Improving Public Health Programs Through
The Use of A Geographically Informed Data
Model: A Strategy for Low Capacity
Envi...
Using data for evaluation and evidence
based decision making
Transitioning to a data rich
environment
Data has become stovepiped
Barriers to using
reporting data
Barriers to using
reporting data
Technical
• Do I have to clean the data?
• Is it in a compatible format?
• Can the data l...
A geographically informed data model can address
both technical and non-technical barriers
Data models
Common in other
sectors but not so
common in global public
health.
Data Model
1. Location of program
2. Service provided
3. Number of beneficiaries
4. Implementing organization
5. Year or d...
Linked using Geography
Use numeric geographic identifiers
District Population
Coast 79,133
Mountain 66,251
North 23,415
ID District Population
10...
Not rocket science
Geography can be the common link across data
Non-technical side
• Creates consistency with data
• Can help achieve buy-in about sharing data
– Makes it easier for data...
At least 4 Steps
1. Standardize names
• Le Tierge
2. Spelling
• Karatu
3. Identify changes in
boundaries
• Totou
4. Defini...
• Numeric code for
districts
• Spelling variation
not an issue
• Accommodates
changes in
geography
Using a data model
ID D...
Not rocket science
Geography can be the common link across data
Growing focus on data
Building blocks for better M&E
Photo by ogimogi http://flic.kr/p/4r9zSK
Coming soon
• Upcoming publication
that goes into more
detail
The research presented here has been supported by the President’s
Emergency Plan for AIDS Relief (PEPFAR) through the Unit...
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Improving Evaluation of International Public Health Programs Through the Use of a Geographically Informed Data Model

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Presented by John Spencer at 2013 American Evaluation Association Meeting in Washington, DC.

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  • Hello my name is John Spencer and I’m the Senior Geospatial Analyst at MEASURE Evaluation, a USAID funded monitoring and evaluation project.
  • My talk today will be about a proposed data model that we think has the potential to make it easier to use data for evaluation and evidence based decision making within global public health, especially in countries with nascent M&E capacity.
  • For most of the first decade of this century, there was a limited amount of data available about health programs and the populations they served. That has started to change as PEPFAR, USAID and other donors required more reporting
  • The problem is that this data is collected independently of each other. The data has become stovepiped and is difficult to link to each other. When users want to link programmatic data with other data it proves to be very challenging.
  • It becomes challenging to find and use data. Then there are issues with compatibility, where once you do get the data it can’t be linked with other data without a lot of data cleaning.
  • Barriers are both technical and non-technical. Technical includes things like file formats and schemas. Non-technical includes things like how does one find out who has the data. In many ways the technical are easier to tackle than the non-technical.
  • We propose addressing both issues through the development of a data model that uses geography as the link.Data models are used to ensure consistency with data by laying out rules for data schema and data content.
  • Data models are common in other sectors and there have been some data models proposed and implemented in public health in the US and other countries but few focusing on international public health. Data models can be very complex. We’ve kept ours simple.
  • It has 5 elements. Location of program, what service has been provided, how many people have been served, what organization implemented it and when it was implemented. Note this is for aggregated data, not individual patient or client data.
  • Geography is the key field. Everything happens somewhere and we can use that to help provide the context for linking data. It is important that there isa clear standard for how to represent geographic data.
  • The data model mandates the use of numeric codes for geographic identifiers instead of text, as is often the case with current data, where administrative units are often recorded as text. Most countries have a numeric system, but it often isn’t widely used. If there isn’t a national code, there are international standards that can be used.
  • From there it is a simple matter of linking as one would with any data. This isn’t necessarily rocket science, but by standardizing the approach it removes one of the barriers for linking data.
  • On the non-technical side, a data model helps as well since it creates consistency with data it can help achieve buy-in about sharing data. Data providers have clear guidance about how to store and collect data and they can use data from other organizations.
  • To illustrate, I’ve taken real data, masked where it really is by changing names and some numbers. To link data, one has to go through at least four steps to join the data together. No big deal for this sample, but a major barrier when done at a national scale.
  • Here’s the process with data structured according to the data model. It’s easier to link because its tied to numeric codes.
  • Again, not rocket science, there just needs to be an effort to build consensus about it. This requires national governments, donors and program implementers to identify their data needs and adjust their collection strategy.
  • Some encouraging signs. AidData, World Bank, USAID, and others are beginning to discuss these issues and the importance of standardizing. In our work we’ve had positive feedback from implementers and governments.
  • We hope these developments are the start of a discussion that can lead to improved data that supports evaluation and evidence based decision making.
  • There’s obviously not enough time in a short presentation to cover everything. There are other details around the data model beyond the use of id, but in the time I have there’s no opportunity to go over them. But we have an upcoming publication that goes into more detail. It will appear on the MEASURE Evaluation website in the coming weeks.
  • This work arises from our efforts to build capacity in monitoring and evaluation and while it may not be needed in all countries, in many of the lowest capacity countries, there are limited data standards. I’d be happy to provide more information or answer questions.
  • Improving Evaluation of International Public Health Programs Through the Use of a Geographically Informed Data Model

    1. 1. Improving Public Health Programs Through The Use of A Geographically Informed Data Model: A Strategy for Low Capacity Environments John Spencer Sr. Geospatial Analyst MEASURE Evaluation American Evaluation Association Meeting Washington D.C. October 16, 2013
    2. 2. Using data for evaluation and evidence based decision making
    3. 3. Transitioning to a data rich environment
    4. 4. Data has become stovepiped
    5. 5. Barriers to using reporting data
    6. 6. Barriers to using reporting data Technical • Do I have to clean the data? • Is it in a compatible format? • Can the data link to other data? Non-Technical • Who has the data? • How do I get a copy of the data? • When was it collected?
    7. 7. A geographically informed data model can address both technical and non-technical barriers
    8. 8. Data models Common in other sectors but not so common in global public health.
    9. 9. Data Model 1. Location of program 2. Service provided 3. Number of beneficiaries 4. Implementing organization 5. Year or date
    10. 10. Linked using Geography
    11. 11. Use numeric geographic identifiers District Population Coast 79,133 Mountain 66,251 North 23,415 ID District Population 101 Coast 79,133 103 Mountain 66,251 105 North 23,415 • Facilitates linking • Many countries have district codes, but they may not be widely used • If there aren’t codes, there are international standards that can be used to create codes. Hard to link Easier to link
    12. 12. Not rocket science Geography can be the common link across data
    13. 13. Non-technical side • Creates consistency with data • Can help achieve buy-in about sharing data – Makes it easier for data producers to link data themselves – More involved in the data community
    14. 14. At least 4 Steps 1. Standardize names • Le Tierge 2. Spelling • Karatu 3. Identify changes in boundaries • Totou 4. Definitional Changes • OVC Illustrative data linking Orphan and Vulnerable Children Programs District Orphan Est. 07 OVC Served by PEPFAR CT HH 2013 Koratu 21821 54 1604 Le Tiergé 21804 5015 2000 Salamansa 471204 2500 2229 Totou 108109 7074 -999 East Totou -999 -999 3473 District Orphan Est. 07 Koratu 21821 Le Tierge 21804 Salamansa 471204 Totou 108109 District OVC Served by PEPFAR Koratu 54 Letierge 5015 Salamansa 2500 Totou 7074 PEPFARHIV Prevalence Report District CT HH 2013 Karatu 1604 Le Tiergé 2000 Salamansa 2229 East Totou 3473 Cash Transfer Database Integrated Data Table
    15. 15. • Numeric code for districts • Spelling variation not an issue • Accommodates changes in geography Using a data model ID District Orphan Est. 07 OVC Served by PEPFAR CT HH 2013 101 Koratu 21821 54 1604 103 Le Tiergé 21804 5015 2000 105 Salamansa 471204 2500 2229 107 Totou 108109 7074 -999 108 East Totou -999 -999 3473 ID District Orphan Est. 07 101 Koratu 21821 103 Le Tierge 21804 105 Salamansa 471204 107 Totou 108109 ID District OVC Served by PEPFAR 101 Koratu 54 103 Letierge 5015 105 Salamansa 2500 107 Totou 7074 PEPFARHIV Prevalence Report ID District CT HH 2013 101 Karatu 1604 103 Le Tiergé 2000 105 Salamansa 2229 108 East Totou 3473 Cash Transfer Database Integrated Data Table
    16. 16. Not rocket science Geography can be the common link across data
    17. 17. Growing focus on data
    18. 18. Building blocks for better M&E Photo by ogimogi http://flic.kr/p/4r9zSK
    19. 19. Coming soon • Upcoming publication that goes into more detail
    20. 20. 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. www.measureevaluation.org

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