John Z. Ayanian, MD, MPP

 Michigan expanded Medicaid under the Affordable Care Act (ACA) through a federal waiver that permitted state-mandated features, including an emphasis on primary care. We investigated the factors associated with Michigan primary care providers (PCPs)’ decision to accept new Medicaid patients under Medicaid expansion.

Statewide survey of PCPs informed by semistructured interviews.

 After Michigan expanded Medicaid on April 1, 2014, we surveyed 2104 PCPs (including physician and nonphysician providers, such as nurse practitioners and physician assistants) with 12 or more assigned Medicaid expansion enrollees (response rate, 56%). To guide survey development and interpretation, we interviewed a separate group of 19 PCPs with Medicaid expansion enrollees from diverse urban and rural regions. Survey questions assessed PCPs’ current acceptance of new Medicaid patients.

 Of the 2104 surveyed PCPs, 78% reported that they were currently accepting additional Medicaid patients; 58% reported having at least some influence on the decision.

Factors considered very/moderately important to the Medicaid acceptance decision included practice capacity to accept any new patients (69%), availability of specialists for Medicaid patients (56%), reimbursement amount (56%), psychosocial needs of Medicaid patients (50%), and illness burden of Medicaid patients (46%). PCPs accepting new Medicaid patients tended to be female, minorities, nonphysician providers, specialized in internal medicine, paid by salary, or working in practices with Medicaid-predominant payer mixes.

 In the era after Medicaid expansion, PCPs placed importance on practice capacity, specialist availability, and patients’ medical and psychosocial needs when deciding whether to accept new Medicaid patients. To maintain primary care access for low-income patients with Medicaid, future efforts should focus on enhancing the diversity of the PCP workforce, encouraging healthcare professional training in underserved settings, and promoting practice-level innovations in scheduling and integration of specialist care.

In the era after Medicaid expansion, the factors considered important to Michigan primary care providers (PCPs) when deciding whether to accept additional Medicaid patients included the capacity to accept any new patients (69%), availability of specialists for Medicaid patients (56%), reimbursement amount (56%), psychosocial needs of Medicaid patients (50%), and illness burden of Medicaid patients (46%).

PCPs continuing to accept new Medicaid patients tended to be female, minorities, nonphysician providers, specialized in internal medicine, paid by salary, or working in practices with Medicaid-predominant payer mixes.

Policy makers should consider these factors in addition to reimbursement policies to ensure adequate PCP capacity in states with expanded Medicaid coverage.

As state and federal governments debate the future of the Affordable Care Act (ACA) and its Medicaid expansion, it seems likely that expanded Medicaid coverage will remain, but it will likely be modified by greater state flexibility in coverage and implementation.

 The impact of expanded Medicaid in any form depends on several factors, including, crucially, the acceptance of Medicaid by healthcare providers and systems.

Payment has long been emphasized as a driver of physician participation in Medicaid. Prior studies have found that reimbursement level is important to healthcare providers’ decisions to accept Medicaid.

 However, it is also important to carefully consider both the financial and nonfinancial factors that may influence providers’ participation in Medicaid and other programs.

Since the 1980s, research has examined several nonfinancial factors associated with physician Medicaid acceptance, including characteristics of “high-share” Medicaid providers, such as younger age, female gender, and nonwhite race.

 However, few studies have comprehensively examined which provider characteristics and practice settings may be associated with provider willingness to accept new Medicaid patients after the 1990s, when this was last examined in depth following the rapid growth of managed care. Given that influences on healthcare providers’ willingness to see Medicaid patients may have changed in the era after ACA Medicaid expansion, this question is worth revisiting.

Because primary care providers (PCPs) deliver frontline access to care for patients, understanding the factors associated with an adequate supply of PCPs who accept Medicaid patients is critically important. Access to care for Medicaid patients depends on several dimensions, including (1) providers’ willingness to accept any Medicaid patients, (2) the geographic distribution of available providers in relation to patients’ location, and (3) providers’ capacity and willingness to accept a greater number of Medicaid patients as demand rises (eg, after insurance coverage expansion). In the setting of Michigan’s Healthy Michigan Plan (HMP)—which expanded Medicaid under the ACA through a federal section 1115 waiver that permitted state-mandated features, including an emphasis on primary care

—prior studies have examined the first 2 dimensions of provider Medicaid acceptance. With regard to whether primary care practices accepted any Medicaid patients, these studies demonstrated increased availability of PCP practices offering appointments to HMP patients after Medicaid expansion

 despite rapid enrollment in the program,

 which was consistent with trends observed in 10 other states.

 Regarding provider distribution, these increases in appointment availability appeared to be geographically concentrated in regions with lower pre-expansion Medicaid acceptance.

The current study examines the third dimension of providers’ capacity and willingness to accept a greater number of Medicaid patients: whether PCPs who already have some Medicaid patients are willing to accept more in response to patient demand. In a recent study, Neprash and colleagues found that most physicians maintained or slightly increased their Medicaid participation rates after ACA Medicaid expansion, with greater increases observed in expansion states.

 Of note, approximately 20% of PCPs accounted for the care of 60% of Medicaid patients in that study. In other words, most increases in PCP Medicaid participation occurred among providers already caring for Medicaid patients. The authors of that study concluded that policy efforts to increase access to primary care should target existing Medicaid providers. Thus, focusing on PCPs already caring for Medicaid patients, we investigated what factors were associated with their continued acceptance of new Medicaid patients.

As part of a formal evaluation under contract with the Michigan Department of Health and Human Services (MDHHS) and required by CMS for a section 1115 waiver, we conducted a statewide survey of PCPs regarding their experiences with HMP enrollees, new practice approaches and innovations adopted or planned in response to HMP, and future plans regarding acceptance and care of HMP patients.

 As an evaluation of a public program, the University of Michigan and MDHHS institutional review boards deemed the study exempt.

The PCP sample was drawn from the MDHHS Data Warehouse, which stores data generated from encounters on all Medicaid and HMP enrollees and their providers, including provider demographics, specialty (assessed by linking National Provider Identifier [NPI] numbers from the Data Warehouse to each respondent’s National Plan and Provider Enumeration System profile), practice setting, and health plan participation. From the Warehouse, 7360 NPI numbers were identified as the assigned PCP for at least 1 HMP managed care enrollee (adult Medicaid expansion group) in April 2015, 1 year after the HMP launch. Nurse practitioners (NPs) and physician assistants (PAs) who were listed as the assigned PCP were also included in the sample. We considered PCPs with at least 12 assigned members (an average of 1 HMP enrollee per month) eligible for the survey in order to assess the perspectives of PCPs who had sufficient experience with HMP patients to provide informed input on the primary care—oriented features of the program for another related study.

 Thus, 2813 PCPs with fewer than 12 assigned members were excluded. Of the remaining 4547 PCPs, exclusions included 25 with an NPI entity code that did not reflect an individual provider (20 organizational NPIs, 4 deactivated, and 1 invalid), 161 with only pediatric specialty, 4 University of Michigan physicians involved in the HMP evaluation, and 35 with out-of-state addresses greater than 30 miles from the Michigan border. After exclusions, 4322 PCPs (3686 physicians and 636 NPs or PAs) remained in the survey sample.

Although we focused on a PCP sample that already had assigned Medicaid patients, we considered the representativeness of the sample relative to the typical PCP. As the average US primary care panel size is approximately 1200 to 1900 patients,

 the median number of assigned HMP members per PCP in our sample was 53; with HMP representing approximately one-third of all Michigan Medicaid enrollees, the estimated Medicaid proportion of the panel was approximately 6% to 15% of the total panel size. This is consistent with the national average Medicaid proportion of PCP panels, which has been reported to range from 9% to 20%.

The survey included standard measures of PCP demographic, professional, and practice characteristics, as well as items from prior surveys on decision making related to Medicaid patients.

 New items related to PCPs’ decisions to accept Medicaid patients in the context of the state Medicaid expansion were developed based on qualitative interviews. Items were subsequently cognitively pretested with 2 PCPs (1 physician from a safety net clinic and 1 PA from a private practice) to ensure understanding prior to survey administration. The final survey was also pretested with 1 PCP to ensure appropriate timing and flow.

 To guide survey development and interpretation, we conducted 19 semistructured interviews with a separate group of PCPs caring for Medicaid/HMP patients between December 2014 and April 2015. These interviews were conducted in 5 geographic regions across Michigan, purposefully selected to include racial/ethnic diversity and a mix of urban, suburban, and rural communities: city of Detroit, western Michigan, central lower Michigan, northeastern Michigan, and Michigan’s Upper Peninsula. Interviewees included both physicians and nonphysician providers (ie, NPs/PAs) who worked at small private practices, federally qualified health centers (FQHCs), free/low-cost clinics, hospital-based practices, or rural practices (

Interview topics included (1) provider awareness of patients’ insurance and experiences caring for HMP patients; (2) PCP involvement in decision making about whether to accept Medicaid/HMP patients; and (3) factors that may affect PCPs’ Medicaid acceptance decisions in the future, including knowledge of reimbursement changes such as the Medicaid primary care rate bump to the level of Medicare rates in 2013-2014.

Interviews were audio recorded and transcribed verbatim. Each transcript was reviewed iteratively by at least 2 members of the research team, with in-depth coding and thematic analysis

 performed using Dedoose software (SocioCultural Research Consultants, LLC; Manhattan Beach, California).

The survey was initially mailed to the PCP sample (N = 4322) in June 2015 and included a personalized cover letter describing the project, a fact sheet about HMP, a paper copy of the survey, a $20 bill, and a postage-paid return envelope. The cover letter also gave information on the option to complete the survey online. Two additional mailings with the same contents were sent to nonrespondents in August and September 2015. Data from mailed and online surveys returned by November 1, 2015, were included in the analysis.

 for the full survey instrument. The dependent variable was PCPs’ current acceptance of new Medicaid or HMP patients (“Are you currently accepting new patients with…[Medicaid; Healthy Michigan Plan; private insurance; Medicare; no insurance (ie, self-pay)]?”). In addition to standard PCP and practice characteristics, survey items measured independent variables of PCP attitudes regarding the importance of various patient- and practice-level factors in their practice’s decision to accept new Medicaid/HMP patients (eg, reimbursement amount, practice capacity to accept any new patients, specialist availability, and illness needs of Medicaid/HMP patients) and their experiences caring for and expressed commitment to caring for underserved populations.

Descriptive statistics were used to report PCP characteristics, current acceptance of new patients, and responses to other individual survey items. Bivariate and multivariable logistic regression analyses were used to assess the association of independent variables with the dependent variable of continued PCP acceptance of new Medicaid patients (yes/no).

PCP personal, professional, and practice characteristics with statistically significant associations at 

 <.01 were included as covariates in multivariable analyses, except for cases with expected collinearity (eg, nonphysician provider variable included but not specialty, payer mix variable included but not payment arrangement). For inclusion of items assessing attitudes toward underserved patients in the regression analyses, we created an index across all underserved attitudes items and calculated a score based on agreement level for multiple items (a score of 1 was assigned to a response of strongly disagree, 2 for disagree, 3 for neither agree nor disagree, 4 for agree, and 5 for strongly agree). Scores for individual items were summed to produce a scaled score for which higher numbers represented stronger agreement with commitment to caring for the underserved. A 2-sided 

 <.05 was considered statistically significant. All analyses were performed using Stata version 13 or 14.2 (Stata Corp; College Station, Texas).

Characteristics of PCP Survey Respondents

After excluding PCPs with undeliverable addresses (n = 501) and who were ineligible (n = 27; eg, retired, moved out of state) from the 4322 surveys sent, the final response rate was 56% (54% for physicians, 65% for NPs/PAs; n = 2104). See 

 for a flowchart of survey response rates. There were no significant differences between respondents and nonrespondents with regard to gender, age, number of affiliated Medicaid managed care plans, and practice setting in an FQHC; PCPs with internal medicine specialty were less likely to respond (see 

 for comparison of PCP survey respondents and nonrespondents).

Nearly half (45%) of respondents were female and 79% were white (

). Nonphysician providers (NPs/PAs) represented 17% of respondents. Family medicine (53%) and internal medicine (27%) were the most common specialties, and 82% of respondents were board certified. Most respondents (74%) had been in practice for at least 10 years. Fifteen percent practiced in an FQHC, and 35% had a payer mix that was predominantly Medicaid.

Although all survey respondents had at least 12 assigned patients with Medicaid/HMP coverage based on the survey sampling, 78% reported continuing to currently accept new Medicaid or HMP patients compared with 87% accepting new patients with private insurance, 84% with Medicare, and 76% with no insurance (

Attitudes Influencing PCPs’ Medicaid Acceptance Decision

Most PCP survey respondents reported having at least some influence on the decision to accept or not accept Medicaid or HMP patients; 23% reported “the decision is entirely mine,” 14% reported “I have a lot of influence,” 21% reported “I have some influence,” and 43% reported “I have no influence.”

In interviews, the 19 PCPs described influences on the Medicaid acceptance decision at various levels (see themes in 

). At the provider level, the illness burden and psychosocial needs of prospective Medicaid patients influenced PCPs’ decisions about whether to accept them, particularly for patients with complex chronic pain or mental health needs. At the practice level, the decision to accept new patients depended on the practice’s capacity to provide sufficient care to established patients, including timeliness of appointments and ability to provide high-quality care. At the health system level, PCPs’ decision making depended on the resources and administrative structure of their health system, including whether specialists were available to see Medicaid patients. Lastly, with regard to the policy environment, although most PCPs thought that reimbursement was important, many lacked knowledge of the 2013-2014 ACA primary care rate bump or other payment details yet continued to accept new Medicaid patients.

Based on these themes identified in the qualitative interviews, we asked PCP survey respondents to rate the importance of several patient- and practice-level factors to their practice’s decision to accept new Medicaid or HMP patients (“Please indicate the importance of each of the following for your practice’s decision to accept new Medicaid or Healthy Michigan Plan patients”) (

). Factors considered very/moderately important to the Medicaid acceptance decision included the capacity to accept new patients with any type of insurance (69%), availability of specialists who see Medicaid patients (56%), reimbursement amount (56%), psychosocial needs of Medicaid patients (50%), and illness burden of Medicaid patients (46%).

We also asked PCP survey respondents about their prior experience and attitudes toward caring for poor or underserved patients. More than half (57%) of respondents reported providing care in the past 3 years in a setting that serves poor and underserved patients with no anticipation of being paid. Nearly three-fourths (73%) felt a responsibility to care for patients regardless of their ability to pay, and nearly three-fourths (72%) agreed or strongly agreed that all practitioners should care for some Medicaid patients (see 

 for full descriptive statistics of PCP attitudes about caring for underserved patients).

Factors Associated With PCPs’ Medicaid Acceptance

In bivariate analyses, survey respondents were more likely to accept new Medicaid patients if the PCPs were younger, female, racial minorities, specialized in internal medicine, nonphysician providers (NPs or PAs), paid by salary, or working in FQHCs, rural practices, or practices with integrated mental health care or Medicaid-predominant payer mixes (

). Multivariable analyses largely confirmed bivariate analyses: PCP respondents who were female (adjusted odds ratio [aOR], 1.32 vs male; 95% CI, 1.01-1.72), racial minorities (black: aOR, 3.46 vs white [95% CI, 1.45-8.25]; Asian: aOR, 1.84 vs white [95% CI, 1.21-2.80]), nonphysician providers (aOR, 2.21 vs physicians; 95% CI, 1.32-3.71), specialized in internal medicine (aOR, 1.47 vs family medicine; 95% CI, 1.09-1.97), paid by salary (aOR, 2.09 vs fee-for-service; 95% CI, 1.58-2.77), or working in practices with Medicaid-predominant payer mixes (aOR, 7.31 vs private-predominant payer mix; 95% CI, 5.05-10.57) or other nonprivate payer mixes were more likely to accept new Medicaid patients (Table 3).

Regarding PCP attitudes, in bivariate analyses, PCP survey respondents were less likely to accept new Medicaid patients if they deemed that overall capacity to accept new patients was very/moderately important or reimbursement was very/moderately important. Not surprisingly, PCPs were also more likely to accept new Medicaid patients if they had provided prior care to the poor or underserved or expressed a greater commitment to caring for this group. In multivariable analyses, we again found that PCPs who had previously provided care to underserved patients (aOR, 1.35; 95% CI, 1.05-1.73) or who expressed stronger commitment to caring for the underserved (aOR, 1.14 for each point in composite score; 95% CI, 1.11-1.18) were more likely to accept new Medicaid patients. PCPs were less likely to accept new Medicaid patients if they considered overall capacity to accept new patients important (aOR, 0.59; 95% CI, 0.44-0.79).

We then repeated the bivariate and multivariable analyses for the subgroup of PCP survey respondents who indicated that they had at least some influence on the decision to accept Medicaid patients. The findings were similar to those of analyses with the full sample (data not shown).

The ACA’s Medicaid expansion has been associated with increased access to care nationally

 and increased Medicaid acceptance among PCPs in Michigan,

 even after expiration of the 2013-2014 primary care rate increase of Medicaid fees to Medicare levels.

 In our study, we found that PCP demographics, salary structure, history of caring for the underserved, and perceived practice capacity were all associated with continued acceptance of new Medicaid patients among PCPs who already had assigned Medicaid patients.

Our findings on provider characteristics associated with an increased likelihood of Medicaid acceptance were consistent with literature that predated the ACA Medicaid expansion. Specifically, female gender, nonwhite race, and nonphysician professional training (NP or PA) have been associated with greater provider Medicaid acceptance in earlier periods.

 These findings suggest that demographic characteristics of the primary care workforce can influence access for Medicaid patients and that integrating nonphysician providers in primary care practices may be beneficial to ensuring access for Medicaid patients. In fact, an earlier Michigan study found that an increase in Medicaid primary care appointment availability after HMP was associated with rising proportions of appointments offered with NPs or PAs, suggesting that nonphysician providers contributed to improved access.

We also found that PCPs in safety net settings and other practices with Medicaid-predominant payer mixes were more likely to continue accepting new Medicaid patients, confirming findings from prior studies.

 Of note, FQHCs and rural health clinics receive higher Medicaid reimbursements than private practices. PCPs choosing to work in these safety net settings may be less likely to consider payment amounts important to their decisions to accept additional Medicaid patients.

 in our adjusted models, PCP attitudes toward reimbursement were not significantly associated with the likelihood of Medicaid acceptance. Whether this relates to PCPs’ lack of knowledge of reimbursement changes during the rate bump period, as found in our interviews, or other factors is uncertain. Other recent studies have demonstrated no association of the 2013-2014 Medicaid primary care rate bump with physician participation in Medicaid.

 Combined with our findings that experience and commitment to caring for the underserved were associated with PCP acceptance of Medicaid, these results suggest that PCPs who continue to accept Medicaid patients do so, at least in part, out of a sense of professional duty.

 It is possible that this may reflect a population-level shift in attitudes among the current generation of healthcare providers.

 To encourage greater provider acceptance of Medicaid, policy makers should consider promoting experiences caring for underserved populations during professional training to broaden the pipeline of future healthcare providers accepting Medicaid.

Articles

In the era after Medicaid expansion, primary care providers placed importance on practice capacity, specialist availability, and reimbursement when deciding whether to accept new Medicaid patients.

Factors Influencing Primary Care Providers’ Decisions to Accept New Medicaid Patients Under Michigan’s Medicaid Expansion

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.

MA plans are currently paid more than TM on average.

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.

 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.

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.

John Z. Ayanian, MD, MPP

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