1. From PHIA* to Facility: Using Data to Drive Program Impact—Zimbabwe, Malawi, & Zambia
Shannon Hader, MD MPH
Director, Division of global HIV & TB
US Centers for Disease control and prevention
*Population HIV Impact Assessment (PHIA)

2. PHIAs are Different: Designed for HIV Response
Sample for precision on primary HIV objectives: national incidence and sub-national VL suppression; targeted oversampling to increase precision for regions of programmatic interest
Assess Continuum of Care: Questionnaires include testing history (“aware v. unaware”—1st 90), history in care, exposure to key prevention activities (DREAMS, VMMC)
Return of results and linkage to care for HIV+ participants:
Return of HIV rapid test, PIMA CD4, viral load, and other test results
Partner with existing linkage programs for HIV+ participants to link to treatment
Expanded biomarkers including CD4, HIV viral load, HIV recency assays, ARV resistance, ARV metabolites, HIV antibody and HIV PCR testing for children
Selective addition of biomarkers: only where there is co-morbidity, benefit and limited impact on field work
HepB (Zam, Uga, Tanz, Cameroon, Ethiopia); HepC (Tanzania); Syphilis (Zim, Zam, Uga, Tanz, Ethiopia)
HIV stakeholders directly involved: Ministries of Health, Government HIV programs, labs and statistics offices, PLWHA advisors, HIV donors involved from development through dissemination

3. “Interventional” Survey: Starts & Ends with People & Action…
Community Mobilization
Household Consent & Interview
Individual Questionnaire
Blood Draw
HIV Testing & Counselling
Return of Results
Linkage to Care
A DIFFERENT KIND OF SURVEY—DRIVING PREMISE: INTERVENTIONS FOR PERSONAL & PROGRAM BENEFITS.
Driving premise shows up throughout the survey, even see throughout ‘survey process’:
Focus on direct beneficiaries: Involvement of PLWHA; Age and gender-specific strategies to improve participation rates
Return of results, to facilitate linkage to care
Consent & Counseling: incorporating the most up to date national program approaches (eg B+), including readiness for “Treat All” (not just last guidelines)
Act on ‘emergency results’ to link to care: Pos EIDs; monitor for any reports of partner violence
Maps CDC/PEPFAR-supported labs to build on—lab staff trained for VL lab tests will be the ones performing VL for programmatic VL scale-up
We had about 230 staff members over 9 months conducting the survey. Similar to many other surveys, the MPHIA team administered a Household Consent and then household questionnaire, and rostered individuals in the household.
Then administered an adult consent and questionnaire to eligible individuals years of age.
We did not have an adolescent questionnaire in MPHIA.
We then did consent for blood draw and future storage – those who agreed have their samples sitting in the MPHIA Repository.
HIV Testing and Counseling was performed as well as CD4 testing on all HIV+ and 5% of Negatives. Results were Returned on the spot and testing was not anonymous.
Measure Successes & Gaps
Change Programs to Expand Successes & Fill Gaps

4. How have PHIA results differed from program data?

5. Population VLS Higher than Predicted Prevalence VLS Among Adult PLHIV (15–59)

6. Progress Toward 90-90-90: First “90” Behind Adults (15–59 years)
*The number within each bar represents the conditional percentage while the height of each bar represents the absolute percentage of all PLHIV.

7. Youth less likely to be aware, major factor of lower VLS Progress Toward by age (3-Country Combined)
63%
30%
*The number within each bar represents the conditional percentage while the height of each bar represents the absolute percentage of all PLHIV.

8. Younger people, especially men, less likely to be virally suppressed (driven by not aware) Viral Load Suppression, by Age and Sex
Prevalence of VLS among HIV-positive people in Zambia is highest among older adults: 73.5 percent among HIV-positive females and 73.0 percent among HIV positive males ages 45 to 59 years.
In contrast, prevalence of VLS is distinctly lower among younger adults: 34.0 percent among HIV-positive females and 35.7 percent among HIV-positive males ages 15 to 24 years.
Preliminary ZAMPHIA Data, December 2016

9. How have PHIA data been used to inform, improve programs?

10. Population HIV Impact Assessment (PHIA) data was foundational to Zimbabwe, Malawi, and Zambia’s FY18 planning
Improved geographic, age and sex targeting through analysis and use of PHIA, program and modeling data
Malawi and Zimbabwe were two of the first countries to conduct the PHIAs. Summary sheets were released on World AIDS Day, last year. This data provided the first clear picture as to how far we truly were along the cascade. This was the first time we had an objective measure of the 3rd 90 – viral suppression at population level, not just within our programs.
Across both countries there had been remarkable progress made in the previous five years – at national level, both countries are now close to the ART and viral load suppression 90’s, and both had made significant progress in the proportion of people who know their status.
We had an updated estimate of the total number of PLHIV living with HIV, both those who know their status and those who don’t.
And not only did we have this information at national level, but we also have it at provincial level, by age and by sex. Suddenly we are able to ‘see the entire elephant’ more clearly.

11. PHIA Data in Use
PHIA data was used drive country operational planning in Zambia, Zimbabwe, Malawi
Population – who are we missing?
Geographic – where are they?
Cascade – where in the cascade are our gaps?
Combine with program and cost data to plan program and cost specific program strategies tailored to specific populations in specific geographies – How are we going to close the gap?

12. Every Province “behind” in the first “90”: Who do we need to find?
ZIMPHIA
at Provincial Level
Analysis of the cascade at provincial level revealed a striking pattern in Zimbabwe: Without exception, every single province was disproportionally behind at the first step of the cascade – and since knowing your HIV status is the first step to the rest of the cascade, this was quite alarming. This was the first step for us to then ask “Who do we need to find”?

13. Nationally, both men & women <30 years-old far less likely to be aware
Preliminary analysis of the cascade by age, even at national level, revealed some startling truths about who was accessing HIV services. Both men and women under the age of 30 were far less likely to know their status than the older population.

14. Zimbabwe: Age distribution of ART gap, by district, age and sex
Who, How many, & Where:
Still need to reach many persons >25 years-old to close the ART gap
Zimbabwe: Age distribution of ART gap, by district, age and sex
In Zimbabwe we applied the ‘who’ to the ‘where’, and started looking in-depth at both volume, age and sex by district. You’ll note here as well that as in Malawi’s case, although younger adults are less likely to know their HIV status, the absolute number of PLHIV we still need to find are the older adults. We also have quite a big of geographic variation in where the unidentified children and young people are. F

15. Index Testing is the most successful strategy by age and sex
What testing strategies needed to reach these people? YIELD
Index Testing is the most successful strategy by age and sex
After index testing, the highest yield across entry points for young men is in the facility
To do this, we brought in our program data to identify which testing strategy has the highest ‘yield’ in which sex and age groups.
You can see that Index testing, or tracing the sex partners of current ART patients, has the highest yield across all ages and sexes.
And for young men, the highest yield after index testing is in routine, provider-initiated testing in health facilities – a finding we were not expecting.

16. Highest yield strategy currently has the lowest volume
Testing strategies: Yield + Volume=absolute number of persons found
More info needed: Are the young men not in facilities, or are we not testing in the right facility entry points?
Highest yield strategy currently has the lowest volume
When we compared the yield of testing to where our testing was actually taking place, or the volume, we saw that although young men were readily identified in facility settings, we weren’t testing a lot of them there – instead our volume was in the community. The reason behind this wasn’t clear, and means we need to go back and take a closer look at our sites – are the young men just not in the facilities, in which case we need to keep the focus on the community, or is it that we haven’t got our testing providers at the right entry points in the facilities?
You’ll also note here that although our new Index testing strategy was averaging 40% positivity, it was only a very small portion of our overall testing portfolio.

17. Very high yield makes the cost of this outreach model comparable or lower than PITC

19. Putting it all together: Gap by District, age, gender, & testing strategies (yield+vol+$)
Gap % male 15-24
Gap % male 25+
Gap %
female 15-24
Gap % female 25+
Mat South
0.0%
61.1%
38.9%
Mash East
13.5%
44.1%
5.2%
37.2%
Bulawayo
28.8%
3.6%
67.6%

20. Final Product: A highly targeted yet nationally coherent testing strategy

21. MPHIA: “Gap to Viral Suppression”
Undiagnosed HIV Infection is the Main Reason for
Non-suppressed Viral Load
Proportional contribution that each step of the cascade provides to ongoing transmission of the virus.
The majority of the gap could be closed by ensuring people knew their HIV status.
Malawi analyzed their ‘gap’ a little differently – they used the “gap to viral suppression” rather than the gap to knowing HIV status, and looked at the proportional contribution that each step of the cascade provides to ongoing transmission of the virus.
They also ended up at the same conclusion: The majority of the gap could be closed by ensuring people knew their HIV status.

22. Together they account for 71% of infectious PLHIV
Higher Proportion of young people (<25yo) were unaware, but higher ABSOLUTE number of PLWHA 25yo+ need to be reached
Malawi National Gap to Viral Suppression by Age and Sex
Males aged 25+ represent the largest number of PLHIV with unsuppressed viral load
Females 25+ are the second largest group of PLHIV with unsuppressed viral load.
Together they account for 71% of infectious PLHIV 1 2 2
When they broke down the data by age and sex, they found that even through a higher PROPORTION of young people didn’t know their status, like Zimbabwe, the ABSOLUTE number of PLHIV they still need to reach are older adults. 1

23. Malawi: Year Olds, and Males 25+ are Still Far from Target Suppression Levels in 10 Scale Up Districts
5 Districts account for 70% of the national gap
MPHIA data on VL suppression in 10 scale up districts. Significant gaps remain in women and in men.

24. Using MPHIA to Refine PEPFAR Malawi’s Geographic Focus: Increased Targets, Accelerating Net New ART Enrollment to Address the Geographic Gap to Saturation

25. Malawi: Scale-up of Index Case Testing & Targeted Community testing
Malawi did the same – although their program showed a need to increase rather than decrease community testing as well as increasing their index testing.
So in summary, the new PEPFAR testing targets for both Zimbabwe and Malawi were developed by drilling right down to the facility level and catchment areas within districts, and very intentionally identifying the who, the where, the how, and the how much it was going to cost us to achieve and achieve HIV epidemic control.

26. Uses of ZAMPHIA Data Population Denominator Results
Validation of national incidence estimate
Validation of national and provincial PLHIV and prevalence estimates
Generation of district age/sex band PLHIV and prevalence SAEs
Calculation of ART coverage and gap using program data
PLHIV Denominator Results
Triangulation of ZAMPHIA vs program data for ART coverage
’s: analysis of unawares, unlinked and unsuppressed
Age/sex and geographic distribution: increased focus on < 35 year olds
Analysis of unaware vs unlinked
3. Further analyses yet to be completed

27. ZAMPHIA: Population Denominator Uses Shifts in Provincial & District Estimates
Validation of National Estimates vs SPECTRUM 2015
15-49 prevalence: % vs 12.9%
15-49 incidence: vs 0.85
Validation of SNU1 Estimates vs SPECTRUM 2015
Copperbelt Province PLHIV: 211,149 vs 166,247
Lusaka Province PLHIV: 215,579 vs 215,796
Generation of SNU2 Small Area Estimates vs 2016 SAEs
Kitwe District PLHIV: 82,224 vs 58,225
Kapiri Mposhi District PLHIV: 16,537 vs 24,409
↑27%
↑41%
↓27%

28. ZAMPHIA: PLHIV Denominator Uses Coverage compared to Program Data; three 90s
Triangulating ART Coverage vs Program Data
The First 10: The ‘Unawares’
The Second 10: The ‘Unlinked’
The Third 10: The ‘Unsuppressed’

29. What might be expected in the future?

30. PHIAs – Looking Forward
Potential areas for tightening the scope for repeat surveys
Subnational - only where need to measure impact of deep program effort
Pediatric sampling efficiencies
CD4 may not be needed
Transition to rapid recency test currently under development
Potential for surveys as validation for use in surveillance and program
Shorten time from data collection to dissemination
Explore expanded use of satellite labs
Complementarity of other data sources – maximize utility of case surveillance data to ensure linkage of new positives to treatment
PHIA/TB joint survey

31. PHIA Repositories (First 7 Countries)
PHIA repositories have DBS and plasma for all participants who agreed to allow future testing
800,000 plasma aliquots
400,000 DBS aliquots
Nationally representative samples of individuals from age 0 up
CDC working with Ministries to develop repository use plans which reflect their public health needs

32. THANK YOUs
*As per Jessica’s slides: All the PHIA Teams & Participants!
National MOHs & Steering Committees
Zimbabwe, Zambia, Malawi
PHIA Field & Lab Teams
PEPFAR
Ambassador Debbi Birx
Irum Zaidi
ICAP PHIA Team
Wafaa El Sadr
Jessica Justman
David Hoos
CDC Teams
Atlanta PHIA & Lab Teams
CDC-Zimbabwe, CDC-Malawi, CDC-Zambia
Laura Porter, Drew Voetch, Kristin Brown
Hank Tomlinson
Beth Barr, Andrew Auld, Sundeep Gupta

33. DIVISION OF GLOBAL HIV & TB
……………………………………………
Center for Global Health
Centers for Disease Control and Prevention