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OVC_HIVSTAT and
Linkages to Care
for Strengthened Collection, Analysis,
and Use of Routine Health Data
Jenny Mwanza, MPH
K...
Global, five-year, $232M cooperative agreement
6 partners, led by the University of North Carolina at Chapel Hill
Strategi...
Local Partners and Capacity
Building Are Key
 Prime: UNC-CH and partners:
 ICF
 John Snow, Inc.
 Management Sciences f...
Global Footprint (more than 40 countries)
4
4
Outline of the Webinar
1. Introduction
2. HIV risk assessment
3. Data analysis and use
4. Recommendations
5
Introduction
6
Activity Objectives
1. Strengthen data collection and management for
reporting on OVC_HIVSTAT
2. Improve tracking of the O...
Activity Deliverables
1. Webinar for all global OVC programs to improve reporting of
OVC_HIVSTAT data for FY18 Q2
2. Study...
Webinar Objectives
1. Clarify rationale for collecting OVC_HIVSTAT data
2. Explain the HIV Risk Assessment cascade and how...
Background
• OVC_HIVSTAT data was requested by PEPFAR in FY2017 Quarter 2
with the aim of strengthening the role of OVC pr...
Rationale for OVC_HIVSTAT
1. Implementing partners should assess the HIV risk of OVC
enrolled in their programs in order t...
HIV risk assessment
12
DATIM
DSD: OVC_HIVSTAT: Total
Auto-calculated
Number of OVC with HIV status reported to implementing partner
(including st...
Assessment Cascade
2. Conduct
HIV Risk
Assessment
1. Register
OVC & elicit
HIV status
3. Refer at-risk
children to
testing...
HIV Unknown – Other Reasons
At Risk
Not at
Risk
Refuse
assess
2. Conduct
HIV Risk
Assessment
1. Register
OVC & elicit
HIV ...
MIS Database Fields
HIV
Assessment
1. HIV positive
2. HIV positive on ART
3. HIV positive not on ART
4. HIV negative
5. HI...
Challenges
Data collection tool
• Risk assessment questions can be confusing
• Outcomes of “At Risk” and “Not at Risk” are...
Data Quality Controls
Data entry clerks record
• HIV Unknown – At Risk
• HIV Unknown – Test Not
Indicated
Community volunt...
Data analysis and use
19
Sample performance feedback
on process indicators
20
OVC_HIVSTAT Logic Model
Input Outcome Impact
High-quality
collection forms
Robust
database
Technical
capacity
Standard
Ope...
Assessment Cascade
Overview
*These de-aggregates are not reported via DATIM;
these “dummy” data are presented to demonstra...
Assessment Cascade
How to identify high performers?
23
HIV Risk Assessment
% of unknown who were assessed
24
Pre-test Counseling
% of at risk who were referred
25
Support for Testing
% of referrals completed
26
ART Treatment Support
% of HIV positive w/ updated Tx
27
Process Indicators
• We notice that children are “lost” at each stage of the
assessment cascade.
• Therefore, we recommend...
Sample performance feedback
on outcome indicators
29
HIV Status
We seek to diminish
HIV Unknown – Other Reasons
through HIV assessment and testing
30
HIV Status by District
31
Outcome Indicator
% of OVC with Known HIV Status or TNI
HIV Positive + HIV Negative + Test Not Indicated
OVC_SERV <18
32
Global OVC Programs
61% of OVC with Known HIV Status
or Test Not Indicated (FY17Q4)
33
Global OVC Programs
HIV Positive, HIV Negative, HIV Unknown – Test
Not Indicated & OVC_SERV
34
Global OVC Programs
Close-up of Kenya and Zimbabwe
• Kenya has strong
performance because
they have tested a
large proport...
Global OVC Programs
89% HIV-positive OVC on ART (FY17Q4)*
*These data probably best measure linkage to treatment
and exclu...
Recommendations for
implementing partners
37
Recommendations (1)
1. Include risk factors which require testing; if any one risk factor is
met then the child would be r...
Recommendations (2)
1. Integrate HIV Risk Assessment within household visits to
ensure sustainability
2. Use paper data co...
Recommendations (3)
1. Implementing partners to provide regular
feedback on process indicators to sub-
recipients.
2. Impl...
Thank you!
To the tireless community
volunteers, M&E officers,
and leaders of
implementing partners
who continually strive...
Acknowledgments
MEASURE Evaluation would like to thank Christine Fu, Amy
Aberra, and Erin Schelar of USAID/Washington, for...
This presentation was produced with the support of the United States Agency for
International Development (USAID) under th...
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OVC_HIVSTAT and Linkages to Care for Strengthened Collection, Analysis, and Use of Routine Health Data

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This webinar focused on explaining the HIV Risk Assessment cascade and how it is related to OVC_HIVSTAT disaggregates. The presenters also provided guidance for how OVC_HIVSTAT data can be analyzed to enhance program outcomes.

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OVC_HIVSTAT and Linkages to Care for Strengthened Collection, Analysis, and Use of Routine Health Data

  1. 1. OVC_HIVSTAT and Linkages to Care for Strengthened Collection, Analysis, and Use of Routine Health Data Jenny Mwanza, MPH Kristen Brugh, PhD Lisa Parker, PhD MEASURE Evaluation Erin Schelar, MPH, RN Amy Aberra, MPH USAID Washington March 14, 2018 Global Webinar for PEPFAR OVC Programs
  2. 2. Global, five-year, $232M cooperative agreement 6 partners, led by the University of North Carolina at Chapel Hill Strategic objective: Strengthen capacity in developing countries to gather, interpret, and use data to improve health MEASURE Evaluation Overview 2 2
  3. 3. Local Partners and Capacity Building Are Key  Prime: UNC-CH and partners:  ICF  John Snow, Inc.  Management Sciences for Health  Palladium  Tulane University  MEASURE Evaluation works with more than 72 smaller sub-awardees in over 27 countries  Over 26 percent of project funding goes back to minor sub-awardees 3
  4. 4. Global Footprint (more than 40 countries) 4 4
  5. 5. Outline of the Webinar 1. Introduction 2. HIV risk assessment 3. Data analysis and use 4. Recommendations 5
  6. 6. Introduction 6
  7. 7. Activity Objectives 1. Strengthen data collection and management for reporting on OVC_HIVSTAT 2. Improve tracking of the OVC platform’s contributions to 95-95-95 7
  8. 8. Activity Deliverables 1. Webinar for all global OVC programs to improve reporting of OVC_HIVSTAT data for FY18 Q2 2. Study Report to expand upon webinar and will include examples of best practices 3. Technical assistance for implementing partners who participated in the study to strengthen their M&E systems in order to improve collection, analysis and use of OVC_HIVSTAT 4. HIV Risk Assessment Prototype to provide a structure for the data collection tool that enables high-quality data collection on risk behaviors for children and adolescents* *Theprototypewillnotprovideguidance on thetypesofquestionstoinclude 8
  9. 9. Webinar Objectives 1. Clarify rationale for collecting OVC_HIVSTAT data 2. Explain the HIV Risk Assessment cascade and how it is related to OVC_HIVSTAT disaggregates 3. Demonstrate how OVC_HIVSTAT data can be analyzed to enhance program outcomes 4. Provide recommendations on how to revise and implement HIV Risk Assessment 9
  10. 10. Background • OVC_HIVSTAT data was requested by PEPFAR in FY2017 Quarter 2 with the aim of strengthening the role of OVC programs to identify children at risk for HIV infection, ensure they are tested, and link them to care and treatment. • Performance, data quality, and contextual factors were considered to select three countries (South Africa, Côte d’Ivoire, and Zimbabwe) for in-depth study. • A total of six implementing partners across three countries were visited between November 2017 and February 2018; 32 qualitative interviews were conducted and over 60 community volunteers participated in workshops. • HIV risk assessments, indicator reference sheets, and standard operating procedures were collected from each implementing partner. 10
  11. 11. Rationale for OVC_HIVSTAT 1. Implementing partners should assess the HIV risk of OVC enrolled in their programs in order to focus HIV counseling and testing services on those OVC determined to be most at risk for HIV infection. 2. Implementing partners should track whether OVC who report to be HIV positive are successfully linked to and retained in treatment and care. 11
  12. 12. HIV risk assessment 12
  13. 13. DATIM DSD: OVC_HIVSTAT: Total Auto-calculated Number of OVC with HIV status reported to implementing partner (including status not reported). Numerator will auto-calculate from Status Type Disaggregate. Numerator Required Disaggregated by Status Type Reported HIV positive to IP (includes tested in the reporting period and known positive) Of those positive: Currently receiving ART Of those positive: Not Currently receiving ART Reported HIV Negative to IP No HIV status reported to the implementing partner Of those not reported: Test not indicated Of those not reported: Other Reasons 13
  14. 14. Assessment Cascade 2. Conduct HIV Risk Assessment 1. Register OVC & elicit HIV status 3. Refer at-risk children to testing 4. Elicit test result & document 14
  15. 15. HIV Unknown – Other Reasons At Risk Not at Risk Refuse assess 2. Conduct HIV Risk Assessment 1. Register OVC & elicit HIV status HIV + not on ART HIV + on ART HIV Un- known HIV Neg HIV + HIV Referral Referral com- plete 3. Refer at-risk children to testing 4. Elicit test result & document HIV + not on ART HIV + on ART Refuse Report HIV Neg HIV + At the end of a reporting period, any OVC recorded in one of the red boxes (A + B + C+ D +E +F) in the MIS database, should be reported in DATIM as “HIV Unknown – Other Reasons” A B C D E F Stop StopStop Stop Stop StopStop 15
  16. 16. MIS Database Fields HIV Assessment 1. HIV positive 2. HIV positive on ART 3. HIV positive not on ART 4. HIV negative 5. HIV unknown 6. HIV unknown – test not indicated 7. HIV unknown – other reasons 1. At risk 2. Not at risk HIV Test Referral 1. Referral made 2. Referral completed 3. Refuse self-report HIV status 16 Required Suggested
  17. 17. Challenges Data collection tool • Risk assessment questions can be confusing • Outcomes of “At Risk” and “Not at Risk” are not clearly labeled • Next steps related to HIV test referral are often missing Record of new test results • Community workers often hesitate to record HIV-positive test results • HIV-negative test results are often not recorded at all • Inconsistent linkage between the paper forms and MIS database Update of HIV treatment status • Despite strong documentation of ART treatment status at enrollment, there was weak documentation of HIV treatment status at 6-month intervals 17
  18. 18. Data Quality Controls Data entry clerks record • HIV Unknown – At Risk • HIV Unknown – Test Not Indicated Community volunteers apply HIV Risk Assessment with guardians to determine if child displays HIV risk factors These risk factors are recorded on a paper data collection tool; the results should be clearly documented In a data quality audit, we must be able to observe these outcomes on the individual child’s form In a contact trace and verify exercise, the guardian may be re- interviewed to determine if the child displays the same risk factors 1. 2. 3. 4. 5. 18
  19. 19. Data analysis and use 19
  20. 20. Sample performance feedback on process indicators 20
  21. 21. OVC_HIVSTAT Logic Model Input Outcome Impact High-quality collection forms Robust database Technical capacity Standard Operating Procedures 95% of all people living with HIV will know their HIV status Process % of unknown who have been assessed % of “at risk” who have been referred % of referrals completed 95% of all HIV positive people on ART (2) % of HIV positive with updated Tx % of HIV positive OVC currently on ART (1) % of OVC for whom HIV status is known or test is not indicated (1) OVC programs measure self-reported ART treatment status (2) Point of care data measures actual ART adherence 21
  22. 22. Assessment Cascade Overview *These de-aggregates are not reported via DATIM; these “dummy” data are presented to demonstrate how MIS data can be used to strengthen internal performance management of IPs and narrative sections of reporting to USAID 22
  23. 23. Assessment Cascade How to identify high performers? 23
  24. 24. HIV Risk Assessment % of unknown who were assessed 24
  25. 25. Pre-test Counseling % of at risk who were referred 25
  26. 26. Support for Testing % of referrals completed 26
  27. 27. ART Treatment Support % of HIV positive w/ updated Tx 27
  28. 28. Process Indicators • We notice that children are “lost” at each stage of the assessment cascade. • Therefore, we recommend that all MIS databases collect these information for internal performance monitoring to target supervision and conduct ongoing training. • Although process indicator data will not be reported via DATIM, we suggest that there is a direct link between process indicators and outcome indicators. • IPs should share feedback on process indicators to both sub-recipients in the field as well Mission focal points/HQ in the narrative sections of their bi-annual reports. 28
  29. 29. Sample performance feedback on outcome indicators 29
  30. 30. HIV Status We seek to diminish HIV Unknown – Other Reasons through HIV assessment and testing 30
  31. 31. HIV Status by District 31
  32. 32. Outcome Indicator % of OVC with Known HIV Status or TNI HIV Positive + HIV Negative + Test Not Indicated OVC_SERV <18 32
  33. 33. Global OVC Programs 61% of OVC with Known HIV Status or Test Not Indicated (FY17Q4) 33
  34. 34. Global OVC Programs HIV Positive, HIV Negative, HIV Unknown – Test Not Indicated & OVC_SERV 34
  35. 35. Global OVC Programs Close-up of Kenya and Zimbabwe • Kenya has strong performance because they have tested a large proportion of their OVC_SERV population. • On the other hand, Zimbabwe shows large numbers of children who have been assessed and determined “test not indicated.” 35
  36. 36. Global OVC Programs 89% HIV-positive OVC on ART (FY17Q4)* *These data probably best measure linkage to treatment and exclude self-reported adherence 36
  37. 37. Recommendations for implementing partners 37
  38. 38. Recommendations (1) 1. Include risk factors which require testing; if any one risk factor is met then the child would be referred for HIV testing. 2. Track the outcomes of “At Risk” and “Not at Risk” on the form 3. Track testing referral and completion for “At Risk” on the form 4. Track new HIV test results on the form (or elsewhere) 5. Ensure new HIV test results are entered into the MIS database 6. Ensure ART treatment status is collected regularly on the form (or elsewhere) to revise HIV Risk Assessment (MEASURE Evaluation will be publishing HIV Risk Assessment Prototype to strengthen M&E aspects of data collection) 38
  39. 39. Recommendations (2) 1. Integrate HIV Risk Assessment within household visits to ensure sustainability 2. Use paper data collection tool during interview to improve reliability of the data 3. Organize a training specifically on how to conduct the HIV Risk Assessment • Discuss each of the HIV risk factors • Identify probing questions that are culturally appropriate • Do role-plays to explore different scenarios to implement HIV Risk Assessment 39
  40. 40. Recommendations (3) 1. Implementing partners to provide regular feedback on process indicators to sub- recipients. 2. Implementing partners to establish internal targets for outcome indicators to use OVC_HIVSTAT data Process Indicators • % assessed of unknown • % referred of at risk • % referrals completed • % HIV Tx status updated Outcome Indicators • % of OVC with known status or Test Not Indicated • % of HIV positive OVC currently on ART 3. Sub-recipients to organize quarterly data analysis meetings to review progress 4. Sub-recipients to identify districts with weak performance and provide supportive supervision and enhanced training. 5. Through regular analysis of data, improve the linkage between risk assessment, testing, and treatment. 40
  41. 41. Thank you! To the tireless community volunteers, M&E officers, and leaders of implementing partners who continually strive to improve linkages among OVC populations and HIV testing and treatment from HIVSA, PACT, REVE, MAVAMBO, HOSPAZ, and FACT. 43
  42. 42. Acknowledgments MEASURE Evaluation would like to thank Christine Fu, Amy Aberra, and Erin Schelar of USAID/Washington, for their continued support and insights into improving HIV Risk Assessment of Orphans and Vulnerable children in PEPFAR priority countries. USAID Health Teams in South Africa, Côte d’Ivoire, and Zimbabwe—Ambereen Jaffer, Anita Sampson, Brilliant Nkomo, Collen Marawanyika, David Chikoka, Kathryn Reichert, Lauren Murphy, Lucie Dagri, Mavis Boateng, Naletsana Masango, Natalie Kruse-Levy, Samson Chidiya — and Brenda Yamba from the Regional Office, provided invaluable guidance and access to local implementing partners. 44
  43. 43. This presentation was produced with the support of the United States Agency for International Development (USAID) under the terms of MEASURE Evaluation cooperative agreement AID-OAA-L-14-00004. MEASURE Evaluation is implemented by the Carolina Population Center, University of North Carolina at Chapel Hill in partnership with ICF International; John Snow, Inc.; Management Sciences for Health; Palladium; and Tulane University. Views expressed are not necessarily those of USAID or the United States government. www.measureevaluation.org

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