Expenditures for Medicaid Patients Treated With Exenatide Compared With Other Diabetes Management Regimens
Published Online: September 27, 2012
Jennie H. Best, PhD; John A. Romley, PhD; Dana P. Goldman, PhD; Ryan M. Conrad, PhD; and Anne L. Peters, MD
In 2010, more than 34 million adults were enrolled in Medicaid; it has been estimated that 11.3% of American adults have diabetes.1,2 In 2007, total annual US direct medical spending for diabetes was estimated to exceed $116 billion.2 Annualized, this translates to adjusted mean medical expenditures of $11,744 for patients with diabetes, compared with $5095 for those without diabetes.3 The Congressional Budget Office projects adult Medicaid enrollment to exceed 43 million people in 2014, and more than 55 million people in 2020.1
Clinical guidelines recommend that patients with newly diagnosed type 2 diabetes mellitus (T2DM) receive initial treatment with metformin unless contraindicated. The addition of other therapies is recommended if target glycated hemoglobin (A1C) levels are not reached. As diabetes is a progressive disease, most patients eventually require combination therapy to maintain A1C goals, although guidelines do not stipulate a clear therapeutic preference vis-à-vis glycemic control.4 While comparative effectiveness research studies have addressed clinical outcomes more broadly, it is not fully understood which therapeutic combinations are associated with lower total costs.5,6
The availability of antidiabetic medications for patients on Medicaid varies from state to state, and formulary restrictions often limit use of drugs. Many drugs have limitations for use in various settings due to restricted Medicaid formulary coverage. The use of pioglitazone, insulin analogues, and newer antidiabetic medications recently approved by the US Food and Drug Administration (FDA) (glucagon-like-peptide-1 [GLP-1] receptor agonists and dipeptidyl peptidase-4-inhibitors [DPP4Is]) is limited to varying degrees based on state and local policies. In the case of exenatide, a GLP-1 receptor agonist, Medicaid boards in several states approved its use soon after FDA approval. For example, in 2005, exenatide was placed on the preferred drug list in West Virginia and Iowa.7,8 However, Medicaid boards in other states have delayed the inclusion of exenatide in their formularies.9,10 In contrast, glucose-lowering agents that have been on the market longer, such as metformin and sulfonylureas (SUs), are widely available and covered by Medicaid preferred drug lists.7-14 Antidiabetic therapies differ in their costs, with 30-day average wholesale prices in this study population ranging from $236 for DPP4Is to $513 for exenatide (2011 dollars).
To address the value of adding exenatide to the treatment regimen of patients on existing therapy consisting of metformin and/or SUs, this study compared Medicaid-enrolled patients with T2DM who had exenatide added to their therapeutic regimen with those who had thiazolidinediones (TZDs), basal insulin, or DPP4Is added. Four spending outcomes—inpatient, outpatient, prescription, and total—were measured for the year following initiation of each of these treatments. Regression analyses were conducted to control for patient characteristics. All comparisons with patients given exenatide were performed with the entire study cohort and again with propensity score–matched groups.
This retrospective cohort analysis applied Medicaid claims data from January 1, 2005, to December 31, 2009, from the Thomson Reuters MarketScan Multi-State Medicaid Database. This database contains inpatient, outpatient, and pharmacy claims of approximately 880,000 Medicaid enrollees with multiple claims with diabetes diagnoses. Also included in this database are demographic information, diagnosis codes, costs, dates of occurrence, and other administrative information. Clinical information, however, such as body mass index, blood pressure measurements, and laboratory results, such as A1C values, are not available. This database contained Medicaid claims information from 12 geographically dispersed states; however, state-level information or identifiers for geographic location were not available.15
Patients between 18 and 65 years of age were eligible for the study if they had T2DM, confirmed by 2 diagnoses of T2DM (International Classification of Diseases, 9th Revision, Clinical Modification codes 250.x0 or 250.x2) at least 30 days apart, or at least 1 T2DM diagnosis and a filled prescription for a 30-day supply of antidiabetic medication. These requirements helped to ensure the stability and accuracy of the diabetes diagnosis. Patients were required to have received an SU and/or metformin for at least 30 days, followed by the addition of exenatide, a TZD, basal insulin (long-acting or intermediate-acting, including detemir, glargine, and neutral protamine Hagedorn), or a DPP4I for at least 90 days within 12 months of initiating these add-on therapies. Patients who received treatment with more than 1 of the 4 specified add-on therapies during the study period were excluded. Those who used rapid-acting insulin prior to, or in combination with, the addition of a new therapy were excluded.
The index date was defined as the first date of add-on therapy initiation. For study inclusion, patient data had to be available for 12 consecutive months prior to the index date (ie, pre-period) and 12 consecutive months after the index date (ie, follow-up period). The pre-period served as a washout to ensure that the study captured new T2DM treatment initiation. Data obtained during the pre-period also enabled identification of comorbidities and calculation of the Charlson Comorbidity Index (CCI). The 12-month follow-up period allowed for an adequate observation window for outcomes.
Spending outcomes were evaluated for the 365 days subsequent to add-on treatment initiation. Outcomes were inpatient, outpatient, and pharmacy claims, and total spending (consisting of these 3 cost components). Costs were converted to 2011 US dollars based on the medical care component of the Consumer Price Index.3
Two potential confounders that could affect study outcomes were identified prospectively. First, physicians make treatment decisions based on a variety of factors, some of which cannot be fully observed in claims data (eg, comorbid disease severity, treatment adherence likelihood, and formulary restrictions).16-18 In addition, patient preferences and other factors can influence treatment initiation. These unobserved factors could confound the effect of therapy on measured outcomes.16,19,20
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