Using Indirect (& Direct) Race/Ethnicity
Data to Target Disparities in Community
Settings
Academy Health Methods Workshop
Allen Fremont, MD, PhD
June 28, 2009
Co-Investigators
(Partial List)
• Marc Elliot, PhD
• Alexandria Felton, MPH
• Nicole Lurie, MD, MSPH • Philip Pantoja, MA
• Mark Hanson, MA
• Spencer Jones, PhD
• Lisa Miyashiro, MA
• Aaron Kofner, MA
• Adrian Overton, MA
• Stephanie Chan, MA
• Malcolm Williams, PhD
Fremont, 2009
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New indirect estimates of R/E composition very
accurate
Proportion of Race/Ethnicity by Estimation Method
100%
Proportion of Race/Ethnicity
90%
80%
52%
52%
70%
White
60%
Hispanic
50%
Black
Asian
40%
26%
26%
18%
18%
4%
4%
State
Indirect Estimate
American Indian
30%
20%
10%
0%
Fremont, 2009
3
Indirect R/E estimates providing QI decision makers
with clearer picture of populations served…
NORTHERN
Region MRA
A
WESTERN
RegionMRA
B
American Indian / Alaskan
Native
2%
11%
13%
28%
12%
Asian / Pacific Islander
31%
Black
49%
53%
White
Hispanic
SOUTHERN
Region MRA
C
GREATER
RegionBOSTON
D MRA
2%
11%
9%
12%
24%
36%
31%
74%
Fremont, 2009
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Indirect R/E measures can be used to assess
disparities at plan or market level
Fremont, 2009
5
Newer Indirect R/E estimates are also accurate
enough to assess disparities at provider group level
Fremont, 2009
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Obtaining R/E data & linking to quality measures
necessary, but not sufficient for reducing disparities
• “Awash in a sea of data”,
“…Where to begin?”
• Current infrastructure does not
support efficient targeting of
disparities & effective
interventions
• Available knowledge does not
translate easily into cost-effective
changes
• Tremendous variation in patterns
& contributing factors across
different markets and local areas
Fremont, 2009
7
GIS mapping and decision can help plans use their
data more efficiently and effectively
• Map distribution of patient subgroups nationally,
regionally, locally
• Rapidly identify and characterize populations and
areas for potential interventions
• Clarify contributing factors and cost-effective
interventions
• Can act as Disruptive Innovation*
*Health Affairs 26, no.3, 2007
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8
Exploratory work revealing ways in which salient
data and tools can help plans and providers
see the problem more clearly:
• Reveal local patterns of care
not apparent with current
approaches
• Increase understanding of
social determinants
• Learn ways to target
interventions more
effectively
• Recognize possibilities for
shared action
Fremont, 2009
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Data and GIS tools are providing plans with more
accurate view of populations they serve
Without race/ethnicity
With race/ethnicity
With GIS tools
Fremont, 2009
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Mapping health data to clarify factors contributing to
health problems & how to address them not new idea
Cluster of Cholera cases,1853
Broad Street
Pump
John Snow used mapping to clarify Cholera spread in London
Fremont, 2009
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Distribution of members can be shown to some
extent with tables and charts but …
30000
100%
80%
80% p
15000
60%
40%
10000
20%
LUFFS
ESTER
THOL
VERLY
UCKET
BURN
EHAM
RHILL
EANS
ALEM
OUTH
RIDGE
THAM
WELL
HBURG
OUTH
DAMS
VILLE
TABLE
NFIELD
LDEN
BORO
LYNN
ESTER
GHAM
UINCY
PTON
FIELD
NTON
ENCE
FIELD
RIVER
KTON
DFORD
EVERE
YOKE
0
MARY
5000
FIELD
Members
20000
% Hispanic Members
25000
0%
Pareto chart of diabetic members without LDL control by service area
Fremont, 2009
12
Maps can highlight service areas where minority
group members are concentrated
Non-White Communities of Plan Area
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Other information, e.g., distribution of members
served by different providers or plans can be shown
Non-White Communities of Plan Area
Type A Provider Group
Members
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Different spatial patterns can inform interpretation of
quality metrics and targeting of interventions
Non-White Communities of Plan Area
Type B Provider Group
Members
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For example, GIS Tools are Helping Target
Potential Opportunities for Intervention
Diabetic Members with and without
LDL Test
GIS Tools can help
highlight “Hotspots” or
areas with clusters of
members with worse
than expected rates
Fremont, 2009
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Region 12 Provider Group Performance: LDL Test
B
C
Regional Average
A
Fremont, 2009
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Mapping distribution of diabetics served by different
provider groups can help assess performance
Provider
Group C
Provider Group A
Ellipses show where 95% of group’s diabetic members live
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Knowing that a given provider group’s service area
overlaps with hotspot can be instructive
Ellipses show where 95% of group’s diabetic members live
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Adding spatial perspective can increase
understanding of contributing factors
Fremont, 2009
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Provider A’s Diabetics & % LDL Test by Ethnicity
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Provider A & B Diabetic Patients
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Individual plans may find targeting hotspots alone
relatively ineffective unless dominate market
11%
89%
% Diabetics not covered
% Diabetics covered by Plan A
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The cost-effectiveness of community interventions
may improve when plans pool efforts
24%
53%
13%
10%
% Diabetics not covered
% Diabetics covered by Plan A
% Diabetics covered by Plan B
% Diabetics covered by Plan C
Fremont, 2009
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Emerging web-based GIS & social networking tools
will also facilitate multi-stakeholder QI efforts
Fremont, 2009
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Allowing stakeholders to interact with the data and
maps always instructive to them & researchers
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New Directions
• Indirect estimates of health literacy
• Regional multi-stakeholder, community-based
initiatives, e.g. California Right Care Initiative
• Facilitating cooperative efforts between health
plans and public health initiatives in community
• Using optimization and simulation techniques to
help decision makers choose intervention approach
at community level
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