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Data Sources & Quality for TB M&E

This facilitator guide provides information on TB data collection methods and sources, including routine data, process monitoring, program evaluation, and special surveys. It emphasizes the importance of data quality for evidence-based decision-making.

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Data Sources & Quality for TB M&E

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  1. Data Sources & Quality for TB M&E Facilitator: Dr. Lucie Blok Delhi, 31st Jan 2006

  2. TB data-collection methods and sources • Routinely collected data (including Global TB reporting) • Process monitoring and evaluation • Program evaluation/reviews • Special surveys • Action research

  3. Routine Recording (facility level) • TB Register • TB-Treatment Card • Laboratory Register • Cough register • Stock cards and consumption reports

  4. Routine Reporting (district and national level) • District TB Register • Quarterly report of new cases and relapses of TB • Quarterly report on the results of treatment of pulmonary TB patients registered 12-15 months earlier

  5. Process Monitoring and Evaluation • Analysis of recording and reporting • Triangulation: population denominators, lab register vs. TB register • Supervision • Observation • Records of trainings held, meetings held, events, job evaluations, etc…

  6. Program Review • Comprehensive review of the entire program • Conducted every 1-5 years • External and internal experts break up into groups and cover a representative sample of the country • Usually provides input for developing or revising the medium-term development plan

  7. Special studies • Prevalence surveys • Population-based surveys • Facility surveys • Vital-registration surveys • Tuberculin surveys • Drug resistance surveys • Action research

  8. Identifying gaps in case detection: The “ONION” Adapted from Chris Dye’s model, 2002, using Piot’s framework

  9. Qualitative vs. Quantitative • Qualitative: answer questions about how well the program elements are being carried out • Quantitative: measures how much and how many.

  10. Quantitative Measure (how many, how much?) Determine causal relationships Representative for target population Sampling: statistical significance Technique: Structured Present: tables/graphs Qualitative Describe (why, how, what in this context) Identify relationships between factors and issues Identify variation in a specific context Sampling: Inclusion all relevant variation Technique: in-depth, semi structured, participatory Present: descriptive Qualitative vs Quantitative

  11. Example of a national leveldata-collection system review Special study Prevalence Survey Prevalence Survey DRS DRS Facility survey Facility survey Facility survey External Monitoring Visits Routine information system & surveillance 2000 2002 2004 2006

  12. Basis for Decision-making

  13. Basis for Decision-making

  14. Why is data quality important? • To enhance evidence-based decision-making • To give substance and weight to recommendations for future actions

  15. Data Quality • Reliability • Validity • Completeness • Relevance • Timeliness

  16. Standard Case definition • Define a case definition that serves the surveillance purpose. • Make sure everyone understands the definition, and the difference between a patient to be treated and a case to be reported. • Make sure every service provider USES the definition.

  17. GIGO: Garbage management Garbage In = Garbage Out More possibilities: QIGO: Quality In-Garbage Out QILO: Quality In-Little Out QIQO: Quality In-Quality Out

  18. Data quality assessments At district level: during supervision: • Step 1: Interview appropriate individual to assess understanding of • the usefulness/value of HMIS • (data collection, analysis and maintenance process) • Step 2: Review reports to determine whether they are consistent

  19. Data quality assessments (con’t) • Step 3: Periodically sample and review data for completeness, accuracy and consistency • Indicator and case definitions are used consistent with NTP guidelines • Data collection is consistent from year to year • Data are complete in coverage • Formula used to calculate indicator (if any) is applied correctly

  20. Source 1 Truth Source 2 Source 3 Data quality assessments (con’t) • Step 4: Compare and triangulate various sources (e.g. central office records with district or district with facility for consistency and accuracy) • Combine qualitative and quantitative data

  21. From Data to Information: Basic Epidemiology • Describe • Ask key questions • Use at-risk populations and target populations • From absolute to relative data (%, rates) • Use defined indicators • Analyze • Use common sense • Compare indicators in time, place, person and with target • Identify high-risk populations

  22. From Information to Evidence • Interpretation of data (relevance, coherence, context)

  23. From Evidence to Decision-Making: Using the Information • Getting on the political agenda of the right decision-maker at the right time • Take user perspective into account (politician, technician, public) • Use effective presentation • Apply advocacy skills

  24. Any Questions so far?

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