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The American Journal of Managed Care September 2018
Food Insecurity, Healthcare Utilization, and High Cost: A Longitudinal Cohort Study
Seth A. Berkowitz, MD, MPH; Hilary K. Seligman, MD, MAS; James B. Meigs, MD, MPH; and Sanjay Basu, MD, PhD
Language Barriers and LDL-C/SBP Control Among Latinos With Diabetes
Alicia Fernandez, MD; E. Margaret Warton, MPH; Dean Schillinger, MD; Howard H. Moffet, MPH; Jenna Kruger, MPH; Nancy Adler, PhD; and Andrew J. Karter, PhD
Hepatitis C Care Cascade Among Persons Born 1945-1965: 3 Medical Centers
Joanne E. Brady, PhD; Claudia Vellozzi, MD, MPH; Susan Hariri, PhD; Danielle L. Kruger, BA; David R. Nerenz, PhD; Kimberly Ann Brown, MD; Alex D. Federman, MD, MPH; Katherine Krauskopf, MD, MPH; Natalie Kil, MPH; Omar I. Massoud, MD; Jenni M. Wise, RN, MSN; Toni Ann Seay, MPH, MA; Bryce D. Smith, PhD; Anthony K. Yartel, MPH; and David B. Rein, PhD
“Precision Health” for High-Need, High-Cost Patients
Dhruv Khullar, MD, MPP, and Rainu Kaushal, MD, MPH
From the Editorial Board: A. Mark Fendrick, MD
A. Mark Fendrick, MD
Health Literacy, Preventive Health Screening, and Medication Adherence Behaviors of Older African Americans at a PCMH
Anil N.F. Aranha, PhD, and Pragnesh J. Patel, MD
Early Experiences With the Acute Community Care Program in Eastern Massachusetts
Lisa I. Iezzoni, MD, MSc; Amy J. Wint, MSc; W. Scott Cluett III; Toyin Ajayi, MD, MPhil; Matthew Goudreau, BS; Bonnie B. Blanchfield, CPA, SM, ScD; Joseph Palmisano, MA, MPH; and Yorghos Tripodis, PhD
Economic Evaluation of Patient-Centered Care Among Long-Term Cancer Survivors
JaeJin An, BPharm, PhD, and Adrian Lau, PharmD
Fragmented Ambulatory Care and Subsequent Healthcare Utilization Among Medicare Beneficiaries
Lisa M. Kern, MD, MPH; Joanna K. Seirup, MPH; Mangala Rajan, MBA; Rachel Jawahar, PhD, MPH; and Susan S. Stuard, MBA
High-Touch Care Leads to Better Outcomes and Lower Costs in a Senior Population
Reyan Ghany, MD; Leonardo Tamariz, MD, MPH; Gordon Chen, MD; Elissa Dawkins, MS; Alina Ghany, MD; Emancia Forbes, RDCS; Thiago Tajiri, MBA; and Ana Palacio, MD, MPH
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Adjusting Medicare Advantage Star Ratings for Socioeconomic Status and Disability
Melony E. Sorbero, PhD, MS, MPH; Susan M. Paddock, PhD; Cheryl L. Damberg, PhD; Ann Haas, MS, MPH; Mallika Kommareddi, MPH; Anagha Tolpadi, MS; Megan Mathews, MA; and Marc N. Elliott, PhD

Adjusting Medicare Advantage Star Ratings for Socioeconomic Status and Disability

Melony E. Sorbero, PhD, MS, MPH; Susan M. Paddock, PhD; Cheryl L. Damberg, PhD; Ann Haas, MS, MPH; Mallika Kommareddi, MPH; Anagha Tolpadi, MS; Megan Mathews, MA; and Marc N. Elliott, PhD
CMS implemented the Categorical Adjustment Index as part of the Medicare Advantage and Part D Star Rating Program in 2017. These analyses informed its development.
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To address gaps in care, public and private payers have undertaken a variety of actions, including performance measurement, public reporting, and performance-based payments. Concerns have been raised that some program designs may create incentives for providers and plans to avoid more challenging patient populations.1 Adjusting performance for differences in the patient populations that plans and providers serve, to level the playing field, is one approach that has been suggested to address potential mismeasurement problems.6,16 Providers caring for low-SES patients may face communication challenges associated with lower education, English proficiency, and health literacy, as well as reduced patient access to care and compliance with medical regimes associated with limited transportation, residential instability, and other barriers.16 Accounting for these differences may reduce the likelihood that providers will avoid lower-SES patients in response to pay-for-performance programs.

We found within-contract disparities in performance on the clinical measures used to assess MA contract and PDP performance, predominantly reflecting lower odds of receiving recommended care for low-SES patients; the magnitude of within-contract disparities varied across measures and contracts. These findings are consistent with those of prior studies demonstrating associations among patient sociodemographic characteristics, including SES, for selected Healthcare Effectiveness Data and Information Set measures in commercially insured populations,21,22 for outcomes measures among the general population with cardiovascular disease or diabetes,23 and for medication adherence measures in the MA population.24

Based on these analyses, CMS implemented the CAI with the 2017 star ratings. Adjustment of star ratings through CAI resulted in increased star ratings for some contracts with higher percentages of DE/LIS beneficiaries; 8.5% of MA contracts with 50% or more DE/LIS received half-star increases and none decreased, and 33.3% of PDPs with 50% or more DE/LIS received half-star increases and none decreased. Of contracts with less than 50% DE/LIS, less than 0.1% of MA contracts and 0% of PDPs had higher star ratings and less than 0.1% of MA and 16.3% of PDP contracts had lower star ratings.

Our study is the first to estimate the effects of adjusting the full set of clinical measures used in the Medicare Star Rating Program for SES factors and to simulate the effect of CAI adjustments for DE/LIS and disability on the star ratings used for quality bonus payments and public reporting. These results should inform future decisions about adjustment for SES.

Strengths and Limitations

Our study has several strengths. First, our analyses used patient-level measures of SES, in contrast to other studies that have used area-level estimates of SES from Census data as proxies for patient-level measures; these estimates measure a combination of the separate effects of the neighborhood in which a person resides and are a less accurate measure of person-level SES. Second, we measured the effect of SES adjustment for the universe of MA and PDP contracts. Third, we measured the effect of adjustment on all clinical measures contained in the Star Rating Program, rather than only a small subset of measures as has previously been reported.21,22,24 Fourth, inclusion of the contract fixed effects in our models allowed for adjustment for within-contract differences in quality for DE/LIS and disability, preserving quality differences between contracts and their affiliated providers that should be the target of improvement efforts.16 Fifth, we translate into policy-relevant terms the effect of risk adjustment at the measure level by examining its effect on the overall star rating used for quality bonus payment determination in MA contracts.

Our study also has several limitations. This study examined the effects of DE and/or receipt of LIS; although Medicaid eligibility varies by state, it is an important and widely available measure of low income and assets and has been recognized as the best proxy for income linkable to the Medicare beneficiary level.9 Furthermore, other measures of SES, such as housing stability, may be important markers of disparity; however, CMS and other payers would face challenges in collecting this measure of disadvantage. We believe that DE/LIS is a partial proxy for housing instability, as it measures the resources available to a beneficiary. This study was not designed to determine what factors allow some contracts to have small or zero disparities in care while others have sizeable disparities. Our findings are limited to beneficiaries in MA and PDP contracts, although other studies have found disparities in care in fee-for-service.25,26


Policy makers, plans, and providers need to understand the effects of case mix on performance scores and to consider whether it is appropriate to adjust for differences. The overall impact of adjustment and the feasibility of adjustment are important considerations.22 In addition, even when risk adjustment does not lead to changes in performance scores for most providers, it provides face validity to the overall measurement effort in signaling to providers that their treatment of more challenging patients will be accounted for in performance assessment. It is important to design performance measures to influence plan and provider behavior in desired ways, and case-mix adjustment could guard against undesired behaviors, improve the accuracy of quality measurement, and increase the incentive for high-performing contracts to enroll low-income and disabled beneficiaries, which, in turn, might help reduce disparities in quality of care. Decisions about whether to adjust and the effects of adjustment will be a function of the existence of within-contract or within-provider disparity, the magnitude of disparity, and the structure of the scoring algorithm used to rate providers.

In addition to adjustment for SES, which primarily addresses issues of quality measurement, policy makers may consider other options to reduce disparities in health and healthcare, including enhancing data collection to better support reporting quality, specifically for patients with social risk factors; developing and including in value-based purchasing programs measures of health equity paired with incentives to improve performance on these measures; changing the payment structure of incentive programs to reward high performance and improvement among beneficiaries with social risk factors; providing support and technical assistance to providers that serve beneficiaries with social risk factors; developing demonstrations that focus on care innovations intended to achieve better outcomes for beneficiaries with social risk factors; and requiring the coordination of benefits between Medicare and Medicaid by contracts that serve dually enrolled beneficiaries.6

Author Affiliations: RAND Corporation, Pittsburgh, PA (MES, AH), and Santa Monica, CA (SMP, CLD, MK, AT, MM, MNE).

Source of Funding: This work was performed under contract for CMS.

Author Disclosures: 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.

Authorship Information: Concept and design (MES, SMP, MNE); acquisition of data (CLD); analysis and interpretation of data (MES, SMP, CLD, AH, MK, AT, MM, MNE); drafting of the manuscript (MES, SMP, CLD, AH, MK, AT, MM); critical revision of the manuscript for important intellectual content (MNE); statistical analysis (SMP, AH, MK, AT, MM); obtaining funding (SMP); administrative, technical, or logistic support (SMP); and supervision (MES, SMP, CLD).

Address Correspondence to: Melony E. Sorbero, PhD, MS, MPH, RAND Corporation, 4570 Fifth Ave, Ste 600, Pittsburgh, PA 15213. Email:

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