A geographically informed data model can help address barriers to using data from public health programs in low capacity environments. The model links different data sources, like estimates of orphans and children served, using numeric geographic identifiers for districts rather than district names. This facilitates linking data across sources when there may be spelling variations or changes in boundaries over time. The data model provides consistency across data sources and can help different organizations more easily share and link their data to evaluate programs and make evidence-based decisions.
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Improving Public Health Data With Geospatial Modeling
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. Using data for evaluation and evidence
based decision making
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?
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
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. 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. • 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
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
Hinweis der Redaktion
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.