OBJECTIVES We aimed to describe the experience of a state Medicaid agency incentivizing reduction of racial and ethnic disparities in a hospital quality incentive program (QIP). STUDY DESIGN Retrospective review of a decade of experience implementing a hospital health disparity (HD) composite measure. METHODS Observational analysis of programwide trends in missed opportunity rates and between-group variance (BGV) for the HD composite from 2011 to 2020 and subanalysis of 16 metrics included in the HD composite for at least 4 years over the decade. RESULTS Programwide missed opportunity rates and BGV fluctuated widely from 2011 to 2020, likely due to variation in measures included in the HD composite. When the 16 measures that were included in the HD composite for at least 4 years were collapsed into a hypothetical 4-year period, missed opportunity rates decreased across the 4 consecutive years, from 47% in year 1 to 20% in year 4. Differences among racial and ethnic subgroups also decreased across the 4-year period, as reflected in the BGV decrease from 7.85 × 10-4 in year 1 to 5.10 × 10-4 in year 4. CONCLUSIONS Construction of a composite measure, use of a summary disparity statistic, and measure selection are key considerations in the design and interpretation of equity-focused payment programs. This analysis revealed improved aggregate quality performance and a modest reduction in racial and ethnic disparities for measures included in the HD composite for at least 4 years. Further research is needed to evaluate the association between equity-oriented incentives and health disparities.

Population and Quantitative Health Science and ForHealth Consulting, UMass Chan Medical School, Worcester, MA The opinions expressed do not necessarily reflect those of any Medicaid or other government agency or any provider system. C.F. and J.D. are employed by ForHealth Consulting at UMass Chan Medical School and serve as Deputy Chief Medical Officer (C.F.) and Chief Medical Officer (J.D.) for MassHealth, the Massachusetts Medicaid Program. J.D. is also a part-time palliative care clinician at Tufts Medical Center and an adjuvant assistant professor of medicine at Tufts School of Medicine. Both are also faculty members within the Department of Population and Quantitative Health Sciences at UMass Chan Medical School. The authors declare no conflict of interest. Correspondence to: Jatin Dave, MBBS, MPH, ForHealth Consulting, UMass Chan Medical School, 333 South Street, Shrewsbury, MA 01545. E-mail: [email protected].

Rural beneficiaries make up nearly one quarter of the Medicare population, yet rural providers and patients face specific challenges with health and health care delivery that remain inadequately understood. Health disparities between rural and urban residents are widespread, barriers to health care in rural communities persist, and the rural health care workforce is limited. To better understand and track the relationship between rurality and performance under Medicare’s payment programs, researchers must be able to identify rural beneficiaries, providers, and hospitals. Although numerous definitions of rurality are applied across the Medicare program, empirical research is lacking comparing the different definitions of rurality and the impact of their application to quality, outcome, or costs. Definitions that recognize rurality as a graded concept, rather than a dichotomous one, hold promise. Understanding the strengths and limitations of different approaches to identifying rurality will help researchers choose the best method for their particular purpose, and help policymakers interpret studies using these approaches.

Disparities by economic status are observed in the health status and health outcomes of Medicare beneficiaries. For health services and health policy researchers, one barrier to addressing these disparities is the ability to use Medicare data to ascertain information about an individual's income level or poverty, because Medicare administrative data contains limited information about individual economic status. Information gleaned from other sources-such as the Medicaid and Supplemental Security Income programs-can be used in some cases to approximate the income of Medicare beneficiaries. However, such information is limited in its availability and applicability to all beneficiaries. Neighborhood-level measures of income can be used to infer individual-level income, but level of neighborhood aggregation impacts accuracy and usability of the data. Community-level composite measures of economic status have been shown to be associated with health and health outcomes of Medicare beneficiaries and may capture neighborhood effects that are separate from individual effects, but are not readily available in Medicare data and do not serve to replace information about individual economic status. There is no single best method of obtaining income data from Medicare files, but understanding strengths and limitations of different approaches to identifying economic status will help researchers choose the best method for their particular purpose, and help policymakers interpret studies using measures of income.

IMPORTANCE Medicare recently launched the Physician Value-Based Payment Modifier (PVBM) Program, a mandatory pay-for-performance program for physician practices. Little is known about performance by practices that serve socially or medically high-risk patients.OBJECTIVE To compare performance in the PVBM Program by practice characteristics.DESIGN, SETTING, AND PARTICIPANTS Cross-sectional observational study using PVBM Program data for payments made in 2015 based on performance of large US physician practices caring for fee-for-service Medicare beneficiaries in 2013.EXPOSURES High social risk (defined as practices in the top quartile of proportion of patients dually eligible for Medicare and Medicaid) and high medical risk (defined as practices in the top quartile of mean Hierarchical Condition Category risk score among fee-for-service beneficiaries).MAIN OUTCOMES AND MEASURES Quality and cost z scores based on a composite of individual measures. Higher z scores reflect better performance on quality; lower scores, better performance on costs.RESULTS Among 899 physician practices with 5 189 880 beneficiaries, 547 practices were categorized as low risk (neither high social nor high medical risk) (mean, 7909 beneficiaries; mean, 320 clinicians), 128 were high medical risk only (mean, 3675 beneficiaries; mean, 370 clinicians), 102 were high social risk only (mean, 1635 beneficiaries; mean, 284 clinicians), and 122 were high medical and social risk (mean, 1858 beneficiaries; mean, 269 clinicians). Practices categorized as low risk performed the best on the composite quality score (z score, 0.18 [95% CI, 0.09 to 0.28]) compared with each of the practices categorized as high risk (high medical risk only: z score, -0.55 [95% CI, -0.77 to -0.32]; high social risk only: z score, -0.86 [95% CI, -1.17 to -0.54]; and high medical and social risk: -0.78 [95% CI, -1.04 to -0.51]) (P < .001 across groups). Practices categorized as high social risk only performed the best on the composite cost score (z score, -0.52 [95% CI, -0.71 to -0.33]), low risk had the next best cost score (z score, -0.18 [95% CI, -0.25 to -0.10]), then high medical and social risk (z score, 0.40 [95% CI, 0.23 to 0.57]), and then high medical risk only (z score, 0.82 [95% CI, 0.65 to 0.99]) (P < .001 across groups). Total per capita costs were $9506 for practices categorized as low risk, $13 683 for high medical risk only, $8214 for high social risk only, and $11 692 for high medical and social risk. These patterns were associated with fewer bonuses and more penalties for high-risk practices.CONCLUSIONS AND RELEVANCE During the first year of the Medicare Physician Value-Based Payment Modifier Program, physician practices that served more socially high-risk patients had lower quality and lower costs, and practices that served more medically high-risk patients had lower quality and higher costs.

Racial and ethnic disparities are observed in the health status and health outcomes of Medicare beneficiaries. Reducing these disparities is a national priority, and having high-quality data on individuals' race and ethnicity is critical for researchers working to do so. However, using Medicare data to identify race and ethnicity is not straightforward. Currently, Medicare largely relies on Social Security Administration data for information about Medicare beneficiary race and ethnicity. Directly self-reported race and ethnicity information is collected for subsets of Medicare beneficiaries but is not explicitly collected for the purpose of populating race/ethnicity information in the Medicare administrative record. As a consequence of historical data collection practices, the quality of Medicare's administrative data on race and ethnicity varies substantially by racial/ethnic group; the data are generally much more accurate for whites and blacks than for other racial/ethnic groups. Identification of Hispanic and Asian/Pacific Islander beneficiaries has improved through use of an imputation algorithm recently applied to the Medicare administrative database. To improve the accuracy of race/ethnicity data for Medicare beneficiaries, researchers have developed techniques such as geocoding and surname analysis that indirectly assign Medicare beneficiary race and ethnicity. However, these techniques are relatively new and data may not be widely available. Understanding the strengths and limitations of different approaches to identifying race and ethnicity will help researchers choose the best method for their particular purpose, and help policymakers interpret studies using these measures.