Accounting for Social Risk Factors in Medicare Payment (2017) / Chapter Skim
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2 Social Risk Factors: Definitions and Data
2 Social Risk Factors: Definitions and Data
Pages 47-76
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From page 47... ...
. Thus, that report provides a conceptual basis and empirical evidence that social risk factors could affect performance indicators used in Medicare value-based payment (VBP)
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The committee notes that the listing of social risk factors does not reflect an order of priority. Socioeconomic Position1 Socioeconomic position (SEP)
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In the short term, CMS should use available area-level income data from the American Community Survey as a proxy for individual income. In the longer term, CMS should explore the feasibility of linking to SSA income data from the uncapped Medicare payroll tax and/or develop standardized measurements and methods for new data collection.3 Education Education is important for health because it shapes future employment and economic resources (Adler and Newman, 2002; IOM, 2014; NASEM, 2016a)
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For the indicators listed in bullets under each social risk factor, bold lettering denotes measurable indicators that could be accounted for in Medicare VBP programs in the short term; italicized lettering denotes measurable indicators that capture the basic underlying constructs and currently present practical challenges, but are worth attention for potential inclusion in accounting methods in Medicare VBP programs in the longer term; and plain lettering denotes indicators that have considerable limitations. a As described in Figure 1-1, health care use captures measures of utilization and clinical processes of care; health care outcomes capture measures of patient safety, patient experience, and health outcomes; and resource use captures cost measures.
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Thus, in the short term, CMS should use these available area-level measures as a proxy for individual education. In the long term, because education is relatively stable for Medicare beneficiaries, CMS should develop standardized measures and methods to collect education data.3 Dual Eligibility In health research, numerous studies assess the effects of insurance coverage on health status (see, for example, IOM, 2009a)
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, and therefore are useful for examining the effect of wealth on health care outcomes. However, as described in the committee's third report (see Appendix C)
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. Because no data sources are available for use in the short term, CMS should conduct more research on both measurement and data collection methods on wealth by CMS or through EHRs.5 In particular, CMS may want to consider the empirical question of whether the addition of wealth data adds sufficient precision above and beyond income data, for which some data is already available and for which methods and measures exist to collect data with less burden, to warrant additional data collection for inclusion in any method to account for social risk factors in Medicare quality measurement and payment.
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Race and Ethnicity Categories of race and ethnicity capture a range of health-relevant dimensions, especially those related to social disadvantage. Race and ethnicity are strongly associated with health and health care outcomes, even after accounting for measures of SEP (Krieger, 2000; LaVeist, 2005; NASEM, 2016a; Williams, 1999; Williams et al., 2010)
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This includes deaf American Sign Language users. Language barriers are strongly associated with health and health care outcomes -- in particular, poorer access to health care, poorer health status, poorer quality care, including less recommended care, and more adverse health events (NASEM, 2016a)
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In the short term, CMS should use its existing data on preferred language while acknowledging its limitations. In the long term, CMS should continue efforts to standardize measures and data collection methods.8 A 2009 IOM report provides guidance on standardization of race, ethnicity, and language data (IOM, 2009b)
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Thus, normative gender is a strong candidate for inclusion in methods to account for social risk factors in Medicare quality measurement and payment programs. However, the committee notes that gender is already included as a risk factor in clinical risk adjustments in Medicare.
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Taken together, like gender identity, emerging literature supports a relationship between sexual orientation and health care outcomes of interest, but poor existing measures have limited available evidence. Although some measures and best practices for data collection exist and CMS has included data collection of sexual orientation and gender identity in its Equity Plan for Improving Quality in Medicare, there are currently no standards for measuring and collecting data on sexual orientation and gender identity (CMS Office of Minority Health, 2015)
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If so, validated measures of partnership exist in the literature, but CMS would need to develop standardized measures and data collection methods for its own collection or provider/plan reporting requirements. An important consideration for the longer term are ongoing demographic shifts in family structure, including the federal Supreme Court ruling making same-sex marriage legal nationally.13 It will be important for CMS to monitor the empirical association between marital/partnership status and health care outcomes and revisit assumptions about marital/ partnership status as an indicator of social support over time.
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Thus, there are no data sources that could be used in the short term. However, for the long term, because living arrangements can change rapidly, especially for older adults and because living alone is clinically useful, living alone may best be captured in the clinical setting, and CMS should develop standardized measures and methods for data collection through EHRs.15 Social Support Social support is a key function of social relationships and includes emotional elements (such as through caring and concern)
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Thus, there are no data sources that could be used in the short term. However, for the long term, because social support can change rapidly especially among older adults and because it is clinically useful, it may best be captured in the clinical setting, and CMS should develop standardized measures and methods for data collection through EHRs.16 Residential and Community Context Residential and community context captures a set of broadly defined characteristics of residential environments, including compositional characteristics that describe aggregate characteristics of individuals residing in a given neighborhood or community, as well as characteristics of social and physical environments.
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Although residential addresses are available from providers, plans, and Medicare records, the latter is preferable, because these are the data CMS already possesses. Neighborhood deprivation can be assessed using a single-item measure such as median household income or using a multi-item composite measure.
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. For the purpose of inclusion in Medicare performance measurement and payment, the urbanicity or rurality of a beneficiary's place of residence is likely to be a more salient indicator of his or her social risk factors than a provider's location.
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Thus, in the short term, the committee recommends that CMS test area-level measures of housing based on a beneficiary's residential address in the Medicare record and contrast their performance. Because other elements of housing, in particular, physical characteristics, occur at the individual level, and these are likely to change over time, individual-level housing data could be collected through EHRs in the long term, but more research is needed on measurement and data collection methods.18 17 See Recommendation 2 in the committee's fourth report (NASEM, 2016c)
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Therefore, CMS should revisit such environmental measures and their appropriate measurement when more evidence is available.19 Table 2-1 summarizes the preceding discussion regarding availability of data for social risk factor indicators. GENERAL CONSIDERATIONS Upon release of the committee's prior reports, several questions arose about the placement of specific risk factors in the committee's framework (in the first report)
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TABLE 2-1 Summary of Data Availability for Social Risk Factor Indicators 67
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However, its predictive power for any given performance indicator used in VBP in a model that accounts for social risk factors in Medicare quality measurement and payment relative to the predictive power of other indicators of SEP is an empirical question that is beyond the scope of the committee's task. For example, to determine the extent to which adjustment for dual eligibility accounts for the adjustment that would occur with a broader set of social risk factors not limited to dual eligibility, CMS could compare the variance in scores and payments from a fully adjusted model to the variance of adjustments from a model adjusting only for dual eligibility.
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Disability The committee considers disability to be a proximal risk factor for poor health care outcomes that is influenced by more distal social risk factors, somewhat like health literacy. The committee recognizes that disability is in part socially determined (IOM, 2007)
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. Additionally, the clinical risk factors that entitle an individual to Medicare benefits are captured in existing clinical risk adjustments for Medicare VBP programs through major clinical diagnoses associated with disability, such as stroke, schizophrenia, or multiple sclerosis.
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2000. Social integration, social networks, social support, and health.
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2013. Behavioral Risk Factor Surveillance System questionnaire.
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2015. Sexual minorities in England have poorer health and worse health care experiences: A national survey.
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2016a. Accounting for social risk factors in Medicare payment: Identifying social risk factors.
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2013a. Race, ethnicity, and language data collection by health plans: Findings from 2010 AHIPF-RWJF survey.
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2012. The validity of race and ethnicity in enrollment data for Medicare beneficiaries.
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