A retrospective cohort study evaluating the relationship between statin medication adherence and economic outcomes in commercial health plans

Longitudinal medication adherence group-based trajectories of aging adults in the US: A retrospective analysis using monthly proportion of days covered calculations

BACKGROUND

It is thought that half of the patients with chronic conditions are not adherent to their medications, which contributes to significant health and economic burden. Many studies estimate medication non-adherence by implementing a threshold of ≥80% of Proportion of Days Covered (PDC), categorizing patients as either adherent or non-adherent. Healthcare quality metrics pertaining to medication use are based on this dichotomous approach of medication adherence, including the Medicare Part D Star Ratings. Among others, the Medicare Part D Star Ratings rewards part D plan sponsors with quality bonus payments based on this dichotomous categorization of beneficiaries' medication adherence.

OBJECTIVES

Describe the longitudinal adherence trajectories of adults ≥65 years of age covered by Medicare for 3 classes of drugs in the Part D Star Ratings: diabetes medications, statins, and select antihypertensives.

METHODS

This study used Medicare healthcare administrative claims data linked to participants from the Health Retirement Study between 2008 and 2016. Group-based trajectory models (GBTM) elicited the number and shape of adherence trajectories from a sample of N = 11,068 participants for the three pharmacotherapeutic classes considered in this study. Medication adherence was estimated using monthly PDC.

RESULTS

GBTM were estimated for the sample population taking antihypertensives (n = 7,272), statins (n = 8,221), and diabetes medications (n = 3,214). The hypertension model found three trajectories: high to very high adherence (47.55%), slow decline (32.99%), and rapid decline (19.47%) trajectories. The statins model found 5 trajectories: high to very high adherence (35.49%), slow decline (17.12%), low then increasing adherence (23.58%), moderate decline (12.62%), and rapid decline (11.20%). The diabetes medications model displayed 6 trajectories: high to very high adherence (24.15%), slow decline (16.84%), high then increasing adherence (25.56%), low then increasing (13.58%), moderate decline (10.60%), and rapid decline (9.27%).

CONCLUSIONS

This study showed the fluid nature of long-term medication adherence to the medications considered in the Medicare Part D Star Ratings and how it varies by pharmacotherapeutic class. These challenge previous assumptions about which patients were considered adherent to chronic medications. Policy and methodological implications about medication adherence are discussed.

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

BACKGROUND

Increasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data-rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery.

METHODS

We conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications.

RESULTS

Our study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision-making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality.

CONCLUSION

Advanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.

Reinforcing adherence to lipid-lowering therapy after an acute coronary syndrome: A pragmatic randomized controlled trial

BACKGROUND AND AIMS

Achieving the low-density lipoprotein cholesterol (LDL-C) goal following an acute coronary syndrome (ACS) is a milestone often missed due to suboptimal adherence to secondary prevention treatments. Whether improved adherence could result in reduced LDL-C levels is unclear. We aimed to evaluate whether an educational-motivational intervention increases long-term lipid-lowering therapy (LLT) adherence and LDL-C goal attainment rate among post-ACS patients.

METHODS

IDEAL-LDL was a parallel, two-arm, single-center, pragmatic, investigator-initiated randomized controlled trial. Hospitalized patients for ACS were randomized to a physician-led integrated intervention consisting of an educational session at baseline, followed by regular motivational interviewing phone sessions or usual care. Co-primary outcomes were the LLT adherence (measured by Proportion of Days Covered (PDC); good adherence defined as PDC>80%), and LDL-C goal (<70 mg/dl or 50% reduction from baseline) achievement rate at one year.

RESULTS

In total, 360 patients (mean age 62 years, 81% male) were randomized. Overall, good adherence was positively associated with LDL-C goal achievement rate at one year. Median PDC was higher in the intervention group than the control group [0.92 (IQR, 0.82-1.00) vs. 0.86 (0.62-0.98); p = 0.03] while the intervention group had increased odds of good adherence (odds ratio: 1.76 (95% confidence interval 1.02 to 2.62; p = 0.04). However, neither the LDL-C goal achievement rate (49.6% in the intervention vs. 44.9% in the control group; p = 0.49) nor clinical outcomes differed significantly between the two groups.

CONCLUSIONS

Α multifaceted intervention improved LLT adherence in post-ACS patients without a significant difference in LDL-C goal attainment.