Patient and financial impact of implementing WHO recommendations for EID testing – expanding entry point testing and introduction of POC Jenna Mezhrahid CHAI Laboratory Services Team July 25th, 2017 Paris, FRANCE

Policy makers are facing The 2016 WHO guidelines introduced new recommendations for how to increase HIV + infant identifications. Policy makers are facing two key questions : WHO recommendations : When to test: Birth/near birth Where to test: Alternative entry point How to test POC How many additional HIV + infants could be identified with these recommendations? What is the investment required to roll out these strategies?

Country specific, flexible, policy oriented. CHAI developed an Excel-based model to help countries answer these questions. Country specific, flexible, policy oriented. EID testing algorithm @ PMTCT entry point Maternal or infant testing @ alternative entry point Conventional technology POC technology Identification of HIV + infants & costs HIV Exposed Infant (HEI) cohort at birth The model maps out the HIV Exposed Infant cascade and identifies the gaps of the EID program. The model allows policy makers to understand the impact of a proposed POC scale up on the number of additional HIV+ infants identifications and its associated costs. The model also assesses the impact of introducing alternative entry point testing strategies : Delivery ward, Immunization, Inpatient ward, Nutrition ward, Outpatient units. Note : birth testing was not modeled out due to insufficient evidence on how introducing birth testing can impact the subsequent testing time points.

A country example was created, with the following inputs (1/2): Population : Enrolled in PMTCT Not enrolled (illustrative example) ~95,000 HIV + pregnant women ~25,000 HIV + preg. women PMTCT coverage (UNAIDS 2016, 12 UNITAID countries) Around 79% of women in need are enrolled in PMTCT 21% Gap HIV Transmission rates (assumptions) PMTCT pop. : 5%* Non -PMTCT pop. : 35%** Only 56% of diagnosed HIV + infants receive a test result back*** 44% Gap Proportion of conventional tests results not delivered to the patient *Assumption made based on a performing PMTCT program but facing challenges in retention of mothers. **Transmission rate used : De Cock,2000 *** Mozambique: Deo et al (2015); Malawi: Dube et al (2012); Tanzania: Nuwagaba-Biribonwoha et al (2010); Kenya: Hassan et al (2011)

A country example was created, with the following inputs (2/2): EID testing algorithm (customizable based on country needs): 1st PCR 2nd PCR Final RDT Test yield 4.24% 1.99% 2.09% LTFU 25% 44% 27.09% Source : Yields and LTFU rates inspired from a high burden country in Sub Saharan Africa. Uganda Entry Point Study (leveraged to model the impact of entry point testing) Source: Charles Kiyaga, 2016 : “Where have all the children gone : HIV prevalence in infants attending nutrition and inpatient entry points” (8th International Workshop on HIV Pediatrics, Durban, South Africa)

Output 1 : The model maps the HEI patient cascade, highlighting the gaps to be addressed. Low 1st PCR coverage & not all the results are delivered back to the patient Low 2nd PCR coverage & not all the results are delivered back to the patient Illustrative HEI cascade- will vary from country to county

(79% of all HIV exposed pregnancies) Output 2 : With our country example*, only ~14% (1,959) of HIV + infants are identified through the PMTCT entry point. HIV + mothers enrolled in PMTCT ~95,000 (79% of all HIV exposed pregnancies) HIV + mothers not enrolled in PMTCT : ~25,000 (21% gap) HIV + Infections : 8,820 Infants*** 65% of total HIV + Infections HIV + infections : 4,740 infants** 35% of total HIV + Infections Total HIV + infections for the HEI @ birth cohort : 13,560 HIV + infants Leaky PMTCT cascade HIV + infants never enrolled in the PMTCT cascade High % of results not sent back to the patient/caregiver HIV + infants identified by the PMTCT cascade : 1,959 *Illustrative purposes only, will vary based on country specific data **Transmission rate used : 5% **Transmission rate used : 35% (De Cock,2000) “Business as usual”

+62% from Business as usual Output 3 : POC testing increases HIV + infant identifications at the PMTCT entry point but is insufficient to close the gap. +62% from Business as usual Business as usual Only 1,959 HIV + infants can be identified through conventional testing at PMTCT. The model runs 4 POC scale up options, defined by the user : 100% conventional testing, conservative POC scale up (11%), aggressive POC scale up (27%) and 100% POC testing. As POC is scaled up, the proportion of tests results not sent back to the patient will decrease. By increasing results delivery, POC could boost HIV + infant identifications by up to +62%. However this would not be sufficient to close the gap.

+166% from Business as usual +58% from Business as usual Output 4 : Testing at the immunization entry point in conjunction with POC could cast a wider net and improve HIV + infant identifications +166% from Business as usual +58% from Business as usual The EPI program traditionally benefits from a high coverage (e.g. 95% for the 6 weeks DPT). Routine maternal retest (followed by an EID test for infants whose mothers are diagnosed HIV+) at the immunization entry point could boost HIV + infant identifications by an additional +58% when 100% of tests are run on a conventional system. POC would increase HIV + infant identifications by up +166% from the “Business as usual” approach.

+168% from Business as usual +59% from Business as usual Output 5 : Routine EID testing in nutrition wards would yield slightly higher results, also leveraging POC to boost identifications. +168% from Business as usual +59% from Business as usual A routine EID test would be performed for all infants under 2 admitted into a nutrition ward. The impact on identifications is similar to Immunization but would be more targeted. Nutrition ward testing would increase infant identifications by 59% in the conventional system. Here again, POC would act as an accelerator for HIV + infant identifications, boosting numbers up to + 168% from conventional testing at the PMTCT entry point.

Output 6 : Nutrition entry point testing with PMTCT testing & a reasonable POC scale up could generate the best ROI. Business as usual Note : Cost of testing includes reagents and HR costs only. Equipment costs are amortized over 5 years

Key limitations: HIV Exposed Infant population The model only focuses on exposure at birth and does not take into account infants who become exposed after delivery through the mother’s seroconversion. This limitation could underestimate the total HEI & HIV + infant population. HIV testing yields at alternative entry points: Most countries do not have programmatic testing yields for alternative entry points so the Uganda Entry Point Study (UEPS) odds ratios were used in the model. Point of Care : The model does not take into account the fact that the introduction of POC has a positive impact on HEI retention in the PMTCT entry point, and as a result, less HIV + infants would be picked up at alternative entry points. General considerations: Any output generated by the model will be driven by programmatic data from a specific time point and will produce a snapshot of the program. PMTCT population in need, coverage, MTCT, EID LTFU and yields are all susceptible to evolve year on year.

Key takeaway : Combining POC scale up and alternative entry point testing will act as an accelerator in increasing the number of HIV + infants identified. Increases HIV + identification outside of the traditional PMTCT cascade but limited impact HIV + infants identification accelerator The gaps remains Strengthens the foundations of the EID program PMTCT only Introduction of entry point testing Conventional testing Introduction of POC testing The model will help policy makers define the right strategy mix based on the country’s programmatic reality and financial considerations

Thank you ! For any questions : Jenna Mezhrahid jmezhrahid@clintonhealthaccess.org