of future nonadherence, and, in fact stronger than variables identified in prior studies[8] which have shown relatively low predictive power of insurance claims, with AUCs under 0.64[14]. We show, however, that while claims data have limited value prior to initiation of the medication, they contain important information after several weeks of filling behavior, as they become an indicator of personal behavior[16].

Because CMS now uses adherence to award bonus payments, adherence is tied to direct financial incentives as well as incentives to prevent morbidity and mortality. We show that the CMS metric of PDC can be predicted far in advance; therefore plan managers could apply an individualized surveillance model to target adherence improvement programs to their Medicare Advantage members who are at risk for nonadherence and who are likely to benefit from such programs.

Prediction of PDC improves with time. Our primary model, which uses 90 days of adherence data, performs well, but sensitivity analysis demonstrates the utility of early monitoring as early as day 31. Little improvement in model performance is gained from monitoring adherence before the first prescription fill is expected to run out. A plan manager can account for the tradeoff between early detection of poor adherers and accuracy, based on the cost and characteristics of the intervention.

A limitation of this study, in common with much claims-based adherence research, and in fact with the very PDC metric used by CMS, is that it treats medication fills as essentially the same as adherence, which is a simplified conception[8]. Our model does not address self-report, pill counts, or other measures of adherence, and further research should use these measures as outcome variables, either singly or in combination. Similarly, all purely claims-based adherence research, including this study, cannot address underlying reasons for treatment discontinuations, including discontinuations that are recommended by the prescriber. Adherence is commonly defined as “the extent to which patients take medications as prescribed by their health care providers,”[1] and this cannot strictly be assessed with prescription claims data alone. Second, there is only slim evidence that the 0.8 PDC threshold is clinically meaningful[8,17]. Third, it may be difficult to separate any true effect of 90-day prescription supplies on adherence from the effect of days supply on the PDC calculation. Further study is particularly needed to determine the effect of days supply on pill taking, not just on metrics of prescription filling. Finally, our source population is commercial insurance members who were initiating statins; we did not study the Medicare population or those who were already taking statins when they entered our dataset. These populations are important for the proposed application of our model, and its performance should be confirmed in these populations.

To further increase accuracy of detection, the use of additional data sources, including electronic medical record information and patient-reported adherence, should be explored.

Acknowledgments

We thank Sebastian Schneeweiss, MD, ScD (Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA) for his assistance with analysis. He received no compensation for this assistance.

Previous Presentation: We have presented this study as a poster at the American Medical Informatics Association’s Summit on Clinical Research Informatics, March 20, 2013, San Francisco, CA.