This presentation was shared by Clara R. Burgert-Brucker, Pete Gething, Andy Tatem, and Tom Bird, all with The DHS Program, at the June 2016 MEASURE Evaluation GIS Working Group Meeting.

1. MEASURE EVALUATION GIS WORKING GROUP June 2016 1
Beyond Dots on a Map:
Spatially Modeled Surfaces of DHS data
Clara R. Burgert-Brucker
Pete Gething
Andy Tatem
Tom Bird
USAID fundedThe DHS Program
ICF International, Oxford University, &
University of Southampton

2. WAY TOO BIG!STILL TOO BIG!too small! (and not representative)
Where
are we
making
health
program
decisions?
Still too small! (Who makes decision at 5 x 5 km squares?
Just right?

3. Increasing DHS survey sample size to enable
increased geographic disaggregation is
EXPENSIVE!
3
Challenge
Aim
Improving the measurement and
understanding of local (geographic) patterns
to support more decentralized decision
making and targeted, efficient program
implementation.

4. • Selecting Indicators: Not rare event, Spatially
distributed, Specific Reference period, No
seasonally/temporally related, and Related to the
current location of the respondent
• Modeled Map Criteria: Use publically available covariate
data, standardize and comparable across countries, and
produce a corresponding map surface with uncertainty
estimates
Burgert et al. DHS Spatial Analysis Reports 9. 2014
Spatial Interpolation with DHS Data:
Key Considerations (SAR 9)

5. Proposed Approach
Bayesian Model-based Geostatistics (MBG)
Exploits spatial relationships within the data,
leverages ancillary information from
geospatial covariates, and rigorously handles
uncertainties at all stages.

6. • Assessment of method for:
– Validity, Covariates, Uncertainty, DHS cluster displacement, &
Urban areas
• Countries:
– Tanzania DHS 2010, Uganda DHS 2011, & Ghana DHS 2008
• Indicators:
– <5 Anemia, <5 stunting, household access to improved
sanitation, & women 15-49 tested for HIV in last 12 months
Gething et al. DHS Spatial Analysis Reports 11. 2015
Creating Spatial Interpolation Surfaces
with DHS Data (SAR11)

9. • Aggregate from 5x5km to administrative units not represented in survey
sample
– Targeting interventions and development
– Where is greatest need?
– Where are X,Y, Z, factors present to implement specific course of
action?
– Compare intervention/non-intervention areas
• Linkage with other data
– Combine with population data to improve burden estimates
– Health Facility data
• Overlay for context
• CAUTION: Direct extraction of values is misleading & re-aggregation back
up to DHS regions or National level may not match DHS data exactly.
How might these surfaces be used?

10. Aggregated to Districts Aggregated to Livelihoods

11. Modeling HIV related variables with survey data
• In our pilot, HIV testing was least well predicted modeled surface likely
due in part to limited socio-economic covariates
• In some countries or areas might be a relatively rare events?
• Prospective versus retrospective, locational bias may be stronger for
HIV related variables?
• Inclusion of multiple data streams?

12. • Provide a standard set of spatially modeled map
surfaces using DHS data
–Routinely create interpolated map surfaces for each
population-based survey
–Make publically available on DHS Spatial Data
Repository (spatialdata.dhsprogram.com)
• First round 10 countries, 15 indicators in 9/2016
• Provide guidance on the use and interpretation of
spatially modeled map surfaces
–Including use cases, Do you have an idea?
• Community awareness & sharing
–Could you use this in your activities next year?
Ongoing Activities

13. Clara R. Burgert-Brucker
clara.burgert@icfi.com

14. • Urban mapping:
–Predicted with uniform values in models
–Reality is urban areas have substantial
heterogeneities
• Covariate library mostly focused on biophysical
data at this time & not demographic or health
data that might be stronger drivers of certain
types of outcomes
–Problem is these would likely be DHS derived
Pilot Activity Limitations

15. • All models performed reasonably well with
relatively low bias & errors
• HIV testing was least well modeled indicator
• Displacement of GPS locations for confidentiality
did not add significant error to models
Pilot Activity Results