Monitoring of antiretroviral therapy and mortality in HIV programmes in Malawi, South Africa and Zambia: mathematical modelling study

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dc.contributor.authorEstill, Janneen_ZA
dc.contributor.authorEgger, Matthiasen_ZA
dc.contributor.authorJohnson, Leigh Fen_ZA
dc.contributor.authorGsponer, Thomasen_ZA
dc.contributor.authorWandeler, Gillesen_ZA
dc.contributor.authorDavies, Mary-Annen_ZA
dc.contributor.authorBoulle, Andrewen_ZA
dc.contributor.authorWood, Robinen_ZA
dc.contributor.authorGarone, Danielaen_ZA
dc.contributor.authorStringer, Jeffrey S Aen_ZA
dc.date.accessioned2015-11-16T04:09:30Z
dc.date.available2015-11-16T04:09:30Z
dc.date.issued2013en_ZA
dc.description.abstractObjectives Mortality in patients starting antiretroviral therapy (ART) is higher in Malawi and Zambia than in South Africa. We examined whether different monitoring of ART (viral load [VL] in South Africa and CD4 count in Malawi and Zambia) could explain this mortality difference. Design: Mathematical modelling study based on data from ART programmes. METHODS: We used a stochastic simulation model to study the effect of VL monitoring on mortality over 5 years. In baseline scenario A all parameters were identical between strategies except for more timely and complete detection of treatment failure with VL monitoring. Additional scenarios introduced delays in switching to second-line ART (scenario B) or higher virologic failure rates (due to worse adherence) when monitoring was based on CD4 counts only (scenario C). Results are presented as relative risks (RR) with 95% prediction intervals and percent of observed mortality difference explained. RESULTS: RRs comparing VL with CD4 cell count monitoring were 0.94 (0.74-1.03) in scenario A, 0.94 (0.77-1.02) with delayed switching (scenario B) and 0.80 (0.44-1.07) when assuming a 3-times higher rate of failure (scenario C). The observed mortality at 3 years was 10.9% in Malawi and Zambia and 8.6% in South Africa (absolute difference 2.3%). The percentage of the mortality difference explained by VL monitoring ranged from 4% (scenario A) to 32% (scenarios B and C combined, assuming a 3-times higher failure rate). Eleven percent was explained by non-HIV related mortality. CONCLUSIONS: VL monitoring reduces mortality moderately when assuming improved adherence and decreased failure rates.en_ZA
dc.identifier.apacitationEstill, J., Egger, M., Johnson, L. F., Gsponer, T., Wandeler, G., Davies, M., ... Stringer, J. S. A. (2013). Monitoring of antiretroviral therapy and mortality in HIV programmes in Malawi, South Africa and Zambia: mathematical modelling study. <i>PLoS One</i>, http://hdl.handle.net/11427/14990en_ZA
dc.identifier.chicagocitationEstill, Janne, Matthias Egger, Leigh F Johnson, Thomas Gsponer, Gilles Wandeler, Mary-Ann Davies, Andrew Boulle, Robin Wood, Daniela Garone, and Jeffrey S A Stringer "Monitoring of antiretroviral therapy and mortality in HIV programmes in Malawi, South Africa and Zambia: mathematical modelling study." <i>PLoS One</i> (2013) http://hdl.handle.net/11427/14990en_ZA
dc.identifier.citationEstill, J., Egger, M., Johnson, L. F., Gsponer, T., Wandeler, G., Davies, M. A., ... & Keiser, O. (2012). Monitoring of antiretroviral therapy and mortality in HIV programmes in Malawi, South Africa and Zambia: mathematical modelling study. PloS one, 8(2), e57611-e57611. doi:10.1371/journal.pone.0057611en_ZA
dc.identifier.ris TY - Journal Article AU - Estill, Janne AU - Egger, Matthias AU - Johnson, Leigh F AU - Gsponer, Thomas AU - Wandeler, Gilles AU - Davies, Mary-Ann AU - Boulle, Andrew AU - Wood, Robin AU - Garone, Daniela AU - Stringer, Jeffrey S A AB - Objectives Mortality in patients starting antiretroviral therapy (ART) is higher in Malawi and Zambia than in South Africa. We examined whether different monitoring of ART (viral load [VL] in South Africa and CD4 count in Malawi and Zambia) could explain this mortality difference. Design: Mathematical modelling study based on data from ART programmes. METHODS: We used a stochastic simulation model to study the effect of VL monitoring on mortality over 5 years. In baseline scenario A all parameters were identical between strategies except for more timely and complete detection of treatment failure with VL monitoring. Additional scenarios introduced delays in switching to second-line ART (scenario B) or higher virologic failure rates (due to worse adherence) when monitoring was based on CD4 counts only (scenario C). Results are presented as relative risks (RR) with 95% prediction intervals and percent of observed mortality difference explained. RESULTS: RRs comparing VL with CD4 cell count monitoring were 0.94 (0.74-1.03) in scenario A, 0.94 (0.77-1.02) with delayed switching (scenario B) and 0.80 (0.44-1.07) when assuming a 3-times higher rate of failure (scenario C). The observed mortality at 3 years was 10.9% in Malawi and Zambia and 8.6% in South Africa (absolute difference 2.3%). The percentage of the mortality difference explained by VL monitoring ranged from 4% (scenario A) to 32% (scenarios B and C combined, assuming a 3-times higher failure rate). Eleven percent was explained by non-HIV related mortality. CONCLUSIONS: VL monitoring reduces mortality moderately when assuming improved adherence and decreased failure rates. DA - 2013 DB - OpenUCT DO - 10.1371/journal.pone.0057611 DP - University of Cape Town J1 - PLoS One LK - https://open.uct.ac.za PB - University of Cape Town PY - 2013 T1 - Monitoring of antiretroviral therapy and mortality in HIV programmes in Malawi, South Africa and Zambia: mathematical modelling study TI - Monitoring of antiretroviral therapy and mortality in HIV programmes in Malawi, South Africa and Zambia: mathematical modelling study UR - http://hdl.handle.net/11427/14990 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/14990
dc.identifier.urihttp://dx.doi.org/10.1371/journal.pone.0057611
dc.identifier.vancouvercitationEstill J, Egger M, Johnson LF, Gsponer T, Wandeler G, Davies M, et al. Monitoring of antiretroviral therapy and mortality in HIV programmes in Malawi, South Africa and Zambia: mathematical modelling study. PLoS One. 2013; http://hdl.handle.net/11427/14990.en_ZA
dc.language.isoengen_ZA
dc.publisherPublic Library of Scienceen_ZA
dc.publisher.departmentInstitute of Infectious Disease and Molecular Medicineen_ZA
dc.publisher.facultyFaculty of Health Sciencesen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_ZA
dc.rights.holder© 2013 Estill et alen_ZA
dc.rights.urihttp://creativecommons.org/licenses/by/4.0en_ZA
dc.sourcePLoS Oneen_ZA
dc.source.urihttp://journals.plos.org/plosoneen_ZA
dc.subject.otherAntiretroviral therapyen_ZA
dc.subject.otherDeath ratesen_ZA
dc.subject.otherViral loaden_ZA
dc.subject.otherMathematical modelsen_ZA
dc.subject.otherHIVen_ZA
dc.titleMonitoring of antiretroviral therapy and mortality in HIV programmes in Malawi, South Africa and Zambia: mathematical modelling studyen_ZA
dc.typeJournal Articleen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceArticleen_ZA
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