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Strategies for HIV Epidemic Control

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Susan Little, MD, of UC San Diego AntiViral Research Center, presents "Strategies for HIV Epidemic Control" at AIDS Clinical Rounds

Susan Little, MD, of UC San Diego AntiViral Research Center, presents "Strategies for HIV Epidemic Control" at AIDS Clinical Rounds

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  • 1. The UC San Diego AntiViral Research Center sponsors weekly presentations by infectious disease clinicians, physicians and researchers. The goal of these presentations is to provide the most current research, clinical practices and trends in HIV, HBV, HCV, TB and other infectious diseases of global significance. The slides from the AIDS Clinical Rounds presentation that you are about to view are intended for the educational purposes of our audience. They may not be used for other purposes without the presenter’s express permission. AIDS CLINICAL ROUNDS
  • 2. Susan Little, M.D. Professor of Medicine University of California San Diego Strategies for HIV Epidemic Control 7/18/141
  • 3. Epidemic spread of infection  Understanding the spread of infectious diseases in populations is key to controlling them.  Epidemic disease spread determined by properties of the pathogen (contagiousness, the length of the infection period, severity, etc.) and network structures within the population.  An understanding of these issues may provide insights for preventing the spread of disease. 7/18/142
  • 4. Epidemic Disease Control  Infection control options  Treatment as prevention/Universal test and treat  Network-focused interventions  Improved methods to guarantee privacy 7/18/143
  • 5. Strategies to prevent/control infectious diseases 7/18/14  Reduce contact rate (case finding & isolation, contact tracing & quarantine, behavior change)  Reduce infectiousness (treatment, vaccination)  Reduce susceptibility (vaccination, immune globulin)  Interrupt transmission (infection control)  Identify and control reservoir/source (pest/vector control, environmental disinfection)  Reduce prevalence of infectious sources (identify and control infectious sources)  Reduce duration of infectiousness (treatment, vaccination)  Increase herd immunity (vaccination) 4
  • 6. Strategies to prevent/control HIV 7/18/14  Reduce contact rate (case finding & isolation, contact tracing & quarantine, behavior change)  Reduce infectiousness (treatment, vaccination)  Reduce susceptibility (vaccination, immune globulin)  Interrupt transmission (infection control ≈ treatment)  Identify and control reservoir/source (pest/vector control, environmental disinfection)  Reduce prevalence of infectious sources (identify and control infectious sources)  Reduce duration of infectiousness (treatment, vaccination)  Increase herd immunity (vaccination) 5
  • 7. Summary of HIV prevention options 7/18/14  Case finding (testing)  Contact tracing (partner services for recently infected)  Behavior change (durable?)  Identify and treat infectious sources  Identify and treat susceptible recipients 6
  • 8. HIV Intervention Strategies 7/18/14  Universal test and treat strategies: theoretically plausible  Acceptability: issues of stigma  Feasibility: model presumes annual testing  Resources: estimated costs are greater initially  ARTAccess: by 2012,ART accessed by 65% of 15 million global “target”  Acute Infection: 20-50% of transmission may occur in setting of acute HIV (not captured by routineAb screening).  Targeted strategies  How to prioritize target populations?  Will locally effective interventions translate to success at the population level? 7
  • 9. Epidemic Disease Control  Infection control options  Treatment as prevention/Universal test and treat  Network-focused interventions  Improved methods to guarantee privacy 7/18/148
  • 10. Treatment as Prevention A strategy that considers “universal” HIV testing with immediate antiretroviral therapy (ART) with the goal of reducing HIV transmission (HIV incidence) and eventual “elimination” of disease 9 7/18/149
  • 11. This image cannot currently be displayed. 0.00 0.05 0.10 0.15 1980 1990 2000 2010 2020 2030 2040 2050 0.00 0.01 0.02 0.00 0.05 0.10 0.15 1980 1990 2000 2010 2020 2030 2040 2050 0.00 0.01 0.02 0.00 0.05 0.10 0.15 1980 1990 2000 2010 2020 2030 2040 2050 0.00 0.01 0.02 Current phase Roll out Elimination Prevalence Incidence Mortality On ART 7/18/1410
  • 12. Conclusions from modeling exercise Universal and annual voluntary HIV testing followed by immediate ART (irrespective of CD4 cell count and combined with other prevention interventions) could:  Eliminate HIV transmission in 5-10 years  Eliminate HIV infection in 40 years  Initial resources would be higher but over time, given the reduction in HIV incidence, this approach may provide cost savings  Estimated costs are within UNAIDS estimates for universal access for a population this size 7/18/14Granich, et al. Lancet 200911
  • 13. Treatment as Prevention: HPTN 052 Sheth PM, et al. AIDS. 2009;23:2050-2054. 7/18/14 Cohen M. NEJM 2011 1,763 sero-discordant couples (97% heterosexual) HIV infected partners: 890 men, 873 women I-ART (CD4 350-550) D-ART (CD4 <250, >200) 39 Transmissions 28 Linked 11 Unlinked I-ART: 1 Transmission D-ART: 27 Transmissions 96% Protection Associated with Immediate ART 12
  • 14. Test and Treat (Treatment as Prevention) Studies (1)  HPTN 071 (PopART): Richard Hayes/Sara Fidler, n=1.2 million  3 arm, 2-country (Zambia & S.Africa) cluster-randomized trial:  Full combination prevention and immediate ART  Combination prevention with SOC ART (CD4<350)  SOC  Endpoint: HIV incidence (at 36 months)  Botswana combination prevention program (BCPP): Max Essex  Pair-matched, community-randomization design (30 villages)  Interventions: enhanced HTC (>90%), MC (>80%), PMTCT, improved LTC/treatment (>90%/60%),ART for those withVL ≥ 10,000 (regardless of CD4)  Endpoint: HIV incidence (within 3 years) 7/18/1413 2013/4 International HIV Treatment as Prevention Workshop
  • 15.  SEARCHTrial: Diane Havlir/Moses Kamya, Uganda and Kenya  Clustered, community-randomized trial (32 pair-matched communities of 10,000 each)  Intervention:ART access w/optimized LTC and streamlined care vs. SOC  Outcome: cumulative 3 and 5-year HIV incidence  MaxART:Velephi Okello (Swaziland – In country study)  Regional access to immediateART and various PMTCT options  Médecines Sans Frontières (MSF) Implementation: RogerTeck  Promote community scale ofART and PMTCT  CombinationTrials in SouthAfrica & Uganda: Ruanne Barnabas  Promote testing, LTC, male circumcision 7/18/1414 Test and Treat (Treatment as Prevention) Studies (2) 2013/4 International HIV Treatment as Prevention Workshop
  • 16. TasP is biologically plausible, but…  Massive mobilization of resources necessary  No specific recommendations for sex workers, drug users, or MSM  Retrospective analyses of HIV transmission and ART are mixed.  HIV incidence has not decreased in Canada, US, EU despite improved access to testing and treatment  Marked increases in risk activity in SF are outpacing any decrease in infectivity due to increasedART use  ~4300 discordant HET couples in China (2006) – no difference in seroconversion rates associated w/ART use 7/18/1415 Lu, et al. JAIDS 2010; Nguyen, et al. AIDS 2011
  • 17. Universal Test and Treat: The Present  HIV testing is the foundation for all ART interventions.  Early treatment substantially reduces the risk of secondary HIV transmission.  The prevention benefit of treatment requires linkage to and retention in care, and adherence to ART (Gardner EM, et al. CID, 2011). 7/18/14 80 % 89 % 66 % 77 % 77 % 16
  • 18. Epidemic Disease Control  Infection control options  Treatment as prevention/Universal test and treat  Network-focused interventions  Improved methods to guarantee privacy 7/18/1417
  • 19. 18 The Challenge…. 7/18/14 “Because of the common route of transmission through sexual networks, interventions against STIs need to be targeted to whole populations.” -World Health Organization,2012 “What network theory teaches us is that connections, even within the most complex systems, are not random (that is, they are not unpredictable). Instead, networks behave in ways that we can theorize, model, and predict.” The PLoS Medicine Editors It's the Network,Stupid:Why Everything in Medicine Is Connected PLoS Med 2008
  • 20. Talking about networks…  Nodes (n): individuals in the network  Degree (d): connections between nodes  The degree or degree centrality of a node is the number of connections (edges) the node has to other nodes in the network 7/18/1419
  • 21. Network Topology Scale Free  Real world networks: www, gang networks, power grid, on-line gaming, HIV, etc.  Large majority of nodes have low degree, but small number (hubs) have high degree. Random  Cannot be used to model real world networks  Nodes are connected (or not) with independent probability: binomial distribution (for large n) 7/18/1420 Degree distribution: the probability (P) that a randomly chosen node has k connections: Pdeg(k)
  • 22. Scale-free Networks: preferential attachment  Connectivity is maintained by a few highly connected “hubs”  Scale-free networks are vulnerable to coordinated attacks - when the nodes with the highest number of edges are targeted, the network breaks down faster than in the case of random node removal. 7/18/1421 Scale-free network (i.e. HIV) Hubs
  • 23. 7/18/14 HIV is transmitted along a complex contact network Concept Contact Network Transmission network Node Individual HIV+ individual Edge A contact that could lead to HIV transmission, e.g. sexual, shared needle Transmission event Degree = edges connected to a node Number of contacts associated with a node Number of transmissions associated with a node Transmission network is a subset of the contact network 22 Contact network
  • 24. It pays to target highly connected nodes Targeting a low degree node has a local effect Targeting a high degree node has a global effect 7/18/1423
  • 25. Example 1: Drug Trafficking Network Simulation  Simulations of illicit networks (drug trafficking networks) to determine areas of vulnerability and resilience.  Four intervention strategies: 1) Target most connected nodes (high degree centrality) 2) Target nodes which play most important roles 3) Target by a combination of degree centrality and role 4) Random selection  Quantify the extent of network disruption – the number of nodes in the largest remaining connected component 7/18/1424 Bright et al, UNSW, Australia (2013)
  • 26. Size of the largest connected component 7/18/1425
  • 27. 7/18/1426 Example 2: Simulations of HIV network Random vs. Preferential ART • RandomART (red): 4.1% fewer infected nodes than without any intervention • PreferentialART (black): 31.3% fewer infected nodes after 3 years than random ART. • Highly connected nodes become infected early and thus receive ART earlier. • These nodes are 96% less likely to transmit to partners NumberofInfected Day ART Non-targeted ART Targeted Trewick C, et al. Computational Social Science Society of the Americas, 2013
  • 28. HIV Network Intervention Strategies 7/18/14  Studies of the UK national HIV database suggest that random (i.e. uniformly applied) interventions will not be sufficient to halt the epidemic.  Interventions must be targeted to high-degree nodes to be effective.  San Diego: Dr. Smith working on an NIH funded study to map HIV transmission dynamics in real time and direct community specific prevention resources. Leigh Brown, et al. JID 2011; Brenner et al. AIDS 2013 27
  • 29. Degree Distribution of SDPIC Network A small proportion of individuals have many connections (high degree) 7/18/1428
  • 30. SDPIC Network Map 7/18/14 29
  • 31. 30 Example 3: Retrospective Analysis of Self-Selected ART ART naive ART >30 days of EDI ART ≤30 days of EDI No data 7/18/14
  • 32. Retrospective study of ART  Network statistic* shows that early (≤30 days since EDI) treatment results in a significantly lower network connectivity than does delayed treatment (p<0.05), even with small samples (N=21 early, N=137 delayed).  While encouraging, must still prove that such interventions when delivered in a targeted fashion can disrupt the entire network. 7/18/14*Wertheim, et al. PLoS One 201131
  • 33. The Future…  Goal is a prospective, real-time, network informed (i.e., targeted interventions) trial to evaluate impact of network HIV incidence Objectives:  Infer the local HIV transmission network - estimate features of the underlying infected population and efficacy of potential interventions.  Assess the potential of molecular epidemiology and network statistics to measure the efficacy of ART as a network-based prevention intervention.  To develop and deploy privacy preserving methods for analysis and release of network data. 7/18/1432
  • 34. Hypothesis We can control the San Diego HIV epidemic (i.e., reduce R0<1) by identifying high degree nodes or “hubs” within the transmission network and selectively interrupting transmission from these clusters. 7/18/1433
  • 35. Study Design  HIV EarlyTest Program – identify persons with HIV  Provide partner services for acutely and recently infected persons  Rapid linkage to care  UCSD AVRC – referral to primary care  Baseline HIV genotype and routine clinical laboratories  Universal access to immediateART – elvitegravir/cobicistat/tenofovir/emtricitabine (Stribild) for 1-5 years  Compare network connectivity (transmission) in persons who initiate ART within ≤30 days EDI vs >30 days. 7/18/1434
  • 36. Epidemic Disease Control  Infection control options  Treatment as prevention/Universal test and treat  Network-focused interventions  Improved methods to guarantee privacy 7/18/1435
  • 37. 7/18/1436 HIV Transmission & The Law: USA  In 1990, Congress passed the RyanWhite CAREAct – mandated that states criminalize the intentional transmission of HIV  In 2000, Congress reauthorized theAct, but removed the criminalization requirement – many states kept their laws  32 States currently have laws that criminalize the transmission of HIV  25 States criminalize one or more behaviors that pose a low or negligible risk for transmission  These laws perpetuate and condone stigma and discrimination against persons with HIV
  • 38. California (7/2010) “Any person who exposes another to HIV by engaging in unprotected sexual activity (anal or vaginal intercourse without a condom) when the infected person knows at the time of the unprotected sex that he or she is infected with HIV, has not disclosed his or her HIV-positive status, and acts with the specific intent to infect the other person with HIV, is guilty of a felony.” 7/18/1437 http://www.hivandhepatitis.com/hiv-policy-advocacy/3803-hiv-medicine-association-calls-for-repeal-of-hiv- criminalization-laws • The divisions between intentional, reckless, and accidental transmission can be blurred • The majority of criminal convictions involving sexual transmission involve an HIV positive person not informing their negative partner about their status
  • 39. Rhoades vs. Iowa (2008)  Nick Rhoades (HIV+,VL undetectable) had sexual encounter with another man – used a condom.  HIV transmission did not occur  Other man found out Rhoades was HIV infected, contacted police  Rhoades received maximum sentence – 25 years in prison and lifetime registration as a sex offender.  Months later, sentence suspended – replaced with supervised probation for 5 years  Iowa Supreme Court (6/13/14) - sentence reversed 7/18/1438
  • 40. HIV Transmission & The Law: Africa  USAID has financed the “Action forWestAfrica Region HIV-AIDS program” since 2004  Instrumental in developing a model for HIV-specific criminal law  27 African countries now have active laws. 7/18/1439 http://www.change.org/petitions/demand-usaid-to-stop-funding-hiv-criminalization-laws-in-africa
  • 41. Limitations of Phylogenetic Analysis • Current techniques are NOT reliable enough to estimate the direction of transmission with certainty. • Similar strains may be found in many more than two individuals, especially if they are part of the same transmission network. 7/18/14 A B C E D F G H ? ? 40
  • 42. Privacy Challenges To develop and deploy privacy preserving methods for analyzing network dynamics in order to share predictions about future network growth with quantitative estimates of privacy risk. 7/18/1441
  • 43. Phylogentics & Criminal Statutes Plans  Assess consumer and provider knowledge and expectations associated with phylogenetic analyses  Assess perceptions of acceptable risk and benefit for public health and personal privacy  Public health use of these data requires decriminalization of unintended HIV transmission during consensual exposure. 7/18/1442
  • 44. 7/18/1443 Acknowledgements OWEN CLINIC

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