Presented by Scott Moreland, Tara Nutley and Leontine Gnassou at the MEASURE Evaluation End-of-Phase-III Event.

1. Data Demand and Use
Facilitating the Use of Data to Inform
Programs and Planning in Health

2. Why We Collect Data
The ultimate goal is
not to gain
information, but to
improve action.
Credits: Pierre Holtz for UNICEF

3. “… without information, things are done arbitrarily
and one becomes unsure of whether a policy or
program will fail or succeed. If we allow our policies to
be guided by empirical facts and data, there will be a
noticeable change in the impact of what we do.”
Director of Policy, National Action Committee on AIDS, Nigeria

4. Data are often underutilized
because of…
 Lack of a “data culture”
 Unclear staff roles or low staff motivation
 Lack of technical skills
 Lack of information technology
 Poor data quality
 Lack of appreciation of existing data

5. Organizational and data quality issues
topped the list of issues identified as
constraints in a DDU study in India.
Source: Data Use in the Indian Health Sector, MEASURE Evaluation

6. What Do We Mean By Data Use?
 Availability use
 Data are reviewed to:
 monitor a program
 create or revise a program or strategic plan
 develop or revise a policy
 advocate for a policy or program
 allocate resources
 Review is linked to a decision making process

7. There is no single “right way”
to use data to support decisions.
 Multiple stakeholders
 Multiple and/or conflicting goals
 Different ways to measure success
 Ambiguous interpretation of what the data
mean
 How much data is sufficient to make an
informed decision

8. Decisions are influenced by factors
other than information and data.
 Political, cultural or religious ideology
 Power and influence of sectional interests
 Corruption
 Arbitrariness
 Anecdote

9. DDU is part of the
data-information-use cycle.
Data
Demand
Information
Availability
Information Use
(Decisions made)
Data Collection and
Analysis
Decision-Making
Process

10. Data Demand And Use
1. Is a systematic and deliberate approach
2. Ideally starts at the beginning of the data cycle
3. Includes users of the data and other stakeholders in
all stages
4. Is facilitated through the use of various tools
and approaches
5. Principles apply at all levels from the facility
and community, to national and international
programs

11. Decisions
The Three DDU Elements

12. Element 1: Stakeholders
and Decision-Makers
 Government
 USG SI officers
 Other Donors
 Implementing Partners
 Beneficiaries
 Program Managers
 Policy Makers
 Journalists/Media
 Private Sector
Engage stakeholders to identify issues
and data required

13. Element 2: Data and Information
 Service statistics
 Surveillance data
 Household surveys
 Vital events data
 Research
 Census
 Mapping of health
facilities and services
 Financial and management
information
 Modeling, estimates and projections
Assemble or collect and analyze data

14. Element 3: Decisions
 Problem identification, awareness
raising & advocacy
 Policy & planning
 Program design & improvement
 Program management &
operations
Facilitate use of data by stakeholders

15. Project’s Vision for DDU
Data Demand and Use Strategy, MEASURE Evaluation Dec 2009
“By the end of the project period it is anticipated that
data use will be fully integrated into and part of the
regular M&E process. Moreover, it is envisioned that
select country collaborators will have
institutionalized data use approaches and tools and
will regularly consider issues relating to data
informed decision making at the outset of all M&E
activities.”

16. Phase 2: Changing the paradigm
 Developed the theory, concepts, tools and
case study examples
 Conceptual framework
 Toolkit
 Case studies
 Arusha meeting
 Training by experts

17. Phase 3: Scale Up and Institutionalization
Refined & standardized DDU
approach
 Revised toolkit – 2 additional tools,
one in draft
 Publications (10)
 Data Use Net (954 members)
 Expanded Conceptual Framework
& Logic Model

18. Phase 3: Scale Up And Institutionalization
Capacity building and technical
assistance in DDU
 Capacity building packages;
eLearning
 Webinars
 DDU advisors in Nigeria,
Kenya, Tanzania, South Africa
 Case studies with service
providers

19. Remember….
The ultimate goal is
not to gain
information, but to
improve action.

20. DDU in Action
 The new DDU framework
and intervention strategy
 DDU in Cote d’Ivoire

21. Data Demand and Use As a
Health Systems Strengthening
Intervention
Tara Nutley
End of Project Meeting
May 22, 2014

22. Expanded
audience
Expanded
reach
Institutionalization
Increased
demand
Outcomes
Phase 2
Phase 3

23. AAAA
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24. AAAA
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25. Data Use Intervention: 8 Activities
1. Assess & improve data use context
2. Engage data users & producers
3. Identify information needs
4. Improve data quality
5. Improve data availability (access, synthesis,
communication)
6. Build capacity in data use core competencies
7. Strengthen organization’s data use infrastructure
8. Evaluate & communicate data use successes

26. Partnership with Pact Worldwide
 Institutionalize DDU tools and strategies
within organizations with a global reach
 Apply DDU intervention in one country for
proof of concept
 Pact Lesotho – OVC & HIV prevention project
 Rely on diffusion of
innovation to reach
an expanded audience

27. DDU Intervention
1) Assess data use context
 Pact HQ & Lesotho, 6 of 12 NGOs
 Rapid assessment
 8 month work plan
 January notice of program closure in
2 & 5 months

28. DDU Intervention
2) Engage data
users & producers
 In depth data
review & use meetings
 Utilized existing data
 Generated demand for additional data

29. DDU Intervention
3) Identify information
needs
 Applied Framework
for Linking Data
with Action
 Question – What
difference did the
program create at
partner & beneficiary
levels?

30. DDU Intervention
4) Improve data quality
5) Improve data availability
6) Build capacity
 On-line course
 Qualitative
methods
course

31. DDU Intervention
7) Strengthen DDU
infrastructure
 Data use policy
8) Monitor & evaluate
 Increased review of &
demand for data

32. Intervention Results – The ‘So What’
 Service delivery
 Increased # of guardians of OVC
receiving services & improved dialogue
 Global diffusion
 Pact Global M&E
standards mandate
datause plan

33. Data Demand and Use
in Cote d’Ivoire
Leontine Gnassou, Resident Advisor
MEASURE Evaluation
End-of-Phase-III Event, May 22, 2014

34. MEval-II in Cote d’Ivoire
 Beginning of Phase II – 2004
 Absence of harmonized HIV indicators
and data collection tools
 Data not available for decision making
 Implementation of Health Information
System Strengthening Plan

35. MEval-III in Cote d’Ivoire
 Beginning of Phase III – 2008
 PRISM found weaknesses in data quality and
use of information
 Focus on data quality and use at central
and decentralized level of the health
system
 First example of data use intervention
integrated in RHIS strengthening plan from
the beginning

36. Data Use Interventions
1. Assess and improve data use context
2. Engage data users and producers
3. Identify information needs
4. Improve data quality
5. Improve data availability (access, synthesis,
communication)
6. Build capacity in data use core competencies
7. Strengthen organization’s data use infrastructure
8. Evaluate and communicate data use successes

37. 1. Assess and Improve
Data Use Context
PRISM 2008
PRISM 2012

38. 2. Engage Data Users and
Producers
 DDU workshop in 2010
 Regional data review meetings every
6 months
 Questions identified
 Additional analysis
 Recommendations for improved programs
 Tool application

39. 3. Identify Information Needs
 Regional strategic information
coordination meetings supported in six out
of the 19 regions in the country
 Data users and producers identified
information by prioritizing their
programmatic questions

40. 4. Improve Data Quality
 Data management procedure manual developed
 Indicators revised
 Trained to use the
Routine Data Quality
Assessment (RDQA)
 RDQA supervision by
DIPE & regional level

41. 5. Improve Data Availability
 Created a DDU module for the OVC database
 Program Plus database for HIV care and
treatment data
(Access, Synthesis,
Communication)

42. 6. Build Capacity in Data Use
Core Competencies
 Data use concepts & tools incorporated into 4 in-
service & pre- service training institutions
 Schools of: health professionals, public health,
statistics & economics, social training
 Individual capacity building
 Trained a total of 479 students, PEPFAR IPs & MOH
staff

43. 7. Strengthen Organization’s
Data Use Infrastructure
 M&E staffing
 MOH mandated new regional positions
 Six regions hired regional M&E specialists
 1region hired 6 district M&E officers
 Regular regional meetings for data review,DQA use
tools, and DQA procedures

45. 8. Evaluate Intervention
 Data use –
 Data quality –
 Data availability –
44% to70% at district level
38% – nochange at facility level
43% to 60%at district level
40% to81% at facility level
7% to29% at facility level
PRISM results 2008 & 2012

46. 8. Evaluate Intervention
 Engagement & ID information needs – New
quarterly strategic data use meetings
 Capacity building – data use curriculum in
national universities
 Institutionalization – National guidelines &
protocols, regular data use fora, new positions to
oversee data use activities
Observed results: