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Lancet Glob Health. 2014 Jan;2(1):e35-43. doi: 10.1016/S2214-109X(13)70048-2. Epub 2013 Dec 10.

Cost-effectiveness of different strategies to monitor adults on antiretroviral treatment: a combined analysis of three mathematical models.

Author information

1
South African Department of Science and Technology/National Research Foundation Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa.
2
Centre for Health Economics, University of York, York, UK.
3
School of Medicine, New York University, New York, NY, USA.
4
Research Department of Infection and Population Health, University College London, London, UK.
5
Division of International and Environmental Health, Institute of Social and Preventive Medicine (ISPM) University of Bern, Bern, Switzerland.
6
Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
7
Massachusetts General Hospital, Boston, MA, USA.
8
The Kirby Institute, The University of New South Wales, Sydney, NSW, Australia.
9
University of Liverpool, Liverpool, UK.
10
Center for Health Decision Science, Harvard School of Public Health, Boston, MA, USA.
11
HIV Programme, WHO, Geneva, Switzerland.
12
Department of Infectious Disease Epidemiology, Imperial College London, London, UK. Electronic address: timothy.hallett@imperial.ac.uk.

Abstract

BACKGROUND:

WHO's 2013 revisions to its Consolidated Guidelines on antiretroviral drugs recommend routine viral load monitoring, rather than clinical or immunological monitoring, as the preferred monitoring approach on the basis of clinical evidence. However, HIV programmes in resource-limited settings require guidance on the most cost-effective use of resources in view of other competing priorities such as expansion of antiretroviral therapy coverage. We assessed the cost-effectiveness of alternative patient monitoring strategies.

METHODS:

We evaluated a range of monitoring strategies, including clinical, CD4 cell count, and viral load monitoring, alone and together, at different frequencies and with different criteria for switching to second-line therapies. We used three independently constructed and validated models simultaneously. We estimated costs on the basis of resource use projected in the models and associated unit costs; we quantified impact as disability-adjusted life years (DALYs) averted. We compared alternatives using incremental cost-effectiveness analysis.

FINDINGS:

All models show that clinical monitoring delivers significant benefit compared with a hypothetical baseline scenario with no monitoring or switching. Regular CD4 cell count monitoring confers a benefit over clinical monitoring alone, at an incremental cost that makes it affordable in more settings than viral load monitoring, which is currently more expensive. Viral load monitoring without CD4 cell count every 6-12 months provides the greatest reductions in morbidity and mortality, but incurs a high cost per DALY averted, resulting in lost opportunities to generate health gains if implemented instead of increasing antiretroviral therapy coverage or expanding antiretroviral therapy eligibility.

INTERPRETATION:

The priority for HIV programmes should be to expand antiretroviral therapy coverage, firstly at CD4 cell count lower than 350 cells per μL, and then at a CD4 cell count lower than 500 cells per μL, using lower-cost clinical or CD4 monitoring. At current costs, viral load monitoring should be considered only after high antiretroviral therapy coverage has been achieved. Point-of-care technologies and other factors reducing costs might make viral load monitoring more affordable in future.

FUNDING:

Bill & Melinda Gates Foundation, WHO.

PMID:
25104633
PMCID:
PMC4404633
DOI:
10.1016/S2214-109X(13)70048-2
[Indexed for MEDLINE]
Free PMC Article
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