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; Ryan, 2013; 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 is accounting for social risk factors in VBP. For an extensive discussion of concerns regarding possible effects of these approaches, 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
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).
VBP, but any approach requires monitoring for adverse effects on health disparities (NASEM, 2016b). If CMS chooses to account for social risk factors, it must first acquire 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 (HHS) 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 (see the Appendix 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. Details of the statement of task and the sequence of reports can be found in Box 1-1.
This report builds on the committee’s earlier reports. In its third report, the committee expanded the conceptual framework introduced in the first report to include specific indicators across five domains of social risk factors. The committee concluded that there are measurable social risk factors that could be accounted for in Medicare VBP programs in the short term. Indicators include
- income, education, and dual (Medicare and Medicaid) 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 basic underlying constructs and currently present practical challenges, but they are worth attention for potential inclusion in the longer term. These include
- 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 these indicators that could be accounted for Medicare quality measurement and payment programs.
The committee considered three broad categories of data sources for these social risk factors: (1) existing or new CMS data; (2) data that providers and plans could report to CMS; and (3) alternative government data. Patients are the underlying source of most social risk factor data. Moreover, for some social risk factors like race, ethnicity, and gender, it is important for patients to self-identify. However, CMS, health care providers and health plans, and government agencies collect and maintain this information and, more importantly, standardize, assess, interpret, and report this information in a valid, consistent, and reliable way. In the future, new, better, and easier methods of data collection could emerge (e.g., methods that are more accurate, less burdensome, or less costly). As these new methods emerge, an ideal system would be responsive to evolving data availability and could adapt to use new data sources. However, at this time and likely in the near term, it is unlikely that technologies and interoperable systems will be available for patients to directly, systematically, and securely submit social risk factor data to CMS for use in Medicare payment. Thus, although patients and enrollees underlie each of the three categories of data sources described above, they are not called out as a separate and unique source.
New and Existing Sources of CMS Data
CMS possesses some data on Medicare beneficiaries’ social risk factors. Existing sources include administrative records and beneficiary surveys. Administrative records include enrollment records as well as claims data. These sources have limited social risk factor data, such as beneficiaries’ race and ethnicity (ResDAC, n.d.). Enrollment data capture the basis of a beneficiary’s entitlement, which plans beneficiaries are enrolled in (Parts A, B, C, D, or alternative payment models), as well as Medicaid enrollment for those who are dually enrolled in Medicare and Medicaid (ResDAC, n.d.). Administrative records also include basic demographic information and vital statistics. Survey data from CMS refer to data derived from one of the surveys of Medicare beneficiaries that CMS routinely conducts.
The primary advantage of using existing sources of data CMS already possesses is precisely that CMS has access to and maintains accurate data it already collects using standardized measures and validated, reliable methods, and which it could apply to performance measurement and payment. If CMS collects new social risk factor data, it could design measures and data collection methodologies to ensure collection of accurate data that meet the needs of the intended method to account for those social risk factors in Medicare quality measurement and payment. New data collection would not be subject to the potentially substantial barriers of collaborating with other federal government agencies, but it would require substantial cost.
Data Sources from Providers and Plans
Data sources from providers and plans include data from electronic health records (EHRs) and administrative data that providers report or submit or could report or submit to CMS. Most EHRs capture some basic information on social risk factors, such as race and ethnicity, and EHRs are beginning to capture more robust social risk factor data. Some more comprehensive EHRs may include data on language, education, housing, and community context (Gottlieb et al., 2015; ONC, n.d.). The Office of the National Coordinator for Health Information Technology (ONC) is the office responsible for supporting and encouraging EHR adoption and health information exchange in HHS. To date, ONC has included some social risk factors in the regulations put forth for the CMS meaningful use incentive programs. Administrative data include data captured through patient enrollment forms and claims, and may also include limited social risk factor data. For example, many health plans collect language data (Lawson et al., 2011; Nerenz et al., 2013a,b), and these data could be reported to CMS for use in performance measurement and payment.
A primary advantage of using data that providers or health plans collect is that some information on social risk factors may be clinically useful to enhance the care or services providers and plans provide. Additionally, CMS already has a reporting infrastructure for claims and performance reporting with standardized reporting requirements, processes, and systems that it could expand. However, collecting social risk factor data through EHRs could increase burdens on individual providers and health care organizations, as well as on patients.
Burdens on patients and enrollees pertain to the ability of patients to recall information about their social risks as well as privacy and security. With respect to the former, patients and enrollees may not know or be willing to share data on certain social risk factors that are sensitive in nature. Concerns about why clinicians or plans are asking about social risk factors and how such data may be used relate to concerns about the privacy and security of patient health information, especially when shared with other providers and with researchers and administrators for nonclinical uses. For a more comprehensive discussion of privacy and security issues as well as mitigation strategies, the committee points the interested reader to the Institute of Medicine’s (IOM’s) earlier reports on EHRs and health information technology (IOM, 2012, 2014).
Alternative Government Data Sources
Alternative government data sources in this report refer to administrative data and national surveys that federal agencies other than CMS (including other agencies within HHS) and state agencies oversee and maintain and that could be linked to Medicare beneficiary data. This includes data that could be linked to Medicare beneficiary data at the individual level, area-level data that could be used to describe a Medicare beneficiary’s residential environment or serve as a proxy for individual effects, and data that could help CMS to determine how to elicit information on social risk factors from Medicare beneficiaries.
The Social Security Administration (SSA) may be the best source of individual-level social risk factor data that could be linked to Medicare data. The SSA maintains data that captures demographics, vital statistics, income, and information related to eligibility for Social Security needs-based benefits, such as disabling conditions and living arrangements (McNabb et al., 2009). The American Community Survey (ACS) may be a useful source of area-level social risk factor data that could be used to assess genuine area-level effects or serve as proxies for individual-level effects. The ACS is a nationwide survey administered by the Census Bureau that
gathers demographic, housing, social, and economic data on local communities (U.S. Census Bureau, 2013). Other national surveys include the Health and Retirement Survey, National Health and Aging Trends Study, National Health and Nutrition Examination Survey, National Health Interview Survey, and National Survey of Family Growth. They all capture social risk factor data that could be useful to CMS when determining how best to elicit information from Medicare beneficiaries on their social risk factors.
The primary advantage of using administrative and survey data from other agencies is that these data sources contain substantial information on social risk factors, and data from these sources are collected using standardized and validated measures and methodologies. However, substantial barriers to linking such data to Medicare data include state and federal regulations and laws relating to the privacy and security that may restrict data sharing (IOM, 2014) and the substantial effort and/or cost required to ensure that data can be linked at the appropriate level.
The committee notes that it has not been asked to recommend whether the CMS should include social risk factor adjustments in its public reporting and payment programs. The recommendations in this report indicate things CMS should do if it decides to move toward accounting for social risk factors. To assess the advantages and disadvantages of specific data sources for specific social risk factor indicators, the committee identified three characteristics to consider: (1) collection burden, (2) accuracy, and (3) clinical utility. Collection burden describes the resources (e.g., time, cost, and effort) required to collect and store data through any given source, and pertains to respondents, as well as providers collecting data, and CMS. For some social risk factors, there may be substantial barriers to data collection (such as high cost). For others, early pilot testing or modeling of an indicator in a multivariable model may suggest only marginal gains. In these cases, CMS may choose not to include the indicator in quality measurement and payment. Because literature does not indicate whether all social risk factors related to performance indicators used in VBP must be individually accounted for to accurately adjust payment and quality measures, these are questions for ASPE/CMS to test empirically.
Conclusion 1: If there are substantial barriers to collecting social risk factor data (such as high cost) and/or if early pilot testing or modeling in a multivariable model suggests only marginal gains from including any given indicator in any method of accounting for social risk factors in Medicare performance measurement and payment, inclusion of that indicator may not be warranted.
Accuracy refers to the degree that a given measure captures the construct that measure represents. In this report, this characteristic also captures related constructs important for data quality, such as validity, reliability, and completeness. The committee considered the extent to which standardized measures and data collection methods for each social risk factor indicator are available and used. Standardization is important to ensure valid comparisons across reporting units and settings. Clinical utility describes whether providers can use information on a social risk factor in the management and treatment of that patient (IOM, 2014). If intervening on or otherwise addressing a social risk factor is beyond the purview of health care providers or can only be done at substantial cost, clinicians may be reluctant to collect data out of concern that patients would expect them to provide services that they do not have the capacity to offer. The committee notes that its focus is on social risk factors important for use in Medicare quality
measurement and payment. The EHR may include information on social and behavioral risk factors important to the clinical encounter, but that would not be relevant or be the best source of data for application to Medicare performance measurement and payment. The committee sees no conflict between the conclusions and recommendations in this report and those in the 2014 IOM report on capturing social and behavioral domains and measures in EHRs (IOM, 2014).
The committee also considered whether an indicator is relatively stable or changes over time. This distinction is not binary, but rather describes a spectrum. Some factors, such as nativity, would not logically change over time, while other factors, such as language, could potentially change over time, but such change is likely to be relatively slow. These factors are relatively stable. Other factors are likely to change more rapidly. For example, a Medicare beneficiary’s marital status could change rapidly owing to the loss of a spouse (NASEM, 2016b).
To weigh the trade-offs between, and identify priorities among, the potential data sources for each individual social risk factor indicator, the committee identified several guiding principles.
Recommendation 1: The committee recommends the Centers for Medicare & Medicaid Services (CMS) use five guiding principles when choosing data sources for specific indicators of social risk to be used in Medicare performance measurement and payment. These guiding principles are
- CMS should first use data it already has.
- CMS should second look for opportunities to use existing data collected by other government agencies (including elsewhere in the Department of Health and Human Services).
- To the extent that a social risk factor is relatively stable, CMS should examine the feasibility of collecting additional data at the time of enrollment in Medicare.
- Where social risk factors change over time and have clinical utility, requiring data collection through electronic health records or other types of provider reporting may be the best approach.
- For social risk factors that reflect a person’s context or environment, existing data sources that can be used to develop area-level measures should be considered.
Once the committee identified potential data sources for each of the social risk factor indicators identified in its third report, the committee assessed each potential data source in terms of the three characteristics (collection burden, accuracy, and clinical utility) and identified the relative advantages and disadvantages of each source. It then weighed the trade-offs for each source to identify preferences and priorities and develop proposed data collection strategies. Based on the committee’s review and assessment of potential data sources for each of the social risk factor indicators, the committee identified the following categories of data that CMS could use for inclusion in Medicare quality measurement and payment:
- Data sources exist that could be used in the short and long term.
- Data sources with some limitations exist that could be used in the short term, and CMS should conduct research on new or improved data collection strategies in the long term. These include indicators for which:
- CMS has some existing data that could be used in the short term, but CMS should research ways to improve accuracy and data collection in the long term;
- Area-level measures could be used in the short term, but CMS should research standardized measurement and data collection for the long term.
- Measures and data collection methods exist, but data sources have considerable limitations and more research is needed to accurately collect data in the long term.
- Some measures exist, but more research is needed on the effect of the social risk factor indicator on health care outcomes of Medicare beneficiaries and on methods to accurately collect data for the Medicare population.
Recommendation 2: The committee recommends that the Centers for Medicare & Medicaid Services use existing data on dual eligibility, nativity, and urbanicity/rurality in Medicare performance measurement and payment.
For the Medicare population, dual eligibility is an indicator of insurance status that can be used as a proxy measure of socioeconomic position (SEP). Because it captures elements of SEP and health status, dual eligibility can be considered a broader measure of health-related resource availability that captures medical need (NASEM, 2016b). CMS administers both Medicare and Medicaid programs, and therefore already possess existing data on dual eligibility among Medicare beneficiaries.
Nativity refers to country of origin. Measures can capture a specific country of origin or a dichotomous variable comparing foreign-born to U.S.-born individuals (NASEM, 2016b). CMS does not currently collect nativity data, nor is nativity routinely captured in EHRs. However, Medicare beneficiaries’ place of birth could be collected either by CMS or via EHRs with relatively little burden to patients, providers and plans, or CMS. Nativity is a stable social risk factor, which supports one-time collection by CMS to reduce burden, but nativity also has clinical utility, which supports collection through EHRs. The SSA collects place of birth including city and state or foreign country. These data could be paired with Medicare beneficiary records.
Urbanicity/rurality describes where a place falls on the spectrum from urban to rural (NASEM, 2016b). Because urbanicity/rurality represents a beneficiary’s residential and community context, an area-level measure based on the beneficiary’s place of residence is appropriate. The Census Bureau classifies census tracts and/or census blocks as urban areas, urban clusters, and rural, and CMS could use this classification. Medicare beneficiaries’ place of residence is available in Medicare administrative records and is also likely to be captured in administrative or EHR data by providers and plans.
Recommendation 3: Data for individual measures of race and ethnicity, language, and marital/partnership status and for area-level measures of income, education, and neighborhood deprivation are currently available, and the committee recommends that the Centers for Medicare & Medicaid Services (CMS) use them for performance measurement and payment applications in the short term. However, owing to limitations in these data, CMS should research ways to improve accuracy and collection of individual-level measures of race and ethnicity, language, marital/partnership status, income, and education, as well as an area-level measure of neighborhood deprivation for use in the future.
Race and ethnicity are conceptually distinct albeit related constructs that are typically identified through self-reported categories. Medicare currently maintains race and ethnicity data in its administrative records. Current Medicare surveys and administrative records capture self-reported race and ethnicity using categories that adhere to federal standards issued by the White House Office of Management and Budget (OMB) (OMB, 1995; Zaslavsky et al., 2012). However, race and ethnicity information for older beneficiaries who enrolled in Medicare prior to when these standards were issued and implemented may reflect outdated racial and ethnic classifications (Zaslavsky et al., 2012). EHRs are also likely to capture race and ethnicity data. CMS should use available self-report and imputed race and ethnicity data in its existing records and methods in the short term. However, the committee acknowledges some limitations with regard to lack of standardization in current measurement and collection, and less accuracy for older age groups. Over the long term, CMS should continue to collect self-reported race and ethnicity following the OMB standards and work on standardizing measures and methods across the various self-report mechanisms it oversees—administrative forms, Medicare sample surveys, and provider and plan reporting requirements.
Language as a social risk factor typically represents language barriers, such as speaking a primary language that is not English, having limited English proficiency, or otherwise needing interpreter services (NASEM, 2016b). CMS currently maintains some data on preferred language, which has high specificity, but poor sensitivity. In the short term, CMS should use its existing data on preferred language while acknowledging their limitations. In the long term, CMS should continue efforts to standardize measures and data collection methods.
Marital/partnership status is a structural element of social relationships and an indicator of social support. CMS maintains data on marital status, because it is important for Social Security benefits, but CMS does not have partnership data. Providers, plans, and other federal government agencies also do not collect data on partnership. However, because partnership changes over time, especially among older adults, and is clinically useful, it could be collected through EHRs. Regardless of the data source CMS chooses, 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. In the short term, CMS should use available data on marital status. In the long term, research is needed on measurement and data collection for partnership. In particular, CMS could examine whether including partnership in any method to account for social risk factors that already includes marital status and living alone adds substantial additional precision and explanatory value.
Individual income can affect health and health care outcomes directly or indirectly (Adler and Newman, 2002; Braveman et al., 2005). CMS does not currently collect or maintain income data, nor do providers and plans. In the short term, an area-level measure of income from the ACS such as median household income could be used as a proxy for individual-level income. In the long term, the SSA maintains several sources of individual-level income data (lifetime earnings, Medicare payroll taxes, Supplemental Security Income), which CMS could link to Medicare data. Several government agencies also collect and maintain income data to determine Medicare Part B and Part D premium amounts for individuals and married couples with higher incomes, which CMS could also link to Medicare data. CMS could also develop standardized measures and methods to collect income data.
Education can affect health directly (Cutler and Lleras-Muney, 2006; IOM, 2014) or through other indicators of SEP—employment, occupation, and income (Adler and Newman,
2002; IOM, 2014; NASEM, 2016a,b). Currently, CMS does not collect or maintain data on education, nor do providers and plans routinely collect it. Although some of the more comprehensive EHRs may capture educational attainment, standardized measures and data collection strategies are needed. With respect to other government sources, area-level measures are available through the ACS. Thus, in the short term, CMS should use these available area-level measures as a proxy for individual education. In the long term, CMS should develop standardized measures and methods to collect education data.
Relevant area-level constructs of neighborhood deprivation include compositional characteristics of communities such as dimensions of SEP (e.g., the proportion of racial and ethnic minority residents, single-parent households, households below the federal poverty level, and English language–proficient residents) as well as elements of residential environments including the physical or built environment (e.g., availability of services—including health care services) and social environments (e.g., safety and violence, the presence of social organizations, and social cohesion). Because neighborhood deprivation captures a beneficiary’s environment or residential context, an area-level measure based on the beneficiary’s residential address is appropriate, and CMS already possesses these data. Neighborhood deprivation can be assessed using a single-item measure such as median household income or using a multi-item composite measure. In the short term, the committee recommends that CMS test a composite measure (such as an existing indicator from the literature) and a simple single-indicator item (such as median household income), contrast their performance at the census tract-level, and also weigh the benefits of simplicity of a single indicator against the increased precision from a composite measure. To increase accuracy in the long term, CMS could conduct research on measurement and data collection such as measures to better capture neighborhood deprivation in rural areas, to identify an improved geospatial unit of analysis for rural settings, and to assess the performance of any given variable (single or composite) across multiple geographic areas.
Recommendation 4: Individual measures of wealth, living alone, and social support exist, but they are sufficiently limited to preclude their use by the Centers for Medicare & Medicaid Services (CMS) in Medicare performance measurement and payment at this time. Therefore, the committee recommends that CMS research ways to accurately collect data on these indicators.
Wealth represents total accumulated economic resources (assets) that, like income, can affect health directly and indirectly (Braveman et al., 2005; Deaton, 2002; NASEM, 2016a). Wealth may capture more variation than income among older persons, and may therefore be a more sensitive indicator of SEP among Medicare beneficiaries (Allin et al., 2009). Collecting self-reported net worth is difficult because it is sensitive and because many individuals simply do not know the value of their net worth or what assets they have (Braveman et al., 2005; Eggleston and Klee, 2015). Wealth data are not currently available through CMS, providers and plans, or other government agencies. Because no data sources are available for use in the short term, CMS should conduct more research on both measurement and data collection methods by CMS or through EHRs. CMS could consider whether inclusion of wealth data adds sufficient precision above and beyond income data.
Living alone is a structural element of social relationships, which is typically an indicator of social isolation or loneliness, and it is likely to capture elements of social support (Berkman and Glass, 2000; Brummett et al., 2001; Cohen, 2004; Eng et al., 2002; House et al., 1988; Wilson et al., 2007). There are no data sources that could be used in the short term. However, for
the long term, because living arrangements can change rapidly for older adults and living alone has clinical utility as an indicator, living alone may best be captured in the clinical setting. CMS should develop standardized measures and methods for data collection through EHRs.
Social support is a crucial function of social relationships that includes instrumental components (e.g., material and other practical supports) and emotional dimensions (e.g., through caring and concern). Currently, no social support data are available within CMS, from providers and plans, or from other government agencies. 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 and has clinical utility, it may best be captured in the clinical setting. CMS should develop standardized measures and methods for data collection through EHRs.
Recommendation 5: Area-level measures exist for housing, but they have limitations for use by the Centers for Medicare & Medicaid Services (CMS) in Medicare performance measurement and payment at this time. The committee recommends that CMS research ways to accurately collect housing data, whether at an individual level or an area level.
Elements of housing that may influence health include housing stability, homelessness, and quality and safety. Currently neither CMS nor providers and plans routinely collect housing information, although some more comprehensive EHRs may collect or link to housing information (Gottlieb et al., 2015; ONC, n.d.). Because housing can change over time and has clinically utility, housing information could be collected through EHRs. Some area-level measures of housing are also available through the ACS and the Department of Housing and Urban Development. Because some dimensions of housing reflect beneficiaries’ environment, an area-level measure could be appropriate. In the short term, the committee recommends that CMS test area-level measures based on a beneficiary’s residential address in the Medicare record. Because other elements of housing, in particular, physical characteristics, occur at the individual level, and can 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.
Recommendation 6: The committee recommends that research be conducted on the effect of acculturation, sexual orientation and gender identity, and environmental measures of residential and community context on health care outcomes of Medicare beneficiaries, and on methods to accurately collect relevant data in the Medicare population.
Acculturation describes how much an individual adheres to the social norms, values, and practices of his or her own home country or ethnic group or to those of the United States (NASEM, 2016a). Evidence on the effect of acculturation and health care outcomes is not well established (Abraído-Lanza et al., 2006; IOM, 2014; NASEM, 2016a). Because more evidence is needed on the empirical association between acculturation and health care outcomes, CMS should revisit this indicator and its appropriate measurement when more evidence is available.
Sexual orientation captures individuals who identify as lesbian, gay, bisexual, queer, questioning, or otherwise nonconforming, and it is typically defined with respect to three dimensions: attraction, behavior, and identity (IOM, 2011). Gender identity typically refers to individuals who identify as gender minorities, including those who identify as transgender, intersex, or otherwise nonconforming (IOM, 2011). 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). Providers and plans also do not typically collect sexual orientation and gender identity data. However, ONC included sexual orientation and gender identity in its stage 3 meaningful use regulations (CMS, 2015). Because, in part, of a lack of standardized measures, there is currently little evidence on the effect of sexual orientation and gender identity on health care outcomes (NASEM, 2016a,b). Because more empirical evidence of an effect on health care outcomes is needed, CMS should revisit this indicator and its appropriate measurement when more evidence is available.2
Environmental measures of residential and community context capture elements of the physical or built environment such as transportation options and proximity to services (including health care and social services), as well as social environments such as safety and violence and the presence of social organizations. There is a conceptual relationship between neighborhood environments and health care outcomes, but evidence is currently limited and environmental measures need to be tested further (NASEM, 2016a). Thus, CMS should revisit such environmental measures and their appropriate measurement when more evidence is available.
Recommendation 7: The committee recommends that the Centers for Medicare & Medicaid Services collect information about relevant, relatively stable social risk factors, such as race and ethnicity, language, and education, at the time of enrollment.
Indicators for which data might best be captured through a revised enrollment form include race and ethnicity, language, and education. Should other methods, such as linking to data from the SSA, prove too difficult or not produce accurate information on other indicators (e.g., income and nativity), these could be considered for inclusion in the revised enrollment form. Should research demonstrate an important explanatory effect of one or more of these indicators and a pilot test shows it is feasible, CMS could supplement the information collected at enrollment with a survey of current beneficiaries, whose information would not have been captured at the time of enrollment.
Table S-1 summarizes the availability of data for social risk factor indicators that could be accounted for in Medicare payment programs.
2 As described in the committee’s third report (NASEM, 2016b), normative gender categories (men and women) are strongly associated with health and health care outcomes, despite the fact the gender effects are difficult to separate from biological sex effects. 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.
TABLE S-1 Summary of Data Availability for Social Risk Factor Indicators
|SOCIAL RISK FACTOR||DATA AVAILABILITY|
|Pace, Ethnicity, and Cultural Context|
|Race and Ethnicity||
|Residential and Community context|
|Other environmental measures||
Available for use now
Available for use now for some outcomes, but research needed for improved, future use
Not sufficiently available now; research needed for improved, future use
Research needed to better understand relationship with health care outcomes and on how to best collect data
The committee identified several general conclusions for CMS in its overall approach to collecting data on social risk factors for use in Medicare payment. Any given indicator may require different data collection strategies depending on its intended use. For example, risk-adjusting health plan quality measures may require data from different sources compared to risk-adjusting hospital quality measures, because social risk factors that affect the outcome or cost of a hospitalization likely differ from those that affect quality or total cost of care measures. This may be particularly relevant for data collected through EHRs, because providers vary in their stage of EHR adoption and capacity for health information exchange. However, this may also be true for other sources of data, where there are limitations to data from existing sources, where data would be collected in different settings (e.g., hospitals, clinical practices, in the home), and when data are collected by different types of individuals (e.g., clinicians and nonclinical staff). Moreover, the specific modes of data collection needed may change over time. For EHR data, needs for complementary modes may diminish with advances in EHR adoption and interoperability. An example of an existing multimodal approach is CMS’s strategy for collecting race and ethnicity data. Data from beneficiaries enrolled since the 1990s are collected via self-report, but for older beneficiaries for whom current categories collected through self-reported data are unavailable, CMS imputes race and ethnicity and also updates older data with newer self-reported data collected through surveys. Additionally, when CMS revised its race and
ethnicity measures, it conducted a survey of certain Medicare beneficiaries to improve the accuracy of its data (Zaslavsky et al., 2012).
Conclusion 2: Different data collection strategies for the same social risk factor indicator may be warranted depending on the purpose or methods used to account for social risk factors in Medicare performance measurement and payment. Additionally, the advantages and disadvantages of any specific source should be considered in reference to the intended use.
Conclusion 3: Any specific social risk factor indicator may require a multimodal approach to data collection.
Conclusion 4: Regardless of the source, research on how to accurately and reliably collect social risk factor data across different modes and in different settings will be needed.
Abraído-Lanza, A. F., A. N. Armbrister, K. R. Flórez, and A. N. Aguirre. 2006. Toward a theory-driven model of acculturation in public health research. American Journal of Public Health 96(8):1342-1346.
Adler, N. E., and K. Newman. 2002. Socioeconomic disparities in health: Pathways and policies. Health Affairs (Millwood) 21(2):60-76.
Allin, S., C. Masseria, and E. Mossialos. 2009. Measuring socioeconomic differences in use of health care services by wealth versus by income. American Journal of Public Health 99(10):1849-1855.
Bach, P. B., H. H. Pham, D. Schrag, R. C. Tate, and J. L. Hargraves. 2004. Primary care physicians who treat blacks and whites. New England Journal of Medicine 351(6):575-584.
Berenson, J., and A. Shih. 2012. Higher readmissions at safety-net hospitals and potential policy solutions. Issue Brief (Commonwealth Fund) 34:1-16.
Berkman, L., and T. Glass. 2000. Social integration, social networks, social support, and health. In Social epidemiology. New York: Oxford University Press.
Bhalla, R., and G. Kalkut. 2010. Could Medicare readmission policy exacerbate health care system inequity? Annals of Internal Medicine 152(2):114-117.
Braveman, P. A., C. Cubbin, S. Egerter, S. Chideya, K. S. Marchi, M. Metzler, and S. Posner. 2005. Socioeconomic status in health research: One size does not fit all. JAMA 294(22):2879-2888.
Brummett, B. H., J. C. Barefoot, I. C. Siegler, N. E. Clapp-Channing, B. L. Lytle, H. B. Bosworth, R. B. Williams, Jr., and D. B. Mark. 2001. Characteristics of socially isolated patients with coronary artery disease who are at elevated risk for mortality. Psychosomatic Medicine 63(2):267-272.
Burwell, S. M. 2015. Setting value-based payment goals—HHS efforts to improve U.S. health care. New England Journal of Medicine 372(10):897-899.
Casalino, L. P., A. Elster, A. Eisenberg, E. Lewis, J. Montgomery, and D. Ramos. 2007. Will pay-for-performance and quality reporting affect health care disparities? Health Affairs (Millwood) 26(3):w405-w414.
Chien, A. T., M. H. Chin, A. M. Davis, and L. P. Casalino. 2007. Pay for performance, public reporting, and racial disparities in health care: How are programs being designed? Medical Care Research and Review 64(5 Suppl):283s-304s.
CMS (Centers for Medicare & Medicaid Services). 2015. Medicare and medicaid programs; electronic health record incentive program- stage 3 and modifications to meaningful use in 2015 through 2017; final rule. Federal Register 80(200):62761-62955.
CMS Office of Minority Health. 2015. The CMS equity plan for improving quality in Medicare. https://www.cms.gov/About-CMS/Agency-Information/OMH/OMH_DwnldCMS_EquityPlanforMedicare_090615.pdf (accessed August 11, 2016).
Cohen, S. 2004. Social relationships and health. American Psychology 59(8):676-684.
Cutler, D. M., and A. Lleras-Muney. 2006. Education and health: Evaluating theories and evidence. Cambridge, MA: National Bureau of Economic Research.
Deaton, A. 2002. Policy implications of the gradient of health and wealth. Health Affairs 21(2):13-30.
Eggleston, J. S., and M. A. Klee. 2015. Reassessing wealth data quality in the survey of income and program participation.http://www.census.gov/content/dam/Census/library/workingpapers/2016/demo/SEHSD-WP2016-17.pdf (accessed September 19, 2016).
Elliott, M. N., M. K. Beckett, W. G. Lehrman, P. D. Cleary, C. Cohea, L. A. Giordano, E. Goldstein, and C. Damberg. In press. Understanding the role Medicare’s patient experience points system plays in hospital reimbursement. Health Affairs.
Eng, P. M., E. B. Rimm, G. Fitzmaurice, and I. Kawachi. 2002. Social ties and change in social ties in relation to subsequent total and cause-specific mortality and coronary heart disease incidence in men. American Journal of Epidemiology 155(8):700-709.
Friedberg, M. W., D. G. Safran, K. Coltin, M. Dresser, and E. C. Schneider. 2010. Paying for performance in primary care: Potential impact on practices and disparities. Health Affairs (Millwood) 29(5):926-932.
Gilman, M., E. K. Adams, J. M. Hockenberry, I. B. Wilson, A. S. Milstein, and E. R. Becker. 2014. California safety-net hospitals likely to be penalized by ACA value, readmission, and meaningful-use programs. Health Affairs (Millwood) 33(8):1314-1322.
Gilman, M., E. K. Adams, J. M. Hockenberry, A. S. Milstein, I. B. Wilson, and E. R. Becker. 2015. Safety-net hospitals more likely than other hospitals to fare poorly under Medicare’s value-based purchasing. Health Affairs (Millwood) 34(3):398-405.
Gottlieb, L. M., K. J. Tirozzi, R. Manchanda, A. R. Burns, and M. T. Sandel. 2015. Moving electronic medical records upstream: Incorporating social determinants of health. American Journal of Preventative Medicine 48(2):215-218.
House, J. S., K. R. Landis, and D. Umberson. 1988. Social relationships and health. Science 241(4865):540-545.
IOM (Institute of Medicine). 2011. The health of lesbian, gay, bisexual, and transgender people: Building a foundation for better understanding. Washington, DC: The National Academies Press.
IOM. 2012. Health IT and patient safety: Building safer systems for better care. Washington, DC: The National Academies Press.
IOM. 2014. Capturing social and behavioral domains and measures in electronic health records: Phase 2. Washington, DC: The National Academies Press.
Joynt, K. E., and A. K. Jha. 2013a. A path forward on Medicare readmissions. New England Journal of Medicine 368(13):1175-1177.
Joynt, K. E., and A. K. Jha. 2013b. Characteristics of hospitals receiving penalties under the hospital readmissions reduction program. JAMA 309(4):342-343.
Joynt, K. E., and M. B. Rosenthal. 2012. Hospital value-based purchasing: Will Medicare’s new policy exacerbate disparities? Circulation: Cardiovascular Quality Outcomes 5(2):148-149.
Lawson, E. H., R. Carreón, G. Veselovskiy, and J. J. Escarce. 2011. Collection of language data and services provided by health plans. American Journal of Managed Care 17(12):e479-e487.
McNabb, J., D. Timmons, J. Song, and C. Puckett. 2009. Uses of administrative data at the Social Security Administration. Social Security Bulletin 69(1).
NASEM (The National Academies of Sciences, Engineering, and Medicine). 2016a. Accounting for social risk factors in Medicare payment: Identifying social risk factors. Washington, DC: The National Academies Press.
NASEM. 2016b. Accounting for social risk factors in Medicare payment: Criteria, factors, and methods. Washington, DC: The National Academies Press.
NASEM. 2016c. Systems practices for the care of socially at-risk populations. Washington, DC: The National Academies Press.
Nerenz, D. R., R. Carreon, and G. Veselovskiy. 2013a. Race, ethnicity, and language data collection by health plans: Findings from 2010 America’s Health Insurance Plans Foundation-Robert Wood Johnson Foundation survey. Journal of Health Care for the Poor Underserved 24(4):1769-1783.
Nerenz, D. R., G. M. Veselovskiy, and R. Carreon. 2013b. Collection of data on race/ethnicity and language proficiency of providers. American Journal of Managed Care 19(12):e408-e414.
OMB (White House Office of Management and Budget). 1995. Standards for the classification of federal data on race and ethnicity. https://www.whitehouse.gov/omb/fedreg_race-ethnicity (accessed April 21, 2016.
ONC (Office of the National Coordinator for Health Information Technology). n.d. CHP participant community: All Chicago making homelessness history. https://www.healthit.gov/sites/default/files/all_chicago_making_homelessness_history-final.pdf (accessed August 15, 2016).
Rajaram, R., J. W. Chung, C. V. Kinnier, C. Barnard, S. Mohanty, E. S. Pavey, M. C. McHugh, and K. Y. Bilimoria. 2015. Hospital characteristics associated with penalties in the Centers for Medicare & Medicaid Services Hospital-Acquired Condition Reduction Program. JAMA 314(4):375-383.
ResDAC (Research Data Assistance Center). n.d. Health and retirement survey—Medicare linked data. https://www.resdac.org/cms-data/files/hrs-medicare (accessed August 4, 2016).
Rosenthal, M. B. 2008. Beyond pay for performance—emerging models of provider-payment reform. New England Journal of Medicine 359(12):1197-1200.
Ryan, A. M. 2013. Will value-based purchasing increase disparities in care? New England Journal of Medicine 369(26):2472-2474.
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.
U.S. Census Bureau. 2013. American Community Survey information guide. https://www.census.gov/content/dam/Census/programssurveys/acs/about/ACS_Information_Guide.pdf (accessed August 11, 2016).
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.
Wilson, R. S., K. R. Krueger, S. E. Arnold, J. A. Schneider, J. F. Kelly, L. L. Barnes, Y. Tang, and D. A. Bennett. 2007. Loneliness and risk of Alzheimer disease. Archives of General Psychiatry 64(2):234-240.
Zaslavsky, A. M., J. Z. Ayanian, and L. B. Zaborski. 2012. The validity of race and ethnicity in enrollment data for Medicare beneficiaries. Health Services Research 47(3 Pt 2):1300-1321.