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Assessment & adjustment for data quality used in the South African DISTRICT HEALTH BAROMETER

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  • 1. Assessment & adjustment for data quality used in the South African DISTRICT HEALTH BAROMETER Candy Day 21 September 2009
  • 2. Assessment of data sources Data Source Contents Capacity & Practices Dissemination Integration and use Total Census Highly adequate 100% Highly adequate 75% Adequate 71% Adequate 56% Highly adequate 75% Vital statistics Highly adequate 89% Adequate 67% Highly adequate 100% Highly Adequate 83% Highly adequate 85% Population-based surveys Adequate 57% Highly Adequate 88% Highly adequate 100% Present but not adequate 33% Adequate 70% Health and disease records (incl. surveillance) Adequate 56% Adequate 59% Present but not adequate 44% Present but not adequate 28% Present but not adequate 47% Health service records Not adequate at all 12% Present but not adequate 41% Highly adequate 78% Adequate 50% Present but not adequate 45% Resource records Adequate 63% Present but not adequate 40% Present but not adequate 33% Present but not adequate 31% Present but not adequate 42% Total Adequate 63% Adequate 62% Adequate 71% Present but not Adequate 47% Adequate 61%
  • 3. Problems of health records
    • Range from burdensome paper-records to high-tech paperless EMRs
    • In general these systems do not function well
    • Incomplete, poor quality, time delays
    • Inadequate staffing and resourcing
    • Poor feedback, dissemination and use
    • Poor integration
  • 4. Record review
    • Challenges for routine health system data management in a large public programme to prevent mother-to-child HIV transmission in South Africa. Kedar S Mate, Brandon Bennett, Wendy Mphatswe, Pierre Barker, Nigel Rollins (2009) PloS one 4 (5) p. e5483
    • An evaluation of the District Health Information System in rural South Africa. A Garrib, N Stoops, A McKenzie, L Dlamini, T Govender, J Rohde, K Herbst (2008) South African medical journal 98 (7) p. 549-52
  • 5. All indicators
  • 6. Single indicator
  • 7. Monthly data
    • Trend smoothing
    • Data variability
    • Noteworthy events
  • 8. Data quality issues
  • 9. HIV prevalence data sources
  • 10. DHIS Survey
  • 11. Vital statistics
  • 12. Linking expenditure to utilisation
  • 13. THANK YOU!
      • Candy Day
      • [email_address]
      • http://www.hst.org.za