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Viral load versus CD4 monitoring - impact on mortality, transmission and cost-effectiveness. Olivia Keiser, Janne Estill, Matthias Egger ISPM, University of Bern, Switzerland okeiser@ispm.unibe.ch; jestill@ispm.unibe.ch;. Introduction: Challenges in monitoring ART.
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Viral load versus CD4 monitoring-impact on mortality, transmission and cost-effectiveness Olivia Keiser, Janne Estill, Matthias Egger ISPM, University of Bern, Switzerland okeiser@ispm.unibe.ch; jestill@ispm.unibe.ch;
Introduction: Challenges in monitoring ART Routine viral load monitoring Less time spent with high viral load Switching with higher CD4 cell count Less transmission Lower mortality
Introduction: Challenges in monitoring ART Routine viral load monitoring Less time spent with high viral load Switching with higher CD4 cell count Less transmission Lower mortality ...But to what extent? Impact on costs?
Mortality – previous studies • Modelling study: Viral load monitoring increased proportion of potential life time survived from 82% to 83% over 5 years (Phillips et al, Lancet 2008) • Modelling study: Viral load monitoring increased life expectancy on ART from 17.5 to 19.3 years (Kimmel et al, JAIDS 2010) • Randomizedcontrolledtrial: Nosignificantdifference in mortalitybetweenstrategies(Merminet al, BMJ 2011)
Mortality – previous studies No routine VL (Malawi, Zambia) 0.10 Routine VL (South Africa) Cumulative mortality 0.05 0.00 0 1 2 3 Years since ART start Keiser et al., AIDS 2011
The IeDEAmathematical model • Individual-based simulation model • The model simulates a cohort of individual patients receiving ART and records the key outcomes such as: • Viral load over time • Time of immunologic failure and clinical events • Twocohortswithfrequent VL monitoring (GugulethuandKhayelitsha) wereusedtoparameterizethemodel.BotharepartoftheInternational epidemiologic Databases toEvaluate AIDS Southern African region (www.iedea-sa.org) Estill et al, AIDS 2012
Mortality – previous studies What could explain this difference? How can we apply the model? Simple comparison of the twostrategies • More accurate detection offailure?
Mortality – previous studies What could explain this difference? How can we apply the model? Simple comparison of the two strategies Adding a strategy-specific delay from failure to switch Increasing the rate of virological failure if viral load monitoring not available • More accurate detection offailure? • More confidence to switch when viral load is known? • Improved adherence and therefore lower failure rates?
Mortality: simulations • 3 times higher hazard of failure if no routine viral loadmonitoring • Delay fromfailuredetectiontoswitchingasobserved in cohorts Estill et al, IAS 2011, Rome
Routine viral load monitoring and transmission: Background • Strong dependencebetween individual viral load and transmission (Quinn et al, NEJM 2000) • Noroutine viral loadmonitoring • longer time on failingregimen • higherriskoftransmission
The mathematical model Estill et al, AIDS 2012
Definitions • What additional benefitstobegainedfromintroducing VL monitoringandathowmuchcost? • Incrementalcost-effectiveness (ICER)=Difference in costsbetween VL and CD4Difference in healtheffectsbetween VL and CD4 • Costeffective: < 3 * per capita GDP ofcountry
Different model assumptions Other differences: time, type, number and price of regimens, ART initiation criteria, disease progression and incidence of OIs, inclusion of pre ART or not
Total costs Assumedcosts(US $): CD4/ test: 4.70 First-line/ year: 150 Visit: 15.80Viral load/ test: 10 Second-line/ year: 500
Cost-effectiveness ratio- adherenceandtransmission not included VL failure: 1000 copies/ml 3* GDP = 3* 1414= 4242
Cost-effectiveness ratio- includingeffect on transmission VL failure: 1000 copies/ml 4242 1749 1053
Cost-effectiveness ratio- includingeffect on transmissionandadherence VL failure: 1000 copies/ml Adherenceeffect: 2* higherfailure rate in CD4 sites 1053 1749
Conclusion Short termbenefitof VL monitoring on survivalsmall; longtermeffectlessclear Effect on adherenceandtransmissionneedstobeconsidered; couldmake VL verycost-effective Differencesacross different modelsshouldbeexplored in moredetail
Acknowledgement Co-authors: Timothy Hallett, Nello Blaser, Luisa Salazar, Mary-Ann Davies, Gilles Wandeler, Robin Wood, Andrew Boulle, Thomas Gsponer, Cindy Aubrière, Daniela Garone, Jeffrey Stringer, Jennifer Campbell, Leigh Johnson All stafffromthecollaboratingIeDEAsitesandfromtheIeDEAdatacentres in Bern, Switzerlandand Cape Town, South Africa Christine Rousseau, Russ Wada Funders: NIH, Bill and Melinda Gates Foundation, Swiss National Science Foundation, UNITAID, Swiss School of Public Health Latebreakerposterby Janne Estill: Tuesday 12.30-14.30 (LBPE15)