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The impact of the new WHO antiretroviral treatment guidelines on HIV epidemic dynamics and cost in South Africa. Jan Hontelez 1,2,3 , Sake de Vlas 1 , Frank Tanser 2 , Roel Bakker 1 , Till Bärnighausen 2 , Marie-Louise Newell 2 , Rob Baltussen 3 , Mark Lurie 4 - PowerPoint PPT Presentation
Citation preview
The impact of the new WHO antiretroviral treatment
guidelines on HIV epidemic dynamics and cost in South
AfricaJan Hontelez1,2,3, Sake de Vlas1, Frank Tanser2, Roel Bakker1, Till
Bärnighausen2, Marie-Louise Newell2, Rob Baltussen3, Mark Lurie4
1Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
2Africa Centre for Health and Population Studies, University of KwaZulu-Natal, Somkhele, South Africa
3Department of Primary and Community Care, Radboud University Nijmegen Medical Center, Netherlands
4Department of Epidemiology and the International Health Institute, Warren Alpert Medical School, Brown University, Providence, RI, USA
Introduction
• WHO: treat patients at CD4+ cell counts of ≤350 cells/µL rather than ≤200 cells/µL
• What would be the impact of these new guidelines in South Africa?– HIV epidemic dynamics– ART program costs
Methods
• STDSIM: an established stochastic microsimulation model– Simulates individuals in a dynamic network of
sexual contacts
• Africa Centre for Health and Population Studies, KwaZulu-Natal, South Africa– Africa Centre Demographic Information System
(ACDIS)1
– Hlabisa Treatment and Care Programme21. Tanser F, Hosegood V, Bärnighausen T, et al. Cohort Profile: Africa Centre Demographic Information System (ACDIS) and
population-based HIV survey. Int J Epidemiol 2008;37:956-62.2. Houlihan CF, Bland R, Mutevedzi P, et al. Cohort Profile: Hlabisa HIV treatment and care programme. Int J Epidemiol 2010
Model quantification
• Demographic module– Published data from ACDIS
• Sexual behavior module– Published and unpublished data from ACDIS– Used to fine-tune predicted HIV epidemic
• Biological module– Same as in previous STDSIM studies
• Treatment module– Unpublished data form ART cohort
Methods• ART:
– Reduces infectiousness by 92% 1,2
– Increases survival by a factor 3– Introduced in 2004
• Drop-out rate 1.27%/year based on data3
• Other interventions like condom use, STI treatment, circumcision rates based on literature
1. Attia S, Egger M, Muller M, Zwahlen M, Low N. Sexual transmission of HIV according to viral load and antiretroviral therapy: systematic review and meta-analysis. AIDS 2009; 23:1397-404.
2. Donnell D, Baeten J, Kiarie J, et al. Heterosexual HIV-1 transmission after initiation of antiretroviral therapy: a prospective cohort analysis. Lancet 2010; 375: 2092-2098
3. Houlihan CF, Bland R, Mutevedzi P, et al. Cohort Profile: Hlabisa HIV treatment and care programme. Int J Epidemiol 2010
Cost values
• One-time cost of dying• Costs for testing and treatment 201-350 in ≤200
scenario• Cost of CD4+ testing and monitoring for patients
>350
Annual ART costs (US$) CD4+ count
(cells/µl) at ART initiation
Pre-ART
First year
Second and third year
Subsequent years
0-100 495
3,664
1,435 1,095
101-200 495 3,060 1,284 1,095 201-350 495 2,304 1,095 1,095
1. Harling G, Wood R. The evolving cost of HIV in South Africa: changes in health care cost with duration on antiretroviral therapy for public sector patients. J Acquir Immune Defic Syndr 2007; 45:348-54.
2. Badri M, Maartens G, Mandalia S, et al. Cost-effectiveness of highly active antiretroviral therapy in South Africa. PLoS Med 2006; 3:e4
HIV prevalence (15-49)
Simulations
Effect of ART at ≤350 cells/µL in mid 2010 versus continued treatment at ≤200 cells/µL:
– Period: 2010 – 2040– 1000 model runs– Population size: 280,000
Epidemic dynamics
28%
30%
48%
37%
Annual treatment costs
ART at ≤200 cells/µL ART at ≤350 cells/µL
Cumulative net costs
Cumulative life-years saved
Conclusions
• New WHO treatment recommendations require limited initial investments, and result in net cost savings in a limited time-horizon
• Societal benefits are high due to increased number of life-years saved, and better health of treated individuals
Limitations
• Mathematical models always imprecise
• Cost values were from Cape Town– Studies indicate values are in same range in KwaZulu-
Natal
• Limited health care infrastructure and funding limit full implementation of WHO guidelines
Thank you for your attention!
For more information, please contact:[email protected]