Presented by Nate Heard, HIU/Dept of State, at the March 2014 MEASURE GIS Working Group meeting.

1. Spatial data for health: what’s changed in terms of
availability and quality?
MEASURE GIS Working Group
March 4, 2014
Nate Heard
1
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2. Overview
• Spatial data for analysis in health
• Tracks of spatial data development
– Authority
– The crowd
– The academy
• Open data
• What’s next?
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3. Attribute
data
Feature
data
Databases
Describe patterns
Analyze patterns
Explain or predict
patterns
Visualization
Exploration
Modelling
GIS
DBMS
Statistical
analysis
Pfeiffer et al. 2008. Spatial Analysis in Epidemiology. Oxford University Press
Conceptual Framework for Spatial
Epidemiological Data Analysis
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4. Sub-national HIV Prevalence – 2001-2007
Percent of women and men age 15-49 who are
HIV-positive
Kimberly
Forkner -
Macro
International
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5. Clara
Burgert,
ICF Macro
Sub-national HIV Prevalence – 2001-2013
Percent of women and men age 15-49 who are
HIV-positive
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6. Spatial Data Repository
Before… After!
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7. # CVL
(mean)
0-20
21-100
101-500
500+
Brazil: Community Viral Load, 2012
# CVL
(quartile)
0 – 449,948
449,949 – 1,570,941
1570942 - 5433788
5433788+
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8. DHIS2
• Used in 46
countries, 25,000
users monthly
• National standard
for HMIS in 11
countries
• In Uganda, DHIS2 is
the facility registry
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http://www.dhis2.org/
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9. Global AIDS Response Progress
Reporting 2014
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“To facilitate data integration and
analysis, geographic markers for data
should be maintained with indicators
at the appropriate level of precision
and using standardized geographic
references and naming conventions...
Attaching geographic information to
the more granular data that compose
aggregate indicators can enable a
wide array of analysis, such as
geographic coverage of services,
spatial distribution of human
resource and expenditures, and the
estimation of change over time
for small areas.”
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10. Geographic Location as Required Data in
Master Lists of Health Facilities, 2012
10
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11. Open Street Map (OSM):
Johannesburg, 2013
11
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12. OSM: Rio de Janeiro, 2013
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13. OSM: Mathare, Nairobi, Kenya
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14. OSM: Bangui, Central Africa Republic
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15. 15
OSM: Bangui, Central Africa Republic
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16. OSM: Lubumbashi, DRC
16
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17. OSM: Ta‘izz, Yemen
17
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18. OSM: Nalayh, Mongolia
18
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19. Haiti MSPP v. OSM 1
19http://www.mspp.gouv.ht/cartographie/
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20. Haiti MSPP v. OSM 2
20http://www.mspp.gouv.ht/cartographie/
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21. Haiti MSPP v. OSM 3
21http://www.mspp.gouv.ht/cartographie/
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22. Haiti MSPP v. OSM 4
22http://www.mspp.gouv.ht/cartographie/
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23. This Wormy World
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Brooker et al. 2009. An updated atlas of human helminth infections: the example of East Africa.
International Journal of Health Geographics 2009, 8:42
http://www.thiswormyworld.org/Global Atlas of Helminth Infections
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24. 24
Patil, A.P., Gething, P.W., Piel, F.B. and Hay, S.I. (2011). Bayesian geostatistics in health
cartography: the perspective of malaria. Trends in Parasitology 27(6): 246-253
The clinical burden of Plasmodium falciparum
map in 2007 in Papua New Guinea
The spatial limits of Plasmodium falciparum
malaria transmission map in 2010 in
Dominican Republic
Malaria Atlas Project
http://www.map.ox.ac.uk http://www.map.ox.ac.uk
UNCLASSIFIED

25. Messina et al. 2010. "Spatial and
socio-behavioral patterns of HIV
prevalence in the Democratic
Republic of Congo" Social Science
& Medicine 71 (2010) 1428e1435.
Montana, L. 2007. Spatial Modeling of HIV
Prevalence in Kenya. DHS Working
Papers. MEASURE DHS, Macro
International Inc., Calverton, MD
Larmarange, J. 2011. Methods for mapping
regional trends of HIV prevalence from
Demographic and Health Surveys (DHS)
Cybergeo: European Journal of
Geography.
HIV Interpolation Using DHS
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26. Gridded Population
26U.S. Census: Demobase
http://www.census.gov/population/international/data/mapping/demobase.html
Landscan http://web.ornl.gov/sci/landscan/
WorldPop
http://www.worldpop.org.uk/
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27. Watch This Space
27
http://www.thummp.org/
Map generated by more than 250 million
public tweets with high-resolution location
information, March 2011 and January 2012.
Salathé M, Bengtsson L, Bodnar TJ, Brewer DD, et al. (2012)
Digital Epidemiology. PLoS Comput Biol 8(7): e1002616.
doi:10.1371/journal.pcbi.1002616
http://www.ploscompbiol.org/article/info:doi/10.1371/journ
al.pcbi.1002616
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28. “The three principles of transparency, participation, and collaboration form the
cornerstone of an open government. Transparency promotes accountability by
providing the public with information about what the Government is
doing. Participation allows members of the public to contribute ideas and
expertise so that their government can make policies with the benefit of
information that is widely dispersed in society. Collaboration improves the
effectiveness of Government by encouraging partnerships and cooperation
within the Federal Government, across levels of government, and between the
Government and private institutions.”
1. Publish Government Information Online
2. Improve the quality of USG information
3. Create and Institutionalize a Culture of Open Government
4. Create an Enabling Policy Framework for Open Government
http://www.whitehouse.gov/open/documents/open-government-directive
Open Government Directive
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29. Federal agencies with more than $100M in R&D expenditures to develop
plans to make the published results of federally funded research
freely available to the public within one year of publication and
requiring researchers to better account for and manage the digital
data resulting from federally funded scientific research.
http://www.whitehouse.gov/sites/default/files/microsites/ostp/ostp_public_access_memo_2013.pdf
Expanding Public Access to the Results of Federally
Funded Research
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30. PLOS’ New Data Policy: Public Access
to Data
30
… We are now revising our data-
sharing policy for all PLOS
journals: authors must make all
data publicly available, without
restriction, immediately upon
publication of the article.
Beginning March 3rd, 2014, all
authors who submit to a PLOS
journal will be asked to provide a
Data Availability Statement,
describing where and how others
can access each dataset that
underlies the findings. This Data
Availability Statement will be
published on the first page of
each article.
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31. Conclusion
• Beyond visualization
• New tools for new data
• Getting closer to the E
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32. Boundary representation is not necessarily authoritative. The views and
conclusions contained in this presentation are those of the author and do
not necessarily reflect the policies of the United States Government. Any
use of trade, product, or firm names in this presentation is for descriptive
purposes only and does not imply endorsement by the U.S. Government.
UNCLASSIFIED
Nathan Heard, DSc
Public Health Analyst
Humanitarian Information Unit
U.S. Department of State
HeardNJ@state.gov
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