Large Study of Statin Users Identifies Fill Behavior Patterns Predicting Adherence

Researchers analyzed pharmacy and medical claims among nearly 94,000 individuals with ongoing statin prescriptions to assess which of their 10 models was most successful at predicting adherence. Their findings, published in the Journal of Managed Care & Specialty Pharmacy, indicated that prescription filling behavior before and after the index fill had the highest predictive value for continued adherence.

Prior studies have found that models based on short-term prescription fill behaviors are most accurate at predicting adherence among patients initiating a new therapy, compared with other models constructed with medical claims data. However, little information existed on which models could best predict adherence among the much larger population of established medication users. As such, a group of researchers set out to evaluate the predictive value of 10 models for adherence among patients with ongoing statin prescriptions.

Using medical and claims data from a large commercial insurer, the study authors identified 93,777 individuals with an index statin fill in January 2012 who had filled at least 1 statin in the last 6 months of 2011. These data included socioeconomic and other demographic information, as well as claims for all procedures, visits, hospitalizations, and filled prescriptions. Adherence was determined by a cutoff of 80% proportion of days covered from the first prescription through the end of 2012 based on observed statin fills.

The investigators identified 6 groups of predictors that could drive adherence: pharmacy and demographic predictors or medical predictors chosen by the researchers, pre-index adherence, post-index filling, and high-dimensional propensity scores calculated with either pharmacy or medical claims data. These propensity scores were complex algorithms that identified the 400 prescription or medical codes most strongly associated with adherence, in an attempt to serve as a proxy for patient-level characteristics.

They then constructed 10 models using combinations of these predictors, and conducted analyses to assess the predictive power of each of the models across varying follow-up periods within the 12-month study period. For instance, the adherence outcomes linked to the post-index fill indicators were measured from April to December 2012 to “exclude temporal overlap between predictors and outcome.”

The researchers found that the models based on observed adherence, whether before or after the index fill, were most accurate at predicting which patients would maintain adherence. The strongest model incorporated pre-index pharmacy claims, demographic information, and medical claims in addition to post-index fills.

Interestingly, the complex propensity scores and the investigator-specified medical claims models performed the worst at predicting adherence. The authors wrote that these findings support the literature on predictors of adherence among new statin users and indicate that “adherence is not only a dynamic process, but also one with predictable patterns of fluctuation over the course of a patient’s lifetime on treatment.”

They recommended that efforts to improve adherence should be sure to provide ongoing support, instead of a one-time intervention. They also noted that the simplicity of the best predictive model could make it easier for clinicians to tackle the complex problem of medication adherence.

“In particular, the success of the 6 simple adherence-based pharmacy claims-derived predictors could form the basis of an intervention that could be easily implemented in a pharmacy or clinical outpatient setting,” they concluded.