Presented by John Spencer

1. Surfing a great wave
Data Science and Global Health
John Spencer, MA
Peter Lance, PhD
Mark Janko
MEASURE Evaluation
University of North Carolina at Chapel Hill
January 13, 2016

2. What is data science?
How is data science
happening at USAID?

3. Global, five-year, $180M cooperative agreement
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.
Project overview

4. MEASURE Evaluation
Phase IV Results Framework
Strengthened collection, analysis,
and use of routine health data
Improved country capacity to
manage health information
systems, resources, and staff
Methods, tools, and approaches
improved and applied to address
health information challenges and
gaps
Increased capacity for rigorous
evaluation

5. Global footprint (more than 25 countries)

6. Data

7. “Health policymakers, international donors, program
managers, service providers and other health system
stakeholders need reliable data to make evidence-based
decisions.”

8. “
”

9. “If we are going to ensure that people everywhere have access
to quality health care … we need to invest in high-quality,
timely, and accurate data and statistics.”

10. The path to better global health is in the data.

11. More data

12. Other organizations

13. Everyone is creating data

14. More data available in the world than ever before.

15. 0
6
12
18
2011 2013 2015 2017
Zettabytes
Digital universe: 1.8 zettabytes in 500 quadrillion “files”
1 zettabyte= 1 trillion gigabytes

17. Happening all around the world

19. Data
Tsunami

23. This data reflects the world around us.

24. We can use it to improve people’s lives.

25. There is considerable opportunity.
But also there are challenges.

26. Data can be hard to find in the
tsunami

27. Data can be so complex it
can be hard to extract and
analyze

28. Lessons from the analysis
of the data can be hard to
communicate

29. The way things have worked
Question
Gather data
from traditional
siloed sources
(DHS, RHS,
Census, etc.)
Hypothesis
testing
Produce static
data product
(Lather, rinse, repeat)

30. Approaches to exploiting data that
served us well in the 1980’s can now
limit our possibilities

31. Narrowly focused, siloed data
influences conclusions and limits
opportunities to see larger context

32. Data science is an
approach that addresses
these challenges.

33. So…what is data science?

34. Data science is a discipline and process that
integrates 3 key skills for surfing this Tsunami:
1. Locating and retrieving key data to that
can inform decision making
2. Turning that data into actionable
information
3. Communicating that information in the
most effective way

36. Data science is the approach
used by organizations that
live and die by data

37. Increasingly making its
way into global health

38. This is happening
at USAID

39. It’s also being used elsewhere in USG
USG Example

40. But we can do more

41. We can turn this into a keystone for
the practice of public health

42. How does it fit within the context of
specific USAID priorities?

43. EPCMD

44. EPCMD
LiST tool
Add data tsunami data to LiST model

45. Quantifying seasonal population fluxes driving
rubella transmission dynamics using mobile phone
data
Wesolowski, Metcalf, et al; PNAS; Vol 112 no. 35; Sept 1 2015

46. Health System
Strengthening

47. Filling in data gaps
Twitter as early warning or post hoc assessment

48. Mom Connect
South Africa
Two way data stream – to and from patient
What can we learn from the moms?

49. AIDS Free Generation

50. Subnational
models using
DHS data
• Travel time to cities
• Population
• Aridity
• Elevaton data from Shuttle
• Daytime/nighttime land surface temperature
• Rainfall
• Landcover
DHS Program: Spatial Analysis Report Number 11
Covariates

51. Bayesian analysis of DHS and PEPFAR reporting data
Stay tuned….
Are PEPFAR sites
underperforming or
over performing?

52. Confidentiality and privacy

53. Who owns all this data?
Him? Her?

54. Conclusion
More data about the world than ever before.
This data tsunami offers unprecedented
opportunities, but also challenges.
Data science is a production process for data
that can harness the data tsunami

55. MEASURE Evaluation and Data Science

56. http://measureevaluation.org/datascience

57. Questions for audience
What are you working on where traditional data
sources and techniques are missing part of the
story?
How is data science happening at USAID?

58. 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 Group, 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