Oral Antidiabetic Medication Adherence and Glycemic Control in Managed Care

Objective: To evaluate adherence to oral diabetes medications (ODMs) in patients with type 2 diabetes and the impact of ODM adherence on glycemic control.

Study Design: Retrospective observational study.

Methods: Medical and pharmacy claims from a managed care plan in Oregon were used to identify adults with diabetes who newly initiated ODM therapy (n = 2741); a subset of this cohort linked to electronic health records was used to evaluate the relationship between adherence and glycemic control (n = 249). Glycemic control was assessed based on most recent glycosylated hemoglobin (A1C) measurement within the study period.

Results: Mean cohort age was 54 years; 46% initiated therapy with metformin, 39% with a sulfonylurea, and 12% with a thiazolidinedione. Mean adherence overall was 81%, and 65% of subjects had good adherence (=80%). Increasing age and comorbidity burden were associated with higher medication adherence. In the patient subset with A1C measurements, mean baseline A1C was 8%. An inverse relationship existed between ODM adherence and A1C; controlling for baseline A1C and therapy regimen, each 10% increase in ODM adherence was associated with a 0.1% A1C decrease (P = .0004).

Conclusion: Although most patients were adherent to ODM therapy, adherent patients were more likely to achieve glycemic control than nonadherent patients. Greater efforts are needed to facilitate diabetes self-management behaviors to improve patient outcomes.

(Am J Manag Care. 2008;14:71-75)

In a managed care setting, this retrospective study demonstrated that although most patients were adherent with prescribed oral diabetes medication (ODM), about 35% of patients were classified as nonadherent.

After adjusting for baseline glycosylated hemoglobin (A1C) and therapy regimen, each 10% increase in adherence with ODM was associated with a 0.1% A1C decrease. n When lab data are not available (as is common in managed care databases), pharmacy claims may serve to identify patients for targeted outcomes/ intervention programs.

Greater efforts are needed to improve medication adherence and patient self-management in type 2 diabetes.

Type 2 diabetes is a growing worldwide epidemic, with approximately 20 million diagnosed and undiagnosed persons in the United States.¹ Of national concern is the finding that fewer than half of Americans with diabetes have achieved a glycosylated hemoglobin (A1C) level of <7%.2 Nonadherence to medication therapy is among several factors contributing to suboptimal glycemic control. A recent systematic review found significant variation in adherence to oral diabetes medications (ODMs), ranging from 36% to 93%.3 Patient nonadherence to medications prescribed for diabetes has been shown to decrease treatment effectiveness4-6 and increase healthcare costs.^7,8 Although published studies address clinical outcomes, there is a continuing need to evaluate the association between medication adherence and diabetes outcomes. The purposes of this study were to evaluate (1) patient adherence to ODM and (2) the relationship between adherence and glycemic control.

METHODSSetting and Study Population

Medication adherence was calculated for all patients with at least 2 fills of the index ODM, and was defined as the sum of the days supply from the index prescription date to the last fill date (excluding days supply that was dispensed at the final prescription fill), divided by the duration of therapy. Patients using free combinations of more than 1 ODM were classified according to the first medication that was filled during the study period, and study metrics were calculated for that drug. For free combination regimens, patients were considered adherent on a specific day if all drugs were considered to be &#8220;on hand&#8221; on that date. Patients with adherence of less than 80% were classified as nonadherent.

Patients&#8217; complete refill history at baseline was used to calculate the modified chronic disease score (CDS) to assess patient comorbidity.⁹ The number of concurrent (non&#8211;diabetes- related) medications also was assessed as another measure of disease burden.

A subset of study patients who also received care from primary care physicians employed within the integrated delivery network was identified to evaluate the relationship between medication use and glycemic control. This subset was limited to patients with an A1C test result at study baseline (180 days before the study index date and 60 days subsequent to the index date). Glycemic control at follow-up was assessed after a stabilization period subsequent to ODM initiation; it was based on the most recent A1C within the study period, collected at least 60 days after the index date.

Statistical Analysis

The cohort of patients who newly initiated ODM therapy included 2741 subjects for evaluation of utilization metrics. Mean cohort age was 54 &#177; 11 years, and 49% were female (Table). Overall, 84% initiated ODM monotherapy, 15% initiated dual combination therapy, and 1% initiated triple combination therapy; 41% were prescribed metformin, 33% SUs, 9% TZDs, 2% other drugs (including a-glucosidase inhibitors and meglitinides), and 15% were prescribed various combination therapies. The mean CDS overall was 2.89 &#177; 0.99, with a small but significant difference between SU and TZD patients (2.85 vs 2.99, P = .04).

DISCUSSION

Overall, study findings indicated good adherence with ODMs in this managed care population with diabetes. An association was found between ODM adherence and glycemic control, such that each 10% increase in ODM adherence was associated with a 0.1% decrease in A1C. These findings highlight the importance of medication adherence for attaining glycemic control, thus reducing the incidence of diabetes complications. Initiatives targeting improved medication adherence in patients with type 2 diabetes are important to patient care and health plans.

We acknowledge Parker Pettus, MS, and K. Arnold Chan, MD, ScD, of the Channing Laboratory, Brigham & Women’s Hospital and Harvard Medical School for the development of the SAS code used to calculate the chronic disease score in this study. We also thank James Slater, PharmD, Providence Health Plan, for guidance and support in the execution of the study.

Author Affiliations: From Providence Physician Division, Beaverton, Ore (YR, JSH); Novartis Pharmaceuticals Corporation (CP), East Hanover, NJ; and Novartis Pharmaceuticals Corporation (KSW), San Marino, Calif.

Funding Source: This study was funded by Novartis Pharmaceuticals Corporation.

Author Disclosure: Drs Plauschinat and Wong are employees of Novartis Pharmaceuticals Corporation, which provided funding for this study. Dr Plauschinat also reports owning stock in Novartis Pharmaceuticals Corporation. The other authors (YR, JSH) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (YR, JSH, CP, KSW); acquisition of data (YR, JSH); analysis and interpretation of data (YR, KSW); drafting of the manuscript (YR, JSH, CP, KSW); critical revision of the manuscript for important intellectual content (YR, JSH, CP, KSW); statistical analysis (YR); obtaining funding (JSH, KSW); administrative, technical, or logistic support (JSH, KSW); and supervision (JSH).

Address correspondence to: Yelena Rozenfeld, MPH, Providence Physician Division, 3601 Murray Blvd, Ste 45, Beaverton, OR 97005. E-mail: yelena.rozenfeld@providence.org.

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