A Comparison of Relative Resource Use and Quality in Medicare Advantage Health Plans Versus Traditional Medicare

ABSTRACT

Objectives: 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.

Study Design: Cross-sectional matched observational study using data for 2007.

Methods: 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.

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

Conclusions: 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.

Am J Manag Care. 2015;21(8):559-566Take-Away Points

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,¹ 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.^2,3

Medicare’s managed care program, Medicare Advantage (MA), currently provides care to 15.7 million Americans, representing 30% of beneficiaries.⁴ 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.⁵ 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.^6-8 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).⁹ 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.

METHODSOverview

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.^2,3 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.^7,10

Data Sources and Measures

Medicare Advantage. 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.⁹ 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.^7,11 CMS has audited HEDIS quality measures reported by Medicare health maintenance organizations (HMOs) and found them to be highly accurate.¹² 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).^7,10

Traditional Medicare. 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.^13,14 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.^15,16 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.

Health Plan Characteristics

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.

Statistical Analyses

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.^17,18 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.

RESULTS

Table 1

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.

eAppendix Table 1

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 (, available at www.ajmc.com). 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

Table 2

eAppendix Table 2

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

Figures 1

2

eAppendix Table 3

Patterns of comparative price-standardized utilization by type of plan are presented inand respectively ( 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

Figure 3

eAppendix Figure

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 ().

DISCUSSION

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.¹⁹ 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.

Policy Implications

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.²⁰ 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.²¹

MA plans are currently paid more than TM on average.²² Because of Medicare regulations and competitionamong plans, many of the savings from these extra payments and the reduced utilization we documented in this study are passed through to beneficiaries in the form of lower premiums, less cost sharing, and benefits for noncovered services.²³ However, providers may also profit from these excess payments, as they may be able to negotiate higher prices from MA plans. To finance its expansion of health insurance, the ACA reduced reimbursement for MA plans; how these reductions will impact plan and beneficiary participation and the future growth of the MA program remains an open question.

Although MA plans as a whole were able to achieve substantially lower utilization rates, we found considerable heteroge-neity among health plan types, consistent with earlier analyses.^5,24 In particular, larger, more established (mostly nonprofit) health plans were able to deliver care of high quality at substantially lower cost. Health plans may use a variety of approaches to influence the costs and quality of care,²⁵ ranging from contractually based incentives and pay-for-performance to care management programs directed to either patients or physicians, and utilization management programs such as prior authorization requirements. Future research will be needed to elucidate more fully how health plans have achieved these savings and what the most effective approaches might be.

Limitations

An important limitation of our research is that our quality measures were limited to basic ambulatory services, and we lacked measures of more complex services (eg, appropriate use of coronary revascularization procedures, such as coronary artery bypass graft surgery, for which rates are higher in MA health plans¹⁰) and outcomes of care. Ultimately, healthcare organizations must be evaluated on their success at controlling spending while improving both intermediate clinical outcomes (eg, control of blood pressure) and ultimate outcomes such as risk-adjusted mortality. Larger, more establishedHMOs may have greater ability to achieve these goals.^25,26 Future extensions of this re-search should evaluate the extent to which health plans achieve savings while improving outcomes of care that are important to patients and delivery systems as a whole.

Our study is subject to several additional limitations. One possible explanation for our findings is favorable selection into MA, as suggested by research using data prior to the time of our study.²⁷ To minimize the impact of such selection effects in our analysis, we matched MA and TM enrollees by age, sex, race/ethnicity, and geographic area, usually at the zip code level, which in the aggregate created cohorts with similar sociodemographic characteristics. Indeed, the health services research literature commonly uses US Census data at this level to impute these characteristics.^28-31 We then compared care for patient populations with specific diagnoses, further controlling for clinical characteristics that might be associated with higher spending. Furthermore, favorable selection into MA appears to have fallen considerably in recent years.^32-34 Also, our data are now several years old. These data, however, are from the most recent year of RRU data for which CMS required reporting by health plans. Nonetheless, the RRU data made possible analyses that would not otherwise be possible given the unavailability of health plan claims data, despite only covering a limited set of conditions. Finally, the RRU data that health plans submitted to CMS were not fully audited and may have been incompletely reported since they did not affect payment.

CONCLUSIONS

Proponents of managed care have long argued that integrated health plans can deliver care more efficiently than traditional fee-for-service care by using their ability to tailor their provider networks to the needs of their population and to implement disease and case management programs to improve chronic disease management.¹⁹ In this large national study of enrollees with diabetes or cardiovascular disease, our findings suggest that many Medicare HMO health plans are able to deliver care of equal or better quality with lower RRU than TM.Author Affiliations: Department of Health Care Policy, Harvard Medical School (BEL, AMZ, JPN, JZA), Boston, MA; Division of Primary Care and General Internal Medicine, Department of Medicine, Beth Israel Deaconess Medical Center (BEL), Boston, MA; National Committee for Quality Assurance (RS), Washington, DC; Stevens and Lee (LGP), Lancaster, PA; Department of Health Policy and Management, Harvard School of Public Health (JPN), Boston, MA; John F. Kennedy School of Government, Harvard University (JPN), Boston, MA; National Bureau of Economic Research (JPN), Cambridge, MA; Institute for Healthcare Policy and Innovation, Gerald R. Ford School of Public Policy, University of Michigan (JZA), Ann Arbor, MI; Division of General Medicine, Medical School, University of Michigan (JZA), Ann Arbor, MI; Department of Health Management and Policy, School of Public Health, University of Michigan (JZA), Ann Arbor, MI.

Source of Funding: This study was supported by a grant from the National Institute on Aging (P01 AG032952). The funding source did not play a role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or preparation, review, and approval of the manuscript.

Author Disclosures: Dr Newhouse is a director of and holds equity in Aetna, which sells Medicare Advantage products. Drs Saunders (current) and Pawlson (past) are current or former employees of NCQA, which holds the copyright for HEDIS measures. Dr Ayanian is a consultant to RTI on risk adjustment models for Medicare Advantage health. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (BEL, JZA, AMZ, JPN); acquisition of data (LGP, JPN, RS); analysis and interpretation of data (BEL, JZA, AMZ, JPN, RS); drafting of the manuscript (BEL, LGP); critical revision of the manuscript for important intellectual content (JZA, LGP, AMZ, JPN, RS); statistical analysis (BEL, AMZ, JPN); obtaining funding (BEL, JPN).

Address correspondence to: Bruce E. Landon, MD, MBA, MSc, Department of Health Care Policy, Harvard Medical School, 180 Longwood Ave, Boston, MA 02215. E-mail: landon@hcp.med.harvard.edu.REFERENCES

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