cop18 target-setting overview · 2018-04-04 · target-setting objectives & overview • cop...
TRANSCRIPT
COP18 Target-Setting Overview
Background/Rationale
“This year, we will take the next critical steps to eliminate HIV from our midst.
By scaling up our testing and treating campaign, we will initiate an additional two million people on antiretroviral treatment by December 2020.”President Cyril Ramaphosa, State of the Nation Address, February 16, 2018
Key Goals and Principles
• Achieve Epidemic Control in the form of 90 90 90 (public sector) and 95 95 95 (public + private sector) by 2020.
• Provide support in alignment to the HIV/AIDS burden i.e. more support to those districts with higher numbers of PLHIV.
• Align PEPFAR and South Africa government targets through the SURGE proposal.
Prologue (as described in COP18 funding letter)• SURGE funding to lead to a 45% and 20% increase for TX_NEW in adults and peds
respectively• FY19 TX_CURR targets to be achieved by increasing retention and TX_NEW in all age, sex
populations• FY19 TX_CURR targets to be aligned with those outlined in SURGE proposal
National publicsector target by
Sept 2018. National public + private (SURGE+) sector target by
Sept 2019.
Target-setting objectives & overview
• COP target-setting has evolved over the past 3-4 years to be more rigorous, transparent, and accountable.
• Targets are based on key programmatic markers (e.g. coverage, linkage, yield, etc.) and goals (TX CURR coverage)
• Relational program areas:• Care/Treatment: includes PMTCT, TB/HIV, pediatrics, previously diagnosed• Prevention: HTC (including HTC/VMMC)
• Additional program areas:• OVC• Prevention: KP, GBV
Target Setting ISNU (District) Targets
Process Overview1. Finalized district sub-national unit (SNU) PLHIV
estimates2. Forecast FY18 (COP17) results (relative to targets)
• Recommend matching these to SURGE proposal targets3. Determine TX coverage levels by end of FY18
• Recommend matching these to SURGE proposal targets4. Input key assumptions into DatapackTargets
generated5. Disaggrate district targets to IMs, age/sex, site (part
II)
What is the Datapack?
• Relational* excel database that incorporates key:• Epidemiologic Data • Program assumptions (coverage, yields, linkage, initiated, retained, etc.)• Assumes ‘Test and Start’ treatment environment• Pre-populated with FY17 results, FY18 targets, and key assumptions
• ….to generate targets at the national and sub-national levels• Also includes: 2015-20 trend summaries, allocation by IM, IM summary
*central to most program areas are the SNU PLHIV, current TX_CURR, and the expected TX_CURR coverage levels
Getting your targets
• For all CDC awarded partners, please contact your PO/AM and SI representatives; USAID targets pending awards
• Redacted Datapacks available • Note that all targets are considered notional pending final COP18
approval and formalization of awards• Final Datapack will be posted on US Embassy website
Methods: PLHIV Estimates
• Estimates TWG established to routinely review and establish national and sub-national PLHIV estimates
• NDoH, Academia, multi-, bi-lateral stakeholders
• COP17: COP16 estimates reviewed/triangulated against other methods, results for validation
• Thembisa (District ANC SS proportion, population), Spectrum, Small-Area Estimate, geo-spatial
• Key stakeholders and technical experts
• COP18: SNU proportions from COP17 to be bench-marked to the provincial and national Thembisa estimates for FY18, FY19, and FY20
• Age, gender denominator • Geo-spatial (Imperial College) age, gender proportion applied to final COP18 PLHIV estimates
0
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COP15 COP16 COP17 COP18
District PLHIV Estimate ComparisonR² = 0.979
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COP17-COP18 Compare
27 Focus Districts (80% of PLHIV)
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Surging towards Epidemic Control: COP18 Tx coverage
• PEPFAR SA has aligned its programs and targets to address the highest HIV burden districts.
• Complementary COP + SURGE support to achieve epidemic control nationally by 2020 and by 2019 in the highest burden districts.
• By end of 2019:• 5.6 million receiving ART via public
sector• By end of 2020:
• 6.1 million receiving ART via public sector
R² = 0.5618
0%
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FY19
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FY19 PLHIV
eThekwini, Tshwane, Jo’burg, and Ekurhuleni
Key Assumptions/Inputs: COP18 (FY19), 1/2• Anticipated FY18 achievement:
• Focus districts: 73-101% based largely on SURGE targets except 4 highest burden districts• Non-focus districts: matched to SURGE targets
• FY19 TX coverage: 73-81% (81% for 4 highest burden districts)• Matched to SURGE targets
Care/Treatment• Previously diagnosed: 5%• Linkage from Testing to TX: 90% • % TX_NEW from previously diagnosed• 12-month retention:
• <1 year cohort: 90%• >1 year cohort: 95%
• PMTCT: PMTCT_STAT: 99%->PMTCT_ART: 98%• TB/HIV: 10% reduction in TB cases from previous year
• TB_STAT: 99%->TB-ART: 97%• TB_PREV: based on ART population; active TB-negative; 65% completion rate
Note: if FY18 targets are not met, FY19 targets are effectively
increased.
Key Assumptions/Inputs: COP18 (FY18), 2/2Counseling and Testing (HTC specific tab in Datapack)
• Calculated based on TX_CURR gap to target coverage level; proportion PLHIV by modality; yield/modality
• 85-90% of HTC_pos from facility-based modalities• 10-20% from community • Reviewed and adjust HTC_pos by modalities based on epi and program data
• Increase index-testing, male-focused yields increased to reflect focused approaches
VMMC• Based on total male population; current coverage levels; required coverage levels (default=80%)• % VMMC HIV+: 2-2.5%
OVC• Based on <18 year old population size, past performance and available budget • Graduated, transferred, exited
KP_PREV• FSW sizes estimates based on SWEAT survey; KP cascade work • Coverage: FSW (80%), MSM (35%), and PWID (80%), inmates (same as FY18), TG (90%)• ART in 90% of HIV+• PrEP: 10% of negative FSW and 25% of MSM• KP_MAT: 10% of PWID
TX_CURR: Private sector in high burden districts
Ref: HE2RO assessment of private sector services (2/2018)
Estimated 275,000 individuals receiving ART via private sector medical schemes (Dec 2017). An estimated 60% of private sector ART services in highest burden districts
TX_CURR: Private sector contribution-age, sex; GP+KZN (Dec 2017)
Ref: SANAC survey of registered medical schemes (2/2018)
Private sector coverage increases with age. Overall, 61% of private sector ART is to women with a leveling of male:female ratio with age.
Note: all COP18 targets are notional pending final COP approval
Please meet with AM/SI staff ASAP to have partner-specific discussions and Datapack orientations
COP18 Topline Targets: Care & Treatment
*TX_CURR targeted to 5.5 million to better align to SURGE targets—SUBJECT to CHANGE**Include eThekwini, Tshwane, Jo’burg, and Ekurhuleni
SURGE SURGE+ (public + private)Indicator 52 Districts 27 Focus Districts 4 Highest Burden
Districts*Indicator 52 Districts 27 Focus Districts 4 Highest Burden
Districts*PLHIV (FY19) 7,424,087 6,017,094 2,295,453 PLHIV (FY19) 7,424,087 6,017,094 2,295,453
CLHIV (FY19) 292,295 236,975 91,818 CLHIV (FY19) 292,295 236,975 91,818HTS (adult) 10,155,365 8,318,664 2,921,693 HTS (adult) 11,170,902 9,150,530 3,283,983
HTS_pos (adult) 925,334 809,826 365,063 HTS_pos (adult) 1,017,867 890,809 410,331HTS (children) 1,320,473 1,236,040 512,316 HTS (children) 1,452,520 1,359,644 575,843
HTS_pos (children) 100,249 91,809 39,902 HTS_pos (children) 110,274 100,990 44,850TX_NEW 1,163,857 1,014,692 445,398 TX_NEW 1,280,243 1,116,161 500,627TX_CURR (all) 5,565,459 4,557,948 1,859,317 TX_CURR (all) 6,122,005 5,013,743 2,089,872
TX_CURR (<15) 286,621 242,859 91,818 TX_CURR (<15) 315,283 267,145 103,203TB_PREV 578,257 578257 170,570 TB_PREV 578,257 578,257 170,570
*Assumes 10% of overall HTS and TX services are provided via the private sector (11.0% in 27 focus districts; 12.5% in 4 highest burden districts).
COP18 Topline Targets: Prevention
**Include eThekwini, Tshwane, Jo’burg, and Ekurhuleni
SURGEIndicator 52 Districts 27 Focus Districts 4 Highest Burden Districts*PLHIV (FY19) 7,424,087 6,017,094 2,295,453
CLHIV (FY19) 292,295 236,975 91,818VMMC 510,489 497,638 178,119
VMMC(15-34) 425,268 414,587 148,088OVC_SERV 626,604 626,604 317,054PP_PREV 686,882 686,882 419,549KP_PREV 245,748 153,733 90,051
Note: all COP18 targets are notional pending final COP approval
Please meet with AM/SI staff ASAP to have partner-specific discussions and Datapack orientations
Target Setting IIAge, Sex disaggregation
Age, sex, site disaggregation: Overview1. Age, sex disags determined at:
• Indicator• District • IM level
2. Age, sex disags extrapolated to sites/level of service delivery3. Auto-uploaded into DATIM
Approach & Inputs to the age, sex allocations• Program to the TX gap:
• HTS, TX_NEW• Higher risk populations for prevention services• VMMC
• Program to the epi:• TX_CURR
• Program to effective coverage levels• PP_PREV, KP_PREV, OVC, GENDER
• Data sources: • Gap: FY17 results, PLHIV estimates, (preliminary) 2017
HSRC survey data, program data• Epi: Thembisa, (preliminary) 2017 HSRC survey data,
program data
Proposed methods for age, sex distribution: TX_NEW, TX_CURR methods• <15 years; all indicators: use existing program data to distribute, adjust as
per program staff inputs• TX_NEW, HTS: program to fill the Tx gap
• Crude program gaps (>15, 15-24, 25+; M, F) x HSRC finer age, sex prevalence• TB_PREV follow similar distribution at TX_NEW
• TX_CURR: match the epidemiology • In district with >=100% coverage by age, sex distribution adjusted to that of nearby
district• TB/HIV; PMTCT_STAT, ART: follow previous program results• Prison, KP partners: need to adjust their distribution to align to age, sex of
targeted population• E.g. prison programs weighted towards older males; FSW programs towards younger
females
• All distributions validated by appropriate program staff
Summary points
• PEPFAR district PLHIV estimates have been updated & aligned to NDoH
• PEPFAR Tx & NDoH coverage targets have been aligned to goal to reach 90 90 90 (95 95 95) by Dec 2020
• Need to accelerate from current trajectory to meet these
• Key programmatic assumptions built into target-setting around: yield/case-finding, linkage to Tx, retention
• Please meet with AM/SI staff ASAP to have partner-specific discussions and Datapack orientations
Resources