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School of Health Systems and Public Health Monitoring & Evaluation of HIV and AIDS Programs

School of Health Systems and Public Health Monitoring & Evaluation of HIV and AIDS Programs Data Quality Wednesday March 2, 2011 Win Brown US AID /South Africa. Slide 1 of 18. Objectives of the Session. To Review and Discuss: A Data Quality approach to M&E

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School of Health Systems and Public Health Monitoring & Evaluation of HIV and AIDS Programs

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  1. School of Health Systems and Public Health Monitoring & Evaluation of HIV and AIDS Programs Data Quality Wednesday March 2, 2011 Win Brown USAID/South Africa Slide 1 of 18

  2. Objectives of the Session • To Review and Discuss: • A Data Quality approach to M&E • Six important elements of data quality • Practical applications Slide 2 of 18

  3. Why Data Quality? • Program is “evidence-based” • Data quality  Data use • Accountability Slide 3 of 18

  4. Data Quality Real World In the real world, activities are implemented in the field. These activities are designed to produce results that are quantifiable. Data Management System An information system represents these activities by collecting the results that were produced and mapping them to a recording system. Data Quality: Howwell the DMS represents the real world ? RealWorld Data Management System Slide 4 of 18

  5. ? = Dimensions of Data Quality Slide 5 of 18

  6. Good Data are Valid and Reliable ≠ Valid ≠Reliable ≠ Valid  Reliable Valid Reliable XXX XXXX XXX X X X X X X X X X X XXX XXXX XXX Slide 6 of 18

  7. What are: • Valid data? • Reliable data? • Complete data? • Precise data? • Timely data? • Data with integrity? Slide 7 of 18

  8. Framework for Enhancing Data Quality Slide 8 of 18

  9. The South Africa Approach • Data Quality Assessment • Training • Data Warehouse • SASI Manual • Standard M&E plan  DQ Plan Slide 9 of 18

  10. Data Quality Assessment • PMTCT Data; District focus • Trace and Verify • Routine Data Quality Assessment Tool (RDQA) Slide 10 of 18

  11. M&E Training? M E • Routine data collection • Data quality • Results reporting • Strategic planning • Internal validity • Operations research • Instrument design • Survey sampling • Data analysis for data use • Local training partners • Participant follow-up • User’s groups/networks Slide 11 of 18

  12. PEPFAR Reporting Issues • Are PEPFAR’s results valid & reliable? • How do you know? • Are your patient numbers valid & reliable? • How do you know? Slide 12 of 18

  13. Data and Statistics are Empowering 100 # random samples drawn 50 1 0% 10% 20% 30% 40% 50% Percent reporting: “I understand statistics.” Slide 13 of 18

  14. Data Warehouse • Online results reporting system • Standardized data capture • Control of data quality • Customized reporting tool • Online indicator guidance Slide 14 of 18

  15. South Africa Strategic Information Manual (SASI Manual) • Operational manual • Standard definitions for PARTNERS • Addresses common data quality problems Slide 15 of 18

  16. Try Making a Data Quality Plan • Component of the M&E plan • Strategically think about data quality Slide 16 of 18

  17. Measurement With monitoring of progress in a clinic or in a community, always try to hit the bull’s eye. Paper Trail Always document progress. Data Use Who is using the data? Slide 17 of 18

  18. Thank you Slide 18 of 18

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