Future Directions for the National Healthcare Quality and Disparities Reports (2010)

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5 Enhancing Data Resources As the nation moves forward with enhanced health information technology (HIT) and building a health care data infrastructure, AHRQ can leverage its position as the producer of the national healthcare reports to identify health care quality measurement and data needs. Subnational data, for example, can inform trends on emerging measures and serve as a model for the development of more widespread data collection on measures that show promise for quality improvement. Race, ethnicity, and language need, among other sociodemographic variables, continue to influence the quality of care individuals receive. For that reason, standardized information regarding these variables is a necessary component of the national health care data infrastructure. Collecting and reporting accurate, comparative data that are useful to measuring health care quality is a “time- consuming” process (NPP, 2008). There is movement among quality improvement stakeholders to harmonize performance measures to reduce the data burden on organizations and health care providers. At the same time, there is extensive development and testing of new measures to fill shortcomings in measurement areas or improve existing measures. ­The Future Directions committee believes AHRQ, by leveraging its position as the producer of the NHQR and NHDR can identify health care quality measurement and data needs for development, and uti- lize subnational data sources when national data do not yet exist. This chapter underscores the importance of the evolving national health care data infrastructure as an emerging source of information for the NHQR and NHDR. The chapter also outlines the pros and cons of using subnational data to fill needs for measurement areas in the NHQR and NHDR and proposes criteria for the use of such data. In addition, the chapter summarizes the independent consensus study of a subcommittee to the Future Direc- tions committee, which culminated in the report Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. The subcommittee’s recommendations in that report (see Appendix G) emphasized the need to increase the availability of standardized race, ethnicity, and language need data across the health care system. This chapter addresses the relationship of the subcommittee’s findings to improving the content and analyses in the NHDR and discusses the utilization of socioeconomic and insurance status data in analyses for the NHDR and NHQR. The full text of Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement is available at http://www.nap. edu/catalog.php?record_id=12696. 89 

90 NATIONAL HEALTHCARE QUALITY AND DISPARITIES REPORTS BUILDING A NATIONAL DATA INFRASTRUCTURE Information on quality and disparities can promote understanding of where health care needs and quality gaps exist. In the mid-1960s, the National Halothane Study first indicated how data on variation in performance can advance our understanding of health care and provide opportunities for improvement. The results from the Halothane Study, which principally evaluated mortality rates in the use of anesthesia, revealed unexpected varia- tion in surgical outcomes across hospitals. After adjusting for differences in procedure, age, and physical status, differences in death rates between institutions remained “very much larger” than the differences among anesthetics used (Subcommittee on the National Halothane Study of the Committee on Anesthesia, 1966, p. 128). Looking beyond anesthesia, health care variation by both institution and geographic region remains very much an issue in 2010, including how variation in quality affects the cost of health care (Fisher et al., 2009; Skinner et al., 2010; Weinstein and Skinner, 2010). We now understand that “unwarranted variation” occurs and must be identified in order to be addressed in a “logical and manageable fashion” (Wennberg and Wennberg, 2003, p. 614). Once health care organizations have evaluated and identified the factors contributing to undesirable variation, they are better positioned to develop and implement quality improvement interventions to reduce or eliminate it. The absence of a national health care data infrastructure hinders the potential for national measurement and reporting to actually improve quality (James, 2003). The development of such an infrastructure has been labeled an “awesome task” (Mechanic, 2007, p. 46) that requires national coordination of performance measures, data aggregation, methodology, and technology (Roski, 2009). Yet AHRQ can play a role in defining the content for such a national health care data infrastructure by identifying and fostering measures and data sources, even if the measures and data are not yet national in scope, and by specifying measurement areas with the greatest potential to improve population health as quality and equity gaps are closed. Data directly related to care processes and outcomes are needed to comprehensively describe the quality and quantity of care provided by individuals and institutions. Accordingly, data illuminating how care is delivered, who is delivering care, and where care is delivered are necessary to identify opportunities for system change. Electronic health records (EHRs), patient-based registries, and all-payer claims data (APCD) offer long-term potential for comprehensive patient data that can be used to measure the quality of care being provided across settings and time. These data sources have the potential to link use of services, intermediate outcomes, and demographics, and may be large enough to address questions about the quality of care provided to specific subpopulations. The American Recovery and Reinvestment Act of 2009 authorizes and provides resources for the Office of the National Coordinator for Health Information Technology (ONC) within HHS to guide the “development of a nation- wide health information technology infrastructure that allows for the electronic use and exchange of information.” Proposed rules on standards to receive Medicare and Medicaid reimbursement incentives for the implementation of EHRs were issued in December 2009 and describe ways in which EHR systems should be used for purposes that include quality improvement and the elimination of disparities in health and health care (CMS, 2010). In addition, there is potential for data linkages between health information exchanges (HIEs) and APCD data- bases (Rogers, 2009). An APCD database would ideally contain information on all covered services, regardless of the setting or the location of the provider, and would include eligibility information and medical, pharmacy, and dental claims. APCD databases may be able to provide data by payers and plans, and could provide the sample size necessary to report on populations and measurement areas where statistical power currently limits quality reporting. Ideally, APCD could be used to define episodes of care and to handle issues of risk and severity adjustment without the need for medical records data. In reality, putting together the requisite data and addressing patient confiden- tiality concerns require significant investment of time and resources. For instance, Maine, New Hampshire, and Vermont, among others, have APCD databases, but these databases do not always capture care for residents who have out-of-state plans and none of these databases have integrated Medicare data to allow long-term follow-up. Kansas’ APCD database, which is called the Kansas Health Insurance Information System (KHIIS), is a repository for data from group insurers, Medicaid, the Children’s Health Insurance Program (CHIP), and the state employee health plan. It does not include Medicare data and faces budgetary, political, and data quality hurdles (Allison,  American Recovery and Reinvestment Act of 2009, Public Law 111-5 § 3002(b)(2)(B)(vii), 111th Cong., 1st sess. (February 17, 2009). 

ENHANCING DATA RESOURCES 91 2009). In December 2009, HHS announced its intent to build a universal claims database for health research.  In the interim, state-based claims databases may provide comparative data. In the near term, multi-site clinical registries may provide data that allow the NHQR and NHDR to illus- trate the potential of a health care data infrastructure for national performance measurement. The Northern New ­England Cardiovascular Disease Study Group, National Surgical Quality Improvement Program, and National Quality Program of the Cystic Fibrosis Foundation are examples of registries with an explicit focus on provider- specific performance, sharing data, exploring the causes of variations in outcomes, and applying established quality improvement techniques (e.g., benchmarking and site visits to high-performing providers) (American College of Surgeons, 2009; Cystic Fibrosis Foundation, 2009; Leavitt et al., 2009; Likosky et al., 2006). These collaboratives may provide insight into what levels of performance are possible. As EHR and other HIT provisions are implemented, and as national registries, health information exchanges, and APCD become more comprehensive and available, the potential to build the NHQR and NHDR on a solid foundation of provider- and community-specific performance measurement will become even greater. These data sources have the potential to complement or replace some of the data sources AHRQ currently uses to monitor specific conditions; however, AHRQ may face resource challenges to analyze and use new data sources without additional funding. In the near term, AHRQ should continue to work with various stakeholders, such as states, the National Quality Forum (NQF), and other HHS agencies to stimulate data development when data do not exist to support desir- able measures. Such data development could be accomplished by adding pertinent questions to existing surveys, or data elements to EHR systems and existing registries. AHRQ could work with the Centers for Medicare and Medicaid Services (CMS), for instance, to further develop datasets on a widening array of clinical services. CMS is already beginning to publicly report on risk-adjusted 30-day outcomes for acute myocardial infarction (AMI) across almost all U.S. hospitals (CMS, 2009); the reported measure tracks outcomes in addition to mortality and could supplement AHRQ’s current measure on AMI mortality rates. Furthermore, AHRQ could capitalize on other opportunities for partnership in measure and data development, particularly given the contract awarded by HHS to the NQF to identify the most important quality and efficiency measures for individuals cared for under Medicare (NQF, 2009). Additionally, in AHRQ’s portfolio of research, including the burgeoning field of comparative effectiveness, there are opportunities to promote the generation of measures that may be of high impact for quality improvement. Previous AHRQ-funded research projects have yielded performance measures. For example, a project focused on aggregating utilization data on psychopharmacology use among children enrolled in Medicaid resulted in several potentially useful quality and safety measures, even though the project was not specifically aimed to develop mea- sures (Crystal et al., 2009). Additionally, AHRQ could fund measure development activities, as it has done in the past. For example, from 1996 through 1999, AHRQ funded the Expansion of Quality of Care Measures (Q-SPAN) project to develop and test clinical performance measures focused on specific conditions, patient populations, or health care settings. AHRQ may need additional resources to support measure development in areas identified in its measurement agenda (see Chapters 4 and 7). The preceding discussion indicates that analysis of quality and disparities can be informed by multiple data sources—nationally representative provider-based and household surveys, administrative databases such as the Medicare and Medicaid programs and hospital discharge data, and clinical data obtained from sources such as EHRs and disease registries (IOM, 2002). Comprehensive quality and disparities reporting currently requires utilizing data available from all of these types of sources. FILLING MEASUREMENT AND DATA NEEDS The NHQR and NHDR are a “mosaic of existent data sources” (IOM, 2001, p. 128). To compile the 2008 NHQR and NHDR, AHRQ used 35 diverse data sources, including population surveys, vital statistics databases, For more information, see the Federal Business Opportunities website: https://www.fbo.gov/?s=opportunity&mode=form&id=71d119aea 45a6f2efdc5862cac9cb6e2&tab=core&_cview=0 (accessed December 20, 2009). 

92 NATIONAL HEALTHCARE QUALITY AND DISPARITIES REPORTS administrative data, and clinical data (see Table 5-1). Despite the use of these data sources, the committee finds important areas of measurement for which data are not included in the NHQR and NHDR (see Chapter 3). In many of these measurement areas (e.g., HIT adoption and care coordination), national data sources do not support such measures. In some cases, though, the Future Directions committee believes that data sources beyond those currently included in the NHDR and NHQR have the potential to provide important insight into certain aspects of quality and disparities measurement. Incorporating information from additional data sources into the NHQR and NHDR could help to ensure that the reports tell a more complete story of the nation’s progress in improving the quality of health care. These additional data sources may be nationally representative or national in scope (e.g., the National Surgical Quality Improvement Program, the Cystic Fibrosis Patient Registry) and may provide clinical information, data on alternate payment streams, and information on populations of interest (e.g., children with special health care needs) that are not represented in large enough numbers in existing datasets used by AHRQ. For example, Healthcare Effective- TABLE 5-1  Data Sources Used in the 2008 NHQR and NHDR Federally Funded National Surveys AHRQ, CAHPS Hospital Survey (HCAHPS) AHRQ, Center for Quality Improvement and Patient Safety (CQUIPS), National CAHPS Benchmarking Database (NCBD) AHRQ, Medical Expenditure Panel Survey (MEPS) CDC, Behavioral Risk Factor Surveillance System (BRFSS) CDC-National Center for Health Statistics (NCHS), National Health Interview Survey (NHIS) CDC-NCHS, National Immunization Survey (NIS) Substance Abuse and Mental Health Services Administration (SAMHSA), National Survey on Drug Use and Health (NSDUH) Health Care Facilities and Clinical Data AHRQ, Healthcare Cost and Utilization Project (HCUP), Nationwide Inpatient Sample (NIS) AHRQ, Healthcare Cost and Utilization Project (HCUP), State Inpatient Database (SID) American Cancer Society (ACS), National Cancer Data Base (NCDB) CDC-NCHS, National Ambulatory Medical Care Survey (NAMCS) CDC-NCHS, National Hospital Ambulatory Medical Care Survey (NHAMCS) CDC-NCHS, National Hospital Discharge Survey (NHDS) CMS, End Stage Renal Disease Clinical Performance Measures Project (ESRD CPMP) CMS, Home Health Outcomes and Assessment Information Set (OASIS) CMS, Medicare Patient Safety Monitoring System (MPSMS) CMS, Nursing Home Minimum Dataset (MDS) CMS, Quality Improvement Organization (QIO) program, Hospital Quality Alliance measures National Institutes of Health (NIH), U.S. Renal Data System (USRDS) SAMHSA, Treatment Episode Datasets (TEDS) Surveillance and Vital Statistics Data CDC, HIV/AIDS Surveillance System CDC, National Program of Cancer Registries (NPCR) CDC, National Tuberculosis Surveillance System (NTBSS) CDC, National Vital Statistics System: Link Birth and Infant Death Data (NVSS-I) CDC, National Vital Statistics System: Mortality Data (NVSS-M) CDC, National Vital Statistics System: Natality (NVSS-N) National Cancer Institute (NCI), Surveillance, Epidemiology, and End Results program (SEER) Other CMS, Medicare Administrative Data (MAD) CMS-National Hospice and Palliative Care Organization (NHPCO), Family Evaluation of Hospice Care Survey (FEHCS) HHS, HIV Research Network (HIV RN) Indian Health Service (IHS), National Patient Information Reporting System (NPIRS) National Committee for Quality Assurance (NCQA), Healthcare Effectiveness Data and Information Set (HEDIS) NIH-National Institute of Mental Health (NIMH), Collaborative Psychiatric Epidemiology Surveys (CPES) University of Michigan, Kidney Epidemiology and Cost Center SOURCES: AHRQ, 2009a,b. 

ENHANCING DATA RESOURCES 93 ness Data and Information Set (HEDIS) data often include ambulatory clinical care measures that expand beyond information available in administrative data to provide details on actual treatment, not just testing. Using Subnational Data in the Absence of National Data As David Lansky of the Pacific Business Group on Health told the Future Directions committee, “a snapshot of national performance is instructive to establish a national vocabulary on quality for trending and benchmark- ing, but there is a risk of ‘looking under the lamppost’ and failing to focus on the right (and evolving) problems” (Lansky, 2009). The committee believes that looking “under the lamppost” and potentially missing important areas of quality measurement is an apt metaphor of caution for the selection of national measures for inclusion in the NHQR, NHDR, and related products. If the reports measure only areas for which national data are currently available, the measure selection process becomes circular, precluding development of new measures in national priority areas for health care quality improvement. For that reason, it is important for AHRQ to identify novel quality measurement possibilities and to look beyond existing data sources. Defining Subnational Datasets Although it is preferable that the national healthcare reports rely on nationally representative data or data that are national in scope, there are instances, whether due to insufficient sample sizes at the national level (e.g., ethnic populations in some surveys) or underdeveloped areas for measure development and reporting (e.g., end-of-life care, adoption of HIT), when subnational data may be informative for additional or otherwise overlooked measures of quality and disparities. The IOM’s 2002 Guidance for the National Healthcare Disparities Report described subnational datasets as “surveys produced by single states” or surveys of “all or multiple” states or localities. Subnational data also includes, for instance, state-based APCDs. Subnational datasets can represent health care entities (e.g., hospitals, payers) in certain areas of the country or contain data on specific population groups. Currently, AHRQ uses several subnational datasets to fill gaps in data on specific population groups and on specific measures. State-based data from states with a high proportion of specific racial or ethnic groups can help portray the health care issues specific to populations not well represented in national datasets (e.g., data for Native Hawaiians in Hawaii or on individuals of specific Asian ethnicities in California). The California Health Interview Survey (CHIS), for instance, provides estimates of insurance coverage and barriers to care for many of the sizable population groups present in California, such as recent immigrants, however, for which national data are lacking. AHRQ uses CHIS to supplement some information in the NHDR that is principally provided by the Medical Expenditure Panel Survey (MEPS). Other state-based surveys (e.g., the Rhode Island Health Interview Survey, the Hawaii Health Survey, and the Massachusetts State Health Survey) may also provide useful data for AHRQ; these surveys tend to have smaller samples sizes than CHIS. Rationale for Using Subnational Datasets For certain areas of quality and disparity reporting, national databases provide insufficient or no data. As an example, quality data for all major population groups—as defined by the Office of Management and Budget (OMB) categories of White, Black or African American, Asian, Hispanic, American Indian or Alaska Native, and Native Hawaiian or Other Pacific Islander—are often unavailable because national survey samples often contain insufficient data to stratify all measures for each population group. Due to small sample sizes, this problem par- ticularly arises for AHRQ in the case of American Indian or Alaska Natives, and Native Hawaiian or Other Pacific Islanders.  A man is on his knees under a lamppost crawling around looking for something. A passerby asks him what is he doing. “Looking for lost keys,” he replies. “Is this where you lost them?” “No, but there is light here” (Rogers and Wright, 1998; Salinger, 2006).  Numerous organizations including Papa Ola Lokahi, the Asian and Pacific Islander American Health Forum, and the National Indian Health Board encourage and foster the development of subnational datasets specific to racial and ethnic groups that are underrepresented in national surveys. 

94 NATIONAL HEALTHCARE QUALITY AND DISPARITIES REPORTS Although oversampling has the potential to resolve information gaps, costs and logistical issues constrain the use of oversampling techniques (Madans, 2009). In an effort to provide information in the 2008 NHDR on qual- ity measures that were otherwise limited by sample size, AHRQ included data from the Indian Health Service for several measures (AHRQ, 2009b). As discussed in Chapter 3 and Appendix D, information gaps exist for measurement areas such as the adoption of HIT, end-of-life care, efficiency, and care coordination. Several subnational datasets provide information that could be used to fill measurement gap areas (as examples, see Box 5-1 and Box 5-2). A principal rationale for using subnational data is that these data would inform a priority area identified by this Future Directions committee or by the Secretary as a result of health reform that is not sufficiently addressed with current national data. Using subnational data could not only fill gaps where important national measures do not currently exist, but also could spur development of nationally representative data for measurement areas. Criteria for the Use of Subnational Data As the previous discussion indicates, subnational datasets have the potential—in both the interim and long- term—to supplement information presented in the NHQR and NHDR. The committee deliberated on the degree to which AHRQ should rely on these data in the national healthcare reports. On one hand, utilizing these datasets in the NHQR and NHDR may provide insight into important opportunities for quality improvement or reduction of disparities. On the other hand, these datasets are, by definition, not nationally representative as they represent only specific populations or geographic regions. The presentation of subnational data has the potential to mislead read- ers; therefore, AHRQ should clearly underscore the limitations of such data. The committee suggests that AHRQ only use subnational data when national data are not available and that AHRQ should clearly present caveats to ensure that readers of the NHQR and NHDR understand what population the data represent (i.e., subnational data should not be advertised as being nationally representative). AHRQ may, for example, explicitly note: “We do not currently have national data for this specific measure; these data represent a region, a particular population, or a sector.” Presenting the information in either textboxes or sidebars would help clarify that subnational data are examples of areas for future measure or data development. Recommendation 4: AHRQ should use subnational data for domains that do not yet have national data in order to illustrate the types of national data that need to be developed to satisfy measurement and data gaps. Subnational data should meet the following minimum requirements for reporting: • The data source allows the calculation of a measure of interest, ideally one identified as a national priority. • The data source uses reliable and well-validated data collection mechanisms and tested measures. • The sample used in the data source is representative of the population intended to be reported on (e.g., a region, state, population group) or is drawn from the entire population group even if it is not necessarily generalizable to the nation. To further the development of strong subnational datasets and encourage the generation of needed national data, AHRQ could collaborate with sponsors of datasets such as the type identified in Table 5-2. This list is meant to illustrate the kinds of subnational datasets that may be useful but is not comprehensive in scope. These datasets share several key characteristics—they are used to generate measures that are robust in their accuracy and action- ability; they have an established infrastructure, and a process for measure development and reporting that has gained credibility and trust among key stakeholders; and, the tools and methods used are not idiosyncratic and are thus replicable in other parts of the country. AHRQ might partner with the Quality Alliance Steering Commit- tee (QASC), the National Committee for Quality Assurance (NCQA), the Institute for Healthcare Improvement (IHI), the Robert Wood Johnson Foundation’s Aligning Forces for Quality initiative, the National Association of  Patient Protection and Affordable Care Act, Public Law 111-148 § 3013, 3014, 111th Cong., 2d sess. (March 23, 2010). 

ENHANCING DATA RESOURCES 95 BOX 5-1 Using Subnational Data to Provide Insight into Potential Health Information Technology Measures While the adoption of HIT is no guarantee of quality, HIT is a stepping stone to quality improvement as it facilitates interoperability, data sharing, and streamlined work processes. Currently, data at the national level are available to report on the adoption and use of HIT in some, but not all, health care settings. This measurement area is therefore considered developmental. While there are not reliable estimates of the rates of HIT use in all health care settings, national data on the adoption of HIT in hospitals have been collected via survey by the American Hospital Association (Jha et al., 2009). Additionally, proprietary data on the uptake of computerized physician order entry (CPOE) and its impact on length of stay and costs are collected by The Leapfrog Group (The Leapfrog Group, 2010). Furthermore, the Healthcare Information and Manage- ment Systems Society (HIMSS) Analytics collects and analyzes proprietary data related to the HIT market in hospitals and integrated health care delivery systems (HIMSS Analytics, 2010). Regional quality improvement initiatives such as Minnesota Community Measurement, the Integrated Healthcare Association, and the Wisconsin Collaborative for Healthcare Quality measure HIT use within their respective states (Minnesota, California, and Wisconsin, respectively) and report on measures of electronic prescribing, use of electronic lab or diagnostic results, and use of electronic clinical reminders (IHA, 2009a; Mayberry and Hunkins, 2008; Minnesota Community Measurement, 2009b; Wisconsin Collaborative for Healthcare Quality, 2009). AHRQ might feature (in a sidebar, for example) some of the measures used by these initiatives to examine the use of HIT and its impact on quality improvement. BOX 5-2 Measuring Medical Home in Large, Population-Based Surveys An important indicator of quality is whether individuals, especially those with chronic conditions, receive their care through a “medical home,” that is, a source of care that provides comprehensive, ongoing, coordinated, patient-centered care. Most questionnaires that measure whether a person has a medical home were developed for studying care coor- dination, communication, and doctor-patient relationships in clinical settings. The UCLA Center for Health Policy Research included medical home measures in the 2009 California Health Inter- view Survey (CHIS), a large, comprehensive population health survey that the state’s policy makers and researchers use to assess the prevalence and care of chronic conditions in California’s ethnically and racially diverse population. CHIS developed a survey module that collects information from respondents on (1) whether they report having a medi- cal home (i.e., a usual source of care and specific health care professional) (RAND, 2000), (2) whether in the last year they contacted their provider’s office with a question about their condition and received a timely answer (AHRQ, 2006), (3) whether their provider worked with them to develop a care management plan (RAND, 2000), (4) whether the patient is confident about managing their own condition (Beal et al., 2007), and (5) whether their provider helps coordinate their medical care. These indicators are considered important elements of a medical home. CHIS’s comprehensive question- naire and large, diverse sample will permit analyses of the extent to which California residents with differing character- istics have a medical home and, of particular interest to AHRQ, the existence of disparities. Beal and colleagues analyzed data from the 2005 Household Component of the Medical Expenditure Panel Survey (MEPS) to identify Latino subgroup variation in having a medical home, the impact of having a medical home on dispari- ties, and the factors associated with Latinos having a medical home. The researchers used MEPS data to determine whether patients had a medical home based on (1) having a regular provider, (2) the role of the provider in total care for the patient (i.e., preventive care, ongoing health problems, referrals), (3) patient engagement in care (e.g., provider asked patient about medications), and (4) patient access to care (e.g., ability to contact provider during business hours, on nights or weekends). Because the MEPS survey oversamples Black and Latino households, the data had enough statistical power to provide unbiased national estimates (Beal et al., 2009). 

96 NATIONAL HEALTHCARE QUALITY AND DISPARITIES REPORTS State Medicaid Directors, the National Association of State Offices of Minority Health, the Association of State and Territorial Health Officials, health information exchanges, and other regional quality collaboratives. Although some of these organizations are national in scope, they often sponsor regional or state-based initiatives that may provide population- or measure-specific data. The committee did not investigate whether costs or confidentiality agreements would interfere with utilization of datasets such as those included in Table 5-2 but encourages AHRQ to explore the feasibility of incorporating additional data sources and enhancing those currently used. The committee understands that AHRQ currently spends about half of its reports-related budget on data acquisition and analysis even though much of the data incorporated in the reports is provided by AHRQ’s federal partners. AHRQ will need additional funding to support and expand its data acquisition to additional external sources (see Chapter 7). IMPROVING RACE, ETHNICITY, LANGUAGE NEED, SOCIOECONOMIC, AND INSURANCE STATUS DATA The NHDR reveals that even as health care quality improves on specific measures, disparities often persist. Addressing such disparities begins with the fundamental step of bringing the nature of the disparities and the groups at risk for those disparities to light by analyzing health care quality information stratified by race, ethnicity, language need, socioeconomic, and insurance status data (IOM, 2009a,b; NRC, 2004). This section of the report briefly examines the need for each of these sociodemographic data elements in documenting disparities in health care, and summarizes a recent IOM report on standardizing race, ethnicity, and language need data for quality improvement. Then, it evaluates the variables by which AHRQ stratifies data, the data sources used to create the NHDR, and the ways in which AHRQ analyzes disparities data. Enhanced Collection, Analysis, and Reporting In 2008, AHRQ contracted with the IOM to form the Subcommittee on Standardized Collection of Race/Eth- nicity Data for Healthcare Quality Improvement in conjunction with the Committee on Future Directions for the National Healthcare Quality and Disparities Reports. As required by the project’s statement of task (see Chapter 1), the subcommittee conducted its own consensus-based, in-depth analysis that was then issued as an independently reviewed, stand-alone report. The subcommittee’s report Race, Ethnicity, and Language Data: Standardization for TABLE 5-2  Examples of Subnational Datasets Not Currently Used in the NHDR and NHQR That May Provide Supporting Data Dataset Responsible Organization California pay for performance (P4P) data Integrated Healthcare Association (IHA) Kaiser Permanente data on its health plan regions Kaiser Permanente Maine Quality Forum data Dirigo Health Agency Minnesota HealthScores Minnesota Community Measurement Northern New England Cardiovascular Disease Study Group (NNECDSG) Dartmouth-Hitchcock Medical Center Database Pennsylvania Health Care Cost Containment Council Interactive Database Pennsylvania Health Care Cost Containment Council (PHC4) State health interview surveys (e.g., Hawaii Health Survey, Massachusetts Various State Health Survey, Rhode Island Health Interview Survey) Wisconsin Performance & Progress Report Wisconsin Collaborative for Healthcare Quality SOURCES: IHA, 2009b; Kaiser Permanente, 2009; Maine Health Data Organization, 2009; Minnesota Community Measurement, 2009a; Wisconsin Collaborative for Healthcare Quality, 2009. 

ENHANCING DATA RESOURCES 97 Health Care Quality Improvement was released on August 31, 2009. It identified current methods for categorizing and coding race, ethnicity, and language need data; discussed the challenges involved in obtaining these data in health care settings; and made recommendations for improvement. The subcommittee’s findings and recommenda- tions (see Appendix G) provide background information relevant to the committee’s task of recommending ways to improve the data reported in the NHQR and NHDR. The committee draws on the subcommittee’s work regarding race, ethnicity, and language need data, but also addresses socioeconomic and insurance status data, which were outside of the scope of work for the subcommittee. Rationale for Granular Ethnicity Data Since the 2003 release of the IOM’s Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care, evidence of disparities in health care among racial and Hispanic populations, as these populations are cat- egorized by the OMB, has continued to accumulate. There is more information on differences in life expectancy (IOM, 2008a) and mortality risks or rates for certain medical conditions (Murthy et al., 2005; Wang et al., 2006), along with knowledge of disparities in general health status, access to health care, and utilization rates (Cohen, 2008; Flores and Tomany-Korman, 2008; Kaiser Family Foundation, 2009a; Ting et al., 2008). Even as quality-of- care indicators show improvement for the overall U.S. population (e.g., screening for colorectal cancer), dispari- ties persist among the OMB race and Hispanic ethnicity categories (Moy, 2009; Trivedi et al., 2005). Therefore, the subcommittee endorsed continued collection of the OMB categories because they are useful for comparative analysis and have been the standard since 1977 (with adjustments in 1997). There has been relatively less attention paid to the issue of disparities as they relate to more discrete ethnic groups within the OMB categories (e.g., persons of Cuban, Russian, Chinese, or Nigerian ethnicity, whether born in the United States or elsewhere). The OMB categories are not always sufficiently precise to capture population groups of interest to national and local quality improvement efforts. Currently, the NHDR presents the OMB- defined race and Hispanic ethnicity groups as homogenous populations. For example, the section of the NHDR that discusses Hispanics as a priority population makes no mention of the wide range of cultures, languages, and health-related behaviors encompassed by the Hispanic ethnicity category. Because some national surveys collect data on individuals of Mexican, Puerto Rican, and Cuban ethnicities, among others, it would be possible to provide illustrative examples of disparities, when they exist, among these specific ethnic groups. These more specific data can highlight quality gaps among more precisely defined populations that differ in the extent of risk factors, degree of health problems, quality of care received, and outcomes. Numerous studies have described heterogeneity in health and cultural factors within the OMB’s Black or African American population, and the need to examine this population in greater detail (e.g., Black individuals of African heritage versus those of Caribbean heritage) (Kington and Nickens, 2001; Pallotto et al., 2000; Read et al., 2005). Similarly, disparities are apparent within other OMB-defined groups, including in the broad OMB-defined White, Asian, Native Hawaiian or Other Pacific Islander, American Indian or Alaska Native, and Hispanic categories. For example, the need for health care services can depend, in many instances, on ancestry: large differences exist in asthma burden between groups of Hispanic children in the United States. One study indicated that compared to children of Mexican heri- tage, children of Puerto Rican heritage had a higher prevalence (10 percent and 26 percent, respectively) and rate of recent asthma attacks (4 percent and 12 percent, respectively) (Lara et al., 2006). Because disparities can exist within the broad OMB categories, there is value in collecting and utilizing data that have more fine-grained ethnicity categories than those put forth by the OMB (Blendon et al., 2007; Jerant et al., 2008; Read et al., 2005; Shah and Carrasquillo, 2006). The subcommittee recommended, and the committee con- curs, that health care-related entities should collect data on granular ethnicity—defined as “a person’s ethnic origin  The full text of Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement is available at http://www.nap. edu/catalog.php?record_id=12696.  The OMB’s Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity (1997) include a minimum of two ethnic categories: (1) Hispanic or Latino and (2) Not Hispanic or Latino, and five race categories: (1) American Indian or Alaskan Native, (2) Asian, (3) Black or African American, (4) Native Hawaiian or Other Pacific Islander, and (5) White. Federal data collection requires that respondents be allowed to select more than one race. 

98 NATIONAL HEALTHCARE QUALITY AND DISPARITIES REPORTS or descent, ‘roots,’ or heritage, or the place of birth of the person or the person’s parents or ancestors” (U.S. Census Bureau, 2008a)—in addition to soliciting data in the OMB race and Hispanic ethnicity categories (Figure 5-1). More discrete population data are necessary to identify opportunities for quality improvement and outreach without unnecessarily and inefficiently targeting interventions to an entire broad race or Hispanic population. The design of the national healthcare reports may make it difficult to display data on a large number of granular ethnicity groups for each measure. For instance, the heart disease measure presented on page 62 of the 2008 NHDR would become overwhelmingly complex if the figure also included data for Americans of Mexican, Japanese, and Jamaican ethnicity. A derivative product of the NHQR and NHDR that focused on subgroups within the broad OMB race or ethnicity groups would be well suited to present more discrete population information. Additionally, online functionalities that allow users to further analyze subgroup data would facilitate more discrete data analyses without imposing additional data into the print version of the NHDR. OMB Hispanic Ethnicity OMB Race Granular Ethnicity (Select one or more) • Hispanic or Latino • Locally relevant choices Race and Ethnicity • Not Hispanic or Latino • Black or African from a national standard American list of approximately 540 • White categories with CDC/HL7 • Asian codes* • American Indian or • “Other, please Alaska Native specify:___” response • Native Hawaiian or Other option Pacific Islander • Rollup to the OMB • Some other race categories Spoken English Language Spoken Language Preferred Proficiency for Health Care • Very well • Locally relevant choices from a Language Need • Well national standard list of • Not well approximately 600 categories • Not at all with coding to be determined* • “Other, please specify:__” (Limited English proficiency is response option defined as “less than very well”) • Inclusion of sign language in spoken language needs list and Braille when written language is elicited FIGURE 5-1  Recommended variables for standardized collection and reporting of race, ethnicity, and language need. * EHR systems should be able to code data elements from national standard sets of response categories. The subcommittee merged lists of granular ethnicities and languages and their corresponding codes to provide templates from which HHS can develop national standard lists of response categories and codes. Every health professional may not actually use, either in data collection processes or in subsequent analyses, all of the hundreds of possible categories of granular ethnicity or language. How- ever, EHR systems should be designed to accommodate all of the national categories and codes so that a provider can choose the top categories encountered in his or her patient population. SOURCE: IOM, 2009b. Figure 5-1 R01677 editable vectors 

ENHANCING DATA RESOURCES 99 The Rationale for Language Need Data Robust evidence exists that patients with limited English-proficiency encounter significant disparities in access to health care (Hu and Covell, 1986), decreased likelihood of having a usual source of care (Kirkman-Liff and Mondragon, 1991; Weinick and Krauss, 2000), increased probability of receiving unnecessary diagnostic tests (Hampers et al., 1999), and more serious adverse outcomes from medical errors (Divi et al., 2007) and drug complications (Gandhi et al., 2000). The most compelling case for collection and use of language need data is that appropriate, understandable communication represents a foundation of quality health care. That is, patient under- standing, comprehension, and informed decision-making are necessary for the provision of high-quality care. Consequently, HHS, in conformance with Department of Justice principles to prevent discrimination and to ensure access to federally funded programs, provides guidance on collecting language need data (HHS, 2003) in its Culturally and Linguistically Appropriate Services (CLAS) standards. However, English language proficiency and preferred language for health care encounters are not often captured in clinical, survey, or administrative datasets. While surveys may capture language need by noting the language in which the survey was administered, surveys are often only administered in Spanish and English, and measures of language need are more detailed than simply listing an individual’s language preference. The subcommittee concluded, and the committee agrees, that language need can best be assessed by asking two questions: one aimed at determining whether an individual speaks English “less than very well” and a second aimed at identifying the individual’s preferred spoken language during a health care encounter (Figure 5-1 above).  In evaluating spoken English proficiency, the subcommittee determined that the threshold of speaking English “less than very well” (as opposed to “less than well”) is the most sensitive for assessing effective communica- tion. Individuals with limited English proficiency may need to have greater English proficiency for health care encounters than for other daily tasks because of the unfamiliarity of health concepts and the complexity of medical terminology (Karliner et al., 2008; Siegel et al., 2001). Collecting and storing standardized language need information allows its use in measuring system-level quality (e.g., the availability of interpreters and translated materials, and evaluating whether patients have been matched with language-concordant providers), and for stratifying measures by English language proficiency. Collecting these data for analysis at the national level could inform the need for culture competency measures or help target areas where culturally and linguistically appropriate policies and interventions are necessary. While the subcommittee principally focused on the categorization of race, ethnicity, and language need—as it was charged to do—it recognized the role of health literacy, among other variables in health care quality. The subcommittee adopted the following definition of health literacy: The degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions. (Ratzan and Parker, 2000, p. vi) Medical information is complex to understand, even without the added barrier of having a primary language other than English. Comprehending many health-related materials requires education at the high school level, as most materials are written at a 10th-grade reading level or higher (D’Alessandro et al., 2001; Downey and Zun, 2007; IOM, 2004a). To ensure effective communication, patients may need to discuss written materials with an inter- preter or bilingual provider even if the materials are translated into the patients’ primary language, which is why the subcommittee prioritized the collection of spoken language ability over written language ability when data systems limit the number of data elements that can be collected. The Rationale for Socioeconomic Data Examining socioeconomic status (SES) and insurance status was outside the scope of the subcommittee’s task, although the subcommittee acknowledged the importance of these factors when assessing health care quality.  The subcommittee’s recommendation to collect English language proficiency and preferred spoken language is closely aligned to how the NQF defines primary language—the self-selected language the patient wishes to use to communicate with his or her health care provider (NQF, 2009). 

100 NATIONAL HEALTHCARE QUALITY AND DISPARITIES REPORTS Therefore, the Future Directions committee looked at other studies to evaluate the usefulness of these data. The multidimensional construct of SES, which can be represented by various measures (e.g., income, education, occu- pation), can act as both a mediator of racial and ethnic health care disparities, and a further source of disparities. The terms socioeconomic status, socioeconomic position, and class are often used interchangeably. Isaacs and Schroeder, for instance, determined that class can be measured by income, wealth, and education (2004). These are the same components that a National Research Council committee concluded to encompass a broad set of socioeconomic characteristics defined as socioeconomic position (SEP) (NRC, 2004). 10 This committee uses the term SES because it is used in the literature more frequently than SEP or class. The committee understands SES to be a broad concept that encompasses income, wealth, and education. Higher SES is related to better health and health care quality (Fiscella et al., 2009). Studies have found, for example, that higher income and education are associated with lower mortality (Deaton and Paxson, 2004; Egerter et al., 2009; Mechanic, 2007; Sorlie et al., 1995) and that SES is correlated with cancer incidence and mortality (Singh, 2003). While the relationship between SES and health care is complex, there are several estab- lished pathways. First, income is related to affordability. Even among the insured, most health care plans include premiums, deductibles, copayments, and non-covered services. Persons with a higher income level are better able to afford these expenses (McWilliams, 2009), as well as to take time off from work to seek care. Second, education is linked with health knowledge, behavior, employment, income, social and psychological factors, and social standing, and is therefore a “crucial path” to health (Egerter et al., 2009). Because education is related to wealth and income, it is therefore related to an individual’s ability to both access and afford the health insurance market (NRC, 2004). Third, a low level of health literacy is associated with less use of preventive services and a greater use of emergency departments (Arispe et al., 2005). Conversely, higher health literacy, which is correlated with education, is generally associated with improved ability to navigate a highly complex and disjointed health delivery and health care payment system (NRC, 2004). Additionally, higher education is associated with greater diffusion and uptake of newer technology, presumably due to a combination of health literacy and social networks (Chang and Lauderdale, 2009). A person’s health and health care are “greatly influenced by powerful social factors such as education and income and the quality of neighborhood environments” (RWJF Commission to Build a Healthier America, 2009, p. 10). While the casual relationships between income, class, neighborhood, and health care are complex, it is clear that where people live, learn, and work have implications for the health services they receive (California ­Newsreel, 2008; Health Policy Institute, 2008; RWJF Commission to Build a Healthier America, 2009). Among other factors, diet, housing conditions, educational quality, and neighborhood environment are a function of class, and neighborhood conditions constrain access to healthful foods, quality medical care, and opportunities for exer- cise (California Newsreel, 2008). Although there is some evidence for reverse causality (e.g., poor health results in lower income due to down- ward occupation drift), the balance of the evidence suggests that the primary pathway is from SES to health and health care (Marmot, 2006). Although measures of SES are correlated, each distinctly influences health and health care outcomes (Mechanic, 2007). For example, although education is associated with income, wealth, and occu- pation, it has independent effects beyond these joint influences (Mechanic, 2007). SES provides a crude index of health status (and thus health care need) within a population and has implications for both allocation of resources and assessment of health performance (Casalino and Elster, 2007; Fiscella et al., 2009). Without collecting SES data, it is difficult to assess whether policies and interventions are mitigating or exacerbating health and health care disparities. The Rationale for Insurance Status Data A 2009 IOM report on the consequences of uninsurance concluded that “health insurance is integral to personal well-being and health” (IOM, 2009a, p. 5) and that high levels of uninsurance undermine the quality of the nation’s 10 In 2004, the National Research Council of the National Academy of Sciences defined SEP as a “complex concept, encompassing a number of elements of a person’s position in society, including economic resources (earnings, income, and wealth), social resources (social networks and connections to community resources), education (formal credentials, communication skills, and health information), and occupation” (NRC, 2004, pp. 33-34). 

ENHANCING DATA RESOURCES 101 health care, even for insured populations. The report presented a robust body of evidence that demonstrated the substantial health and health care benefits of insurance and supported a previous IOM report’s conclusion that “health insurance contributes essentially to obtaining the kind and quality of health care that can express the equal- ity and dignity of every person” (IOM, 2004b, p. 159). AHRQ reviewed the impact of uninsurance on many of the measures included in the 2006 NHQR and NHDR and found, for instance, that uninsured individuals were much less likely than those with private or public insurance to have a usual primary care provider (AHRQ, 2008). The Availability of Data for Disparities Analysis and Reporting The categories for collection and methods of aggregation for reporting race, ethnicity, and language need data vary across the data sources used to create the NHDR. As previously indicated, the 2008 NHQR and NHDR are comprised of data from a variety of sources; these data sources do not uniformly report on all variables (e.g., poor, White, Black or African American, Hispanic, American Indian or Alaska Native, Asian, and Native Hawaiian or Other Pacific Islander) for each measure. For example, all core quality measures in the NHDR cannot be broken down even into each of the OMB race and Hispanic ethnicity categories. This is evident in the 2008 NHDR where 24 of the 46 core measures are missing data from at least one of the OMB categories. For these 24 measures, reli- able data were unavailable for specific groups, most commonly the American Indian or Alaska Native population (AHRQ, 2009a). More recently, AHRQ has indicated that it can analyze most of the core measures by insurance status. The subcommittee report Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement recommended actions to improve data processes across the health care system. These recommenda- tions, with which the Future Directions committee agrees, are as follows: • The necessary variables for disparities measurement (i.e., race, Hispanic ethnicity, granular ethnicity, Eng- lish language proficiency, and preferred spoken language) should be included in clinical records, surveys, and administrative data. • HHS, states, and accreditation and standards setting organizations can require or encourage this adoption through a variety of mechanisms (see Appendix G). AHRQ’s ability to analyze such data for the national healthcare reports is dependent on the uptake of these recom- mendations; AHRQ should work with its data partners to increase the availability of these descriptors. Federally Funded National Surveys National-level surveys, which include the National Health Interview Survey (NHIS), the Health and Retirement Study (HRS), the National Health and Nutrition Examination Survey (NHANES), and the National Immunization Survey (NIS), are designed—among other purposes—to make comparisons across time, providers, and geographic areas (Madans, 2009). Much of what is known about racial and ethnic disparities has been derived from surveys of the national population (Sequist and Schneider, 2006). For example, the available evidence on health and health care disparities among granular ethnicity groups in the U.S. population is limited primarily to those groups for which there is currently discrete categorization on national survey instruments. The various federally funded health surveys that provide data for the NHQR and NHDR collect race and Hispanic ethnicity data in the six categories specified by the OMB and a usually common set of 9 to 12 more granular ethnicity categories. For example, the NHIS, National Survey on Drug Use and Health (NSDUH), and Medical Expenditure Panel Survey (MEPS) all include the OMB categories plus Mexican, Cuban, Puerto Rican, Asian Indian, Chinese, Filipino, Japanese, Korean, and Vietnamese categories, among others. 11 Many studies using data from large national datasets still often need to pool data over multiple years to get sample sizes sufficient to support reliable inferences and conclusions for racial and ethnic groups. As an example, 11 These categories generally correspond to the check-off boxes included in Census 2000, Census 2010, and intercensal American Community Survey (ACS) questions on race and ethnicity. 

102 NATIONAL HEALTHCARE QUALITY AND DISPARITIES REPORTS using logistic regression analyses of MEPS data pooled from 2002 through 2005, AHRQ identified the independent effects of socioeconomic factors on obese adults given advice by a doctor about exercise (AHRQ, 2009a). Without pooling the data, information on subgroups would have been small and less reliable for analysis. Health Care Facilities and Clinical Data AHRQ utilizes a variety of clinical data sources in the NHQR and NHDR. The subcommittee found, and the committee concurs, that a lack of standardization of race, ethnicity, and language need variables and categories has been a barrier to the widespread collection, aggregation, and utilization of these data. Hospitals, health plans, and accrediting bodies, for example, have expressed reluctance to implement data collection because they did not have guidance on what exactly to collect (Taylor and Gold, 2009; Weinick et al., 2008). Standardization can promote greater comparability and ability to aggregate data collected by providers or plans, or, for instance, transferred from providers to multiple plans or from multiple plans to a state. The American Recovery and Reinvestment Act of 2009 (ARRA)12 lays out expectations for the collection of race, ethnicity, and language data by specifying the inclusion of these variables in EHRs (CMS, 2010). Clinical data would be valuable for the NHQR and NHDR because provider settings supply data otherwise not collected in surveys or administrative datasets. Administrative Data Surveys are useful to capture information for which patients are considered the best reporters (e.g., patient- centeredness), whereas administrative data sources generally provide more reliable and detailed information about aspects of care that are not based on patient recall (e.g., utilization of services, costs, efficiency). Ensuring the collection of race, ethnicity, language need, and SES in Medicare, Medicaid, and Children’s Health Insurance Program (CHIP) claims and enrollment data is important to documenting disparities. As indicated in Table 5-1, the NHQR and NHDR utilize several CMS data sources, including data from the Nursing Home Minimum Dataset and the Home Health Outcomes and Assessment Information Set, but there is potential to use additional CMS data sources, including data from Medicare Part D. As a byproduct of administer- ing the Medicare program, CMS has a wealth of information on enrollment, utilization, and costs, among other variables (McGann, 2009; Reilly, 2009), on the nearly 100 million individuals it insures. 13 Thus, Medicaid and Medicare datasets are particularly useful in determining utilization rates for different types of services (IOM, 2002), although they may not contain sufficient clinical information (such as the need for a particular service or its outcome) and they often contain incomplete, inaccurate, or even no data on race, ethnicity, language need, or SES (Bonito et al., 2008).14 These are critical limitations because Medicare and Medicaid claims data are among the few publicly available data sources that would be large enough to provide data on small population subgroups. Improvement in the collection of race, ethnicity, language need, and SES data in Medicare and Medicaid files is needed. To date, CMS has conducted some preliminary studies using indirect estimation tools to enhance race and ethnicity data obtained through current collection methods. Under the Medicare Improvements for Patients 12 American Recovery and Reinvestment Act of 2009, Public Law 111-5 § 3002(b)(2)(B)(vii), 111th Cong., 1st sess. (February 17, 2009). 13 At least 100 million of the 300 million people in the U.S. are served by three programs administered by HHS—Medicare, Medicaid, and community health centers. There were 44.8 million Medicare beneficiaries in 2008, 58.7 million Medicaid and CHIP recipients in 2006, 10 mil- lion with dual enrollment, and 8.9 million uninsured or privately insured individuals served by health centers. The U.S. population, as of July 1, 2008, was 304 million (HRSA, 2008; Kaiser Family Foundation, 2009b; U.S. Census Bureau, 2008b). 14 Because Medicare historically relied on the race and ethnicity data individuals provided when they applied for a Social Security number (SSN), racial and ethnic identifiers were limited to “Black,” “White,” and “Other” responses included on the SSN application form (unless the individual changed enrollment to a specific health plan). Consequently, Medicare data have been of limited use in studying differences in pat- terns of care for populations identified by the OMB categories (Bilheimer and Sisk, 2008; Bonito et al., 2008; U.S. House Committee on Ways and Means Subcommittee on Health, 2008). The limitations of the Medicare data for race and Hispanic ethnicity have been acknowledged by CMS officials, and CMS is actively working to improve its coding of race and ethnicity within existing datasets (Bonito et al., 2008). As of August 2009, the Social Security Administration (SSA) has updated its SS-5 form (to include all of the OMB race and Hispanic ethnicity cat- egories) (Social Security Administration, 2009). This is an important update as SSA provides demographic information to Medicare. 

ENHANCING DATA RESOURCES 103 and Providers Act of 2008,15 CMS is required to address quality reporting by race and ethnicity, and a report by CMS detailing its proposed actions is due to be publicly available in 2010. Using Indirectly Estimated Data When directly collected race or ethnicity data are incomplete or unavailable in a dataset, estimating the prob- ability of a person’s race or ethnicity from other information (e.g., zip code, surname) may be useful. Indirect estimates of race and ethnicity can allow for analyses of associations between race and ethnicity and outcomes of interest. The subcommittee’s report recommended that such inferences can be useful when the limits of direct collection of racial and ethnic data have been reached. One of the simplest indirect approaches is to use area-level population data derived from the Census. Such data include the racial and ethnic composition of an area, as well as socioeconomic measures such as median income, percent in poverty, distribution by years of educational attainment, percent reporting speaking a language other than English at home, and proficiency with English. Substantial literature on the use of “geocoding” in health research compares the effects of using data aggregated to various geographic levels (Fiscella and Fremont, 2006; Fremont et al., 2005; Krieger et al., 2003a,b,c, 2005; Rehkopf et al., 2006; Subramanian et al., 2006); generally, research has concluded that effects are detected more sensitively when data are linked to smaller (more detailed) geographic units. Additionally, names have been used as indicators of racial and ethnic identity. For some names, there is a corresponding racial and ethnic composition based on self-identification of people with that name in Census data. These data have been summarized in lists of common Spanish and Asian surnames and more specific lists of surnames associated with different Asian-origin ethnicities (Elliott et al., 2008; Fiscella and Fremont, 2006; Wei et al., 2006). The distributions of race and ethnicity in an area or for a particular name can be interpreted as probabilities that a randomly chosen person from the group (of residents of the area or persons with that name) is a member of each race or ethnicity. Under the assumption that information such as area composition and name are independent given the person’s race, the information can be combined using Bayes’s theorem to produce a posterior probability for each race and ethnicity (Elliott et al., 2008; Fiscella and Fremont, 2006). Although the use of indirectly estimated data at the individual level is limited by the probabilistic nature of the data and the consequent possibility of error, the subcommittee concluded—and the committee concurs—that these techniques can be used to bridge gaps for analysis until directly collected data are available. In several illustrative analyses, disparities identified with these methodologies closely matched those identified using self-reported race and ethnicity data (Elliott et al., 2008). However, users of indirectly estimated data should be cautioned against interpreting such data to make conclusions about individual characteristics (e.g., assigning a race to a person’s individual medical chart). Stratifying Quality Measures The most analytically simple approach to reporting disparities is to calculate and present the differences between groups being compared. The NQF has noted that addressing issues of quality within “vulnerable patient populations” requires stratifying measures by “gender, race, ethnicity, SES, primary language, and insurance status.” This chapter’s discussion of the rationale for race, ethnicity, language need, SES, and insurance status data highlights the importance of exploring quality measures by these variables. Analyzing these measures within the context of social determinants of health (e.g., neighborhood environments) could also be an effective strategy to explore complex relationships between race, ethnicity, income, education, class, and health care. Further, the ability to stratify measures by gender and age is important to consider as females, children, and older adults are among AHRQ’s priority populations. Studies have shown, for instance, that women with cardio- vascular disease are treated less aggressively than men and are less likely to undergo cardiac procedures (Chou 15 Medicare Improvements for Patients and Providers Act of 2008, Public Law 110-275 § 118, 110th Cong., 2d sess. (July 15, 2008). 

104 NATIONAL HEALTHCARE QUALITY AND DISPARITIES REPORTS et al., 2007). Further stratification may be particularly important, however, in the context of intra- and inter-race variability. Studies that have stratified cardiac care patients by gender and race have found higher rates of clini- cally appropriate care among men and underuse of clinically appropriate care among Blacks (Epstein et al., 2003), with the lowest rates of clinically appropriate care utilization being for Black women (Steiner and Miller, 2008). In addition, the analysis of disparity measures by age will provide important insight. For example, a measure that depicts receipt of a vaccine by the elderly population could adjust for age to show whether the likelihood of being vaccinated by a given age is the same for all population groups. With perfect data, AHRQ might be able to control for a variety of factors (e.g., age, gender, SES, comorbid behavioral and health disorders) to determine whether such factors confound or mediate relationships between high-quality care and race or ethnicity. However, these data are not uniformly available. When possible, AHRQ might discuss in text whether uncontrolled factors would likely mitigate or worsen disparities and could also discuss data limitations. The 2008 NHDR includes a table listing AHRQ’s ability to stratify the core measures by the OMB race and Hispanic ethnicity categories, and by whether individuals have household incomes less than 100 percent of federal poverty thresholds16 (AHRQ, 2009a, p. 287). The committee commends AHRQ for indicating where reliable data are and are not available and encourages AHRQ to expand its table of data availability to include not only all of the OMB race and Hispanic ethnicity categories, but also availability of granular ethnicity, language need, SES, and insurance status data. The IOM’s 2002 Guidance for the National Healthcare Disparities Report advised AHRQ to present analyses of racial and ethnic disparities that take into account the effect of SES (IOM, 2002). Similarly, the 2008 IOM report State of the USA Health Indicators recommended that data be first presented by race, ethnicity, and SES, and then by race and ethnicity data stratified by SES (e.g., a bar chart in which each part represents an income group within a specific race) (IOM, 2008b). Stakeholders have suggested that data presentation in the NHDR could be further strengthened by stratifying race and ethnicity by SES or, in some cases, controlling for SES via multivariate regressions (IOM, 2008b). AHRQ has only done this to a limited extent (e.g., see pages 199 and 143 of the 2008 NHDR for examples of how AHRQ presents multiple stratifications). Figure 5-2 shows another way in which AHRQ might present such data. This format would allow readers to examine racial, ethnic, and SES aspects of a specific disparity and would show the independent and combined contributions of each of these fac- tors. In the 2008 NHDR, AHRQ presented multivariate regression analyses for three measures: obese adults who were given advice about exercise, people without insurance, and people who have a usual primary care provider (AHRQ, 2009a). There are both positive and negative implications of controlling for various factors depending on whether they are viewed primarily as confounders or mediators. The IOM report Unequal Treatment acknowledges that income is one of many intervening variables between race, ethnicity, and disparities (IOM, 2003). However, controlling for SES may possibly “mask” the “main effects” of disparities (IOM, 2008b). Moreover, controlling for SES may obscure important differences among providers that deserve attention, such as poorer performance among provid- ers caring for disadvantaged populations or lack of resources available to provide services in low-income areas (Williams, 2008). For these reasons, it is best to present data both with and without adjustment for income and insurance status. One way of teasing out its potential mediating role is by examining the relationships between race, ethnicity, and quality both with and without income included. The committee does not intend that AHRQ report on all measures stratified by all of the above-discussed variables; rather, AHRQ should present data when they reveal disparities or should note that the analyses were performed and did not reveal a disparity. Recommendation 5: AHRQ should: • Continue to stratify all quality measures in the NHDR by at least the OMB race and Hispanic ethnicity categories, by socioeconomic status variables (e.g., income, education), and by insur- ance status. • Strive toward stratifying measures by language need (i.e., English language proficiency and preferred spoken language for health care-related encounters), and extend its analyses in 16 Twenty-three measures are not assessed by income level. 

ENHANCING DATA RESOURCES 105 New HIV diagnoses per 10,000 adults 35 32 Neighborhood Income 30 High Moderate Low 25 Very Low 20 20 20 17 16 15 15 10 10 9 5 5 5 2 2 0 White Black Hispanic FIGURE 5-2  Both poor and wealthy New York City neighborhoods have high rates of new HIV diagnoses; overall, very low income Black New Yorkers have the highest rates of HIV diagnoses. SOURCE: Karpati et al., 2004. Reprinted, with permission, from the New York City Department of Health and Mental Hygiene. Copyright 2004 by the New York City Department of Health and Mental Hygiene. Figure 5-2 from original the NHDR and derivative products source to include quality measures stratified by more granular ethnicity groups within the OMB categories whenever the data are available. replaces blurry low-resolution bitmap image • Document shortcomings in the availability of OMB-level race and Hispanic ethnicity data, granular ethnicity data, language need, and socioeconomic and insurance status data to sup- port these analyses; work to enhance the collection of these data in future iterations of the source datasets; and whenever necessary, should utilize alternative valid and reliable data sources to provide needed information even if it is not available nationally. SUMMARY This chapter has detailed a variety of shortcomings in health care quality and disparities data. First, national data are often removed from the clinical setting. Although surveys and administrative databases are enormously valuable, measuring outcomes often requires detailed clinical data collected at the point of care. Second, national data are not available on all measures of health care priority areas, including measures of care coordination, effi- ciency, and HIT. And finally, high-quality data on race, ethnicity, language need, SES, and insurance status are not always available for stratifying quality metrics and assessing disparities. The committee finds that these limitations can be addressed by AHRQ in several ways—showcasing subna- tional datasets in the reports when they illustrate measurement opportunities; noting when shortcomings in data exist so attention can be focused on filling them; and supporting measures and data source development for the future through its research agenda, whether by collaboration or direct funding. Efforts are under way to institute national standards for HIT, performance measurement, and data aggregation and exchange that complement local data collection and experiences with performance improvement and reporting (Roski, 2009). AHRQ has the opportunity to exhibit leadership on the content for national reporting that should be embedded in the nation’s health care data infrastructure. The committee envisions AHRQ providing information 

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