Alan M. Zaslavsky, PhD

 To compare use of diabetes medications between beneficiaries enrolled in Medicare Advantage (MA) and traditional Medicare (TM).

 Retrospective cohort analysis of Medicare enrollment and Part D event claims during 2015-2016.

 Data came from 1,027,884 TM and 838,420 MA beneficiaries who received at least 1 prescription for an oral or injectable diabetes medication. After matching MA and TM enrollees by demographic characteristics and geography, we analyzed use of medication overall, choices of first diabetes medication for those new to medication, and patterns of adding medications.

Overall and for patients on 1, 2, or 3 diabetes medications, use of metformin was higher in MA by about 3 percentage points, but use of newer medication classes was 5.1 percentage points higher in TM overall (21.3% vs 16.2%). Use of guideline-recommended first-line agents was higher in MA. For those who started metformin first, use of a sulfonylurea as a second medication was 7.8 percentage points higher in MA than TM (61.5% vs 53.7%), whereas use of medications from newer classes was 7.7 percentage points lower (22.0% vs 29.7%). Mean total spending was $149 higher in TM for those taking 1 medication and $298 higher for those taking 2 medications. Differences in spending among MA plans were of similar magnitude to the MA-TM differences.

 MA enrollees are more likely to be treated with metformin and sulfonylureas and less likely to receive costly newer medications than those in TM, but there also is substantial variation within MA. A limitation of the study is that we could not assess glucose control using glycated hemoglobin levels.

Medicare Advantage (MA) is associated with decreased utilization and higher quality relative to traditional Medicare (TM), but little is known about differences in medication management of chronic medical conditions such as diabetes. Using retrospective analyses of Medicare claims, we find that:

MA enrollees were more likely to use metformin and sulfonylureas and less likely to receive newer medications.

Mean plan and patient out-of-pocket spending for patients on 1, 2, or 3 medications was substantially higher for patients in TM.

There is substantial variation in the approaches to medication management across MA health plans.

Enrollment in Medicare Advantage (MA), Medicare’s managed care program, continues to expand, with more than one-third of Medicare beneficiaries currently enrolled in an MA plan.

 Relative to traditional Medicare (TM), care management by MA plans may enable them to treat patients at lower costs while attaining equal or superior quality through their control of enrollee benefits, network contracting, and coordination of care.

 This may be of particular relevance for patients with chronic medical conditions, for whom plans may employ a variety of care management approaches and financial incentives to promote the delivery of appropriate services and influence the quantity of services delivered.

 Prior research has demonstrated higher quality of ambulatory care for those enrolled in MA for process measures such as preventive care and monitoring of chronic diseases, as well as lower utilization of emergency department and inpatient services and of some elective services such as joint replacements.

 Differences in choices of medication might be another method by which MA plans achieve savings compared with TM. Most MA enrollment is in plans that include Part D coverage in an integrated product. TM enrollees generally enroll in stand-alone Part D prescription drug plans, although some have non-CMS coverage (retiree or other private plans) or none. However, no prior research that we are aware of has compared patterns of medication management in the 2 programs.

Diabetes is a prototypical example of a chronic and often progressive medical condition that may be affected by managed care approaches. Diabetes currently affects more than one-fourth of Medicare beneficiaries and is among the most common and costly chronic medical conditions affecting elderly Medicare enrollees.

 The principal medical treatment for diabetes is pharmacologic therapy aimed at controlling blood glucose levels and thereby preventing downstream cardiovascular, renal, and retinal complications that may arise from poorly controlled diabetes. The American Diabetes Association estimated that in 2017, almost $50 billion was spent on prescription diabetes medications and testing.

 Consensus recommendations uniformly support lifestyle modification plus metformin as the preferred initial treatment strategy for type 2 diabetes, but many classes of medication compete for use as second- or third-line therapy, including those new in the past decade.

 More recently, some emerging data suggest improved outcomes with several new agents in high-risk populations with cardiovascular or chronic kidney disease, but health plans and patients who use these newer medications, which have no generic alternatives, also face higher plan and out-of-pocket costs, respectively.

 For instance, according to GoodRx.com, mean monthly costs of glucagon-like peptide-1 (GLP-1) receptor agonists are approximately $800/month or higher compared with less than $100 for even the most costly dose of metformin. In addition, recommendations specifically for elderly patients include avoidance of long-acting sulfonylureas, which are associated with increased rates of hypoglycemia, and application of less stringent treatment goals for glucose control, particularly for those with limited life expectancy or multiple coexisting comorbid conditions.

 The large number of treatment options and potential intensity of treatment, as well as the wide range in prices of medications used to treat diabetes, present an opportunity for MA plans to improve the care of diabetes using approaches that are less expensive but similarly effective. Moreover, lower out-of-pocket costs to patients are associated with improved adherence to treatment recommendations, which ultimately can influence patient outcomes, as well.

Previous research indicates that diabetes is among the clinical areas in which MA plans achieve their highest rates of savings for medical services,

 but little research has examined how choices of medications for enrollees with diabetes differ for those enrolled in MA vs TM. Given the high cost of medical treatment of diabetes, we therefore used comprehensive data on beneficiaries being treated for diabetes in TM and MA to compare patterns of drug use in the 2 programs, as well as total pharmaceutical spending and adherence. We hypothesized that, relative to TM, MA plans would promote higher rates of use of medications that are recommended by consensus guidelines and that they also would promote use of less expensive generic medications when more expensive alternatives have not demonstrated clear benefits.

We analyzed Medicare claims and enrollment data provided by CMS for the period 2015-2016, the 2 most recent years available to us. Demographic and enrollment characteristics drawn from the Beneficiary Summary File were available for all enrollees. Data on prescription medication use from the Part D prescription drug event files were available for the subset (more than 67.8% in the study years) of the 20% random sample of TM beneficiaries enrolled in a Part D plan, and for a substantially higher percentage (94.6%) of those enrolled in MA plans, which typically are bundled with Part D coverage.

Our sample included all persons who were continuously enrolled in Medicare parts A and B throughout the year and were at least 65 years old on January 1. We excluded residents of long-stay nursing homes, whom we identified using a validated algorithm,

 because these beneficiaries rarely enroll in MA plans other than the dedicated special needs plans aimed at this population,

 and Medicare beneficiaries with end-stage renal disease. We also excluded MA enrollees in special needs plans that most commonly are restricted to low-income enrollees and have different rules related to enrollee cost sharing. We restricted some analyses focused on initiation or up-titration of prescription drugs to beneficiaries 66 years or older who had at least 1 year of continuous Part D enrollment prior to the respective study year.

Because medical claims data are not available for enrollees in MA plans, we restricted our analyses to those with diabetes who had received at least 1 prescription for a diabetes medication during the year of study.

 Thus, our study sample focuses on enrollees with diabetes being treated with medication and excludes those who received a diagnosis of diabetes but were being treated conservatively through lifestyle modification and diet.

Beneficiary and Health Plan Characteristics

The Medicare Master Beneficiary Summary Files provided data on age, sex, race/ethnicity, region of the country, and, when applicable, Medicaid coverage and disability as the original reason for Medicare eligibility.

Using CMS data, we categorized health plans (defined based on Medicare contracts) as large (> 25,000 enrollees) vs small, and we identified health plans new to MA since 2006.

Characterizing Medication Use and Guideline Concordance

We lacked data on clinical indicators, such as glycated hemoglobin levels, that typically drive decisions regarding use of medication; therefore, in addition to analyzing medication use overall, we focused on prespecified cohorts that we defined for this study to identify those with diabetes at a similar level of disease control for whom a clinician had decided that initiation or escalation of medical therapy was warranted. Medications were identified through drug or product names (see 

] for a full list and how they were classified).

For patients who received a new prescription for any diabetes medication in the year of interest and had received no diabetes medications in the prior year, we categorized the 

by class. These enrollees are likely those who previously had been treated with diet and lifestyle modification and who a physician now thought needed to start medication treatment. We considered metformin to be the only guideline-concordant initial choice of medication.

For those treated with a single drug in the prior year, we then characterized the 

 for those started on the 2 most commonly prescribed initial drug classes (metformin and sulfonylureas). Although guidelines regarding second medications allow for individualized use of medications from any of multiple classes, the guidelines also suggest that cost could be a consideration.

 Because of their less frequent use, we grouped into 1 category all relatively newly available medications without a generic alternative, such as sodium-glucose cotransporter-2 (SGLT-2) inhibitors, dipeptidyl peptidase-4 (DPP-4) inhibitors, and GLP-1 receptor agonists.

Finally, we examined whether those patients who were 

 received short-/rapid-acting, intermediate-acting, long-acting, or combination insulin, counting patients filling prescriptions for 2 types of insulin within 30 days as receiving combination therapy. When insulin therapy is necessary, guidelines consistently recommend beginning with basal or long-acting insulin. We did not differentiate between human and analogue insulin.

We based our classifications on largely consistent recommendations from the American College of Physicians, the American Diabetes Association, and the American Geriatrics Society for the treatment of elderly adults with diabetes during the time period of our study.

 Finally, we considered long-acting sulfonylureas to be guideline discordant at any stage of treatment because of increased risks of hypoglycemia in the elderly population.

These analyses included all patients taking any diabetes medication in a year. We calculated each beneficiary’s total, out-of-pocket, and Part D plan monthly diabetes drug spending over the year. Finally, for each medication taken, we calculated the medication possession ratio as a measure of adherence.

 This measure counts the number of days of supply that a patient had starting from the date of the first prescription fill to the date of the last prescription filled during the year plus the days supplied from that last prescription. Higher numbers indicate greater adherence.

We first compared the characteristics of enrollees in TM and MA who were identified as users of diabetes medications by available Part D prescription claims. As in previous studies, for all comparisons between the MA and TM samples, we first reweighted the TM sample to match the MA sample distribution by geography and demographic characteristics (age [in 5-year ranges], sex, race/ethnicity) and then aggregated these results to obtain national estimates that were weighted by the enrollment of MA plans.

 Matching on geography controlled for variation in practice patterns within Medicare across regions.

 By matching at the zip code level where possible (73% of the prevalent cohort), we also controlled for unmeasured socioeconomic characteristics associated with residence at this level of geography, as well as the proportion who were dually eligible for Medicaid. Of note, excluding those dually enrolled in Medicaid did not substantively affect our results, so dual-eligible enrollees were included in the study.

We tabulated characteristics of all enrollees with diabetes on medication as well as those initiating any drug treatment and those initiating treatment with insulin, testing differences between MA and TM proportions or means using χ

 tests as appropriate. We calculated the unadjusted and adjusted MA mean, the matched TM mean, the difference, and their respective standard errors, and we performed χ

 tests as appropriate to assess significance. Because of the size of our sample, most overall MA-TM differences that we report were statistically significant at the 

 < .001 level unless otherwise noted in the tables. Therefore, comparisons were not adjusted for multiple comparisons.

Lastly, as an illustrative example to examine variation in spending among MA plans, we modeled total spending for beneficiaries on 2 medications, using hierarchical linear regression models that included a random effect for each MA contract, as well as random effects for MA and TM at the state and hospital referral region levels. The random effects variance parameters quantify the variation across plans of savings relative to matched TM enrollees in its same service area after accounting for market- and state-level variation and sampling error.

All analyses were conducted using SAS software version 9.2 (SAS Institute). Our study protocol was approved by the Harvard Medical School Human Studies Committee. This work was funded by the National Institute on Aging, which had no role in the design of the study or in the collection, analysis, and interpretation of the data.

During 2015 and 2016, we studied 1,027,884 enrollees in TM and 838,420 enrollees in MA who were enrolled for the full year in Medicare and had available Part D pharmaceutical claims. Their respective characteristics are shown in 

. Among these enrollees, 62,265 from TM and 49,089 from MA initiated treatment with an initial diabetes drug (including all oral medications or insulin) during this time period, and, of these, 24,530 in TM and 16,635 in MA first started using insulin (including as an initial medication). In each treatment category, White enrollees were overrepresented in TM (near 75%) relative to MA (60%), and Hispanic enrollees were underrepresented in TM (7%-9%) relative to MA (21%-23%). More TM enrollees were eligible for Medicaid (25.7% vs 21.2% overall). After weighting, there were no observable demographic differences between the MA and TM populations.

Overall, use of metformin was lower in TM by about 3 percentage points (

). For example, among those on 1 medication, 59.8% of TM enrollees were on metformin compared with 62.7% of MA enrollees. Use of short-acting sulfonylureas was also lower in TM (19.7% vs 23.6%), but use of long-acting sulfonylureas, which should be avoided in elderly patients, was slightly higher in TM (17.1% vs 16.3%). Rates of insulin use were higher in TM (19.7% vs 17.4%). The largest TM-MA difference was for use of any of the new classes of medication (such as DPP-4 or SGLT-2 inhibitors), which was 5.1 percentage points higher in TM (21.3% vs 16.2%).

Consistent with guidelines for the initial treatment of older patients with diabetes, metformin was the predominant first medication, but it was used slightly less frequently in TM than in MA (71.7% vs 72.8%; absolute difference, 1.1 percentage points) (

). For the remaining approximately 30% of patients started on a medication, short-acting sulfonylureas were less often used in TM (6.9% vs 9.1%). These differences were offset predominantly by higher use in TM of a medication from a newer class (9.9% vs 6.8%). Thiazolidinediones were rarely the initial medication (less than 2% in both programs).

These differences varied somewhat by region and plan characteristics. Most notably, TM enrollees in the South were started on metformin 2.3 percentage points less frequently than MA enrollees, and TM enrollees in the West were started on a new medication 5 percentage points more frequently than those in MA. Those enrolled in large and more established plans were about 1 percentage point less likely to start a medication from another class.

Either a sulfonylurea (for those on metformin) or metformin (for those on a sulfonylurea) was the second-choice medicine for more than half of those given a second medicine (

). However, for those who started metformin first, use of a sulfonylurea as a second medication was 8 percentage points lower in TM (53.7% vs 61.5%), which was primarily offset by use of medication from a new class (29.7% vs 22.0%). Patterns were similar for those who started a sulfonylurea first (with metformin the most common choice of second drug), but the differences were about half the magnitude. Differences for smaller and newer plans were larger than for established and larger MA plans.

Among those new to insulin, the percentage of patients started on long-acting insulin was higher in TM than in MA (63.5% vs 58.2%) (

), with the largest differences in the Midwest.

Annual diabetes-related medication costs are summarized in 

, stratified by the number of medications being taken. In each stratum, the percentage of enrollees with total costs of more than $1000 was greater in TM than MA (differences of 4.0, 8.2, and 2.9 percentage points for those taking 1, 2, and 3 medications, respectively). Similarly, mean spending was $149 higher in TM for those taking 1 medication and $298 higher for those on 2 medications. These differences were largest in the West and Midwest, as well as in large and established health plans. Out-of-pocket spending showed similar patterns. Medication possession ratios generally were 90% or higher and generally did not substantively differ between TM and MA or by plan characteristics (

Analyses of estimated differences in mean spending for those taking 2 medications arrayed by MA plan (contract) showed that the variation in spending across MA plans after controlling for geographical factors was similar in magnitude (SD, $240) to the observed overall difference in spending between MA and TM (mean difference, $298, as noted above) (

Patterns of use of diabetes medications for beneficiaries enrolled in TM and MA differ, even after identifying enrollees at a stage of disease who either start medication therapy or require an additional medication. More importantly, these prescribing patterns have important implications for both the quality and costs of care. Metformin, which is recommended as first-line treatment in type 2 diabetes by all major guidelines and is available as a relatively inexpensive generic medication, was used more frequently in MA as both a first and second agent. In contrast, medication choices in TM generally were costlier, with greater use of expensive new classes of medications as both first- and second-line treatment leading to higher levels of both plan and out-of-pocket spending for TM beneficiaries. To achieve these savings, however, enrollees in MA plans more frequently receive sulfonylureas, which have the potential to cause hypoglycemia in older patients.

Our study found substantially greater use of new diabetes agents that do not yet have generic alternatives as both first- and second-line treatment options in TM, substantially contributing to higher spending for those in TM. The use of some of these newer classes of medications, however, could represent higher quality of care, particularly for higher-risk populations with established cardiovascular or chronic kidney disease. For instance, emerging evidence supports the use of SGLT-2 and GLP-1 inhibitors to treat specific high-risk populations with established cardiovascular disease or chronic kidney disease.

 We note, however, that studies have not found consistent advantages in these groups with respect to cardiovascular events or mortality and that these benefits have not been shown for average-risk patients.

 Moreover, older patients with more limited life expectancy may not receive these potential benefits. Consequently, guidelines continue to recommend that the choice of a second medication should be individualized based on patient preferences and, potentially, cost.

 Although our study could not assess patient preferences, MA treatment regimens in most plans demonstrated lower mean costs for both plans and patients, even among patients using the same number of medications, and these differences could be substantial. Whether these differences will translate into improved outcomes is not clear and will require future research.

Surprisingly, we found more frequent use of basal insulin in TM than in MA when first starting treatment with insulin. Multi-injection insulin regimens may be difficult for elderly patients to manage and administer, with little incremental value over once-daily administration of a single long-acting agent, while also carrying increased risk for hypoglycemia.

 Although this finding might reflect that MA physicians may turn to insulin later in the course of treatment, the reasons for this difference are not clear.

Our study results suggest that managed care approaches to diabetes can bring about care that is both more guideline concordant (at least in the case of first-line medications) and less expensive, although this was not uniformly the case for all MA health plans. How might some health plans achieve such results? Although our study did not explore mechanisms, there are several potential explanations. Most MA enrollees choose integrated MA–Part D plans, which may facilitate a more integrated approach to the management of all aspects of diabetes, including the use of medications, than is practiced by the stand-alone Part D plans that are used by TM beneficiaries.

Some health plans employ various approaches such as increased use of guidelines, audits, and feedback to encourage a more uniform approach to treatment within the plan, including the use of less costly medications as initial selections.

 Our results showed greater TM-MA differences for larger and more established health plans, which might be more likely to have the capacity for managing such programs.

 Second, health plans can employ financial incentives to physicians and physician organizations that encourage use of preferred lower-cost agents, thus delegating responsibility for management of prescribing patterns to the providers. For instance, some MA health plans have entered into capitation-type arrangements with physician organizations that also put these organizations at risk for drug spending. Plans using management mechanisms like these might choose to discourage adoption of some newer and more expensive medications until their marginal value is clearly proven in the literature. Of note, many of the studies noted above—those supporting the use of some newer agents in higher-risk populations—came out after the period of our study. However, it will be important to monitor choices of medications in such plans to ensure that patients are not denied more expensive medications when they may be of benefit. Finally, plans also have direct control over their formularies, and integrated plans may make different choices related to inclusion, tiering, or cost sharing that can also influence use of medications.

Our study has several potential limitations. First, our analyses were limited to claims data, so we could not assess the adequacy of diabetes control using measures such as glycated hemoglobin levels. Second, we lacked data on functional status and frailty, each of which may affect decision-making and treatment goals. Third, because we lacked information on the date of first diagnosis, we could not control for the duration of diabetes when treatment began, which might have differed for MA and TM. For this reason, we focus much of our analyses on initial treatment choices for patients who had previously not been on a medication, as well as patterns of adding medications for those who had been on a single agent, which effectively controls for disease status. Fourth, we were unable to assess formulary status of medications, which also can influence medication choice. Fifth, our data extend back almost 5 years, so these findings might differ using more current data. In particular, use of newer agents with no generic alternatives might have accelerated over this time period. Finally, because we lacked information on comorbid conditions for MA enrollees (for whom we lacked claims), we could not examine the extent to which comorbid conditions such as cardiovascular disease or heart failure influenced medication choices, nor could we examine longer-term complications of diabetes such as retinopathy, nephropathy, or cardiovascular disease.

We found substantial differences in patterns of medication use between beneficiaries in TM and enrollees in MA health plans, although with substantial variation among MA plans. Costly and complex regimens are used more frequently in TM than MA across all treatment levels, reflecting more frequent use within MA of medications that can cause hypoglycemia (eg, sulfonylureas) and less frequent use of newer agents, which might be of particular benefit for higher-risk patients. These findings suggest that managed care approaches adopted by at least some plans influence diabetes treatment practices. As our health system strives to find opportunities to lower health spending and improve the value of care, chronic diseases such as diabetes present a substantial opportunity for cost savings that increase the value of care, but they also raise concerns about stinting on care that might ultimately benefit some subgroups of patients.


 Department of Health Care Policy, Harvard Medical School (BEL, AMZ, JS), Boston, MA; Division of General Medicine and Primary Care, Beth Israel Deaconess Medical Center and Harvard Medical School (BEL), Boston, MA; Institute for Healthcare Policy and Innovation; Division of General Medicine, Medical School; Department of Health Management and Policy, School of Public Health; Gerald R. Ford School of Public Policy, University of Michigan (JZA), Ann Arbor, MI.

 Supported by a grant from the National Institute on Aging (P01 AG032952).

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (BEL, JZA); acquisition of data (BEL); analysis and interpretation of data (BEL, AMZ, JS, JZA); drafting of the manuscript (BEL); critical revision of the manuscript for important intellectual content (AMZ, JS, JZA); statistical analysis (BEL, AMZ, JS); obtaining funding (BEL); administrative, technical, or logistic support (BEL).

 Bruce E. Landon, MD, MBA, Department of Health Care Policy, Harvard Medical School, 180 Longwood Ave, Boston, MA 02215. Email: landon@hcp.med.harvard.edu.

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Articles

Medicare Advantage enrollees are more likely to be treated with metformin and sulfonylureas and less likely to receive costly newer medications than those in traditional Medicare.

Use of Diabetes Medications in Traditional Medicare and Medicare Advantage

Prior analyses of Medicare health plans have exam-ined either utilization of services or quality of care, but not both jointly. Our objective was to compare utilization and quality for Medicare Advantage (MA) enrollees with diabetes or cardiovascular disease to that for similarly defined traditional Medicare (TM) beneficiaries.

Cross-sectional matched observational study using data for 2007.

 We obtained individual-level Healthcare Effectiveness Data and Information Set (HEDIS) relative resource use (RRU) and quality data for patients enrolled in MA, and then developed comparable claims-based measures for matched samples of TM beneficiaries. Main outcome measures: utilization levels for inpatient care, evaluation and management services, and surgery; number of emergency department (ED) and inpatient visits; and quality of ambulatory care measures.

We studied approximately 680,000 MA health maintenance organization (HMO) enrollees with diabetes and 270,000 HMO enrollees with cardiovascular conditions. For both conditions and almost all major strata, the RRU was lower for those enrolled in MA than for those in TM. Spending for those with diabetes was $5223 for MA HMO enrollees compared with $6413 for those in TM (cost ratio, 0.81; 

 <.001). ED utilization rates were consistently lower in MA than TM (567 vs 719 visits/1000 enrollees; rate ratio, 0.79; 

 <.001). Health plans that are more established, nonprofit, and/or larger generally had lower resource use and better relative quality than did smaller, newer, for-profit HMOs or preferred provider organizations.

RRU for those with diabetes or cardiovascular disease is lower in MA, while quality of care is higher. Better MA plans may add value to the care of these major chronic medical conditions.

We compared price-standardized utilization and quality of care for Medicare Advan-tage (MA) enrollees with diabetes or cardiovascular disease with matched beneficiaries from traditional Medicare in geographic areas. We found that:

		For both conditions, relative resource use was lower for those enrolled in MA than for those in traditional Medicare.

		Quality of care for diabetes and cardiovascular disease measures was higher in MA for the 4 measures examined, although plans varied greatly in their performance.

		Health plans that are more established, nonprofit, and/or larger generally had lower relative resource use and better relative quality than did HMOs or PPOs that were smaller, newer, and/or for-profit.

Under managed competition, health plans are expected to compete to provide health services of high quality while also controlling the overall costs of care. A key element of a health plan’s ability to contain cost is its ability to control utilization. Although some transparency initiatives are now publicizing the costs that health plans pay for specific services,

such approaches have not assessed differences in the full range of medical services used by patients with particular health conditions. Moreover, many have concerns that health plans that are more successful at controlling utilization might also deliver care of lower quality, though existing data from commercial health plans show no clear relationship between spending and quality.

Medicare’s managed care program, Medicare Advantage (MA), currently provides care to 15.7 million Americans, representing 30% of beneficiaries.

 Relative to traditional Medicare (TM), MA plans may be able to treat patients with particular diagnoses with greater efficiency while attaining equal or superior quality through their flexibility in enrollee benefits, network contracting, and coordination of care, but whether they do so is not known.

 Since 1997, health plans participating in Medicare have been required to report annually on measures of the quality of preventive care and of the management of chronic diseases such as diabetes.

 In 2006 and 2007, CMS also required MA plans to report a set of relative resource use (RRU) measures focused on the care of patients with diabetes (2006 and 2007) and cardiovascular disease (2007 only).

 Such measures of price-adjusted utilization allow for direct comparisons of utilization among health plans as well as between MA and TM.

In this study, we evaluated both the utilization of services and the quality of ambulatory care provided by MA health plans by comparing annual standardized spending and quality of care for 2 specific medical conditions for MA health plan enrollees with corresponding measures among TM beneficiaries.

From CMS and the National Committee for Quality As-surance (NCQA), we obtained health-plan-level Healthcare Effectiveness Data and Information Set (HEDIS) measures of RRU and individual-level HEDIS data on quality of ambulatory care for patients with diabetes and cardiovascular disease enrolled in MA health plans. We focused on care delivered in 2007, the most recent year in which MA plans were required to report these measures before reporting became voluntary and much less consistent.

 We constructed similar measures within TM from Medicare claims data for a random 20% sample of beneficiaries. Given the well-known geographic variation in Medicare services, we created matched samples for each health plan based on its geographic region and enrollee demographic characteristics.

. The RRU measures use standardized pricing applied uniformly to services delivered within MA and TM, thereby accounting for variation in prices due to geography and negotiated prices in MA.

 Spending on all services for all patients with qualifying diagnoses is calculated over the entire year. The RRUs are risk-stratified by age (categorized into 5-year intervals for patients aged 65-85 years), sex, type of diabetes (type 1 or type 2) or cardiovascular disease (acute myocardial infarction, congestive heart failure, angina, or coronary artery disease) and the presence or absence of 1 or more major comorbidities (ie, cardiovascular conditions, chronic obstructive pulmonary disease [COPD], depression, hypertension, or chronic kidney disease for diabetes, and asthma, COPD, diabetes, and hypertension for cardiovascular disease). We then aggregated these strata using nationally determined weights to create a standardized measure of overall resource use. Spending categories include inpatient care, surgery and other procedures, and evaluation and management services, and we also sum spending across these categories. Rates of emergency department (ED) visits and inpatient admissions are also reported. Beneficiaries with concomitant specified dominant medical conditions including active cancer, end-stage renal disease, human immunodeficiency virus/AIDS, and organ transplants are excluded.

HEDIS quality data are collected from administrative billing or encounter records, or by using a hybrid approach in which medical records are also reviewed for services that may not be recorded in administrative data.

CMS has audited HEDIS quality measures reported by Medicare health maintenance organizations (HMOs) and found them to be highly accurate.

 In order to compare MA and TM, we focused on measures that can be constructed from Medicare claims for TM enrollees, including low-density lipoprotein (LDL) cholesterol testing in the current year for enrollees aged 65 to 75 years with cardiovascular disease, and 3 services for enrollees aged 65 to 75 years with diabetes: glycated hemoglobin (A1C) testing in the current year; LDL cholesterol testing in the current year; and a diabetic retinal exam in the current or prior year.

We defined health plans as CMS contracts, meaning a health plan unit operating in a single state, or in a few cases, up to 3 adjoining states, and we included both HMOs and preferred provider organizations (PPOs). We focused on beneficiaries 65 years or older who were enrolled for the entire calendar year. We excluded beneficiaries in legacy health plans that were reimbursed on a cost basis rather than by capitation. In addition, we excluded beneficiaries in private fee-for-service plans because these plans are not required to report HEDIS data to CMS, as well as those enrolled in special needs plans because such plans serve nonrepresentative beneficiaries. Finally, we excluded HMOs with fewer than 500 enrollees (accounting for <0.2% of enrollees).

To create a comparison sample for each health plan for both the RRU and the diabetes quality analyses, we used the TM enrollment file and Part A and Part B claims files for a random 20% of beneficiaries to identify all persons who were continuously enrolled in Medicare Part A and Part B for the entire reporting year and were 65 years or older as of January 1, 2007.

We excluded residents of long-stay nursing homes—identified using a validated algorithm&mdash;because these beneficiaries rarely enroll in MA; however, we had no similar method to exclude these beneficiaries from the MA data.

We applied NCQA specifications to identify the eligible populations for the measures, assigned a standardized priceobtained from NCQA to each delivered service identified in the claims, and aggregated spending across groups using the exact specifications from NCQA.

Medicare Beneficiary Summary files provided demographic data (age, sex, race/ethnicity, zip code, county, and state of residence), vital status, and health plan and Medicare enrollment information for each beneficiary.

We categorized health plans as large (>25,000 enrollees) versus small, and identified health plans new to MA since 2006. CMS provided the tax status.

We compared RRUs and quality of care in each MA plan with a TM sample matched by geographic distribution (RRUs and quality measures) and demographic characteristics (quality measures only) and then aggregated these results to obtain national estimates. For the RRU measures, the control TM population was weighted to match the exact distribution of health plan enrollees across all zip codes in which it operated. Matching on geography controlled for variation in practice patterns within Medicare across regions.

By matching at the zip code level where possible, we also controlled for unmeasured socioeconomic characteristics associated with residence at this level of geography.

To provide nationally representative estimates of over-all HMO and PPO performance relative to traditional Medicare, we averaged HEDIS RRUs and quality mea-sures, respectively, over MA plans and matched cohorts of traditional Medicare enrollees, weighted by MA enrollment. To assess variation in performance of health plans based on particular characteristics relative to matched traditional Medicare in local areas, we used hierarchical regression models with correlated bivariate random effects for each health plan and its matched traditional Medicare sample and fixed effects for the health plan characteristics noted above, including an indicator vari-able for PPOs, with separate coefficients for the MA and traditional Medicare samples. Because more than 80% of PPOs were small, new, and for-profit, the PPO measure pertains to just this category of PPOs (the few other PPOs were not included in these models).

Finally, for each HMO plan, we created a composite of the 3 diabetes quality measures by taking the mean across the measures, and similarly created an aggregate measure of spending by summing over the 3 spending categories: inpatient care, surgery and other procedures, and evaluation and management services. We constructed similar summary measures of quality and spending for the matched TM cohort for each health plan. We then plotted mean quality relative to the local TM comparison group against mean spending relative to the same comparison group to provide a visual representation of the marginal contribution versus TM for each health plan. We also created a similar plot for the cardiovascular cohort using the single quality measure available for this group.

Analyses were conducted with SAS version 9.2 (SAS Institute, Cary, North Carolina). Two-tailed P values are reported for statistical tests. Our study protocol was approved by the Human Studies Committee of Harvard Medical School and the CMS Privacy Board.

Characteristics of the Medicare HMOs and PPOs in our study appear in 

. We studied data from 190 HMOs and 67 PPOs that included 4,207,433, and 318,293 enrollees, respectively, in 2007. About two-thirds of enrollees were in for-profit health plans. Although 75% of the health plans were small (<25,000 members), these HMOs represented only about 25% of enrollment. Most HMOs had participated in Medicare prior to 2006, but only 11 PPOs had done so.

The MA HMOs in our study enrolled approximately 680,000 beneficiaries with diabetes (and the PPOs approximately 50,000) and approximately 268,000 enrollees with 1 of the 4 cardiovascular conditions (and approximately 12,000 PPO enrollees; sample sizes vary slightly by measure). For the diabetes cohort, just under half were male (48.8%) and more than 80% were white. The largest proportion was from the south and more than 85% had at least 1 comorbid condition (

). After weighting the TM sample to match the MA distribution, the 2 samples had identical demographic characteristics.

Utilization in MA and TM by Types of Diabetes and Cardiovascular Disease

With 1 exception, total standardized spending, as well as each of the 3 categories of spending, was markedly lower for MA HMO enrollees than for matched TM enrollees (

). For instance, total spending was 19% less for diabetics enrolled in MA than for those enrolled in TM ($5223 vs $6413; 

 <.001). The single exception was for diabetics without comorbidity (eg, inpatient spending of $1309 for MA vs $925 for TM; 

 <.001). Visits to the ED were consistently lower in MA, as were rates of hospital inpatient discharges. Similar results were observed for those enrolled in PPOs (

Spending and Utilization by Type of Health Plan 

Patterns of comparative price-standardized utilization by type of plan are presented in

 shows the detailed results). For large, established, nonprofit HMOs, all 3 categories of spending were lower than for the matched TM sample, with differences ranging from 16% (evaluation and man-agement services for diabetics) to 70% (surgery rates for those with cardiovascular disease). In contrast, results were mixed for new, for-profit, small HMOs, where aggregate spending was higher in some categories in MA (eg, inpatient spending for cardiovascular disease, 16% higher), and lower for others (eg, surgery and procedures 43% lower for diabetics in MA). Results for PPO health plans were similar to those for new, for-profit, small HMOs.

Spending and Quality of Care by Health Plan Characteristics 

The mean plan-weighted rates of A1C testing, LDL cholesterol testing, and diabetic retinal exams were 89.7%, 87.6%, and 65.2%, respectively. 

 presents scatter plots of HMO health plan spending and the composite measure of quality for the diabetes cohort by health plan age (established prior to 2006 or not), size (>25,000 members), and tax status. Several findings are apparent: first, while members of most health plans experienced higher quality than the matched TM population in their area and therefore are plotted above the solid horizontal axis, the MA-TM difference varied substantially and for some plans was negative (MA worse than TM). Similarly, MA spending was lower than that in TM for most health plans, but the magnitude of the difference varied and was sometimes reversed (plotted to right of solid vertical axis). Second, there was little association between the spending and quality effects (Pearson correlation coefficient, 0.16), as manifested by the nearly equal distribution of plans across the 4 quadrants formed by median splits (dashed lines) on the 2 variables. Finally, although HMO health plans of each type are present in all 4 quadrants of each plot, the upper left hand quadrant (higher quality/lower spending) contains the most established, large, nonprofit HMO plans and the lower right hand quadrant (lower quality/higher spending) contains a higher proportion of new, small, for-profit HMO plans. A similar pattern was seen for the cardiovascular measures (

This study provides the first compre-hensive comparison between MA and TM of price-standardized utilization and quality of care for those with diabetes and cardiovascular disease, 2 prevalent and costly chronic medical conditions. We found several notable results: first, for both cohorts, RRU—which is a measure of total utilization using a standardized set of prices&mdash;was lower in MA health plans than in TM in each of the main categories of spending examined. Moreover, MA plans achieved higher performance on measures of ambulatory quality.

 Second, marked heterogeneity was evident among MA plans. Most older, larger, nonprofit health plans were able to achieve substantial reductions in service utilization while delivering care of high quality, whereas many newer, smaller, for-profit plans had similar or greater utilization when compared with TM. Finally, utilization among PPOs—an alternative arrangement to HMOs that is generally less managed and coordinated&mdash;showed patterns that were similar to new, smaller HMOs and to TM.

Our findings have important implications for policy. With the 2010 passage of the Affordable Care Act (ACA), the attention of policy makers has now shifted to controlling the seemingly inexorable growth in healthcare costs.

Delivering high-value care requires decreasing utilization of services of low value while simultaneously maintaining or increasing the delivery of services of high value. We show that substantial numbers of larger, nonprofit HMO MA plans appear to be delivering care of high quality, while doing so with substantially fewer resources. Whether this is more due to their actions, as payers perhaps linked to their size and ability to influence provider behavior, or to use of more limited and selected networks of providers, or both, is unknown. Although there has been much focus on payment reform in TM—such as the launching of accountable care organizations (ACOs)&mdash;30% of Medicare enrollees are in MA health plans, a far larger proportion than are currently in ACOs.

Compared with traditional Medicare, relative resource use for those with diabetes or cardiovascular disease is lower in Medicare Advantage, while quality of care is higher.

Alan M. Zaslavsky, PhD

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