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

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

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

  1. 1. Data Demand and Use Facilitating the Use of Data to Inform Programs and Planning in Health
  2. 2. Why We Collect Data The ultimate goal is not to gain information, but to improve action. Credits: Pierre Holtz for UNICEF
  3. 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. 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. 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. 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. 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. 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. 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. 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. 11. Decisions The Three DDU Elements
  12. 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. 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. 14. Element 3: Decisions  Problem identification, awareness raising & advocacy  Policy & planning  Program design & improvement  Program management & operations Facilitate use of data by stakeholders
  15. 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. 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. 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. 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. 19. Remember…. The ultimate goal is not to gain information, but to improve action.
  20. 20. DDU in Action  The new DDU framework and intervention strategy  DDU in Cote d’Ivoire
  21. 21. Data Demand and Use As a Health Systems Strengthening Intervention Tara Nutley End of Project Meeting May 22, 2014
  22. 22. Expanded audience Expanded reach Institutionalization Increased demand Outcomes Phase 2 Phase 3
  23. 23. AAAA AAAAA A A A A
  24. 24. AAAA AAAAA A A A A ?
  25. 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. 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. 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. 28. DDU Intervention 2) Engage data users & producers  In depth data review & use meetings  Utilized existing data  Generated demand for additional data
  29. 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. 30. DDU Intervention 4) Improve data quality 5) Improve data availability 6) Build capacity  On-line course  Qualitative methods course
  31. 31. DDU Intervention 7) Strengthen DDU infrastructure  Data use policy 8) Monitor & evaluate  Increased review of & demand for data
  32. 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. 33. Data Demand and Use in Cote d’Ivoire Leontine Gnassou, Resident Advisor MEASURE Evaluation End-of-Phase-III Event, May 22, 2014
  34. 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. 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. 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. 37. 1. Assess and Improve Data Use Context PRISM 2008 PRISM 2012
  38. 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. 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. 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. 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. 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. 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
  44. 44. 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
  45. 45. 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:

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