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The American Journal of Managed Care October 2016
Cost-Effectiveness of a Statewide Falls Prevention Program in Pennsylvania: Healthy Steps for Older Adults
Steven M. Albert, PhD; Jonathan Raviotta, MPH; Chyongchiou J. Lin, PhD; Offer Edelstein, PhD; and Kenneth J. Smith, MD
Economic Value of Pharmacist-Led Medication Reconciliation for Reducing Medication Errors After Hospital Discharge
Mehdi Najafzadeh, PhD; Jeffrey L. Schnipper, MD, MPH; William H. Shrank, MD, MSHS; Steven Kymes, PhD; Troyen A. Brennan, MD, JD, MPH; and Niteesh K. Choudhry, MD, PhD
Benchmarking Health-Related Quality-of-Life Data From a Clinical Setting
Janel Hanmer, MD, PhD; Rachel Hess, MD, MS; Sarah Sullivan, BS; Lan Yu, PhD; Winifred Teuteberg, MD; Jeffrey Teuteberg, MD; and Dio Kavalieratos, PhD
Patients' Success in Negotiating Out-of-Network Bills
Kelly A. Kyanko, MD, MHS, and Susan H. Busch, PhD
Connected Care: Improving Outcomes for Adults With Serious Mental Illness
James M. Schuster, MD, MBA; Suzanne M. Kinsky, MPH, PhD; Jung Y. Kim, MPH; Jane N. Kogan, PhD; Allison Hamblin, MSPH; Cara Nikolajski, MPH; and John Lovelace, MS
A Call for a Statewide Medication Reconciliation Program
Elisabeth Askin, MD, and David Margolius, MD
Postdischarge Telephone Calls by Hospitalists as a Transitional Care Strategy
Sarah A. Stella, MD; Angela Keniston, MSPH; Maria G. Frank, MD; Dan Heppe, MD; Katarzyna Mastalerz, MD; Jason Lones, BA; David Brody, MD; Richard K. Albert, MD; and Marisha Burden, MD
Mortality Following Hip Fracture in Chinese, Japanese, and Filipina Women
Minal C. Patel, MD; Malini Chandra, MS, MBA; and Joan C. Lo, MD
Estimating the Social Value of G-CSF Therapies in the United States
Jacqueline Vanderpuye-Orgle, PhD; Alison Sexton Ward, PhD; Caroline Huber, MPH; Chelsey Kamson, BS; and Anupam B. Jena, MD, PhD
Periodic Health Examinations and Missed Opportunities Among Patients Likely Needing Mental Health Care
Ming Tai-Seale, PhD; Laura A. Hatfield, PhD; Caroline J. Wilson, MSc; Cheryl D. Stults, PhD; Thomas G. McGuire, PhD; Lisa C. Diamond, MD; Richard M. Frankel, PhD; Lisa MacLean, MD; Ashley Stone, MPH; and Jennifer Elston Lafata, PhD
Currently Reading
Does Medicare Managed Care Reduce Racial/Ethnic Disparities in Diabetes Preventive Care and Healthcare Expenditures?
Elham Mahmoudi, PhD; Wassim Tarraf, PhD; Brianna L. Maroukis, BS; and Helen G. Levy, PhD

Does Medicare Managed Care Reduce Racial/Ethnic Disparities in Diabetes Preventive Care and Healthcare Expenditures?

Elham Mahmoudi, PhD; Wassim Tarraf, PhD; Brianna L. Maroukis, BS; and Helen G. Levy, PhD
The authors aimed to examine whether participation in Medicare managed care, compared with fee-for-service, has any effects on racial/ethnic disparities in diabetes care and healthcare expenditures among older adults.

Objectives: Large and persistent racial/ethnic disparities exist in diabetes care. Considering the rapid rate of growth of Medicare Managed Care (MMC) plans among minority populations, our aim was to investigate whether disparities in diabetes management and healthcare expenditures are smaller in MMC versus Medicare fee-for-service (MFFS) plans. We hypothesized that racial/ethnic disparities in diabetes care and in health expenditures would be less pronounced in MMC compared with MFFS plans.

Study Design: Nationally representative data from the 2006 to 2011 Medical Expenditure Panel Survey on white, African American, and Hispanic seniors with diabetes were analyzed.

Methods: We examined 4 measures of diabetes care—regular foot check, eye exam, cholesterol check, and flu vaccine—and total and out-of-pocket (OOP) healthcare expenditures. We implemented the Institute of Medicine’s definition of disparity, applied propensity score weighting to adjust for potential differential selection, and used a difference-in-differences generalized linear framework to estimate outcome measures.

Results: For African Americans, MMC was associated with a $1183 (P <.036) reduction and a $547 (P <.001) increase in disparities in total and OOP healthcare expenditures, respectively. For Hispanics, disparities in foot exam, flu shot, and cholesterol check decreased by 5, 10, and 7 percentage points (P <.001); additionally, disparities in total and OOP healthcare expenditures were reduced by $3588 and $276 (P <.001), respectively. MMC plans spend less on everyone, including whites.

Conclusions: Hispanic/white disparities in diabetes management and healthcare expenditures were smaller in MMC than in MFFS plans. African American/white disparities were not consistently larger in 1 setting than the other.

Am J Manag Care. 2016;22(10):e360-e367
Take-Away Points

Disparities in care and rising healthcare costs continue to plague our healthcare system. Our study indicates that: 
  • Compared with fee-for-service, Medicare managed care (MMC) spent less on every patient, regardless of race/ethnicity and had substantially reduced Hispanic/white disparities in diabetes care. 
  • MMC had mixed effects on African American/white disparities, which may be explained by the degree of market penetration and geographic variations of MMC plans. 
  • Promoting some MMC strategies—for instance, incentivizing the use of preventative services, promoting organized outreach programs, mandating a primary care provider, and monitoring patients with chronic conditions—may reduce racial/ethnic disparities in diabetes care and lower healthcare expenditures.
Disparities in healthcare are a persistent national problem, and reducing them has been the goal of various national healthcare policies for decades.1,2 In particular, racial/ethnic disparity in diabetes care is coupled with a rapid increase in the prevalence of this illness among older minorities.3 Between 1994 and 2001, there was a 37% increase in the number of individuals 67 years or older who were diagnosed with diabetes, with the highest rates seen among minorities.4 If their health is not properly managed, individuals with diabetes are at risk of serious and costly adverse events.3,5 Additionally, minority groups report lower rates of recommended preventive health services for diabetes, such as influenza vaccinations6 and cholesterol screenings.7

Over the past 20 years, managed care plans have rapidly increased in size and influence, mainly as solutions to enduring problems of quality and cost in US healthcare.8 Similarly, Medicare Managed Care (MMC) plans were intended to address these issues by providing lower costs and better quality care for beneficiaries.9 Evidence is mixed on whether MMC plans have consistently achieved these goals.9 We have summarized some of the main differences between traditional Medicare fee-for-service (MFFS) and MMC plans in eAppendix A (eAppendices available at Certain characteristics of MMC plans—including having a mandatory primary care physician; offering more preventive services, such as eye exams; and capping out-of-pocket (OOP) spending—may reduce disparities in quality of care by making valuable services more accessible to vulnerable populations.9,12 Diabetes-specific preventive care, diabetes screening, and self-management training are covered under both MFFS and MMC plans. MMC plans, however, cap OOP spending, usually have lower deductible rates than MFFS plans, and charge co-pays (fixed amounts) instead of coinsurance (percentages).10,11 On the other hand, by applying gate-keeping policies, MMC plans might worsen access to care among racial/ethnic minorities by restricting their access to providers who might be geographically closer or linguistically or culturally more trusted.13 Whether enrollment into MMC versus traditional MFFS would worsen or ameliorate racial/ethnic disparities in access to diabetes care is not known.

Additionally, research indicates the possibility of nonrandom selection into MMC by: a) differential selection by health and geographic location14 and/or b) differential selection by race/ethnicity.14 There is a possibility that beneficiaries with lower incomes and less education (mostly minorities) choose MMC plans mainly because of lower OOP costs, not because of higher levels of care received. Considering the literature on the existence of selection bias towards healthier individuals (with lower risks) among MMC plans,14 chronically ill minorities may be worse off in MMC plans compared with nonrestricted, traditional MFFS.15

Using a nationally representative sample of Medicare beneficiaries diagnosed with diabetes, our specific aims were to: a) examine differences in racial/ethnic disparities between MMC and MFFS enrollees in 4 American Diabetes Association-recommended measures of diabetes management, and b) assess differences between MMC and MFFS in annual total and OOP healthcare expenditures. We hypothesized that MMC plans would reduce racial/ethnic disparities in diabetes management.



We used the Household Component files of the 2006 to 2011 Medical Expenditure Panel Survey (MEPS).16 The MEPS is a nationally representative survey of the US noninstitutionalized population.17 The Agency for Healthcare Research and Quality (AHRQ) conducts the survey and verifies the survey information. We limited our analysis to Medicare beneficiaries 65 years or older who reported being diagnosed with diabetes and reported being (non-Hispanic) African American, Hispanic, or (non-Hispanic) white. Our sample included 3735 individuals (MMC = 1235; MFFS = 2500) (Figure 1). Although the variable-specific nonresponse rate was less than 3%, without the imputation of missing values, we would have lost 13% of the initial sample size in our regression models. Thus, we used a multiple imputation technique (with 5 complete sets) to estimate missing values for independent variables.18,19

Dependent Variables

We used a total of 6 dependent variables. We examined diabetes management across 4 domains during the year: 1) whether feet were checked for sores, 2) whether individuals had a dilated eye exam, 3) whether individuals had their cholesterol checked, and 4) whether individuals had a flu vaccination. Additionally, we examined annual total and OOP healthcare expenditures. Dollar values were inflated, using the 2013 all-items Consumer Price Index.

Independent Variables

In addition to race/ethnicity, sociodemographic variables including age, sex, marital status, location based on 4 US Census regions, and living in a metropolitan statistical area were obtained at the time of survey completion. Education was measured using a 4-category variable (less than high school, high school, college degree, or other professional degree) with high school degree set as the reference category. Household income was measured based on percentage relative to the federal poverty level (FPL), using 5 mutually exclusive categories (poor or <100% of the FPL; near poor or 100%-125% of the FPL; low or 125%-199% of the FPL; middle or 200%-399% of the FPL; and high or ≥400% of the FPL), with the middle-income group serving as the reference category. For health insurance, we included 2 variables that measured whether an individual reported being enrolled in: a) MMC (vs MFFS) and b) whether an individual reported being enrolled in Medicaid (dual eligible). All individuals in our sample reported being enrolled in Medicare. For health-related variables, we accounted for 2 measures of self-rated physical and mental health, coded as “fair” or “poor” versus “good,” “very good,” or “excellent.” We also controlled for 4 reported chronic conditions: asthma, high blood pressure, any heart problems, and arthritis. Finally, we adjusted for whether an individual reported having a usual source of care. We checked for multicollinearity among our independent variables and did not find any strong correlations.

Statistical Analysis

First, we used propensity score weighting (PSW) to adjust for any potential selection bias into MMC versus MFFS. We used all independent variables (listed in Table 1) to estimate an inverse probability of treatment weighting; we then generated “synthetic” MMC and MFFS Medicare groups with distributional equivalence.20 Second, we applied the Institute of Medicine (IOM) conceptual framework in our analysis. In its 2002 report, Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care, the IOM defines disparity as “a difference in access or treatment provided to members of different racial or ethnic groups that is not justified by the underlying health conditions or treatment preferences of patients.”21

Quantifying the IOM Definition of Racial/Ethnic Disparities

In order to implement the IOM definition of a racial/ethnic disparity, we applied the rank-and-replace techniques recommended by Cook and colleagues.22 A full discussion of the rank-and-replace method has been described in detail elsewhere.23-25 Briefly, we applied a 4-step method to estimate the IOM disparities. First, we ran a multivariable difference-in-differences regression model for each of our outcome variables (eAppendices B and C). For all 4 dichotomous diabetes management measures, we used logistic regression models. For the continuous spending measures, we used generalized linear regression models.26 First, we applied the rank-and-replacement method to replace minorities’ health distributions with those of whites. To do so, we created a health index for each white and minority individual based on the sum of multiplications of the health variables’ coefficients and the actual health values in regression models described in step 1. Second, we ranked the health index values for whites and minorities, separately. Third, we replaced the proportionately ranked values of whites with those of minorities. Finally, using the original fitted regression models from step 1 and transformed health index values of minorities from step 3, we predicted the counterfactual outcome values for minorities and real values for whites.23 The differences in predicted averages between racial/ethnic groups were calculated within MMC and MFFS groups. Standard errors of all estimated measures were calculated using bootstrapping techniques applied in the context of complex survey design (replicated 100 times with replacement).27 We describe the IOM method in detail in eAppendix D.

We used Stata version 13 (StataCorp LP, College Station, Texas) for all analyses and adjusted for the clustered and stratified complex survey design of the MEPS.28 We weighted all estimates using the AHRQ-supplied and propensity score-adjusted diabetes weights.28


Table 1 reports the population characteristics of Medicare patients diagnosed with diabetes stratified by race and insurance type. Compared with whites, among both MFFS and MMC enrollees, certain characteristics persist: a) African Americans had a higher proportion of female enrollees and a lower rate of being married, b) African Americans and Hispanics had lower incomes and were less educated, c) African Americans and Hispanics had higher prevalence of hypertension and lower prevalence of stroke (Hispanics only) and emphysema, and d) significant geographic variation exists among minorities between MMC and MFFS. For example, in the West, MMC had greater penetration than MFFS among whites (29% compared with 13%; P <.001) and Hispanics (44% compared with 34%; P = .008). In contrast, in the South, MFFS had greater penetration than MMC among all population groups. Additionally, regardless of race, more MMC enrollees (by 13 percentage points; P <.001) live in large metropolitan areas compared with MFFS enrollees. To summarize, although there are differences within racial/ethnic groups between those enrolled in MMC and those enrolled in MFFS, the data did not present a clear pattern on differential selection based on race/ethnicity into MMC.

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