Classifying patients into particular groups may be a better indicator of their long-term medication adherence behavior, researchers at CVS Caremark Corp. and Brigham and Women's Hospital found.

CVS Caremark, medication adherence, Brigham and Women's Hospital, adherence behavior, September 2013 issue of Medical Care, trajectory modeling, statin users, medication filling, Troy Brennan, Niteesh Choudhry

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CVS research classifies adherence behavior by group

September 12th, 2013

WOONSOCKET, R.I. – Classifying patients into particular groups may be a better indicator of their long-term medication adherence behavior, researchers at CVS Caremark Corp. and Brigham and Women's Hospital found.

CVS said Thursday that in a study published in the September 2013 issue of Medical Care, researchers followed more than 264,000 statin users over a 15-month period and devised measures to account for various adherence behaviors. Next, they identified key groups into which patients would fall based on those measures in order to predict and compare their long-term adherence patterns.

The study employed trajectory modeling, a new method of categorizing adherence that's based on observed patterns of medication filling over time. The researchers noted that this method enables them to more accurately capture and describe adherence, compared with techniques that simply classify patients as adherent or not, using average levels of adherence.

In addition, the trajectory patterns created should allow for more targeted interventions to address nonadherence, CVS said, adding that in the study the trajectory modeling was particularly useful in accounting for variable patterns of intermittent and long-term adherence behaviors.

"CVS Caremark continues to engage in cutting-edge research using novel approaches to analyze data so we can better understand the adherence behaviors and needs of the patients we support," Troy Brennan, executive vice president and chief medical officer at CVS Caremark, said in a statement. "The use of trajectory models could help us more accurately identify patients at risk for medication nonadherence so we can develop and implement targeted interventions to help them stay on their medications for chronic health conditions."

Researchers categorized patients into six groups according to their characteristics: nearly always adherent (23.4%); brief gap in medication use or filled irregularly during the first nine months but improved during the last six months (11.4%); slowly declining adherence throughout the 15 month period (11.3%); used statins only occasionally across the 15-month study period (15%); rapid decline in statin use after initiation (19.3%); and virtually no fills after their initial fill (23.4%).

"Our findings could help facilitate research on medication adherence and medication effectiveness in a variety of ways," explained Niteesh Choudhry, associate physician in the division of pharmacoepidemiology and pharmacoeconomics at Brigham and Women's Hospital

"For example, by using group-based trajectory modeling, we could help identify patients with distinct patterns of adherence so that health care professionals could appropriately target interventions," said Choudhry, who's also an associate professor at Harvard Medical School. " In addition, data related to the quantity and timing of medication availability could help improve our understanding of the effects of nonadherence on clinical outcomes."

Group trajectory modeling also identified certain characteristics that impact medication adherence, according to CVS. For example, among the study participants, on average, patients with the best adherence were older, more likely to have a higher income, more likely to be a high school graduate, and less likely to be black. In addition, those with the best adherence were more likely to be a Medicare Part D beneficiary or live in New England. Those with the lowest adherence rates tended to be generally younger, male and less likely to have an initial prescription that provided them with more than a 30-day supply of medication.