Presented by John Spencer at the February 2013 WWHGD Working Group Meeting.

1. Geographic data
in public health:
lessons from the
field
John Spencer
MEASURE Evaluation
WWHGD Working Group Meeting
February 26, 2013

2. MEASURE Evaluation
USAID funded international
monitoring and evaluation project
Based at University of North
Carolina at Chapel Hill

3. MEASURE Evaluation use of
spatial data and tools
Spatial data and tools are an important
resource for monitoring and evaluation of
health programs and outcomes

4. MEASURE Evaluation
 Technical assistance to USAID
missions, national governments, implementing
partners and USAID Washington
 Trainings and workshops
 Guidance documents
 Participation in expert consultations

5. The big picture
Data barriers have hampered progress
more than technical/capacity barriers

6. We’ve transitioned to a
data rich environment

7. Data Issues
 A lot of paper based records
 Data quality
 Lack of geographic identifier
 Some aspects of health can’t be tied to geography
neatly
 Boundary/administrative issues

8. Health and social service sector as a
whole, lags behind other sectors in the
use of geospatial data and tools

9. Two data challenges to confront
1. Isolated data (data silos)
2. Participation of health sector in national spatial
data infrastructure efforts

10. Isolated data

11. A lot of actors on the stage
within a country, all with a data
and reporting mandate

12. Data streams

13. Barriers to integration of data

14. Payoff can be substantial if effort
is made

15. Rural Poverty Estimates 2003 and Percent
Orphans Served by PEPFAR 2009,
Nyanza Province
Kisumu
Siaya
Nyando
Bondo
Rachuonyo
Suba
Nyamira
Homa
Bay Cash Transfer District
Kisii
Central
Gucha
Migori (South Kisii)
Kuria
Map produced May 2010
Poverty Source: Geographic Dimensions of Well Being in Kenya Report, Kenya Cent. Bur. of
Stat., 2003
Percent Orphans Served Calculated by dividing 2009 KPMS Number of Orphans Served (table 8.1) by
Estimated Number of OVC from NACC Prevalence Report, 2007 (Appx. 3)

16. Not rocket science
Geography can be the common link across data

17. Solutions for data silos
1. Requirements for inclusion of geographic
identifiers
2. Data standards
 Data schema
 File formats
 Indicators
3. Some degree of openness with data

18. Spatial data infrastructure

19. Health and social service sector as a whole,
lags behind other sectors in the use of
geospatial data and tools

20. Health sector and SDI
 Data, capacity and knowledge sharing often
greater outside health sector
 Leads to duplication of effort in health sector
 Recreating boundary files
 Inhibits capacity development
 Leads to inaction in health sector

21. When you bring the health sector to the
SDI table, there are benefits across all
sectors

22. Health sector and SDI
CODIST Workshop, Addis Ababa Ethiopia 2009
Nigeria National Mapping Summit 2011
 Goals of both events:
 Bring together health sector and other sectors
 Identify common goals, data needs, capacity
 Build a community of practice

23. Health sector and SDI
CODIST
 UNECA resolutions including committing member states to
ensure that key players in the health sector (especially Nat’l
AIDS Commissions) participate in NSDI

24. Health sector and SDI
Nigeria Mapping Summit
 Identification of issues across all sectors affecting Nigeria’s
ability to improve health outcomes (especially coordination
of health and NSDI efforts)
 Communique presented to executive and legislative
branches to encourage collaboration between NSDI and
HIV/AIDS efforts

25. We’re all in the same boat, when health
sector is a partner in SDI, all sectors benefit

26. Conclusion
 More than most sectors, the health sector has deep
data roots, yet in many countries, lags behind other
sectors in use of spatial tools
 Software can be taught, capacity in use of software
can be built, but strengthening data requires
addressing issues at a more systematic level

27. Capacity and technical challenges can be
overcome, data issues are more
challenging

28. The research presented here has been supported by the
President’s Emergency Plan for AIDS Relief (PEPFAR) through
the United States Agency for International Development
(USAID) under the terms of MEASURE Evaluation cooperative
agreement GHA-A-00-08-00003-00. Views expressed are not
necessarily those of PEPFAR, USAID or the United States
government.
MEASURE Evaluation is implemented by the Carolina
Population Center at the University of North Carolina at Chapel
Hill in partnership with Futures Group, ICF International, John
Snow, Inc., Management Sciences for Health, and Tulane
University.

29. www.measureevaluation.org