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Viral load monitoring for antiretroviral therapy in resource-poor settings: an evolving role

Kahn, James G.a,b; Marseille, Elliot A.c

doi: 10.1097/QAD.0b013e3283617544
Editorial Comment

aPhilip R. Lee Institute for Health Policy Studies

bGlobal Health Sciences, University of California, San Francisco, San Francisco

cHealth Strategies International, Oakland, California, USA.

Correspondence to James G. Kahn, MD, MPH, Philip R. Lee Institute for Health Policy Studies, University of California, San Francisco, 3333 California Street, Suite 265, Box 0936, San Francisco, CA 94118, USA. E-mail:

Received 18 February, 2013

Accepted 20 March, 2013

Soon after its introduction, HIV-1 plasma RNA level (viral load) monitoring became integral to antiretroviral therapy (ART) management in well resourced settings. Clinical guidelines recommend viral loads to monitor implementation of ART, and to guide antiretroviral drug regimen-switching in cases of treatment failure. The logic of viral monitoring for HIV is compelling: maintaining an undetectable viral load defines ART success. Long survival times in the United States and Europe appear to support this view. Biological plausibility aside, it is unclear what portion of enhanced survival is attributable to viral load monitoring versus contemporaneous changes in antiretroviral drug regimens and practice. Yet in resource-rich settings, the broad acceptance of viral load testing as an integral part of high-quality HIV care assures its widespread use.

Should viral load monitoring be similarly adopted as a standard in resource-limited settings, and under what conditions? This question is particularly vexing in the current era of nearly flat HIV and global health budgets, such as those from PEPFAR (the President's Emergency Plan for AIDS Relief) [1–3]. Success in fulfilling the dictum ‘do more with less’ may determine whether the global health community can continue its spectacular progress in expanding access to ART. This goal is especially challenging as impressive efficiencies have already been achieved by reducing the cost of antiretroviral drugs (through generics and price reductions) and boosting the overall productivity of teams of personnel (through task shifting). With tightening budgets, what is the optimal role for viral load monitoring?

In this issue of AIDS, a cost-effectiveness analysis (CEA) suggests that under the right conditions, viral load monitoring can be economically attractive, and potentially cost saving, in developing countries [4]. These conditions include low test cost ($5), a high viral load threshold (to reduce unnecessary antiretroviral regimen switching), and increased drug adherence via targeted intervention derived from viral load test results. However, other economic analyses, both theoretical and empirical, which used higher current viral load costs (e.g. $30) and no hypothesized adherence benefit, have been much less favorable, suggesting a cost of $3000 or more per added healthy year of life. [The health metric most used in cost-effectiveness studies in the developing world is ‘disability-adjusted life years’ (DALY), a measure of disease burden. One DALY corresponds to the loss of 1 year of healthy life, due to premature mortality or illness. The other standard metric, ‘quality adjusted year of life’ (QALY), is reasonably understood as the health equivalent of the disease burden DALY. One DALY lost approximates one QALY gained [5–7]]. Indeed, in this analysis, relaxing the optimistic assumptions results in findings similar to past studies.

Currently, the gains from viral load monitoring, vis-à-vis clinical and CD4 cell monitoring, appear modest, even below statistical significance, in models and empirical analysis [6,8]. Thus, potential gains from viral load monitoring come at the cost of greater health benefits if the same resources were put into other purposes. From this perspective, the policy implication may be ‘Hold off on wide implementation of viral load monitoring’. However, CEA may help create an environment in which the benefits of viral load monitoring can be realized at acceptable costs – by identifying the cost and use conditions under which viral load monitoring can be cost-effective, and thus help to catalyze the adoption of these conditions. We have favorable experience with the use of CEA to influence policies on costs [9].

There is reason for optimism. The history of ART implementation in the developing world has been one of commodity and service price reductions, and the discovery that conditions for implementation were, in important respects, better than had been predicted. Among the most important such developments were the reductions in antiretroviral drug prices achieved over the past decade [10], and the observation that drug adherence in developing countries is at least as good as, and in many cases superior to, developed country settings [11]. In view of these historical trends, it is sensible to estimate the price and other conditions under which viral load monitoring could be cost-effective. This includes the $5 cost of point-of-care tests and adherence enhancement examined in this issue's CEA.

There is another sense in which sound decisions about viral load monitoring adoption depend on a long-term perspective. The most important empirical finding of modest to nonexistent viral load benefits is based on 24 months of follow-up. This may be too short to capture the potential superiority of viral load over CD4 cell monitoring when the primary outcome is death, as detectable plasma viremia over this period of time apparently does not increase mortality or major morbidity [8]. The suspected immediate consequences of detectable viremia (drug resistance, morbidity) may lead to important short-term and long-term costs. If more favorable effects flowing from suppression of viral replication are observed over extended time frames, or viral load prices drop substantially, or both, it will likely make compelling medical and economic sense to incorporate viral load monitoring into routine care.

Because viral load monitoring is an adjunct to a clinic-based intervention, there may be a tendency to compare viral load costs and benefits within a narrow clinical context. This would be an error, in our view, as life-years saved should be considered equivalent whatever their source – refinements of treatment regimens, expanded access to treatment, or prevention. Indeed, there are new prevention opportunities and an eagerness to deploy them broadly and effectively [12]. There is also growing evidence on strategies to address HIV by capitalizing on synergies with other priority global health areas. One example is access to antenatal care, which allows prevention of mother-to-child transmission of HIV and facilitates getting mothers onto ART. There are cross-disease synergies too: prevention of malaria and diarrhea in HIV-infected individuals slows CD4 cell decline and yields net savings through delayed need for ART [8,13–15].

We believe that the relevant resource allocation question should be broad, namely, ‘What is the best use of available funds to reduce the burden of HIV?’ rather than ‘What is the best use of funds to improve ART outcomes?’ The results of additional outcomes research or price reductions may establish viral load monitoring as an important and cost-effective adjunct to ART. In the meantime, HIV planners should weigh viral load monitoring against the full range of treatment and prevention strategies.

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Funding from NIDA R01-DA15612.

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Conflicts of interest

There are no conflicts of interest to declare.

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1. Leeper SC, Reddi A. United States Global Health Policy: HIV/AIDS, maternal and child health, and The President's Emergency Plan for AIDS Relief (PEPFAR). AIDS 2010; 24:2145–2149.
2. Bristol N. Slow going for the global health initiative. Health Aff (Millwood) 2011; 30:1007–1009.
3. Moszynski P. Global Fund suspends new projects until 2014 because of lack of funding. BMJ 2011; 343:d7755.
4. Estill J, Egger M, Blaser N, Vizcaya LS, Garone D, Wood R, et al. Cost-effectiveness of point-of-care viral load monitoring of antiretroviral therapy in resource-limited settings: mathematical modelling study. AIDS 2013; 27:1483–1492.
5. Kahn JG, Marseille E, Moore D, Bunnell R, Were W, Degerman R, et al. CD4 cell count and viral load monitoring in patients undergoing antiretroviral therapy in Uganda: cost effectiveness study. BMJ 2011; 343:d6884.
6. Phillips AN, Pillay D, Miners AH, Bennett DE, Gilks CF, Lundgren JD. Outcomes from monitoring of patients on antiretroviral therapy in resource-limited settings with viral load, CD4 cell count, or clinical observation alone: a computer simulation model. Lancet 2008; 371:1443–1451.
7. Bendavid E, Young SD, Katzenstein DA, Bayoumi AM, Sanders GD, Owens DK. Cost-effectiveness of HIV monitoring strategies in resource-limited settings: a southern African analysis. Arch Intern Med 2008; 168:1910–1918.
8. Mermin J, Ekwaru JP, Were W, Degerman R, Bunnell R, Kaharuza F, et al. Utility of routine viral load, CD4 cell count, and clinical monitoring among adults with HIV receiving antiretroviral therapy in Uganda: randomised trial. BMJ 2011; 343:d6792.
9. Kahn JG, Marseille E. A saga in international HIV policy modeling: preventing mother-to-child HIV transmission. J Policy Anal Manage 2002; 21:499–505.
10. Medecins Sans Frontieres. Untangling the web of antiretroviral price reductions, 15th ed. Geneva: MSF Access Campaign; 2012; 1–120.
11. Mills EJ, Nachega JB, Buchan I, Orbinski J, Attaran A, Singh S, et al. Adherence to antiretroviral therapy in sub-Saharan Africa and North America: a meta-analysis. JAMA 2006; 296:679–690.
12. Sepulveda J. The ’third wave’ of HIV prevention: filling gaps in integrated interventions, knowledge, and funding. Health Aff (Millwood) 2012; 31:1545–1552.
13. Walson JL, Sangare LR, Singa BO, Naulikha JM, Piper BK, Yuhas K, et al.Evaluation of impact of long-lasting insecticide-treated bed nets and point-of-use water filters on HIV-1 disease progression in Kenya. AIDS 2013 [Epub ahead of print].
14. Mermin J, Lule JR, Ekwaru JP. Association between malaria and CD4 cell count decline among persons with HIV. J Acquir Immune Defic Syndr 2006; 41:129–130.
15. S. Verguet 2013. working paper;

antiretroviral therapy; cost and cost analysis; highly active; viral load

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