Geographic data in public health: Lessons from the field


Published on

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

  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide
  • We’re all probably familiar with many of these issues. {REVIEW LIST}But I want to focus on something that was a common theme for our work in Phase III.
  • Case studies to illustrate two significant challenges
  • Geographic data in public health: Lessons from the field

    1. 1. Geographic data in public health:lessons from the field John Spencer MEASURE EvaluationWWHGD Working Group Meeting February 26, 2013
    2. 2. MEASURE Evaluation USAID funded internationalmonitoring and evaluation project Based at University of North Carolina at Chapel Hill
    3. 3. MEASURE Evaluation use ofspatial data and tools Spatial data and tools are an important resource for monitoring and evaluation of health programs and outcomes
    4. 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. 5. The big pictureData barriers have hampered progressmore than technical/capacity barriers
    6. 6. We’ve transitioned to adata rich environment
    7. 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. 8. Health and social service sector as awhole, lags behind other sectors in theuse of geospatial data and tools
    9. 9. Two data challenges to confront1. Isolated data (data silos)2. Participation of health sector in national spatial data infrastructure efforts
    10. 10. Isolated data
    11. 11. A lot of actors on the stagewithin a country, all with a data and reporting mandate
    12. 12. Data streams
    13. 13. Barriers to integration of data
    14. 14. Payoff can be substantial if effortis made
    15. 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) KuriaMap produced May 2010Poverty Source: Geographic Dimensions of Well Being in Kenya Report, Kenya Cent. Bur. ofStat., 2003Percent Orphans Served Calculated by dividing 2009 KPMS Number of Orphans Served (table 8.1) byEstimated Number of OVC from NACC Prevalence Report, 2007 (Appx. 3)
    16. 16. Not rocket scienceGeography can be the common link across data
    17. 17. Solutions for data silos1. Requirements for inclusion of geographic identifiers2. Data standards  Data schema  File formats  Indicators3. Some degree of openness with data
    18. 18. Spatial data infrastructure
    19. 19. Health and social service sector as a whole, lags behind other sectors in the use of geospatial data and tools
    20. 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. 21. When you bring the health sector to theSDI table, there are benefits across all sectors
    22. 22. Health sector and SDICODIST Workshop, Addis Ababa Ethiopia 2009Nigeria 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. 23. Health sector and SDICODIST  UNECA resolutions including committing member states to ensure that key players in the health sector (especially Nat’l AIDS Commissions) participate in NSDI
    24. 24. Health sector and SDINigeria 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. 25. We’re all in the same boat, when healthsector is a partner in SDI, all sectors benefit
    26. 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. 27. Capacity and technical challenges can beovercome, data issues are morechallenging
    28. 28. The research presented here has been supported by thePresident’s Emergency Plan for AIDS Relief (PEPFAR) throughthe United States Agency for International Development(USAID) under the terms of MEASURE Evaluation cooperativeagreement GHA-A-00-08-00003-00. Views expressed are notnecessarily those of PEPFAR, USAID or the United Statesgovernment.MEASURE Evaluation is implemented by the CarolinaPopulation Center at the University of North Carolina at ChapelHill in partnership with Futures Group, ICF International, JohnSnow, Inc., Management Sciences for Health, and TulaneUniversity.
    29. 29.