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Introduction

The Centers for Medicare & Medicaid Services (CMS) is increasingly paying providers (e.g., hospitals, health plans, provider groups) through value-based payment (VBP) programs.¹ VBP ties quality and cost performance to payment in order to hold providers accountable for the quality and efficiency of the health care they provide and for the health care outcomes they achieve (Burwell, 2015; Rosenthal, 2008). In so doing, VBP schemes shift greater financial risk to providers. Because current VBP programs do not account for social risk factors for poor health outcomes, these programs may underestimate the quality of care provided by providers disproportionally serving socially at-risk populations. Consequently, these providers may be more likely to fare poorly on quality rankings (Berenson and Shih, 2012; Elliott et al., in press; Gilman et al., 2014, 2015; Joynt and Jha, 2013a; Rajaram et al., 2015; Shih et al., 2015; Williams et al., 2014). When payment is tied to quality rankings under VBP, these providers may also be more likely to receive penalties and less likely to receive incentive payments (Chien et al., 2007; Joynt and Jha, 2013a,b; Joynt and Rosenthal, 2012; Ryan, 2013). Moreover, these providers have historically been less well reimbursed than providers serving more advantaged patients and have fewer resources (Bach et al., 2004; Chien et al., 2007). If providers disproportionately serving socially at-risk populations have fewer resources to begin with and are more likely to fare poorly on quality rankings and receive financial penalties under VBP, the limited resources to care for socially at-risk populations and those who care for them may be further reduced. This has led some stakeholders to raise concerns that current VBP programs may increase health disparities (Bhalla and Kalkut, 2010; Casalino et al., 2007; Chien et al., 2007; Friedberg et al., 2010; Ryan, 2013).

A primary method proposed to address these concerns has been to account for social risk factors in quality measurement and payment programs, including VBP. Proponents of such methods view social risk factors as difficult to address through provider actions and may also

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¹ As described in the committee’s first and third reports (NASEM, 2016a,b), CMS payment models cover a spectrum of approaches from traditional fee-for-service to population-based payment models. The committee uses the term value-based payment to describe models that fall into two broad categories, which the committee roughly categorizes as financial incentives and alternative payment models (APMs). Financial incentives (such as pay-for-performance schemes) link financial bonuses and/or penalties to the quality and efficiency of care, whereas APMs (such as episode- or population-based payments) shift greater financial risk to providers in order to hold them accountable for the quality and efficiency of care delivered as well as for the health care outcomes achieved. For more information on specific Medicare VBP programs, the committee points the interested reader to its first and third reports (NASEM, 2016a,b).

believe that the costs of addressing social risk factors are high. Thus, they suggest that social risk factors must be accounted for in VBP even if it is appropriate to expect providers to address social risk factors. Opponents are concerned that methods like risk adjustment could obscure real disparities and thereby reduce incentives to improve care and reduce health disparities. Thus, they might argue that providers disproportionately serving socially at-risk populations should be held responsible for providing services in a manner that compensates for social risk factors. For a more extensive discussion of these concerns, the committee directs the interested reader to its first three reports (NASEM, 2016a,b,c). As described in the committee’s third report (NASEM, 2016b), to the extent that social risk factors influence performance indicators independently of provider actions and those factors are unevenly distributed across providers, it may be appropriate to account for social risk factors in VBP (NASEM, 2016b). However, any specific approach to accounting for social risk factors in Medicare quality and payment programs requires continuous monitoring to ensure the absence of any unanticipated adverse effects on health disparities (NASEM, 2016b). If CMS proceeds with accounting for social risk factors, doing so first requires accurate data on the social risk factors of Medicare beneficiaries.

STATEMENT OF TASK

In response to the Improving Medicare Post-Acute Care Transformation (IMPACT) Act of 2014, the Department of Health and Human Services acting through the Office of the Assistant Secretary for Planning and Evaluation (ASPE) contracted with the National Academies of Sciences, Engineering, and Medicine to convene an ad hoc committee to provide a definition of socioeconomic status for the purposes of application to Medicare quality measurement and payment programs; identify the social factors that have been shown to impact health outcomes of Medicare beneficiaries; specify criteria that could be used in determining which social factors should be accounted for in Medicare quality measurement and payment programs; identify methods that could be used in the application of these social factors to quality measurement and/or payment methodologies; and recommend existing or new sources of data and/or strategies for data collection. The committee comprises expertise in health care quality, clinical medicine, health services research, health disparities, social determinants of health, risk adjustment, and Medicare programs (see the Appendix A for biographical sketches). This report is the fourth in a series of five brief reports that aim to inform ASPE analyses that account for social risk factors in Medicare payment programs mandated through the IMPACT Act. In the first report, the committee presented a conceptual framework and described the results of a literature search linking five social risk factors and health literacy to health-related measures of importance to Medicare quality measurement and payment programs. In the second report, the committee reviewed the performance of providers disproportionately serving socially at-risk populations, discussed drivers of variations in performance, and identified six community-informed and patient-centered systems practices that show promise to improve care for socially at-risk populations. The committee’s third report identified social risk factors that could be considered for inclusion in Medicare quality measurement and payment, criteria to identify these factors, and methods to account for them in ways that can promote health equity and improve care for all patients. Details of the statement of task and the sequence of reports can be found in Box 1-1. The committee will release reports every 3 months, addressing each item in the statement of task in turn.

COMMITTEE PROCESS

This report builds on the committee’s earlier reports. In particular, the committee presented a conceptual framework by which five social risk factors (socioeconomic position [SEP]; race, ethnicity, and cultural context; gender; social relationships; and neighborhood and residential context) and health literacy may influence performance indicators used in VBP in its first report (NASEM, 2016a). In the committee’s third report, the committee expanded the conceptual framework to include specific indicators across the five domains of social risk factors. Indicators are ways to measure the underlying constructs of the social risk factors and are distinct from individual measures. For example, education is an indicator of SEP that can be measured in different ways (e.g., years of schooling, highest degree attained). The committee also identified criteria that could be used to select social risk factors that should be included in Medicare quality measurement and payment programs, and then applied these criteria to indicators of the social risk factors and health literacy. Based on this activity, the committee concluded that there are measurable social risk factors that could be accounted for in Medicare VBP programs in the short term, for which indicators include

• income, education, and dual eligibility;
• race, ethnicity, language, and nativity;
• marital/partnership status and living alone; and
• neighborhood deprivation, urbanicity, and housing.

The committee also concluded that some indicators of social risk factors capture the underlying constructs and currently present practical challenges, but they are worth attention for potential inclusion in accounting methods in Medicare VBP programs in the longer term. These include

• wealth,
• acculturation,
• gender identity and sexual orientation,
• emotional and instrumental social support, and
• environmental measures of residential and community context.

In this report, the committee provides guidance on data sources for and strategies to collect data on the indicators that could be included in Medicare quality measurement and payment programs that the committee identified in its third report. Chapter 2 describes three general categories of data sources the committee considered—existing and new sources of CMS data, data sources from providers, and alternative government data sources. Chapter 2 also describes general advantages of and barriers to using each data source. Chapter 3 then presents guiding principles the committee used to assess each potential data source for each social risk factor indicator as well as the specific potential data sources that could be used for each indicator along with their advantages and disadvantages. Chapter 3 closes with general conclusions for CMS in its approach to collecting social risk factor data for use in Medicare quality measurement and payment.

REFERENCES

Shih, T., A. M. Ryan, A. A. Gonzalez, and J. B. Dimick. 2015. Medicare’s hospital readmissions reduction program in surgery may disproportionately affect minority-serving hospitals. Annals of Surgery 261(6):1027-1031.

Williams, K. A., Sr., A. A. Javed, M. S. Hamid, and A. M. Williams. 2014. Medicare readmission penalties in Detroit. New England Journal of Medicine 371(11):1077-1078.