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Data Sources & Quality for TB M&E

Data Sources & Quality for TB M&E. Facilitator: Dr. Lucie Blok Delhi, 31 st Jan 2006. TB data-collection methods and sources. Routinely collected data (including Global TB reporting) Process monitoring and evaluation Program evaluation/reviews Special surveys Action research.

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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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