Collecting Race and Ethnicity Data is Not Enough: Measuring and Reporting Disparities

1
Collecting Race and Ethnicity Data is Not
Enough Measuring and Reporting Disparities
Joseph R. Betancourt, MD, MPH Director, The
Disparities Solutions Center at Massachusetts
General Hospital Moderator
Karen Kar-Yee Ho, MHS Lead Staff, NHDR, Agency
for Healthcare Research and Quality
Bruce Siegel, MD, MPH Research
Professor, George Washington University School of
Public Health and Health Services
David R. Nerenz, PhD Director, Center for Health
Services Research, Director, Outcomes Research,
Neuroscience Institute, Henry Ford Health System
2
Methods for the National Healthcare
Disparities Report
Karen Ho, MHS Lead Staff, NHDR October 16, 2007
3
2006 National Healthcare Quality and Disparities
Reports
Released Jan 11, 2007
4
How the Reports are Related
5
Types of Data

• Surveys collected from populations
• AHRQ, Medical Expenditure Panel Survey (MEPS),
  2002-2004
• CAHPS Hospital Survey, 2007
• California Health Interview Survey, 2001-2005
• Centers for Disease Control and Prevention (CDC),
  Behavioral Risk Factor Surveillance System
  (BRFSS), 2001-2005
• CDC-NCHS, National Health and Nutrition
  Examination Survey (NHANES), 1999-2004
• CDC-NCHS, National Health Interview Survey
  (NHIS), 1998-2005
• CDC-NCHS/National Immunization Program, National
  Immunization Survey (NIS), 1998-2005
• CDC-NCHS, National Survey of Family Growth
  (NSFG), 2002
• Centers for Medicare Medicaid (CMS), Medicare
  Current Beneficiary Survey (MCBS), 1998-2003
• 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), 2002-2005
• U.S. Census Bureau, American Community Survey,
  2004
• National Center for Education Statistics,
  National Assessment of Adult Literacy, Health
  Literacy Component, 2003

6

• 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), 1997-2004
• CDC-NCHS, National Hospital Ambulatory Medical
  Care Survey-Outpatient Department (NHAMCS-OPD),
  1997-2004
• CDC-NCHS, National Hospital Ambulatory Medical
  Care Survey-Emergency Department (NHAMCS-ED),
  1997-2004
• CDC-NCHS, National Hospital Discharge Survey
  (NHDS), 1998-2005
• CMS, End Stage Renal Disease Clinical Performance
  Measures Project (ESRD CPMP), 2001-2005
• American Cancer Society and American College of
  Surgeons, National Cancer Data Base (NCDB),
  1999-2004
• CDC-NCHS National Nursing Home Survey (NNHS),
  2004

7

• Data extracted from data systems of health care
  organizations
• AHRQ, Healthcare Cost and Utilization Project
  (HCUP) State Inpatient Databases disparities
  analysis file, 2001-2004
• CMS, Hospital Compare, 2006
• CMS, Medicare Patient Safety Monitoring System,
  2003-2005
• CMS, Home Health Outcomes and Assessment
  Information Set (OASIS), 2002-2005
• CMS, Nursing Home Minimum Data Set, 2002-2005
• CMS, Quality Improvement Organization (QIO)
  program, Hospital Quality Alliance (HQA)
  measures, 2000-2004
• HIV Research Network data (HIVRN) data, 2001-2003
• Indian Health Service, National Patient
  Information Reporting System (NPIRS), 2002-2004
• National Committee for Quality Assurance, Health
  Plan Employer Data and Information Set (HEDIS),
  2001-2005
• National Institutes of Health (NIH), United
  States Renal Data System (USRDS), 1998-2003
• SAMHSA, Treatment Episode Data Set (TEDS),
  2002-2004

8

• Data from surveillance and vital statistics
  systems
• CDC, National Program of Cancer Registries
  (NPCR), 2000-2004
• CDC-National Center for HIV, STD, and TB
  Prevention, HIV/AIDS Surveillance System,
  1998-2005
• CDC-National Center for HIV, STD, and TB
  Prevention, TB Surveillance System, 1999-2003
• CDC-NCHS, National Vital Statistics System
  (NVSS), 1999-2004
• NIH, Surveillance, Epidemiology, and End Results
  (SEER) program, 1992-2004

9
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??

10
(No Transcript)
11
(No Transcript)
12
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 plt0.05, assessed
  using z-tests.
• Change over time
• Difference must have plt0.05 and a geometric rate
  of change of gt1 per year.

13
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

14
Race Data Collection

• New Office of Management and Budget guidelines
  for the collection of race and ethnicity in
  federal data in effect since 1997.
• Expands collection of racial data from 4 groups
  to 5 groups
• Identify gt1 race
• Federal agencies had until 2003 to implement
• Non-federal data are not subject to OMB standards

15
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

16
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.

17
Collecting Race and Ethnicity Data is Not
Enough Measuring and Reporting Disparities
October 16, 2007

• Bruce Siegel, MD, MPH

18
Expecting Success Sites
Montefiore Medical Center New York, NY
Mount Sinai Hospital Chicago, IL
Sinai-Grace Hospital Detroit, MI
Washington Hospital Center Washington, DC
Duke University Hospital Durham, NC
Del Sol Medical Center El Paso, TX
Memorial Regional Hospital Hollywood, FL
University Health System San Antonio, TX
University of Mississippi Medical Center Jackson,
MS
Delta Regional Medical Center Greenville, MS
19
Expecting Success Hospital Applicants, 2005
Reported QI Initiatives to Reduce Care
Disparities
Collecting R/E Data
n 4
n 6
n 118
n 112
Siegel, Bretsch, Sears, Regenstein, Wilson.
Assumed equity early observations from the first
hospital disparities collaborative. Journal for
Healthcare Quality 200729(5)11-15.
20
Collecting standardized R/E/L DataLessons 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

21
(No Transcript)
22
Collecting standardized R/E/L DataLessons
Learned (contd)

• 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!!

23
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

24
R/E/L Promises and Challenges
Hospital X
25
A challenge to R/E/L data collection
55
26
3
26
Closing the Gap
Hospital Y
Percent of Heart Failure Patients Receiving
Discharge Instructions by Ethnicity
2005 Quarter 4 - 2006 Quarter 4
100.0
100.0
92.9
90.0
85.7
100.0
90.0
83.3
88.3
91.3
80.0
75.3
70.0
65.6
60.0
Hispanic Patients
Percent of Patients
50.0
Not Hispanic Patients
40.0
30.0
20.0
10.0
0.0
2005Q4
2006Q1
2006Q2
2006Q3
2006Q4
Year/Quarter
27
Transitions and Disparities
Hospital X
Readmissions Within 30 Days by Race
Q4 2005 through Q4 2006
25
18.7
20
15
Black
11.0
10.2
Percent of HF Discharges
White
10
5
3.0
1.3
1.3
0
Readmits HF (HFR)
Patient Readmit HF
Patient Readmit
(HFR-P)
(any cause, HFR-P2)

plt.05
28
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

29
Broadening the Use of R/E/L Data
Hospital Z 30-Day Same Cause Readmission Rates Q4
2006 Discharges
0
10
20
30
40
50
ALL
HIV (N225)
RACE
Black
Ped. Asthma
White
(N487)
ETHNICITY
Hispanic
COPD (N260)
Non-Hispanic
LANGUAGE
English
Stroke (N172)
Spanish
30
www.expectingsuccess.org
31
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

32
Essential Steps
No More Disparity!
Evaluate Impact
Plan QI Project(s)
Identify Significant Disparities
Stratify HEDIS/CAHPS Data
Link to HEDIS/CAHPS Data
Obtain Data on Race/Ethnicity
33
Examples of HEDIS Data Stratified by
Race/Ethnicity at the Individual Health Plan
Level
34
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

35
Comprehensive Diabetes CareFoot Exam Performed
Rate
White vs. African American (plt0.001), White vs.
Hispanic (plt0.001) and White vs. Asian (plt0.001).

36
Multiple Disparities in HEDIS Measuresin Single
Health Plan(Six-State Medicaid Project)
Percent
Source Single Health Plan analysis of HEDIS
data 2003, unpublished
37
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
38
Asthma Medication ManagementReporting Year 2003
39
Breast Cancer Screening
Reporting Year 2003
40
Examples of Tracking Stratified HEDIS Data over
Time
41
Improvements in Quality of Care for African
American Health Plan Members with Diabetes
42
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
43
Comparison of Caucasian and African American
HbA1c Testing in a Single Plan
Rate
44
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

45
Childhood Immunization Combo I (HEDIS 1999
Definition)
Percent
46
Number of Mammograms Billed per Month for
African American women
Mammogram Blitz
Barrier Analysis Survey
Provider focus groups
Mammograms
Month in 2006
47
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.

48

• Question and Answer Period

Type your question in the chat box on the lower
right of your screen, select host and click on
send to submit your question.
49

• www.mghdisparitiessolutions.org

To receive email 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.
Of all the forms of inequality, injustice in
healthcare is the most shocking and inhumane.
Dr. Martin Luther King, Jr.