Introduction to Mapping for
Health Equity Advocacy
“There’s a Map for That” - Health Equity Webinar Series
Session 1 – January 15th 2012
In collaboration with the Michigan Minority Health Coalition
January 15, 2013
Jason Reece, Director of Research
David Norris, Opportunity Mapping Director
Matt Martin, Research Associate II
Kirwan Institute for the Study of Race & Ethnicity
The Ohio State University
Kirwan Institute:
Solving Problems/Building Opportunity
2
The Kirwan Institute:
Finding Solutions/Building
Opportunity
New Ways of Thinking About Public Health Problems
Social Determinants of Health
Why treat people...
...without changing what makes them
sick?
Social Determinants of Health
Health, Community & Opportunity:
Understanding the Role of Place 6
Factors Impacting Health:
Where does Place Fit?
Biological
Environmental Socioeconomic
Place
The Socio-ecological Model:
Intersection with Place…
The Life Course Perspective:
Where Does Place Fit?
Individuals w/ Challenging Life Course Experiences
Concentrated Poverty,
Community Stressors & Segregation
Infant Mortality Hot Spot:
High Concentration of Individuals with
Challenging Life Course Experiences Living in
Distressed Communities & Isolated from
Opportunity
Community Conditions & Health
SYSTEMS
There’s A Map For That
Mapping to understand
community conditions.
Measuring how
neighborhoods
compare to each other
and/or other
benchmarks.
What can you tell us about this
neighborhood?
What is the life experience of residents?
Building Capacity with an Eye to
Equity
• Mapping provides a
framework and “space” for
engaging a broad number
of community stakeholders,
while simultaneously
focusing on the equity
concerns of marginalized
communities.
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Our Guidance to Our Local Advocacy
Partners
• Data gathering as a collaborative process
• Think of data as informative, not necessarily prescriptive
• Ground data with narrative, human experience
• Document the problems/flaws with data
• Supplement data as needed
– Surveys
– Participatory research
– Interviews and narrative
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• Where are the healthiest places to live?
• Where are the least healthy places?
• Who lives in these places?
• What are social determinants in poor health
outcome communities?
• Where are investments and programs going?
• Are health care resources distributed equitably?
• How might disparities be addressed?
• What are assets (and community strenghts) to build
upon?
What Questions Can Mapping
Help to Answer?
C – Collect
O – Organize
L – Learn
A – Act
Collect
Data Collection
• What data will we collect?
• Where will we get it?
• What issues arise with using health
data?
Disease Incidence: Where, How
Much?
• Cancer
• Heart disease
• Diabetes
• Asthma
• HIV/AIDS
• Accidents
Correlates of Healthy Living
Personal
• Birth weight
• Life expectancy
• Educational attainment
• Causes of death
• At-risk populations
– Age (youth, elderly), gender
– Genetic predisposition
» Can complicate picture
» Definition of "race"
Correlates of Healthy Living
Community and environmental factors
• Access to healthy food
• Parks, bike lanes
• Walkability (may be problematic)
• Social cohesion, supports
• Stressors: Crime, fire runs,
foreclosures
• Septic systems
• Toxic release sources (cancers), air quality
(asthma), water quality (sanitation)
Health Care Access
• Health care delivery
– Service provider locations
» Hospitals, clinics
» Service types (Family practice, pediatrics, OBGYN, etc)
» Who provides affordable care?
– Pharmacy locations
• Transportation
• Health insurance coverage
Where do you get all this data?
• Public data
Census, BLS
• Agency administrative data
Births, deaths, SNAP, crime, property
• Proprietary business data
Hospitals, other care providers, employers
• Surveys and outreach
– Important information source for work with some groups and
communities (e.g., LGBT; immigrants)
– Requires connections and trust within the community
Emerging: Public Participation
GIS (PPGIS)
Crowd-sourced data
• Passive: Google Flu Trends
– Data mining of flu-related search
term data
• Active: Detroit trucks
– Truck traffic related to asthma
– Residents text TRUCK when they
see a semi
• Self-selecting, difficult to verify
Google Flu Trends
• Passive PPGIS
• Data mining of flu-related search terms
http://www.google.org/flutrends/
Detroit Trucks and Asthma
• Active PPGIS: WDET Public Radio Detroit
• Text TRUCK when you see a semi
• Resulted in $200K donation by owners of Ambassador Bridge for a neighborhood clinic
http://www.mobilecommons.com/blog/2011/12/crowdsourced-health-reports-make-a-
difference-in-detroit/
Health Data Considerations
Privacy concerns, HIPPA
• Limits access to health
data
• May need to pass
muster with institutional
review boards (IRBs)
• May require you to sign
a memorandum of
understanding (MOU)
Health Data Considerations
Small-area limitations
• County-level or ZIP
Code often smallest
geography available
• Data suppression for
small areas
– Policy set by data source
39.6
42.1
38.5
34.7
40.5
31.8
26.324.9
27.5
30.5
21.3 20.3 21.1 21.322.5
8.7 8.5 8.3 8.3 8.2
6
10
14
18
22
26
30
34
38
42
46
50
Nov-11 Dec-11 Jan-12 Feb-12 Mar-12
Black Teens Latino Teens White Teens Total U.S. Unemployment
Organize
GEOID10 TOT_POP YOUTH PCT_YTH SENIORS PCT_SNR WHITE BLK_AA OTHER ASIAN HISP_LAT
390490001101 809 60 7.4 190 23.5 730 0 7 36 36
390490001102 847 86 10.2 121 14.3 784 0 0 46 17
390490001103 857 65 7.6 61 7.1 846 0 0 11 0
390490001104 1098 204 18.6 235 21.4 1073 0 15 0 10
390490001201 718 108 15.0 136 18.9 647 0 51 10 10
390490001202 1728 354 20.5 430 24.9 1687 10 12 19 0
390490001203 612 76 12.4 95 15.5 522 45 0 0 45
390490002101 697 100 14.3 155 22.2 616 0 0 28 53
390490002102 612 81 13.2 50 8.2 594 18 0 0 0
390490002103 784 127 16.2 69 8.8 714 0 0 0 70
Austin Metro 2000
Austin Metro 2010
Austin City 2000
Austin City 2010
Asian 44,029 82,433 30,960 49,864
Black or African
American 99,432 127,397 65,956 64,406
Hispanic or Latino
327,760 538,313 200,579 277,707
Other 200,332 256,127 130,546 136,360
White 905,970 1,250,332 429,100 539,760
Organizing Data for Analysis
Software for data organization
• Spreadsheets: Excel, Google Spreadsheets,
LibreOffice
– Multiple spreadsheets per file
– Basic arithmetic operations
– Pivot tables
– Visualizations: Graphs, charts
– NOT recommended for advanced statistics
Organizing Data for Analysis
Software for data organization
• Relational databases: Access, MySQL
– Efficient data storage in multiple tables
– “Relate” tables to one another
– Complex queries
– Can store spatial data, but not ideal to visualize it
Organizing Data for Analysis
Software for data organization
• GIS / Geodatabases: ArcGIS, Quantum GIS
– Relational databases
– Store spatial data
– Visualizations: Graphs, charts, MAPS
– Complex spatial queries
Learn
What Can We Learn by Mapping
Data?
Look for patterns and themes
• Where are the healthiest places to live?
• Where are the least healthy places?
• Who lives in these places?
• Are health care resources distributed
equitably?
• How might disparities be addressed?
Act
How Do You Use Maps to Move
People to Action?
Map making as community engagement
Systems approach
Choose attainable goals
Education and outreach
• Community
• Media
Direct advocacy with policy makers
Mapping Health Investment in Ohio
• Identifying disparities
in spending and
access to health care
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Opportunity Mapping and Health
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Linking Mapping, Data & Capacity Building to
Understand and Support Food Security in Mississippi
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Linking Mapping, Data & Capacity Building to
Understand and Support Food Security in Mississippi
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Moving from Analysis to
Intervention:
How can we use spatial analysis
and resources to guide our efforts
to promote infant and maternal
health?
Developing a State Health Equity
Interactive Mapping Tool:
http://www.arcgis.com/explorer/
Interactive Infant Mortality Prevention
Site:
Beta Version for Central OH
Lessons Learned
• Engagement process is critical
– Working within complexity of real
communities
– Data is never perfect
• Using data to move policy
through strong narrative and
visualization
• Also important to overlay and
identify assets
• Focus goal on building long term
internal capacity
– Technical skills
– Relationships
– Collective knowledge
– Common frameworks
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Gaps to Address • Finding the balance between participatory data/community
empowerment and validity of data/indicators
• Balancing tensions between diverse stakeholders
– Who is at the table (can we assure agency for the communities we
are working for)
• Building important (but poorly developed) data (e.g. criminal
justice)
• Working with geographic disparities in capacity
• Working to assure data isn’t manipulated for nefarious
purposes
• Understanding best methods/strategies for empowering
communities with data
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What’s Next (In Session 2 and 3)
• Session 2: Designing a Health Equity
Mapping Initiative From Start to Finish
(February 2013)
• Session 3: Building Capacity &
Implementation: Developing Partnerships
and Building Technical Support to Support
Mapping Initiatives, Moving from Maps to
Action (March 2013)
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