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Using Epidemiology for Data-Driven Decision-Making in Tuberculosis Programs February 24, 2016
Visualization to Enhance Data Impact 1
Visualization to Enhance
Data Impact
Cynthia Driver, DrPH
Deputy Director
Public Health Programs
• 20 countries and cities
• 3 components
– Civil registration and vital statistics
– Mobile phone survey of NCD risk factors
– Data Impact Program
Data for Health Initiative
Goal: Data-driven Policymaking
Management
Collection
Data-driven
Policymaking
Demand
for data
Analysis
Communication
Using Epidemiology for Data-Driven Decision-Making in Tuberculosis Programs February 24, 2016
Visualization to Enhance Data Impact 2
• Difficult to turn data into clear, succinct
communications products that advance
policy
• Challenges in both directions:
– Technical experts—articulating importance of
data to lay audiences
– Decision-makers—incorporating data into
communications products
Challenge
Scenario
• Advocate for additional funds to expand
TB program services in a neighborhood
with high TB rate
• Stakeholders
– Health department decision-makers
– Community partners
Data from: NYC DOHMH Bureau of Tuberculosis Control, Annual Summary 2014
You have data…now what? Tuberculosis Rates by Neighborhood, Brooklyn, NYC, 2014
Neighborhood Rate per 100,000
Bedford Stuyvesant-Crown Heights 6.5
Williamsburg-Bushwick 4.7
East Flatbush-Flatbush 9.6
Downtown-Brooklyn Heights-Park 2.6
Sunset Park 23.1
Coney Island 6.9
East New York 4.8
Borough Park 10.3
Bensonhurst- Bay Ridge 7.7
Greenpoint 1.5
Canarsie-Flatland 5.0
Brooklyn 7.4
City-Wide 7.2
Using Epidemiology for Data-Driven Decision-Making in Tuberculosis Programs February 24, 2016
Visualization to Enhance Data Impact 3
Why Visualize?
• Data findings more readily apparent in visual
images
• Easier to grasp meaning of data points in
graphical or pictorial format
• Visualize to:
- understand what the data are saying
- communicate efficiently to others
- support good decision-making
A Data Visualization…
… conveys meaningful and memorable
insights through graphical representation
of data
0
5
10
15
20
25
Rat
e p
er
10
0,0
00
TB Rates by Neighborhood, Brooklyn, NYC, 2014
Data from: NYC DOHMH Bureau of Tuberculosis Control Annual Summary, 2014
Using Epidemiology for Data-Driven Decision-Making in Tuberculosis Programs February 24, 2016
Visualization to Enhance Data Impact 4
Source: NYC DOHMH Bureau of Tuberculosis Control Annual Summary, 2014
TB Rates by Neighborhood, Brooklyn, NYC, 2014
1.5
2.6
4.7
4.8
5.0
6.5
6.9
7.7
9.6
10.3
23.1
0 5 10 15 20 25
Greenpoint
Downtown-Brooklyn Heights-Park
Williamsburg-Bushwick
East New York
Canarsie-Flatland
Bedford Stuyvesant-Crown Heights
Coney Island
Bensonhurst- Bay Ridge
East Flatbush-Flatbush
Borough Park
Sunset Park
Rate per 100,000
Citywide rate
(7.2 per 100,000)
Effective Visualization
• Visual perception
• Design principles
• Visualization type
• Data stories
Visual Perception
• Humans ‘see’ differences in color, form,
space, sequencing
• Data presentation impacts what we see
and how we interpret it
Using Epidemiology for Data-Driven Decision-Making in Tuberculosis Programs February 24, 2016
Visualization to Enhance Data Impact 5
10 Design Principles
Orient audience
1. Label sufficiently
2. Visually link related elements
3. Create a visual hierarchy
4. Simplify data comparisons
5. Display reference points
10 Design Principles
Eliminate distractions
6. Present text horizontally
7. Limit non-data elements
8. Use color purposively
9. Be cautious with images
10. Avoid distortions.
Data Visualization Types
Figures
• Bar
• Line
Maps
• Choropleth
• Dot density
• Proportional symbol
Using Epidemiology for Data-Driven Decision-Making in Tuberculosis Programs February 24, 2016
Visualization to Enhance Data Impact 6
TB Case Rates by Age Group and Sex, US, 2014
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
Under 5 5 - 14 15 - 24 25 - 44 45 - 64 ≥65
Male Female
Cas
es p
er 1
00,0
00
Source: CDC. Reported Tuberculosis in the United States, 2014. Surveillance Slide set.
20 Leading Causes of DALYs, NYC, 2005
Reported TB Cases, US, 1982–2014*
*Updated as of June 5, 2015.
0
5,000
10,000
15,000
20,000
25,000
30,000
No
. of
Cas
es
Year
Source: CDC. Reported Tuberculosis in the United States, 2014. Surveillance Slide set.
Using Epidemiology for Data-Driven Decision-Making in Tuberculosis Programs February 24, 2016
Visualization to Enhance Data Impact 7
TB Case Rates in U.S.-born vs. Foreign-born Persons, United States,* 1993 – 2014**
*Includes the same data as slide 17, but rates presented on a logarithmic scale.
**Updated as of June 5, 2015.
Cas
es
pe
r 1
00
,00
0
1
10
100
1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013
U.S. Overall
U.S. -born
Foreign-born
Source: CDC. Reported Tuberculosis in the United States, 2014. Surveillance Slide set.
Importance of geography – why map?
• Clustering of disease cases in specific location
• Visualizing health outcomes by geography
• Identifying geographic trends
More focused
interventions
Improved resource
allocation
Better understanding of disease patterns
When to Map?
• Location key to message
– When not key, map not useful for conveying message
• Tables, charts, graphs more useful
Using Epidemiology for Data-Driven Decision-Making in Tuberculosis Programs February 24, 2016
Visualization to Enhance Data Impact 8
Types of Maps
• Choropleth
• Dot density
• Proportional symbol
TB Rates by Neighborhood, Brooklyn, 2014
Sunset Park
2004 2014
DC
Percentage of TB Cases Among Foreign-born Persons, United States*
*Updated as of June 5, 2015.
DC
>50%
25%–49% <25% No cases
Source: CDC. Reported Tuberculosis in the United States, 2014. Surveillance Slide set.
Using Epidemiology for Data-Driven Decision-Making in Tuberculosis Programs February 24, 2016
Visualization to Enhance Data Impact 9
Dental Hygienists in South Carolina by Primary Practice Location, 2010
Source: Office for Healthcare Workforce Analysis and Planning, 2011
US Homeless Population by State, 2009
Source: StateHealthFacts.org
Strategic Communications
Using Data • Communicate priorities,
justify policy decisions
• Establish systems to
regularly share visually
compelling data
o Reports highlight specific
health issues or health status of
populations
o Press releases, speeches,
testimonies regularly
incorporate data
Using Epidemiology for Data-Driven Decision-Making in Tuberculosis Programs February 24, 2016
Visualization to Enhance Data Impact 10
A Policy Brief
A concise document that presents findings of policy-relevant data analysis and evaluates policy options for non-technical audiences
Includes:
• Context, scope, impact of problem
• Viable solutions
• Rationale for changing policies
Design Considerations
• Thoughtful design and length (2-4 pages)
• “Hook” that entices audiences to read
beyond the Key Messages
• Clearly worded
• Use graphs, maps, or figures
Resources
Excel
QGIS
ArcGIS
EpiInfo Map
Using Epidemiology for Data-Driven Decision-Making in Tuberculosis Programs February 24, 2016
Visualization to Enhance Data Impact 11
Thank you!