1. Data Visualization that Works
Facilitating HIV Program Targeting:
Case examples and considerations
Tara Nutley, Isabel Brodsky
MEASURE Evaluation
Stacey Berlow
Project Balance
MEASURE Evaluation Webinar
April 27, 2016

2. MEASURE Evaluation
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Strategic objective:
To strengthen health information systems–the capacity to gather,
interpret, and use data–so countries can make better decisions and
sustain good health outcomes over time.

3. How are data visualization tools being used to improve
the use of data in HIV programs?
What impact do they have on decision making?
What are key elements of success?

4. Contacted 45 people from 17 PEPFAR
implementing partner organizations

6. SouthAfrica
Using proxies to map HIV
services in Nkomazi District

8. HIV prevalence
18.8% National
14.1% Mpumalanga
Province

9. We can improve access to health services if we know where
PLHIV are located in relation to health facilities.

10. How can we fill data gaps through estimation and
the development of proxy indicators?

11. Visualization methodology
Estimate HIV positivity using PMTCT testing
data at each health facility
Use HIV positivity to estimate HIV
prevalence at each facility
Interpolate to estimate HIV prevalence
for all areas between health facilities
Map results using ArcGIS
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12. Visualization result

13. Most PLHIV in Nkomazi are migrant
farm workers. Many of them are undocumented
and unwilling to access services due to
fear of stigma and deportation.
Photo source: TechnoServe

14. Prevalence decreased from
42.7% in 2012 to 40.5% in 2014.

15. Zambia MOH Dashboard
Question: What kind of early warning
system can be put into place to help
the Zambian MOH to more effectively
provide prevention of mother-to-child
transmission (PMTCT)/Option B+ services
across the country?
Look at ANC/patient trends, commodity stock-outs and lab results

16. Collaborate with stakeholders
• Understanding how the
data will be used for
decision making
• Types of graphs and
displays
• User access

17. PMTCT dashboard architecture
PMTCT
dashboard
database
Three national databases
PMTCT dashboard

18. Data aspects
• Extracting the source data
• Calculating indicators
• Data quality
• Refreshing the data

19. Software development team
• Development team who knows
how to develop dashboards
• Excellent testing methodology

20. Lessons Learned

21. Collaborate with data users during
visualization development.

22. Zambia: Collaboration with MOH ensured that
developers understood what information was available and
what ideally was needed to monitor the PMTCT program. A
working group had previously identified a set of indicators
that would help the ministry and implementing partners (IPs)
manage the PMTCT program.
South Africa: A partnership between implementing
partners, local government, and geographic information
system (GIS) specialists enabled programmatic questions to
be answered and needs to be met.

23. Consider developing proxy
indicators through estimation or
modeling techniques

24. Zambia: Developed proxy PMTCT indicators for nine of
the eleven core indicators on the dashboard in order to fill
missing data gaps in the interim while data are collected
South Africa: Developed methodology to estimate
district-level HIV prevalence using proxy data

25. Include training on how to
interpret visualizations

26. South Africa: District team members trained on map
reading and interpretation to understand what the maps were
showing and the implications of the maps in their day-to-day
work
Zambia: It was agreed that training to use the dashboard
would be easy for users. It was also acknowledged that
interpreting the graphs and data in tables will be more
challenging and will likely require MOH policies. For example:
if a target was missed by X percent, what action should the
organization take?

27. Standardize data sources

28. Cleaning and matching data takes time.
Even the most accurate data source may
have errors or duplicates.
Zambia: Multi-step data standardization process
South Africa: Creating a map using a GIS requires a
single, authoritative, and accurate master facility list

29. Ensure good-quality data

30. If users realize they cannot trust the data to help
them make decisions, they may not use the
visualization in the future.
Zambia: Found a lot of missing data. The creation of a new
dashboard for PMTCT gives developers and MOH the
opportunity to ensure data are of good quality so that the
dashboard is ultimately useful to decision makers.
South Africa: Having up-to-date estimates of HIV prevalence
prevalence data at the district level is crucial for decision making.
If decision makers are using old data, they may not be making
accurate targeting and program planning choices.

31. Consider sustainability when selecting
GIS and dashboard software

32. Data visualizations vary in cost and
levelof sophistication.
Zambia: Dashboard created in free and open source
software, which is a cost-effective option. However, it is
generally not well documented and some code may not work.
South Africa: The maps were created on ArcGIS, a
sophisticated but proprietary software. If Nkomazi wants the
maps to be updated in the future, the analysis must be re-
and they would need access to the software.

33. MEASURE Evaluation is funded by the U.S. Agency for
International Development (USAID) under terms of
Cooperative Agreement AID-OAA-L-14-00004 and
implemented by the Carolina Population Center, University of
North Carolina at Chapel Hill in partnership with ICF
International, John Snow, Inc., Management Sciences for
Health, Palladium, and Tulane University. The views
expressed in this presentation do not necessarily reflect the
views of USAID or the United States government.
www.measureevaluation.org