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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
Currently Reading
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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ABSTRACT

Objectives: Studies have identified potential unintended effects of not adjusting clinical performance measures in value-based purchasing programs for socioeconomic status (SES) factors. We examine the impact of SES and disability adjustments on Medicare Advantage (MA) plans’ and prescription drug plans’ (PDPs’) contract star ratings. These analyses informed the development of the Categorical Adjustment Index (CAI), which CMS implemented with the 2017 star ratings.

Study Design: Retrospective analyses of MA and PDP performance using 2012 Medicare beneficiary-level characteristics and performance data from the Star Rating Program.

Methods: We modeled within-contract associations of beneficiary SES (Medicaid and Medicare dual eligibility [DE] or receipt of a low-income subsidy [LIS]) and disability with performance on 16 clinical measures. We estimated variability in contract-level DE/LIS and disability disparities using mixed-effects regression models. We simulated the impact of applying the CAI to adjust star ratings for DE/LIS and disability to construct the 2017 star ratings.

Results: DE/LIS was negatively associated with performance for 12 of 16 measures and positively associated for 2 of 16 measures. Disability was negatively associated with performance for 11 of 15 measures and positively associated for 3 of 15 measures. Adjusting star ratings using the CAI resulted in half-star rating increases for 8.5% of MA and 33.3% of PDP contracts that exceeded 50% DE/LIS beneficiaries.

Conclusions: Increases in star ratings following adjustment of clinical performance for SES and disability using the CAI focused on contracts with higher percentages of DE/LIS beneficiaries. Adjustment for enrollee characteristics may improve the accuracy of quality measurement and remove incentives for providers to avoid caring for more challenging patient populations.

Am J Manag Care. 2018;24(9):e285-e291
Takeaway Points

CMS implemented the Categorical Adjustment Index (CAI) as part of the Medicare Advantage and Part D Star Rating Program in 2017. These analyses informed its development.
  • Low socioeconomic status and disability are negatively associated with performance for most measures included in the Medicare Advantage Star Rating Program, controlling for between-contract effects.
  • The CAI most benefits contracts with at least 50% of enrollees who are dually eligible for Medicare and Medicaid or receive a Part D low-income subsidy.
  • Adjusting the star ratings using the recently implemented CAI changes the star ratings for a modest number of contracts by half a star.
Policy makers use quality measurement, public accountability, and financial incentives to induce health plans and providers to improve performance. Frequently, the process-of-care and intermediate outcome measures on which plans and providers are evaluated are not adjusted for differences in patient socioeconomic status (SES) across plans and providers. Pay-for-performance programs that do not account for differences in patients across providers in their quality measurement risk reducing funding to providers that treat medically complex, disabled, and socioeconomically disadvantaged patients, potentially reinforcing or exacerbating existing SES disparities, as more resources may be required to achieve high quality for such patients.1-3 Moreover, providers caring for disadvantaged patients tend to have fewer resources available to invest in quality improvement due to lower reimbursement rates compared with providers with predominantly commercially insured patients.1,4-7

Low-SES individuals receive recommended care less often and experience worse health outcomes than those with higher SES,2,8,9 possibly because they have greater health burdens and barriers to care, including limited transportation and lower health literacy,10-13 which may discourage providers from treating disadvantaged patients. A recent examination of associations between social risk factors (dual eligibility [DE] for Medicare and Medicaid, black race, Hispanic ethnicity, disability, and rural residence) and performance on quality measures included in 9 Medicare value-based purchasing programs found that beneficiaries with social risk factors received recommended care less often.14 Providers disproportionately serving high-risk beneficiaries performed worse on average, even after controlling for beneficiary differences.

Policy makers have identified closing these quality gaps as a key policy priority.15 One approach to accomplish this is to adjust quality measures for socioeconomic factors. The National Quality Forum (NQF)16 and HHS14 have called on sponsors of value-based measurement and payment programs to determine whether quality measures should be adjusted for differences in providers’ patient mix. The National Academies of Sciences, Engineering, and Medicine developed criteria for determining which social risk factors to address.8,9

Although adjustment of process-of-care and intermediate outcome measures for socioeconomic factors is rare, the use of SES-adjusted patient experience measures in the Medicare Advantage (MA), prescription drug plan (PDP), and Hospital Consumer Assessment of Healthcare Providers and Systems surveys are examples of nationwide implementation of such adjustment.17 Clinical measures in the Medicare Star Ratings Program are not currently adjusted for SES.

Annually, Medicare computes star ratings based on MA and PDP contract performance on clinical, patient experience, customer service, and complaint measures. The star ratings are reported on Medicare Plan Finder,18 determine MA quality-based bonus payments, and affect MA rebates and enrollment (see eAppendix [available at ajmc.com] for star ratings description). To address concerns that the star ratings disadvantage contracts serving low-SES and disabled beneficiaries, CMS implemented a Categorical Adjustment Index (CAI)19 beginning with the 2017 star ratings as an interim policy until measure developers evaluate which clinical measures should be adjusted. The CAI approximates the effect of case mix on star ratings, adjusting the underlying clinical measures for SES characteristics available in CMS administrative data (DE/low-income subsidy [LIS]) and disabled status.

We present analyses that informed the development of the CAI. Our analyses addressed 3 questions: (1) Do within-contract SES and disability performance disparities exist for clinical measures used in the Medicare Star Ratings Program?, (2) How consistent are within-contract disparities across contracts?, and (3) How does adjusting for SES differences affect the overall star rating of MA and PDP contracts, particularly for contracts serving a large portion of beneficiaries who are dually eligible for Medicare and Medicaid, receive a Part D LIS, or are disabled?


 
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