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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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Study Population

We used patient-level data from the 2014 star ratings (measurement year 2012) to assess the relationship of contract performance with SES (ie, DE/LIS) and disability and to develop the CAI. The 2014 star ratings used 48 Part D (prescription drug) and Part C (health plan) measures to rate MA prescription drug contracts, 36 Part C measures to rate MA-only contracts, and 15 Part D measures to rate PDPs. Analyses included all MA and PDP contracts eligible for star ratings. We excluded Puerto Rico contracts from analyses due to program differences.

Study Variables

Performance measures. We examined the effect of SES and disability adjustment for 16 (13 Part C and 3 Part D) clinical measures (Table 1; see eAppendix for description). We excluded from evaluation those measures that were already adjusted for SES (n = 10 measures), being retired or revised (n = 6), used only for Special Needs Plans (SNPs; n = 3), addressing plan-level customer service (n = 12), or under direct provider or plan control (n = 1; high-risk medication). Measures were coded to indicate whether the beneficiary received the recommended care or achieved the measured outcome (0 = no; 1 = yes).

Low SES. Beneficiaries were classified as low SES if they were partially or fully dually eligible for Medicare and Medicaid as of December 2012 or if they applied and were approved for an LIS.

Disability. Beneficiaries were classified as disabled based on their original reason for Medicare eligibility.

Regression Analysis

NQF recommends considering adjustment for within-provider disparities (the extent to which low-SES patients receive lower-quality care than high-SES patients within the same provider) while preserving between-provider differences in performance (the extent to which all patients of a given provider receive lower-quality care than others). Consistent with this recommendation, we assessed average within-contract DE/LIS disparities for each of the 16 measures by fitting logistic regressions predicting performance from the DE/LIS indicator, using fixed effects for MA and PDP contracts to control for between-contract performance differences (see eAppendix for additional detail). A sensitivity test examined the effect on DE/LIS and disabled effects after adjusting for Census-based SES characteristics (block group–level education and income/poverty; see eAppendix for additional detail). We performed similar analyses for the disability indicator.

Contract-level variation in disparity in performance for DE/LIS versus non-DE/LIS beneficiaries was estimated in percentage points using a linear mixed effects model that included DE/LIS as a predictor, mean-centered at the contract level, and random effects for contract and the contract-by-DE/LIS interaction, using empirical best linear unbiased predictions (BLUPs) to account for sampling error in contract-level disparity estimates. As expected from the sample sizes,20 results were insensitive to normality assumptions (not shown). We performed similar analyses for the disability indicator.

Categorical Adjustment Index

The CAI adjustment factor is applied to groups of contracts. Each contract is assigned to an adjustment group based on the percentage of its beneficiaries who are DE or LIS or disabled. The measure subset selected by CMS for the 2017 star ratings CAI is limited to measures with large and/or consistent within-contract disparities, as determined by measures for which the within-contract DE/LIS disparities based on BLUPs were large (median absolute difference in performance of 5 or more percentage points between DE/LIS and non-DE/LIS enrollees) or consistent (DE/LIS performed worse/better than non-DE/LIS enrollees in all contracts) in the 2012 measurement year data. Adjusted scores for the CAI measure subset were derived from logistic regression with contract fixed effects, DE/LIS, and disabled status as righthand-side variables. An adjusted overall star rating for each contract was simulated based on these adjusted measure scores plus all other star rating measure scores. The value of the CAI adjustment factor was computed as the average difference between contract-level adjusted and unadjusted overall star ratings within each CAI adjustment group. To simulate the effect of DE/LIS and disability adjustment on star ratings, we applied the CAI to the star ratings separately for MA and PDP contracts using the 2015 measurement year data (2017 star ratings). The results are summarized for contracts overall and stratified by contract percentage of DE/LIS beneficiaries (<50% DE/LIS and ≥50% DE/LIS).

The study was approved by RAND Corporation’s Human Subjects Protection Committee.

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