“Collecting Race and Ethnicity Data is Not Enough: Measuring and Reporting Disparities” Karen Kar-Yee Ho, MHS Lead Staff, NHDR, Agency for Healthcare Research and Quality David R. Nerenz, PhD Director, Center for Health Services Research, Director, Outcomes Research, Neuroscience Institute, Henry Ford Health System Bruce Siegel, MD, MPH Research Professor, George Washington University School of Public Health and Health Services Joseph R. Betancourt, MD, MPH Director, The Disparities Solutions Center at Massachusetts General Hospital Moderator

Methods for the National Healthcare Disparities Report Karen Ho, MHS Lead Staff, NHDR October 16, 2007

2006 National Healthcare Quality and Disparities Reports Released Jan 11, 2007

How the Reports are Related NHQRNHDR Snapshot of quality of health care in America Snapshot of disparities in health care in American QualityQuality + Access Safety, Effectiveness, Timeliness, Patient Centeredness Safety, Effectiveness, Timeliness, Patient Centeredness + Equity Variation across statesVariation across populations

Types of Data Surveys collected from populations:  AHRQ, Medical Expenditure Panel Survey (MEPS),  CAHPS® Hospital Survey, 2007  California Health Interview Survey,  Centers for Disease Control and Prevention (CDC), Behavioral Risk Factor Surveillance System (BRFSS),  CDC-NCHS, National Health and Nutrition Examination Survey (NHANES),  CDC-NCHS, National Health Interview Survey (NHIS),  CDC-NCHS/National Immunization Program, National Immunization Survey (NIS),  CDC-NCHS, National Survey of Family Growth (NSFG), 2002  Centers for Medicare & Medicaid (CMS), Medicare Current Beneficiary Survey (MCBS),  National Hospice and Palliative Care Organization, Family Evaluation of Hospice Care, 2005  Substance Abuse and Mental Health Services Administration (SAMHSA), National Survey on Drug Use and Health (NSDUH),  U.S. Census Bureau, American Community Survey, 2004  National Center for Education Statistics, National Assessment of Adult Literacy, Health Literacy Component, 2003

Data collected from samples of health care facilities and providers:  National Sample Survey of Registered Nurses, 2004  CDC-NCHS, National Ambulatory Medical Care Survey (NAMCS),  CDC-NCHS, National Hospital Ambulatory Medical Care Survey-Outpatient Department (NHAMCS-OPD),  CDC-NCHS, National Hospital Ambulatory Medical Care Survey- Emergency Department (NHAMCS-ED),  CDC-NCHS, National Hospital Discharge Survey (NHDS),  CMS, End Stage Renal Disease Clinical Performance Measures Project (ESRD CPMP),  American Cancer Society and American College of Surgeons, National Cancer Data Base (NCDB),  CDC-NCHS National Nursing Home Survey (NNHS), 2004

Data extracted from data systems of health care organizations:  AHRQ, Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases disparities analysis file,*  CMS, Hospital Compare, 2006  CMS, Medicare Patient Safety Monitoring System,  CMS, Home Health Outcomes and Assessment Information Set (OASIS),  CMS, Nursing Home Minimum Data Set,  CMS, Quality Improvement Organization (QIO) program, Hospital Quality Alliance (HQA) measures,  HIV Research Network data (HIVRN) data,  Indian Health Service, National Patient Information Reporting System (NPIRS),  National Committee for Quality Assurance, Health Plan Employer Data and Information Set (HEDIS®),  National Institutes of Health (NIH), United States Renal Data System (USRDS),  SAMHSA, Treatment Episode Data Set (TEDS),

Data from surveillance and vital statistics systems:  CDC, National Program of Cancer Registries (NPCR),  CDC-National Center for HIV, STD, and TB Prevention, HIV/AIDS Surveillance System,  CDC-National Center for HIV, STD, and TB Prevention, TB Surveillance System,  CDC-NCHS, National Vital Statistics System (NVSS),  NIH, Surveillance, Epidemiology, and End Results (SEER) program,

Stratified data  By race and ethnicity  By income  By education  By insurance status Multi-stratifications:  By race/ethnicity and income  By race/ethnicity and education  By race/ethnicity and insurance  Regression models??

Comparisons for disparities Reference group  Each race group is compared with data for whites  Hispanics are compared with non-Hispanic whites  Poor populations are compared with high income populations  Uninsured and publicly insured are compared with privately insured  Earliest data year (1999/2000) is compared with most recent data year (2004/2005) available. Disparities exist if  Relative differences are at least 10% and statistically significant with p<0.05, assessed using z-tests. Change over time  Difference must have p 1% per year.

Challenges Faced in National Data  Data on specific racial, ethnic, and socioeconomic groups were often –Not collected –Collected in different ways –Sample size could not provide reliable estimates

Race Data Collection  New Office of Management and Budget guidelines for the collection of race and ethnicity in federal data in effect since –Expands collection of racial data from 4 groups to 5 groups –Identify >1 race –Federal agencies had until 2003 to implement –Non-federal data are not subject to OMB standards

Data gaps Gaps in data for:  Racial groups –Native Hawaiians –American Indians –Asians –Mixed race  Priority Populations  Children  Rural residents  People with special healthcare needs

Why Identify and Track Disparities? To reduce disparities in health care by:  Informing targeted improvement strategies- Health care achieved for one population should be achievable for all populations.  Establishing a national standard- States, communities, and providers can measure their successes and opportunities for improvement in comparison to the nation.  Evaluating progress and change over time- Data are needed to identify successful interventions and opportunities for improvement.

Collecting Race and Ethnicity Data is Not Enough: Measuring and Reporting Disparities October 16, 2007 Bruce Siegel, MD, MPH

Expecting Success Sites Duke University Hospital Durham, NC Mount Sinai Hospital Chicago, IL Sinai-Grace Hospital Detroit, MI Montefiore Medical Center New York, NY Memorial Regional Hospital Hollywood, FL University of Mississippi Medical Center Jackson, MS Delta Regional Medical Center Greenville, MS University Health System San Antonio, TX Del Sol Medical Center El Paso, TX Washington Hospital Center Washington, DC

Expecting Success Hospital Applicants, 2005 Siegel, Bretsch, Sears, Regenstein, & Wilson. Assumed equity: early observations from the first hospital disparities collaborative. Journal for Healthcare Quality 2007;29(5): % Collecting R/E Data % Reported QI Initiatives to Reduce Care Disparities n = 4 n = 118 n = 6 n = 112

Collecting standardized R/E/L Data Lessons Learned □Engage all stakeholders  Changing R/E/L fields affects registration staff, ambulatory sites, patient registries, language services □Work with Information Systems – altering fields and testing changes □Educate staff on why collecting the data is important □Address registration staff anxiety  Patients did not “pushback” as expected, esp. when told “why” up front

Collecting standardized R/E/L Data Lessons Learned (cont’d) □Post-implementation check-in  Focus group three months □Monitor individual performance and provide feedback  Directly observe patient registration  Record pre-registration phone calls  Include R/E/L in performance reports and evaluation  Regularly provide data on unknowns, declines back to registration staff  Make it routine!!

The many uses of R/E/L data With reliable R/E/L data, hospitals can: □Provide person centered care  Target cultural and linguistic competence efforts  From menus to interpreters  Understand educational needs, customize materials □Analyze service lines  Truly understand your market - it may be different from what you expected □Capture changes in hospital demographic trends □Stratify quality measures and find quality opportunities

R/E/L: Promises and Challenges Hospital X

A challenge to R/E/L data collection 26% 3% 55%

Hospital Y Percent of Heart Failure Patients Receiving Discharge Instructions by Ethnicity 2005 Quarter Quarter 4 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0% 2005Q42006Q12006Q22006Q32006Q4 Year/Quarter Percent of Patients Hispanic Patients Not Hispanic Patients 83.3% 65.6% 85.7% 75.3% 92.9% 88.3% 91.3% 90.0% 100.0% Closing the Gap

Transitions and Disparities Hospital X Readmissions Within 30 Days by Race Q through Q % 10.2% 18.7% 1.3% 3.0% 0% 5% 10% 15% 20% 25% *Readmits HF (HFR)*Patient Readmit HF (HFR-P) *Patient Readmit (any cause, HFR-P2) *p<.05 Percent of HF Discharges Black White

Measurement caveats □If looking at “core” or HQA measures:  Large numbers of exclusions  Thus small sample sizes and many measures to review  Potential solutions  Aggregate data  Use “all or nothing” measures:  Larger samples  Fewer measures  More patient-centered

Hospital Z 30-Day Same Cause Readmission Rates Q Discharges 0%10%20%30%40%50% HIV (N=225) Ped. Asthma (N=487) COPD (N=260) Stroke (N=172) ALL RACE Black White ETHNICITY Hispanic Non-Hispanic LANGUAGE English Spanish Broadening the Use of R/E/L Data

Using Data on Race/Ethnicity to Identify Disparities in Quality of Care and to Track Progress of Efforts to Reduce Disparities David R. Nerenz, Ph.D. Center for Health Services Research Henry Ford Health System October 16, 2007

Essential Steps Obtain Data on Race/Ethnicity Link to HEDIS/CAHPS Data Stratify HEDIS/CAHPS Data Identify “Significant” Disparities Plan QI Project(s) Evaluate Impact No More Disparity!

Examples of HEDIS Data Stratified by Race/Ethnicity at the Individual Health Plan Level

Asthma: Outpatient Follow-up After Acute Episodes Core concept: Outpatient follow-up after either ER visit or admission Children 5-17 years old Standard based on national expert panel guidelines

Comprehensive Diabetes Care: Foot Exam Performed White vs. African American (p<0.001), White vs. Hispanic (p<0.001) and White vs. Asian (p<0.001). Rate

Multiple Disparities in HEDIS Measures in Single Health Plan (Six-State Medicaid Project) Percent Source: Single Health Plan analysis of HEDIS data – 2003, unpublished

Comparison of non-Hispanic/Hispanic Breast Cancer Screening by Commercial, Medicare Risk, and Medicaid Products in a Single Health Plan, 2000 P=.001 non-Hispanic population

Asthma Medication Management Reporting Year 2003 African-AmericanCaucasian NumeratorDenominatorRateNumeratorDenominatorRate All Co’s % % A % % B % % C % %

Breast Cancer Screening Reporting Year 2003 African-AmericanCaucasian NumeratorDenominatorRateNumeratorDenominatorRate All Co’s % % A % % B % % C % %

Examples of Tracking Stratified HEDIS Data over Time

Improvements in Quality of Care for African American Health Plan Members with Diabetes

Another Approach to Evaluating QI Program Success Asthma severity definition involving ER visits and admissions Focus on African- American members with asthma Used shift in distribution of severity categories as measure of program success Statistically significant using Chi-square test Percent

Comparison of Caucasian and African American HbA1c Testing in a Single Plan Rate

Disparities in Medicare Managed Care (HEDIS) Measures Over Time Standard, widely-used quality measures Trends from 1997 or 1999 to present Improvements in quality overall, reduction in disparities in some HEDIS measures, but not all Trivedi et al, NEJM, August 18, 2005

Childhood Immunization – Combo I – (HEDIS 1999 Definition) Percent

Month in 2006 Mammograms Number of Mammograms Billed per Month for African American women Barrier Analysis Survey Provider focus groups Mammogram Blitz

Summary There are a number of health plans that have been able to collect race/ethnicity data, link it to HEDIS or other quality of care data bases, and identify disparities in quality of care. The same basic methods can be used to repeat analyses in future time periods in order to track progress on reducing disparities. In some cases, “supplemental” analyses can be done to identify associations between specific initiatives and changes in process of care measures.

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“Of all the forms of inequality, injustice in healthcare is the most shocking and inhumane.” –Dr. Martin Luther King, Jr. To receive notifications of upcoming free web seminars and other future activities at the Disparities Solutions Center, please go to our website and click on the “sign up” link on our homepage.