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Dr. Michael Melchior

Managing and using data in improving targeting and coverage of services in achieving epidemic control. Dr. Michael Melchior. HIV/AIDS Global Goal. Goal of Controlling the HIV Pandemic by 2030 Objectives to achieve the goal was 90/90/90 by 2020 95/95/95 by 2030 UNAIDS Fast Track Strategy.

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Dr. Michael Melchior

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  1. Managing and using data in improving targeting and coverage of services in achieving epidemic control Dr. Michael Melchior

  2. HIV/AIDS Global Goal • Goal of Controlling the HIV Pandemic by 2030 • Objectives to achieve the goal was • 90/90/90 by 2020 • 95/95/95 by 2030 • UNAIDS Fast Track Strategy

  3. Epidemic control requires data and reporting systems to be valid, real-time, and accurate

  4. Program Monitoring for Impact Process Outcome Impact

  5. Which Strategic Information is needed for monitoring the epidemic and the response? Set up sustainable and routine data systems Use data to identify programme gaps Health information systems with real-time data HIV case reporting Unique identifiers Community based data collection Adjust the response Modelled estimates using routine data at granular levels Data use and visualization Identify gaps in services Accountability Fast track HIV response: Fill gaps Improve efficiency

  6. WHO Strategic Information Priorities: 5 key areas from reporting to local data use • Global Accountability • Priorities • Areas and Complementarity • Global Reporting and validation of treatment, policies and prices • M&E Guidelines: closing cascade gaps and impact reviews • Surveillance and strengthen district health information systems • Procurement Supply Chain and Drug Access linked to DHIMS • Patient monitoring and individual level M&E to improve retention • Harmonise reporting working with UNAIDS, CDC, USAID, Global Fund and partners • Cascade Analysis: subnational and KPs, Epi and impact reviews for planning • StrengthenDistrict Health Information Systems (DHIMS) and district data use • DrugQuantification and strengthen LMIS, triangulation with UNAIDS • Strengthen Patient and Case Reporting and disseminate new guidelines • Local data use

  7. Use of Multiple of Data Sources

  8. Setting Targets

  9. Where to start? • Main outcome of interest • Current on treatment (TX_CURR) • Able to work backwards to determine number of tests • Directly determines number of viral load tests • Confounding factors • Current in care/pre-ART • Attrition • Loss to follow-up • Death • Yield by modality and setting • Data Sources • Current program data • Epidemiological data • Should be reasonably ambitious

  10. Target Setting Example (simplified) • Target: 100,000 current on treatment • Start of year current treatment enrollment of 75,000 • How many NEW clients need to be enrolled on treatment? • 100,00 – 75,000 = 25,000 • Assuming a 10% testing yield, how many individuals need to be tested to identify 25,000 positives? • 25,000/0.10 = 250,000 • How many viral load tests, if conducted at 12 month mark? • What is Ghana’s policy? • 75,000 (on for ≥ 1 year) + 50,000 (6 months) = 125,000

  11. Target Setting Example (simple +) • Target: 100,000 current on treatment by end of year • Start of year current treatment enrollment = 75,000 • How many NEW clients need to be enrolled on treatment? • 100,00 – 75,000 = 25,000 + 3,750 = 28,750 • Assuming a 10% testing yield, how many individuals need to be tested to identify 25,000 positives? • 8,750 - Current in care • 20,000/0.10 = 200,000 • How many viral load tests, if conducted at 12 months on treatment? • 75,000 (on for ≥ 1 year) + 50,000 (6 months) = 125,000 Accounting for attrition: If 5% of the 75,000 are going to be lost, we need an additional 3,750. So the total NEW is now 28,750 Consider yield by modality, site type, age, gender and target appropriately Not all new on treatment need to be from testing. Consider how many are already in care.

  12. Progress by Partner Against Targets

  13. Target Questions/Insight • Overachievement – poor target setting • Significant underachievement – poor performance and/or poor target setting • What is a high/reasonably performing partner doing differently that could help a poorly performing partner? • How is poor performance in the beginning of the cascade affecting downstream indicators? • Poor performance in the middle of the cascade (TX) means wasted resources at the beginning of the cascade (testing). • Is poor performance due to the partner or external factors?

  14. Youth Bulge?

  15. PEPFAR Examples

  16. PEPFAR Data Streams

  17. Publicly available at: https://data.pepfar.net/quarterlyData/

  18. These data publicly available for all countries here: https://data.pepfar.net/

  19. These data publicly available for all countries here: https://data.pepfar.net/

  20. Cascades

  21. Culture of Monitoring & Analysis • Routine and timely reviews of cascade data • Disaggregated by age, sex, geography, populations, etc. • Monitor trends over time • Trigger further analyses (“deep dives”)

  22. Testing and Treatment Continuum

  23. Clinical Cascade by Sex

  24. KP Cascade

  25. Clinical Cascade – PMTCT-EID % of pregnant/BF women who know their status Among HIV +, % on ART % of HEI who have been tested HIV - % Virally Suppressed HIV - Infant Mother

  26. PMTCT

  27. Viral Load Cascade

  28. Considerations for Interpretation • Critical to understand value and limitations of data in cascade during analysis and use • Potential limitations: • Geographic or program coverage • Data quality • Client-level data may not be linked across steps • Linkage proxy • Testing does not represent “ever diagnosed” • Can still provide a strong indication of program performance and trigger additional analysis

  29. Key Questions to Ask of Cascades • Where are the leaks? • Geographic and Programmatic • Who is most affected by the leaks? • Why are there leaks? • How do we best address the leaks? • What resulted from the actions taken?

  30. Example Analyses

  31. HIV Testing Services • Filters include: • District • Prioritization • Indicator • Location • Test result

  32. Ghana Help Line Counseling – Test Referral

  33. PLHIV Identified and Yield by Entry Point • Further disaggregate by • Age • Sex • Population • Human-centered programming

  34. FY17 TX_NEW; Time from HIV Diagnosis to ART Initiation

  35. Time from HIV Diagnosis to Enrollment by District

  36. Linkage

  37. Cumulative cohort retention on ART, by District

  38. VL Suppression among children by age group

  39. Population overlaid with PLHIV and Current on Treatment • Not enough to reach 90-90-90 overall • Aim for 90-90-90 by age and sex disaggregates

  40. HIV Prevalence vs. High School Education • Think outside of HIV program and health data • Consider demographic correlates

  41. HIV Prevalence vs. Median Income

  42. Hot Spot Mapping • On a map, identify known hot spots, clinics, ART sites and roads

  43. Let’s look at Ghana

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