Improving the evidence base of Markov models used to estimate the costs of scaling up antiretroviral programmes in resource-limited settings

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dc.contributor.authorLeisegang, Roryen_ZA
dc.contributor.authorMaartens, Garyen_ZA
dc.contributor.authorHislop, Michaelen_ZA
dc.contributor.authorRegensberg, Leonen_ZA
dc.contributor.authorCleary, Susanen_ZA
dc.date.accessioned2015-11-11T12:02:02Z
dc.date.available2015-11-11T12:02:02Z
dc.date.issued2010en_ZA
dc.description.abstractBACKGROUND: Despite concerns about affordability and sustainability, many models of the lifetime costs of antiretroviral therapy (ART) used in resource limited settings are based on data from small research cohorts, together with pragmatic assumptions about life-expectancy. This paper revisits these modelling assumptions in order to provide input to future attempts to model the lifetime costs and the costs of scaling up ART. METHODS: We analysed the determinants of costs and outcomes in patients receiving ART in line with standard World Health Organization (WHO) guidelines for resource poor settings in a private sector managed ART programme in South Africa. The cohort included over 5,000 patients with up to 4 years (median 19 months) on ART. Generalized linear and Cox proportional hazards regression models were used to establish cost and outcome determinants respectively. RESULTS: The key variables associated with changes in mean monthly costs were: being on the second line regimen; receiving ART from 4 months prior to 4 months post treatment initiation; having a recent or current CD4 count <50 cells/uL or 50-199 cells/ul; having mean ART adherence <75% as determined by monthly pharmacy refill data; and having a current or recent viral load >100,000 copies/mL. In terms of the likelihood of dying, the key variables were: baseline CD4 count<50 cells/ul (particularly during the first 4 months on treatment); current CD4 count <50 cells/ul and 50-199 cells/ul (particularly during later periods on treatment); and being on the second line regimen. Being poorly adherent and having an unsuppressed viral load was also associated with a higher likelihood of dying. CONCLUSIONS: While there are many unknowns associated with modelling the resources needed to scale-up ART, our analysis has suggested a number of key variables which can be used to improve the state of the art of modelling ART. While the magnitude of the effects associated with these variables would be likely to differ in other settings, the variables influencing costs and survival are likely to be generalizable. This is of direct relevance to those concerned about assessing the long-term costs and sustainability of expanded access to ART.en_ZA
dc.identifier.apacitationLeisegang, R., Maartens, G., Hislop, M., Regensberg, L., & Cleary, S. (2010). Improving the evidence base of Markov models used to estimate the costs of scaling up antiretroviral programmes in resource-limited settings. <i>BMC Health Services Research</i>, http://hdl.handle.net/11427/14888en_ZA
dc.identifier.chicagocitationLeisegang, Rory, Gary Maartens, Michael Hislop, Leon Regensberg, and Susan Cleary "Improving the evidence base of Markov models used to estimate the costs of scaling up antiretroviral programmes in resource-limited settings." <i>BMC Health Services Research</i> (2010) http://hdl.handle.net/11427/14888en_ZA
dc.identifier.citationLeisegang, R., Maartens, G., Hislop, M., Regensberg, L., & Cleary, S. (2010). Improving the evidence base of Markov models used to estimate the costs of scaling up antiretroviral programmes in resource-limited settings. BMC health services research, 10(Suppl 1), S3.en_ZA
dc.identifier.ris TY - Journal Article AU - Leisegang, Rory AU - Maartens, Gary AU - Hislop, Michael AU - Regensberg, Leon AU - Cleary, Susan AB - BACKGROUND: Despite concerns about affordability and sustainability, many models of the lifetime costs of antiretroviral therapy (ART) used in resource limited settings are based on data from small research cohorts, together with pragmatic assumptions about life-expectancy. This paper revisits these modelling assumptions in order to provide input to future attempts to model the lifetime costs and the costs of scaling up ART. METHODS: We analysed the determinants of costs and outcomes in patients receiving ART in line with standard World Health Organization (WHO) guidelines for resource poor settings in a private sector managed ART programme in South Africa. The cohort included over 5,000 patients with up to 4 years (median 19 months) on ART. Generalized linear and Cox proportional hazards regression models were used to establish cost and outcome determinants respectively. RESULTS: The key variables associated with changes in mean monthly costs were: being on the second line regimen; receiving ART from 4 months prior to 4 months post treatment initiation; having a recent or current CD4 count <50 cells/uL or 50-199 cells/ul; having mean ART adherence <75% as determined by monthly pharmacy refill data; and having a current or recent viral load >100,000 copies/mL. In terms of the likelihood of dying, the key variables were: baseline CD4 count<50 cells/ul (particularly during the first 4 months on treatment); current CD4 count <50 cells/ul and 50-199 cells/ul (particularly during later periods on treatment); and being on the second line regimen. Being poorly adherent and having an unsuppressed viral load was also associated with a higher likelihood of dying. CONCLUSIONS: While there are many unknowns associated with modelling the resources needed to scale-up ART, our analysis has suggested a number of key variables which can be used to improve the state of the art of modelling ART. While the magnitude of the effects associated with these variables would be likely to differ in other settings, the variables influencing costs and survival are likely to be generalizable. This is of direct relevance to those concerned about assessing the long-term costs and sustainability of expanded access to ART. DA - 2010 DB - OpenUCT DO - 10.1186/1472-6963-10-S1-S3 DP - University of Cape Town J1 - BMC Health Services Research LK - https://open.uct.ac.za PB - University of Cape Town PY - 2010 T1 - Improving the evidence base of Markov models used to estimate the costs of scaling up antiretroviral programmes in resource-limited settings TI - Improving the evidence base of Markov models used to estimate the costs of scaling up antiretroviral programmes in resource-limited settings UR - http://hdl.handle.net/11427/14888 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/14888
dc.identifier.urihttp://dx.doi.org/10.1186/1472-6963-10-S1-S3
dc.identifier.vancouvercitationLeisegang R, Maartens G, Hislop M, Regensberg L, Cleary S. Improving the evidence base of Markov models used to estimate the costs of scaling up antiretroviral programmes in resource-limited settings. BMC Health Services Research. 2010; http://hdl.handle.net/11427/14888.en_ZA
dc.language.isoengen_ZA
dc.publisherBioMed Central Ltden_ZA
dc.publisher.departmentDivision of Clinical Pharmacologyen_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 Licenseen_ZA
dc.rights.holder2010 Leisegang et al; licensee BioMed Central Ltd.en_ZA
dc.rights.urihttp://creativecommons.org/licenses/by/2.0en_ZA
dc.sourceBMC Health Services Researchen_ZA
dc.source.urihttp://www.biomedcentral.com/bmchealthservres/en_ZA
dc.subject.otherAntiretroviral therapyen_ZA
dc.subject.otherDirect Service Costsen_ZA
dc.subject.otherHealth Plan Implementationen_ZA
dc.subject.otherMarkov Chainsen_ZA
dc.subject.otherMedication Adherenceen_ZA
dc.titleImproving the evidence base of Markov models used to estimate the costs of scaling up antiretroviral programmes in resource-limited settingsen_ZA
dc.typeJournal Articleen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceArticleen_ZA
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