Anne L Peters, MD

This study examined the relationship between medication adherence, cost sharing measured as out-of-pocket spending, and total annual spending in Medicare beneficiaries with type 2 diabetes (T2D) to evaluate whether pharmacy cost-sharing programs have the potential to decrease adherence. These programs may unintentionally increase the risk of medical complications and may result in higher spending overall.

This retrospective study used 2006 to 2009 Medicare claims data. The sample included patients 65 years or older with T2D (at least 1 claim with International Classification of Diseases, 9th Revision, Clinical Modification codes 250.x0 and 250.x2 and at least 1 antidiabetes drug claim). 

Medication adherence was measured as proportion of days covered over the first 12 months of observation. Spending and adherence outcomes were defined in deciles. 

The sample included 12,305 patient-year observations. Pharmacy spending for patients in the most adherent (10th) decile was 59% higher than that for patients in the least adherent (1st) decile ($4839 vs $3046). Yet, patients in the 10th decile had 49% lower total ($12,531 vs $24,468) and 64% lower medical spending ($7692 vs $21,421) than patients in the 1st decile. Greater out-of-pocket spending was correlated with lower adherence and higher total and medical spending. 

 This study describes a widespread variation in medication adherence, pharmacy cost sharing, and medical spending in a sample of Medicare beneficiaries with T2D. We found that lower adherence was correlated with higher cost sharing in the Medicare population, perhaps because of unobserved confounding factors. However, the existing literature on patients with employer-sponsored insurance suggests some of this correlation may be indicative of causal relationships.

		Increased cost sharing could decrease the likelihood that patients with type 2 diabetes will adhere to their medication and increase diabetes complications and total diabetes spending. 

		Using Medicare claims data (2006-2009), we examined the relationships between medication adherence, cost sharing measured as out-of-pocket spending, and total annual spending to evaluate whether pharmacy cost-sharing programs have the potential to decrease adherence. 

		We found a widespread variation in medication adherence, pharmacy cost sharing, and medical spending, and that lower adherence is correlated with higher cost sharing in the sample of Medicare beneficiaries with type 2 diabetes.

In 2014, nearly 30 million Americans (9.3%) were living with diabetes, including an estimated 11.2 million who were 65 years or older.

 In 2012, the total national spending on diabetes was $245 billion, including $176 billion in direct medical spending. Patients 65 years or older use more healthcare services than younger patients and incur the majority of costs associated with diabetes. Ultimately, the Medicare program—in which 28% of beneficiaries have diabetes&mdash;bears most of this burden.

 Interventions targeted at individuals using the most resources and who have the greatest expenditures have the potential for a disproportionate impact on spending.

Several studies evaluating the economic burden of diabetes have estimated that antidiabetes agents and supplies account for 12% of total healthcare spending.

 Payers have implemented various cost-sharing schemes to reduce pharmacy spending, including incentive-based formularies, coinsurance or co-payments, and benefit caps.

 Research consistently shows that higher co-payments or coinsurance reduce pharmaceutical utilization and adherence among patients with chronic diseases like type 2 diabetes (T2D), heart failure, and schizophrenia,

 and that reducing co-payments in employer-sponsored insurance plans can increase adherence.

Although cost sharing may reduce overall pharmacy spending, it could also have consequences for patient health and result in unintended increases in nonpharmacy healthcare spending, thereby offsetting the savings on pharmaceuticals. The limited existing research shows that cost sharing increases emergency department visits and hospitalizations, especially among chronically ill patients.

 The impact of cost sharing on therapeutic adherence is particularly relevant for patients with diabetes, given that improved adherence is associated with a greater likelihood of reaching glycated hemoglobin (A1C) targets—the primary measure of effective disease management

—and cost sharing has increased since the introduction of Medicare Part D.

 By some estimates, a 1% increase in antidiabetes prescription utilization would reduce medical spending for Medicare beneficiaries with T2D by 0.83%.

Despite the large overarching financial burden of T2D, few studies have examined the extent to which pharmacy and medical spending contribute to total diabetes-related spending.

 This retrospective claims study of a Medicare population examined the relationship between adherence, cost sharing as measured by out-of-pocket (OOP) spending, and total annual spending.

The study used claims data for a 20% random sample of age-eligible Medicare fee-for-service beneficiaries for the years 2006 to 2009. Data were acquired from the CMS Medicare Research Identifiable Files, which provide medical and pharmacy claims, including diagnoses, procedure codes, and measures of charges and spending. Overall spending in any category (total healthcare, medical, or pharmacy) equaled the sum of the patient OOP component and the payer-covered component.

A sample was created using the following criteria: 1) Medicare beneficiaries 65 years or older; and 2) with at least 1 claim with an 

International Classification of Diseases, 9th Revision, Clinical Modification

codes 250.x0 and 250.x2) and at least 1 claim for an antidiabetes medication in the period following the first observed claim with diabetes diagnosis. The index date for patients in the sample was the date of the first observed claim with a T2D diagnosis. Only patients 65 years or older at the time of the first observed claim with a diabetes diagnosis were included. The sample included patients with a T2D diagnosis or antidiabetes medication use in the year prior to the first observed claim in the database if the patient’s index date was less than 1 year after the date of Medicare enrollment. Patients with a follow-up period of less than 1 year after the index date or those who were pregnant or less than 6 months post partum were excluded.

Adherence was measured as the proportion of days covered (PDC), a well-established method that has been used for several decades to evaluate adherence in diabetes and other chronic conditions (eg, cardiovascular disease, dyslipidemia, hypertension).

 PDC was calculated as the total number days when a patient had medication on hand during the observation window (ie, date of first claim to exhaustion of last prescription, segmented into calendar years).

This retrospective study used descriptive analysis to evaluate the extent to which patients’ health status differed across deciles of spending. In particular, we calculated the prevalence of diabetic comorbidities, complications, and drug therapy type by decile of total annual spending. To investigate the relationship between medication adherence and spending, average pharmacy, medical, and total spending were calculated within each adherence decile. To examine the relationship between cost sharing—as measured by OOP spending (total, medical, and pharmacy)&mdash;and medication adherence, average OOP pharmacy, medical, and total spending were calculated within each decile of adherence.

Dividing the sample into deciles created enough bins to characterize the spending distribution. Using deciles to map the distribution of total, pharmaceutical, and medical spending among Medicare beneficiaries with T2D also enabled the identification and description of the degree of skew in annual spending distribution.

Based on the inclusion and exclusion criteria, the sample included 12,305 patients with a mean follow-up period of 3.2 years and a mean age of 74.4 years. The sample was 64% female and 36% male.

Distribution of Total, Medical, and Pharmacy Spending

Considerably higher average total and medical spending were observed for patients in the upper deciles of total spending compared with patients in the lower deciles, but the between-decile difference in pharmacy spending for patients was not as substantial (Figure 1). The average annual total spend for patients in the lowest spending (1st) decile was $794; this increased by nearly 100-fold, to $78,641, for patients in the highest spending (10th) decile. The average annual medical spend for patients in the 1st decile was $417 and increased more than 170-fold, to $71,203, for patients in the 10th decile. However, the average annual pharmacy spend for patients in the 1st decile was $377 compared with $7439 for patients in the 10th decile, a less than 20-fold increase.

Patient Descriptive Statistics and Health Status by Spending

Patient age and sex were similar across all total annual deciles of spending; however, patients in the upper deciles were substantially more likely to have comorbid conditions, including congestive heart failure (74% in the 10th decile vs 17% in the 1st decile), cardiac arrhythmias (68% vs 24%), complicated hypertension (57% vs 17%), or moderate/severe renal impairment (18% vs 1%), among others. The use of certain antihyperglycemic drug therapies was also more prevalent in the upper deciles of total spending. Compared with patients in the 1st annual spending decile, patients with T2D in the 10th decile had a nearly 5-fold higher probability of using long- or rapid-acting insulin to treat their diabetes (31%-34% vs 3%-7%, respectively). Dipeptidyl peptidase-4 inhibitors, meglitinide, and thiazolidinedione use was also higher for patients in the upper deciles of total annual spending, whereas metformin (a biguanide) use was less common in the 10th versus 1st decile (31% vs 53%). Use of sulfonylureas and glucagon-like peptide-1 receptor agonists did not vary significantly across the deciles of total annual spending.

Mean PDC was divided into deciles and ranged from 0.20 for patients in the 1st decile to 1.00 for patients in the 10th decile. As shown in Figure 2, patients with greater medication adherence, as measured by PDC, had lower average annual medical and total spending and only moderately increased pharmacy spending. For patients in the 1st decile of PDC (lowest adherence) to the 10th decile, average annual total spending decreased 49% (from $24,468 to $12,531), and average annual medical spending fell 64% (from $21,421 to $7692). By comparison, pharmacy spending between the 1st decile of PDC and the 10th decile increased 59% (from $3046 to $4839).

An examination of the relationship between total OOP spending and adherence found analogous results. Average annual pharmacy OOP spending for patients in the 1st to 10th PDC decile increased 67% (from $441 to $737), but average annual total OOP spending decreased 36% (from $2116 to $1344), and average annual medical OOP spending decreased 64% (from $1675 to $607).

Based on the present study’s findings, Medicare patients with T2D have highly skewed total annual spending; patients in the upper deciles of total spending account for a disproportionate share of aggregate total spending. As previous studies had similar findings,

 this analysis confirms that medical spending accounts for the vast majority of the skew in total spending on T2D treatment, and that pharmacy spending is a relatively small proportion of total spending.

An analysis of comorbidities and medication use according to total spending showed that patients in the highest deciles were more likely to have comorbid conditions and to use certain antidiabetes drugs (in particular, long-acting, short-acting, and premix insulins). It is likely that the differences by decile in the prevalence of diabetic comorbidities, complications, and therapy utilization in patients play a large role in driving healthcare spending variation. This finding is supported by existing studies.

This study also examined the distribution of total, medical, and pharmacy spending in Medicare patients with T2D based on their level of medication adherence. More adherent patients had substantially lower total spending and medical spending, but moderately increased pharmacy spending, suggesting that, for Medicare patients with T2D, lower adherence is related to increased spending in general. Increased spending may be the result of poorer outcomes, increased comorbidities, and a need for additional treatment, as medication adherence has been significantly associated with improved health status, fewer complications, and less medical resource utilization in patients with T2D.

This study corroborates previous research and suggests that increasing adherence among Medicare beneficiaries with T2D could generate significant cost savings.

 The use of pharmacy cost sharing in Medicare has gone up since the introduction of Medicare Part D, which extended pharmacy benefit coverage and increased pharmaceutical utilization,

 but the availability of generic drugs has prevented significant increases in OOP spending. If this cost-sharing trend continues, adherence may fall and any savings on pharmacy spending could be offset by increased medical spending.

 Recent proposals to limit annual OOP spending for Medicare beneficiaries would reduce the financial burden on high-spend Medicare beneficiaries with T2D, potentially improving medication adherence and health outcomes.

This study has certain limitations. The analysis was descriptive, with data randomly drawn from an administrative claims database representing the years 2006 to 2009. Although the levels of pharmacy and medical spending may have changed since 2009, our qualitative results and the correlations between cost sharing, adherence, and medical spending should not be impaired by the age of the data, as many similar studies have found the same relationships in other patient samples and time periods.

 Additionally, because the study drew from the Medicare database, and because only patients 65 years or older at the time of the first observed T2D diagnosis claim were included, the mean age of the sample (74 years) was higher than the average age of T2D diagnosis (54 years) in the United States.

 Last, only limited medical history is available for patients in claims data research, thereby increasing the potential for confounding, and the date of patients’ initial T2D diagnosis could not be determined with certainty.

This study has several implications. First, the study results demonstrates the existence of a skew in spending on T2D treatment. Medicare patients in the highest deciles of total spending had dramatically higher total and medical spending, clearly indicating that most spending on diabetes-related treatment is from medical—and not pharmacy&mdash;spending among a subset of high-cost patients. In addition, the study expands on available studies of patients with employer-sponsored insurance to demonstrate that improved adherence is related to reduced resource use and spending in Medicare beneficiaries with T2D and suggest that interventions aimed at improving adherence among high-cost patients have the greatest potential to reduce health expenditures. Most importantly, the study results confirm the negative connection established in the existing literature between cost sharing and medication adherence, which varied widely among Medicare beneficiaries with T2D.

 If pharmacy cost-sharing programs decrease medication adherence, initiatives that promote pharmacy cost sharing may unintentionally increase the risk of medical complications offsetting some (or in targeting cases, all) of the savings associated with lower pharmaceutical spending.&ensp;

 Precision Health Economics (JPM, JV-O, JS, DP), Los Angeles, CA; AstraZeneca (JJS, IK), Fort Washington, PA; University of California, Los Angeles (WY), Los Angeles, CA; University of Southern California (ALP), Los Angeles, CA.

 This study was supported by AstraZeneca.

 Dr MacEwan, Mr Sullivan, and Ms Peneva are employees of Precision Health Economics (PHE), which is a consultant for biotech/pharma companies and was compensated by AstraZeneca to perform the study. Dr Vanderpuye-Orgle was an employee of PHE during the time of the study and is currently employed by Amgen. Dr Yin is a consultant for PHE. Dr Sheehan has been employed by AstraZeneca, and Dr Kalsekar has been employed by and holds stock in AstraZeneca and Johnson & Johnson, which manufacture drugs for the treatment of diabetes. Dr Peters has been a consultant and grant recipient from numerous pharmaceutical companies including AstraZeneca, and receives editorial fees from Medscape.

 Concept and design (JPM, JJS, WY, JV-O, JS, IK, ALP); acquisition of data (JS, IK); analysis and interpretation of data (JPM, JJS, WY, JV-O, JS, DP, IK); drafting of the manuscript (JPM, WY, DP); critical revision of the manuscript for important intellectual content (JPM, JJS, WY, JS, ALP); statistical analysis (WY, JS); provision of patients or study materials (ALP); obtaining funding (JJS, IK); administrative, technical, or logistic support (DP, IK); and supervision (JJS, WY, JV-O, IK).

 Joanna P. MacEwan, PhD, Precision Health Economics, 11100 Santa Monica Blvd, Suite 500, Los Angeles, CA 90025. E-mail: joanna.macewan@pheconomics.com. 

1. National diabetes statistics report. CDC website. https://www.cdc.gov/diabetes/pubs/statsreport14/national-diabetes-report-web.pdf. Published October 24, 2014. Accessed January 21, 2015.

2. American Diabetes Association. Economic costs of diabetes in the US in 2012. 

. 2013;36(4):1033-1046. doi: 10.2337/dc12-2625.

3. American Diabetes Association. Economic costs of diabetes in the US in 2007. 

. 2008;31(3):596-615. doi: 10.2337/dc08-9017.

4. Goldman DP, Joyce GF, Zheng Y. Prescription drug cost sharing: associations with medication and medical utilization and spending and health. 

5. Goldman DP, Joyce GF, Escarce JJ, et al. Pharmacy benefits and the use of drugs by the chronically ill. 

6. Sokol MC, McGuigan KA, Verbrugge RR, Epstein RS. Impact of medication adherence on hospitalization risk and healthcare cost. 

Articles

This study describes a widespread variation in medication adherence, pharmacy cost sharing, and medical spending. Increased cost sharing may decrease adherence and increase total diabetes spending.

The Relationship Between Adherence and Total Spending Among Medicare Beneficiaries With Type 2 Diabetes

To compare Medicaid spending among patients with type 2 diabetes mellitus (T2DM) receiving exenatide or other add-on therapies.

Medicaid data in patients with T2DM were compared among those who initiated exenatide, thiazolidinediones, dipeptidyl peptidase-4 inhibitors, or basal insulin. Patients were on a regimen of metformin and/or sulfonylurea for 30 days and continued the next-line therapy for at least 90 days.

Total inpatient, outpatient, prescription, and total annual health expenditures were compared for 1 year following treatment initiation. Regression analyses were conducted to compare spending; analyses controlled for patient characteristics, year of initiation, and prior therapy. Propensity score matching was used to match patients receiving exenatide with those receiving other therapies, and analyses were repeated with matched cohorts.

 Of 23,966 eligible patients, 1345 initiated exenatide and 22,621 initiated other therapies. In the unmatched analysis, medical spending was significantly lower in those given exenatide compared with those given other therapies for inpatient ($1945 vs $3893), prescription ($4505 vs $5714), and total costs ($11,830 vs $15,459) (

 <.01 for all); outpatient spending was not significantly different ($5380 vs $5853, 

 = .15). In the matched analysis (n = 1345 for exenatide, n = 1345 non-exenatide), patients receiving exenatide had significantly lower spending in all 4 categories: inpatient ($1945 vs $4242), outpatient ($5380 vs $6826), prescription ($4505 vs $5878), and total ($11,830 vs $16,945) (

 Patients with T2DM receiving exenatide had lower annual Medicaid claims costs compared with patients receiving other therapies.

In 2010, more than 34 million adults were enrolled in Medicaid; it has been estimated that 11.3% of American adults have diabetes.

 In 2007, total annual US direct medical spending for diabetes was estimated to exceed $116 billion.

 Annualized, this translates to adjusted mean medical expenditures of $11,744 for patients with diabetes, compared with $5095 for those without diabetes.

 The Congressional Budget Office projects adult Medicaid enrollment to exceed 43 million people in 2014, and more than 55 million people in 2020.

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.

 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.

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.

 However, Medicaid boards in other states have delayed the inclusion of exenatide in their formularies.

 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.

 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&mdash;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.

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.

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).

 In addition, patient preferences and other factors can influence treatment initiation. These unobserved factors could confound the effect of therapy on measured outcomes.

To address measured potential confounders, propensity score matching was used to match patients who were given exenatide with patients using 1 of the other 3 therapies (TZD, basal insulin, or DPP4I). Matching was based on patient age, gender, race, pre-period CCI, comorbidities (including congestive heart failure [CHF], hyperlipidemia, hypertension, stroke, chronic obstructive pulmonary disease [COPD], and end-stage renal disease [ESRD]), year of treatment initiation, and prior therapy (ie, an SU and/or metformin used in the 90 days prior to initiation). Predicted probabilities from a multivariate logistic regression analysis of exenatide use were used to identify and match the “nearest neighbor” who received exenatide to each patient who did not.

Baseline characteristics, including demographics (age, race, sex), CCI, comorbidities (from CCI), prior diabetes therapy, and year of treatment initiation, were compared among patients who received exenatide and those who did not.

Mean medical spending in patients receiving exenatide was compared with that in patients receiving other add-on therapies. Additionally, results in patients not receiving exenatide were analyzed by individual treatment groups (ie, TZD, basal insulin, and DPP4I). Analyses were first completed using all patients who did not receive exenatide, then limited to the propensity score—matched sample.

Regression-adjusted models controlled for patient characteristics including age, sex, race, comorbidities (CHF, hypertension, stroke, COPD, hyperlipidemia, and ESRD), CCI, year of drug initiation, and prior therapy. With a large presented as regression-adjusted mean outcomes.

 Analyses were conducted using Stata version 11.1 software; results with a 

A total of 23,966 patients met the criteria defining the population of interest. Of these, 1345 had exenatide added as a next-line treatment. Patients who did not receive exenatide (n = 22,621) were predominantly initiated on TZDs (n = 18,208), followed by basal insulin (n = 3823) or DPP4Is (n = 590). 

 shows how the analysis sample was determined as the inclusion and exclusion criteria were applied.

Once matched, data were included for 1345 patients who received exenatide, and 1345 patients who received a TZD (n = 1061), basal insulin (n = 224), or DPP4I (n = 60), for a total sample size of 2690. Matched results including baseline characteristics, unadjusted outcomes, and adjusted mean outcomes were consistent with the unmatched results. Only unmatched results are presented below (matched results are presented in the 

Baseline characteristics of the study populations are shown in 

. Results in patients not receiving exenatide are also reported by individual therapy (eg, TZD, basal insulin, and DPP4I). Compared with patients who initiated another therapy (n = 22,621), those who initiated exenatide therapy (n = 1345) were a mean of 3.6 years younger (51.5 vs 47.9 years, respectively; 

 <.01). The exenatide group had a higher proportion of female patients compared with the non-exenatide group (79% vs 66%, respectively; 

 <.01). There were also differences in the racial composition of the 2 groups; 68% of patients receiving exenatide were white, compared with 51% of patients receiving other therapy (

The exenatide group had a mean CCI score of 2.0, while the non-exenatide group had a mean score of 1.5 (

 <.01). Generally, patients receiving exenatide were significantly more likely than those receiving other treatments to have comorbid conditions: hypertension (49% vs 45%), hyperlipidemia (33% vs 24%), and COPD (23% vs 17%) (

 <.01 for all). However, compared with patients receiving other therapies, fewer patients receiving exenatide had ESRD (0.9% vs 0.3%, respectively, 

The baseline analysis also examined prescribing patterns of patients’ physicians. A significantly lower percentage of patients receiving exenatide were given an SU and/or metformin compared with those not receiving exenatide (SU, 58% vs 68%, respectively; metformin, 58% vs 63%; SU and metformin, 31% vs 39%; 

 <.05 for all). Of the 22,621 patients in the non-exenatide group, 58% visited physicians who had prescribed exenatide for any of their Medicaid patients (42% visited physicians who had never prescribed exenatide).

, patients receiving exenatide averaged $11,830 (standard deviation [SD] = $15,986) in total annual healthcare expenditures, with a median of $7082 (data not shown). In comparison, patients receiving other add-on therapies averaged $15,459 (SD = $26,012) (

 <.01). Compared with the non-exenatide group, the exenatide group had lower inpatient costs ($3893 vs $1945, 

 <.01) and prescription costs ($5714 vs $4505, 

 <.01); outpatient costs were not significantly different ($5853 vs $5380). Focusing on specific therapies, the TZD and basal insulin groups had higher expenditures than the exenatide group. For example, total annual healthcare spending averaged $14,779 among those receiving a TZD compared with $11,830 for those receiving exenatide (

Differences in utilization were more muted. Mean total days supplied within a year of initiation of therapy were not significantly different in the exenatide, DPP4I, and basal insulin groups (233.2 days, 235.9 days, and 229.1 days, respectively). Mean TZD use was higher (249.0 days, 

The results reported here correspond to the full sample; results for the propensity-score matched sample were similar, and are reported in the Appendix.

Regression-adjusted mean medical spending in all patients who met the inclusion criteria (n = 23,966) is shown by physician prescribing status for exenatide in 

. After adjusting for covariates, inpatient claims for patients of physicians who prescribed exenatide were $560 less per year than those for patients of physicians who prescribed other therapies (

 = .05) (Figure 2A). Compared with patients whose physicians prescribed other add-on therapies, those with physicians that prescribed exenatide had lower outpatient spending ($158 less) and lower total spending ($561 less), although these differences were not significant (Figures 2B and 2D). However, adjusted prescription medication spending for patients of physicians who prescribed exenatide was significantly greater ($157 more [2.7% higher]) than that for patients of physicians who prescribed other therapies (

Compared with patients receiving other add-on therapies, Medicaid programs paid lower claims for patients receiving exenatide across all spending categories. After adjusting for covariates, inpatient spending for patients given exenatide was $1939 (

 <.01) less than that for patients given other therapies. Similarly, adjusted outpatient spending was $478 less per year with exenatide (

= .08); this difference represented an 8.2% reduction in outpatient costs compared with other therapies. Adjusted prescription medication spending and total spending were also significantly lower with exenatide compared with other add-on therapies ($397 less, 

 shows spending based on adjusted data and according to the specific add-on therapy that patients received. Spending outcomes for patients given exenatide were less than those for patients using other therapies. Regression analysis showed that outpatient spending in patients receiving exenatide was $390 less (

 = .16) than for patients receiving a TZD, $660 less (

 = .04) than for patients receiving basal insulin, and $1056 less (

 = .045) than for patients receiving a DPP4I (Figure 3B). Prescription medication spending with exenatide was $347 less than for a TZD (

 = .04), $499 less than for basal insulin (

 <.01) (Figure 3C), and $733 less than for a DPP4I (

 <.01). Total spending in patients receiving exenatide was $2303 less than for those receiving a TZD (

 <.01), $5317 less than for those receiving basal insulin (

 <.01), and $2540 less than for those receiving a DPP4I (

Drug expenditures were significantly higher, while inpatient spending was significantly lower, for Medicaid patients with T2DM whose physicians prescribed exenatide at least once during 2005 to 2009. Compared with other add-on therapies, patients given exenatide had significantly lower inpatient, outpatient, prescription medication, and total spending.

Together, these results suggest that patients on Medicaid who are cared for by physicians with the flexibility to prescribe exenatide had lower healthcare spending than those who are cared for by physicians who never prescribed exenatide. These findings add to some recent evidence that exenatide is associated with lower healthcare costs than other T2DM therapies (these earlier studies also found that exenatide was not associated with poor clinical outcomes).

There are several limitations to this study. One limitation was that the data did not contain any state identifiers. Since Medicaid policies vary from state to state,

 similar patients who live in different states will have varying levels of access to antidiabetic medications. This difference could lead to biased results if left uncontrolled. To address this concern, each physician in the data set was identified as to whether or not they have prescribed exenatide to patients. Each patient was categorized as having a physician who prescribed or never prescribed exenatide.

Because insulin is generally initiated later in the progression of T2DM compared with other therapies, patients receiving insulin may have poorer health outcomes and other comorbidities, and thus higher medical costs, compared with those receiving other treatments.

 Certain unobservable factors may account for some of the differences in outcomes observed between exenatide and insulin. Therefore, to better understand the outcomes in patients initiating various treatments, an analysis was performed that stratified patients by specific treatment received. In all categories of medical spending, costs were lower in those given exenatide compared with those given insulin.

Likewise, any study approach that directly compares outcomes between insulin and non-insulin therapies may be biased. This is because patient and physician resistance to insulin initiation is well documented, and can be predicated on concerns about weight gain, hypoglycemia, patient adherence, or regimen complexity.

 For these reasons, insulin may not be prescribed until other treatments fail, resulting in a group of patients that have T2DM of longer duration. To mitigate these unobserved differences, propensity scores were used in this study to create a matched sample. Propensity score matching on characteristics likely to be correlated with these unobservable differences, such as the presence of comorbidities, can help reduce potential biases.

 Finally, it is important to note that these models may not necessarily measure how the specific type of medication produces differences in outcomes.

Another study limitation was that the DPP4I group had the fewest patients from the overall sample (n = 590), and once the propensity score—matched groups were established, this small size was amplified (n = 60). Given the small sample size, results in those given DPP4Is are more likely to have been skewed by outliers (eg, a few patients with very high medical spending due to medication or procedures) and may be less reliable. Results in this group should be validated in larger studies if future investigators are interested in tracking the costs associated with these specific drugs.

The current data suggest that patients given exenatide had reduced costs compared with those given a TZD, basal insulin, or a DPP4I. Additionally, these results indicated that patients who are cared for by exenatide prescribers experienced lower spending outcomes. Medicaid policies that provide exenatide coverage may result in fewer inpatient, outpatient, and prescription claims, and lower total medical spending. These results also suggest that patients who visit physicians who use a wide array of diabetes treatments may be more likely to be given the treatment that is best suited to maintain their health.

Comparative effectiveness research continues to build interest around the efficacy, safety, and value of existing and new T2DM therapies. As further evidence is generated, the role of these medications in clinical guidelines and algorithms for diabetes management is being reassessed. The long-term clinical efficacy and safety profile of exenatide has been established in a series of successful clinical trials.

 The current analysis indicates that there is also potential economic value in positioning exenatide as a preferred second-line therapy. 

The authors thank Amy Blickensderfer for input on study design.

Amylin Pharmaceuticals, Inc, San Diego, California (JHB); Precision Health Economics, LLC, Los Angeles, California (RMC); Price School of Public Policy and Leonard D. Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, CA (DPG); Division of Endocrinology, Keck School of Medicine and Clinical Diabetes Program, University of Southern California, Los Angeles, CA (ALP); Price School of Public Policy, University of Southern California, Los Angeles, CA (JAR). 

 This supplement was supported by Amylin Pharmaceuticals, Inc.

Dr Best reports employment and stock ownership with Amylin Pharmaceuticals, Inc. Dr Conrad reports consultancy with Amylin Pharmaceuticals, Inc. Dr Goldman reports consultancy with Amylin Pharmaceuticals, Inc. Dr Peters reports consultancy with Amylin Pharmaceuticals, Inc, Boehringer Ingelheim, CellNovo, Lilly, Novo Nordisk, and sanofi-aventis, and honoraria from Dexcom, Medtronic MiniMed, Merck, Medscape, and Perrigo. She also reports membership in speakers’ bureaus for Amylin Pharmaceuticals, Inc, Takeda, Lilly, and Novo Nordisk. Dr Romley reports consultancy with Amylin Pharmaceuticals, Inc.

Concept and design (JHB, RMC, DPG, ALP, JAR); acquisition of data (RMC); analysis and interpretation of data (JHB, RMC, DPG, ALP, JAR); drafting of the manuscript (RMC, DPG, JAR); ALP); statistical analysis (RMC); obtaining funding (JHB); supervision (JHB, DPG, ALP, JAR).

 Jennie H. Best, PhD, Amylin Pharmaceuticals, Inc, 9360 Towne Centre Dr, San Diego, CA 92121. E-mail: 

. Spending and Enrollment Detail for CBO’s March 2011 Baseline: Medicaid. 

www.cbo.gov/sites/default/files/cbofiles/attachments/Medicaid_0.pdf

. Published 2011. Accessed July 27, 2012.

Centers for Disease Control and Prevention

. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States. 

www.cdc.gov/diabetes/pubs/pdf/ndfs_2011.pdf

. Published 2011. Accessed September 20, 2011.

. Consumer Price Index Detailed Report Tables: Annual Average Index, 2011. www.bls.gov/cpi/cpid11av.pdf. Published 2011. Accessed July 27, 2012.

Handelsman Y, Mechanick JI, Blonde L, et al

. American Association of Clinical Endocrinologists Medical Guidelines for Clinical Practice for developing a diabetes mellitus comprehensive care plan. 

. Application of comparative effectiveness research in evaluation of treatments for type 2 diabetes mellitus. 

. Comparative Effectiveness and Safety of Oral Diabetes Medications for Adults With Type 2 Diabetes. Rockville, MD: 2007. 

www.effectivehealthcare.ahrq.gov/repFiles/OralFullReport.pdf

www.dhhr.wv.gov/bms/Pharmacy/dur/Documents/Preferred%20Drug%20List/pdl_44.pdf

. Published 2005. Accessed September 20, 2011.

. Approved PDL for Iowa Medicaid Program: Iowa Medicaid Enterprise: Iowa Medicaid PDL. 

www.iowamedicaidpdl.com/uploads/1H/fA/1HfAqUmB7I401LBkOi9TIQ/report_1010_pdl_a1_pdl_drug_list_vers_x9e_desi_cilostazol.pdf

. Published 2005. Revised November 16, 2005. Accessed September 20, 2011.

Nevada Department of Health and Human Services

. Division of Health Care Financing and Policy. Nevada Medicaid Preferred Drug List (Effective July 1, 2010). 

www.medicaid.nv.gov/Downloads/provider/NV_PDL_20100701.pdf

Colorado Department of Health Care Policy and Financing

. Preferred Drug List (PDL): Effective October 1, 2010. 

www.colorado.gov/cs/Satellite?blobcol=urldata&blobheader=application%2Fpdf&blobheadername1=Content-Disposition&blobheadername2=MDT-Type&blobheadervalue1=inline%3B+filename%3D578%2F142%2FMicrosoft+Word+-+PDL+effective+0--200_2_doc+-111.pdf&blobheadervalue2=abinary%3B+charset%3DUTF-8&blobkey=id&blobtable=MungoBlobs&blobwhere=1251655552502&ssbinary=true

. Published 2010. Accessed September 20, 2011.

. Preferred Drug List/Prior Authorization List. www.lamedicaid.com/provweb1/forms/rxpa/PDL_NPDL_10_1.pdf. Published 2011. Accessed September 20, 2011.

. Wisconsin Medicaid Preferred Drug List. 

https://www.forwardhealth.wi.gov/WIPortal/Tab/42/icscontent/provider/medicaid/pharmacy/pdl/pdfs/pdl_list.pdf.spage

Ross JS, Jackevicius C, Krumholz HM, et al

. State Medicaid programs did not make use of prior authorization to promote safer prescribing after rosiglitazone warning. 

Anne L Peters, MD

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