Home-based and mobile HIV testing with linkages to care & prevention Ruanne Barnabas, MD, DPhil Departments of Global Health and Medicine University of Washington UNICEF webinar March 2014

The case for HIV prevention Expanded ART access has resulted in a decline in AIDS deaths. The annual rate of new infections remains constant. Longer lifespans with ART will mean that the number of persons living with HIV will continue to increase. Continuing on as we are, without stopping new infections, will not work. UNAIDS World AIDS Day Report 2011

Lessons from HIV prevention efforts HIV prevention isn’t easy; one must: –Understand the epidemiology of HIV Identify most-at risk populations Quantify attributable fraction of risk factors Determine most modifiable risk factors –Test interventions to reduce those risk factors Big resources & collaborations, investment, & objectivity –Challenges in scale-up of effective interventions End goal: Implement effective interventions at scale to have a public health impact

Focus on key populations Consider local epidemiology –HIV incidence: indicator of where epidemic is going –HIV prevalence: indicator of where epidemic as been Identify ‘most at risk’ populations –How to access them? Target key risk factors –Are they modifiable? Understand motivators & barriers –Will at risk populations use evidence-based interventions?

Combination HIV prevention: a package What works for HIV prevention: –Male circumcision (F  M risk) –Male condoms, female condoms (probably) –Counseling and testing, particularly as a couple (probably) –↓ partner #, delayed sexual debut, abstinence – Treatment of STIs (probably best to decrease infectiousness in HIV+s) –Conditional cash transfer –ART –Oral/topical PrEP Multiple, integrated, biomedical and behavioral interventions Combination prevention. Coates, et al. Lancet 2009

Efficacy of biomedical HIV prevention strategies Maartens Lancet in press

Combination prevention at the population level Cremin et al., AIDS 2013 Mathematical model of sequential additions to a combination package. ART alone – even in an optimized scenario – is insufficient. Biggest bang is in combination.

Clinic perspective of ART implementation

Parsimony is important: What combination prevention should NOT look like…. 9 Slide courtesy of Susan Buchbinder

Our concept for combination HIV prevention A defined set of modifiable factors drive HIV transmission in heterosexual populations in East and southern Africa and key interventions can target these factors: –Knowledge of serostatus, ART, male circumcision, behavior –High coverage is key to have a population effect The starting point for intervention is to massively increase knowledge of HIV serostatus –Home-based VCT get high coverage of HIV testing Target prevention interventions to individual risk, & measure population effect –Example for a serodiscordant Couple: 1) Couples counseling, 2) ART referral for HIV+ female & 3) MC referral for HIV- male (?PrEP)

The ‘leaky’ cascade decreases intervention efficacy Proportion of adults who know their HIV status in Africa (UNAIDS 2011, HSRC 2009) & Meta-analysis of linkages to HIV care (Mugglin et al, TM&IH 2012) HIV+ in population CD4 measurement Eligible for ART Start ART Proportion tested Virally suppressed 30% 72% 40% 25% 80% 100% N

Testing is the ‘gateway’ to delivery & uptake of effective HIV care & prevention

Knowledge of serostatus Cochrane review: 19 studies –12 facility-based, 3 employment based, 1 mobile VCT, 1 home-based VCT Significant 30% reduction in SPs; significant only for those who tested HIV+ Significant 3-fold increase in condom use for those who tested HIV+ HIV testing has to be part of combination HIV prevention –Long way to go to universal knowledge of HIV status Fonner et al Cochrane Database Syst Rev 2012

HIV testing strategies HIV testing strategy Characteristic Facility-basedRequires perceived risk or symptoms Provider-initiatedEfficient; identifies persons with lower CD4 Household-basedHigh coverage; identifies asymptomatic persons at higher CD4; reduces stigma MobileReaches men and mobile populations Self-testingAcceptable in Malawi; may increase care linkage if have home ART initiation option Couples counselingIdentifies HIV serodiscordant couples

Project Accept Community mobile VCT in Tanzania, Zimbabwe, South Africa & Thailand compared to standard VCT HIV testing increased 45% among men and 15% among women Lower number of SPs & multiple SP in intervention communities, especially among HIV+ men 14% reduction in HIV incidence (p=0.08) –Subgroup analysis: 30% decreased HIV incidence among women ages >24 Potentially greater reduction in HIV incidence if coupled with facilitated linkages to care & prevention? Khumalo-Sakutukwa JAIDS 2008; Sweat Lancet ID, Coates CROI 2013

Uptake of HIV testing through different strategies Suthar PLoS Med 2013 Mobile HTC as part of multi-disease health campaigns achieved high coverage in shortest period of time Community-based HTC increased HIV testing 7-fold over facility-based testing,& reached HIV+ at earlier stage

HBCT in Uganda: starting point Integrated Community Based Interventions, ICOBI: Rural district in southwestern Uganda 264,966 (89% of adults) tested and received HIV results between % of HIV+ persons initiated ART Tumwesigye AIDS Patient Care and STDs 2010

Home HIV counseling and testing (HCT) is a platform for Combination HIV Prevention Massively increases knowledge of HIV serostatus Potential to targets combination prevention activities –HIV seropositive individuals –HIV serodiscordant couples –Link HIV negative persons to prevention Can home-based HCT be used to increase linkages to care and prevention? Present the results from: –Pilot studies in Uganda and South Africa –Observational study in South Africa and Uganda

Community HCT study results Pilot studies in Uganda and South Africa Uganda link to HIV care and MMC for HIV- males South Africa – proof of concept, added POC CD4 testing Intervention study in South Africa and Uganda Large scale, POC CD4, 12 month follow-up Mathematical modeling results Discussion

Home-based HCT study design & point-of-care CD4 test Community Sensitization Household Consent Individual Consent Questionnaire Pre-test counseling HIV test HIV+ Post-test counseling POC CD4 test Referral for CD4 & HIV care Follow-up visits VL testing and counseling Field based point of care [POC] testing has high agreement with flow cytometry Mean difference bet. POC & flow: 16 cells/µL (CI: -1 to 32 cells/µL) Data collection

Community HCT study results Pilot studies in Uganda and South Africa Uganda link to HIV care and MC for HIV- males South Africa – proof of concept, POC CD4 count Intervention study in South Africa and Uganda Large scale, POC CD4, 12 month follow-up Mathematical modeling results Discussion

Results of initial HBCT pilots in South Africa and Uganda Uganda: N(%)South Africa: N (%) HIV testing coverage1558 (80%)671 (91%) HIV prevalence152 (9.8%)201 (30%) Median CD4 count467 cells/µL425 cells/µL

HBCT results 6 months after testing Uganda: N(%)South Africa: N (%) Visited an HIV Clinic133 (88%)195 (97%) ART uptake among those eligible 22 (79%)15 (80%) MC uptake in Uganda75 (62%)- Proportion with viral load <1,000 copies/mL among ART eligible participants -Increased from 20% at baseline to 80% at 6 months* Change in mean viral load over 6 months among ART eligible participants log 10 copies/mL* *p  ≤0.01

The ‘leaky’ cascade & solutions Impact of HBCT & linkages to care on suppressed viral load Increasing knowledge of HIV status, motivating ART uptake, & facilitating care linkages among asymptomatic HIV+ persons are necessary steps to achieve population level impact 91% 100% 40% 83% Home-based HIV testing, POC CD4 & lay counselor follow- up for linkages to HIV care (Barnabas et al, AIDS XIX Conf. 2012) 80% N HIV+ in population CD4 measurement Eligible for ART ART Proportion tested Virally suppressed 70% 59% 33% 20% 100% Baseline N

Main Findings from the pilot studies Community HCT with POC CD4 & lay counselor follow- up: –Acceptable and achieves high coverage in South Africa and Uganda –Identifies HIV+ persons unaware of serostatus & at high CD4 count –Facilitates effective linkage to HIV care Sample sizes were small in both pilot studies Feasibility of a scalable, long-term intervention needed

Community HCT study results Pilot studies in Uganda and South Africa Uganda link to HIV care and MC for HIV- males South Africa – proof of concept, POC CD4 count Intervention study in South Africa and Uganda Large scale, POC CD4, 12 month follow-up Mathematical modeling results Discussion

Participants tested through HBCT N=3,394 Aware of HIV+ status Not on ART* N=152 (5%) Aware of HIV+ status on ART N=254 (7%) HIV-negative N=2758 (81%) Newly diagnosed HIV+* N=230 (7%) Phase II of home HCT: Study population *Median CD4 baseline, HIV+ persons not on ART: 472 in So Africa & 423 in Uganda

Home HCT Results: South Africa & Uganda (September 2011 – May 2013) Baseline findingsSAUganda Households enrolled Adults tested1273 (98%)2121 (94%) HIV+ identified404 (32%)232 (11%) Newly identified HIV+29%48% On ART at enrollment39%41% Median CD4 baseline (not on ART) NIH Directors Award RC4 AI (Celum)

Home HCT Results: South Africa & Uganda (September 2011 – May 2013) NIH Directors Award RC4 AI (Celum) Results at 12 monthsSAUganda Visited an HIV clinic97% Initiated ART (among eligible with CD4≤350) 74%89%

Initiated ART by 6 months N=42 (28%) Did not initiate ART by 6 months N= 106 (71%) Initiated ART by 6 months N=64 (30%) Did not initiate ART by 6 months N=148 (70%) Participants tested N=3,394 Aware of HIV+ status Not on ART N=152 (5%) Aware of HIV+ status On ART N=254 (7%) HIV-negative N=2758 (81%) Newly diagnosed HIV+ N=230 (7%) Initial HBCT visit (baseline) Visited HIV clinic by 6 months N=252 (99%) Visited HIV clinic by 6 months N=148 (97%) Visited HIV clinic by 6 months N=212 (92%) 6 participants died and 22 participants withdrew from the study Month 6 follow-up visit Not ART eligible (CD4>350) N=83 (78%) ART eligible (CD4≤350) N=25 (17%) Not ART eligible (CD4>350) N=123 (83%) ART eligible (CD4≤350) N=23 (22%) Phase II of home HCT: Study flow

Clinic uptake: 96% at 6 months among HIV-infected participants not on ART at baseline

ART uptake: 65% at 9 months among participants not on ART at baseline with CD4≤350 In multivariate analysis, only predictor of ART uptake was CD4 count at baseline Follow-up visit in months

South Africa: ART uptake by CD4 count among those not on ART at enrollment ART uptake was lower among participants with CD compared to CD4≤200

Uganda: ART uptake by CD4 count among those not on ART at enrollment ART uptake was lower among participants with CD compared to CD4≤250

Reported reasons for no ART uptake among ART eligible participants ReasonSouth AfricaUganda I was told I was not eligible64%22% Clinic repeating CD4 count/waiting for appts 8%46% Too busy8% I do not feel sick5%8% Stigma5%8% I am not ready to start ART5%- Other5%8% Individuals with higher CD4 counts may require different strategies, e.g. support and training of ART providers on revised ART initiation criteria Client support, such as simplified ART delivery & couples counseling, maybe required to increase ART uptake & adherence among persons with higher CD4 counts

TMP-SMX uptake in Uganda All HIV+ eligible, home initiation and delivery 98.6% Uptake at 12 months Among those not on Bactrim at enrollment No. at risk With few barriers to uptake, high and sustained coverage for TMP-SMX is achieved

No. at risk In a couple Not in a couple In a couple: 84.8% Not in a couple: 75.6% Log-rank p-value: 0.26 Testing with partner increases likelihood of ART initiation than those testing alone (among ART eligible with CD4 ≤350)

Community survey pre and post home HCT: Community level impact with increased HIV testing & disclosure CharacteristicSouth Africa (N=270)Uganda (N=230) Pre-HBCT (%)Post-HBCT (%)p-valuePre-HBCT (%)Post-HBCT (%)p-value Had HIV test7390< <0.001 Tested in the past year 4669< <0.001 Disclosure to spouse increases respect <0.001 Have partner with HIV 911< <0.001

Community HCT study results Pilot studies in Uganda and South Africa Uganda link to HIV care and MC for HIV- males South Africa – proof of concept, POC CD4 count Intervention study in South Africa and Uganda Large scale, POC CD4, 12 month follow-up Mathematical modeling results Discussion

Mathematical modeling estimate of effectiveness Compartmental, deterministic model for HIV in KwaZulu-Natal, SA Stratified for gender, age, sexual activity classes, CD4 and viral load *Force of infection – per susceptible risk of acquiring HIV (function of sexual mixing, HIV prevalence, transmission probability, viral load) – captures indirect effects Roger Ying, ISSTDR, 2013 Susceptible Acute Stage CD4 >500 CD CD ART CD4 ≤200 *

Mathematical modeling estimate of effectiveness Model prevalence and incidence output compared well with observed data Model assumptions –Viral suppression on ART decreases HIV transmission by 90% –Modeled the impact of HCT every 5 years with 6% annual dropout –Used proportion of all HIV-positive participants w/ viral suppression (73%) for ART adherence

Assuming 5 yearly HCT campaigns, 6% annual dropout and 73% adherence among HIV+ persons on ART. Campaign HCT and ART linkage can decrease HIV incidence in KwaZulu-Natal Change in HIV incidence (%) ART guideline5 years10 years20 years Baseline HCT CD4 ≤ HCT CD4 ≤ Prop. infections attributable to acute HIV infection

Campaign HCT and ART linkage can decrease HIV prevalence in KwaZulu-Natal Decrease in HIVprevalence(%)Cumulative HIV deaths averted ART guideline5 years10 years20 years5 years10 years20 years Baseline ,549848,3201,771,203 CD4 ≤ ,8711,012,9382,255,965 CD4 ≤ ,8951,157,4712,743,997

Community HCT study results Pilot studies in Uganda and South Africa Uganda link to HIV care and MC for HIV- males South Africa – proof of concept, POC CD4 count Intervention study in South Africa and Uganda Large scale, POC CD4, 12 month follow-up Mathematical modeling results Discussion

Limitations Household residents enrolled – did not account for migration e.g. for employment Lower uptake of testing among couples, men and youth in South Africa Data not linked to clinic records ART uptake not evaluated from the provider perspective Study not powered to evaluate whether couples counseling and testing had an impact on ART uptake

Summary of Testing & Linkages Studies Community HCT with POC CD4 & lay counselor follow-up: –Acceptable and achieves high coverage in South Africa and Uganda –Identifies HIV+ persons unaware of serostatus & at high CD4 count –Facilitate effective linkage to HIV care –With follow-up visits, see increase linkage to care and ART initiation –In multivariate analysis, only predictor of ART uptake was CD4 count at baseline –Significant change in viral load suppression at population level –Significant increase in HIV testing at population level –Model could be incorporated into existing cadre community health workers and avert HIV infection and HIV-associated mortality

Summary of Testing & Linkages Studies Despite high HIV clinic attendance, ART uptake lagged behind engagement in care –Individuals with higher CD4 counts may require different strategies, e.g. support and training of ART providers on revised ART initiation criteria –Client support, such as simplified ART delivery & couples counseling, maybe required to increase ART uptake & adherence among persons with higher CD4 counts Additional strategies are needed to reach youth, and men who are working away from homes, and to promote couples testing and disclosure

Summary of Testing & Linkages Studies Ongoing work –Impact evaluation, costing and incremental cost effectiveness analysis –Evaluate minimum package needed for linkage to ART and VL suppression –Develop a model with tools that can be implemented by existing CHWs in SA and Uganda

Next phase in our linkages to HIV care and MC studies: Comparison of strategies

Thank you Study Participants ICOBI and HSRC Staff Connie Celum, Carol Levin, Jared Baeten, Roger Ying, Aditya Khanna, Monisha Sharma, Sarah Roberts, Susie Cassels, Jim Hughes, Geoff Garnett, Meighan Krows, Hilton Humphries, Bosco Turyamureeba, Katherine Murray, Elioda Tumwesigye, Heidi van Rooyen & Judy Wasserheit Funding: NIH 5 R01 AI083034, 3 R0 AI S2 and NIH Directors Award RC4 AI RVB acknowledges funding from NCATS/NIH (KL2 TR000421) and the Centers for AIDS Research (CFAR)/NIH (P30 AI027757).