We tested strategies where CD4 counts alone or in combination with viral load were available for patient monitoring. Viral load monitoring is relatively expensive and rarely available in Africa, although there is interest in increasing viral load capacity with the scale-up on HAART as it is the most direct virologic measure of treatment failure.49 Failure was defined as 2 successive measurements above 1000 copies per microliter.14 When CD4 counts only were available, a decline to half the highest measured CD4 count after an initial response was considered failure and prompted a regimen change.4 Although the WHO currently recommends treatment initiation at 200 cells per microliter, recent evidence and ongoing trials are favoring earlier treatment initiation; thus in our model, treatment was initiated at a CD4 threshold of 200-350 cells per microliter.53,54
We included all direct costs of HIV care: inpatient, outpatient, HAART, and monitoring costs. Inpatient and outpatient costs were derived from costing studies in Cape Town, and costs of HAART were taken from published sources and estimated at the lowest available price in US $2007. Second-generation bPI are rarely available in South Africa, and we used expected prices for low-income and middle-income countries.48 We measured the cost-effectiveness as the ratio of the incremental costs to the incremental benefits of each strategy compared with the next least cost-effective strategy in US $2007 per year of life gained. We adopted a societal perspective, although some indirect costs were excluded as we assumed that they would be equivalent between strategies. We discounted all costs and benefits at 3% annually.
In sensitivity analysis, we varied the rates of virologic failure to reflect uncertainty in published estimates. We also varied several important cost parameters that are expected to change. Specifically, bPIs are likely to become less expensive due to entry of additional drugs and continued price negotiations; and viral load monitoring is increasingly affordable due to cheaper technologies, durable measurement devices, and improving infrastructure.55 We performed a probabilistic sensitivity analysis where we specified distributions for model parameters and employed a Monte Carlo simulation to sample from these distributions. We used the results to calculate confidence intervals (CIs) around our incremental cost-effectiveness ratio estimates.56 Additional details on the distributions used is in the Supplementary Appendix.
We estimate that over the lifetime of the cohort, 25.6% of individuals would experience virologic failure of WHO's first-line and second-line regimens. All individuals with virologic failure who remained in care were detected where viral load monitoring was available. However, where CD4 monitoring alone was available, treatment failure based on immunologic criteria was detected in only 6.5% of the cohort. Monitoring individuals with CD4 counts alone led to lower rates of detecting treatment failure primarily due to the insensitivity of immunologic criteria for detecting virologic failure.57 In addition, monitoring CD4 counts only was associated with a delay of 16.1 months on average in detecting failure. This, in turn, was associated with higher rates of mortality (Table 3) and decreasing the opportunities for detecting treatment failure.
When viral load monitoring was available, the WHO strategy was associated with a discounted life expectancy of 81.0 months and a lifetime cost of $7645. Compared with the WHO strategy, adding a regimen with a second-generation bPI (c) was associated with a gain in life expectancy of 1.6 months and additional $881 in lifetime costs, an incremental cost-effectiveness ratio of $6519 per year of life gained (95% CI 5673 to 8129). The cost of HAART alone was $873 higher in strategy C compared with the WHO strategy. Strategy C was associated with a lower incidence of severe opportunistic diseases, 9.7 per 100 patient-years, compared with 10.9 per 100 patient-years in the WHO strategy. With viral load monitoring, 25.6% of individuals were eligible for third-line treatment under strategy C. The life expectancy of those who were placed on a second-generation bPI was 10.4 undiscounted months longer than the equivalent population in the WHO strategy. Strategy B was dominated by extended dominance. That is, it was more costly and less effective than a blend of strategies A and C but not compared with either strategy alone.
Rates of failure vary widely based on the HAART regimen, geography, clinical setting (eg,. trial or cohort) and individual treatment history. For that reason, we varied the rates of virologic suppression to reflect a broad range of uncertainty. Decreasing the rates of virologic failure from highest to lowest was associated with an average increase in life expectancy of 5.6 months across all strategies (range 4.9-6.1 months). Decreasing rates of virologic failure were also associated with a less attractive incremental cost-effectiveness ratio of additional HAART regimens. With viral load monitoring, the incremental cost effectiveness ratio of strategy C compared with the WHO strategy varied from $12,098 to $5178 per life-year gained as the rate of virologic failure varied from lowest to highest rate. Thus, the value adding an effective third-line HAART regimen is greatest where rates of failure are relatively high (Fig. 2).
We varied several cost parameters to examine the implications as drug and diagnostic technologies are increasingly affordable. Specifically, we varied the costs of second-generation bPIs and the cost of viral load testing. When the annual cost of a second-generation bPI dropped below $540, strategy C with CD4 count monitoring dominated the WHO strategy. That is, it saved costs and improved outcomes relative to the WHO strategy. Reducing the cost of viral load monitoring from $75 to $15 per test decreased the incremental cost-effectiveness ratio of adding a second-generation bPI to $5427 per life-year gained. Figure 2 shows the effect of reducing the cost of viral load monitoring on the incremental cost-effectiveness ratio of strategy C compared with the WHO strategy.
In probabilistic sensitivity analysis, we examined the joint effect of parameter uncertainty. The analysis was repeated 1000 times, and we used the results to obtain CIs for our estimates. Our 95% confidence bounds for the portion of the population who had virologic failure to WHO's first and second line were 23.4% to 28.8%. In our analysis, the strategy where a triple NRTI was used in initial regimen with CD4 count monitoring was dominated in 88% of the scenarios, we simulated and it never dominated the WHO strategy.
We analyzed the benefits, costs, and cost-effectiveness of adding HAART regimens for resource-limited settings using data from South Africa. We show that adding an effective third antiretroviral regimen could provide substantial benefits for those who fail first-line and second-line therapy. Our estimates suggest that individuals who fail both existing regimens may gain between 6.7 and 8.9 months of life with third-line HAART. Although at most a quarter of the infected population could derive benefit from an effective third line, we estimate that that is sufficient to improve the average life expectancy of the entire infected population by 0.6-1.6 months, depending on the monitoring technology. Our estimates of the need for additional regimens, which suggest that the current recommended regimens will provide adequate lifelong benefits for about three quarters of the infected population on HAART, are consistent with recent evidence showing low rates of failure in low-income countries.58
The WHO and World Bank suggest that interventions with an incremental cost-effectiveness ratio less than 3 times the gross domestic product (GDP) per capita represent good value.59,60 By that criteria, adding a third-line regimen based on a second-generation bPI to the existing WHO regimens should be acceptable in South Africa. Although the incremental cost-effectiveness ratio is higher with viral load monitoring (due to higher HAART and monitoring costs), adding a third regimen may be acceptable in countries with an annual per capita GDP above $2000. Further reductions in the price of second-generation bPIs will improve the cost-effectiveness of adding a third regimen and may be cost saving below $540 per year.
Our analysis, however, suggests that adding a less efficacious first-line regimen may worsen outcomes where viral load monitoring is not available. When methods for timing of regimen change are associated with a significant delay, such as when using CD4 counts, adding an initial regimen with rates of failure higher than subsequent regimens may lead to worse outcomes. Although preserving drug classes has intuitive appeal, the delay in diagnosis of treatment failure without viral load monitoring led to additional opportunistic diseases and higher mortality in our study. Even when viral load monitoring is available, we estimate that the cost-effectiveness of adding an initial triple-NRTI regimen is not as cost effective as adding a second-generation bPI as a third regimen.
We also show the importance of preventing virologic failure in improving patient outcomes. The variability in the rates of failure reported in the literature may be attributed to clinical practices and behavioral factors. Taking HAART regularly is directly related to maintaining virologic suppression, and our study suggests that HAART outcomes improve substantially with lower rates of failure. We find that the relative value of additional HAART regimens is highest where the rates of failure are also high.
Use of CD4 counts to determine when to initiate HAART in resource-limited settings improves life expectancy substantially and may reduce costs.11 Here we highlight several important roles which viral load monitoring plays. It is the preferred method for timing regimen change: it is more accurate than using CD4 counts for determining treatment failure and leads to a significantly shorter lag in diagnosis and fewer opportunistic diseases. The findings in our analysis dovetail with increasing evidence about the inaccuracy of using CD4 count monitoring alone for determining treatment failure.57,61 In addition, the benefits of viral load monitoring are greater with more complex treatment options. However, substantial expenditures, lack of infrastructure, and shortage of skilled labor needed for viral load monitoring may continue to be a barrier in many places.
Our model has several important limitations. We estimate rates of virologic failure and medication toxicities from clinical trials and cohort trials. Most of those were done in sub-Saharan Africa on non-HIV subtype B, but where no estimates were available from our region of interest, we used data from developed countries. In addition, available data on the effectiveness of sequential regimens is sparse, and our estimates are partly based on conditional predictions. We also do not account for rates of loss to follow-up, which some suggest may be lower where diagnostic monitoring and medical care is more extensive. Finally, it is also possible that the pathways to resistance of non-HIV subtype B may differ from reported experience. For all these reasons, we vary the rates of failure across a wide range and indicate the limitations of our estimates.
As access to HIV treatment continues to expand across sub-Saharan Africa, where over 20 million are infected and thousands are started on treatment weekly, the number of people who will fail the existing regimens will continue to increase. We suggest that offering additional effective regimens provide substantial benefits to individuals who fail existing therapies, is cost effective in many parts of southern Africa where CD4 count monitoring is available, and may be cost saving with substantial price reductions of second-generation bPIs. Our analysis also shows that reducing treatment failure is an effective way to minimize the need for additional regimens and maximize the benefits of the regimens that are currently available.
1. UNAIDS. Report on the Global AIDS Epidemic
. Geneva, Switzerland: Joint United Nations Programme on HIV/AIDS; 2008.
2. UNAIDS. Towards Universal Access: Scaling Up Priority HIV/AIDS Interventions in the Health Sector. Progress Report
. Geneva, Switzerland: World Health Organization, UNAIDS; 2007.
3. Hammer S, Havlir D, Klement E, et al. Scaling up Antiretroviral Therapy in Resource-Limited Settings: Treatment Guidelines for a Public health Approach
. Geneva, Switzerland: World Health Organization; 2003.
4. Antiretroviral Therapy For HIV Infection in Adults And Adolescents: Recommendations for a Public Health Approach
. Geneva, Switzerland: World Health Organization; 2006.
5. Hardy D, Reynes J, Konourina I, et al. Efficacy and safety of maraviroc plus optimized background therapy in treatment-experienced patients infected with CCR5-tropic HIV-1: 48-week combined analysis of the MOTIVATE Studies. Presented at: Conference on Retroviruses and Opportunistic Infections; February 3-6, 2008; Boston, MA.
6. Steigbigel R, Kumar P, Eron J, et al. 48-week results from BENCHMRK-2, a Phase III Study of raltegravir (RAL) in patients failing antiretroviral therapy (ART) with triple-class resistant HIV-1. Presented at: Conference on Retroviruses and Opportunistic Infections; February 3-6, 2008; Boston.
7. Egger M. Outcome of ART in resource-limited and industrialized countries. Presented at: Conference on Retroviruses and Opportunistic Infections; February 25-28, 2007; LA Conference Center, CA.
8. Mills EJ, Nachega JB. A wake-up call for global access to salvage HIV drug regimens. Lancet
9. Goldie SJ, Yazdanpanah Y, Losina E, et al. Cost-effectiveness of HIV treatment in resource-poor settings-the case of Cote d'Ivoire. N Engl J Med
10. Bishai D, Colchero A, Durack DT. The cost effectiveness of antiretroviral treatment strategies in resource-limited settings. AIDS
11. Bendavid E, Young SD, Katzenstein DA, et al. Cost-effectiveness of HIV monitoring strategies in resource-limited settings-a southern African analysis. Arch Intern Med
12. Phillips A, Pillay D, Miners AH, et al. 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
13. Boulle A, Orrel C, Kaplan R, et al. Substitutions due to antiretroviral toxicity or contraindication in the first 3 years of antiretroviral therapy in a large South African cohort. Antivir Ther
14. Orrell C, Harling G, Lawn SD, et al. Conservation of first-line antiretroviral treatment regimen where therapeutic options are limited. Antivir Ther
15. Calmy A. Outcomes of Adults Receiving Second-line ART in Médecins Sans Frontières-supported Projects in Resources-Limited Countries. Presented at: Conference on Retroviruses and Opportunistic Infections; February 25-28, 2007; Los Angeles Conference Center, CA.
16. de Mendoza C, Valer L, Ribera E, et al. Performance of six different ritonavir-boosted protease inhibitor-based regimens in heavily antiretroviral-experienced HIV-infected patients. HIV Clin Trials
17. Kaufmann GR, Khanna N, Weber R, et al. Long-term virological response to multiple sequential regimens of highly active antiretroviral therapy for HIV infection. Antivir Ther
18. Robbins GK, De Gruttola V, Shafer RW, et al. Comparison of sequential three-drug regimens as initial therapy for HIV-1 infection. N Engl J Med
19. Staszewski S, Keiser P, Montaner J, et al. Abacavir-lamivudine-zidovudine vs indinavir-lamivudine-zidovudine in antiretroviral-naive HIV-infected adults: a randomized equivalence trial. JAMA
20. DART Virology Group and Trial Team. Virological response to a triple nucleoside/nucleotide analogue regimen over 48 weeks in HIV-1-infected adults in Africa. AIDS
21. Srikantiah P, Walusimbi MN, Kayanja HK, et al. Early virological response of zidovudine/lamivudine/abacavir for patients co-infected with HIV and tuberculosis in Uganda. AIDS. 2007;21:1972-1974.
22. Gulick RM, Lalama CM, Ribaudo HJ, et al. Intensification of a triple-nucleoside regimen with tenofovir or efavirenz in HIV-1-infected patients with virological suppression. AIDS
23. Madruga JV, Berger D, McMurchie M, et al. Efficacy and safety of darunavir-ritonavir compared with that of lopinavir-ritonavir at 48 weeks in treatment-experienced, HIV-infected patients in TITAN: a randomised controlled phase III trial. Lancet
24. Gulick RM, Ribaudo HJ, Shikuma CM, et al. Triple-nucleoside regimens versus efavirenz-containing regimens for the initial treatment of HIV-1 infection. N Engl J Med
25. Walker S, Kityo C, Kaleebu P, et al. Superior virological suppression with nevirapine, zidovudine, and lamivudine vs abacavir, zidovudine, and lamivudine without evidence of clinical benefit to 48 weeks: a randomized comparison in patients with low CD4 counts in Africa. Presented at: Conference on Retroviruses and Opportunistic Infections; February 25-28, 2007; Los Angeles, CA.
26. Clotet B, Bellos N, Molina JM, et al. Efficacy and safety of darunavir-ritonavir at week 48 in treatment-experienced patients with HIV-1 infection in POWER 1 and 2: a pooled subgroup analysis of data from two randomised trials. Lancet
27. Taylor BS, Sobieszczyk ME, McCutchan FE, et al. The challenge of HIV-1 subtype diversity. N Engl J Med
28. Conway B. The role of adherence to antiretroviral therapy in the management of HIV infection. J Acquir immune defic syndr
. 2007;45(Suppl 1):S14-S18.
29. Nettles RE, Kieffer TL, Kwon P, et al. Intermittent HIV-1 viremia (Blips) and drug resistance in patients receiving HAART. JAMA
30. Orrell C, Bangsberg DR, Badri M, et al. Adherence is not a barrier to successful antiretroviral therapy in South Africa. AIDS
31. Ramadhani HO, Thielman NM, Landman KZ, et al. Predictors of incomplete adherence, virologic failure, and antiviral drug resistance among HIV-infected adults receiving antiretroviral therapy in Tanzania. Clin Infect Dis
32. Kantor R, Katzenstein DA, Efron B, et al. Impact of HIV-1 subtype and antiretroviral therapy on protease and reverse transcriptase genotype: results of a global collaboration. PLoS Med
33. Holmes CB, Wood R, Badri M, et al. CD4 decline and incidence of opportunistic infections in Cape Town, South Africa: implications for prophylaxis and treatment. J Acquir Immune Defic Syndr
34. Badri M, Wilson D, Wood R. Effect of highly active antiretroviral therapy on incidence of tuberculosis in South Africa: a cohort study. Lancet. 2002;359:2059-2064.
35. Badri M, Lawn SD, Wood R. Short-term risk of AIDS or death in people infected with HIV-1 before antiretroviral therapy in South Africa: a longitudinal study. Lancet
36. Badri M, Cleary S, Maartens G, et al. When to initiate highly active antiretroviral therapy in sub-Saharan Africa? A South African cost-effectiveness study. Antivir Ther
37. Mellors JW, Muñoz A, Giorgi JV, et al. Plasma viral load and CD4+ lymphocytes as prognostic markers of HIV-1 infection. Ann Intern Med
38. Rodríguez B, Sethi AK, Cheruvu VK, et al. Predictive value of plasma HIV RNA level on rate of CD4 T-cell decline in untreated HIV infection. JAMA
39. Ledergerber B, Lundgren JD, Walker AS, et al. Predictors of trend in CD4-positive T-cell count and mortality among HIV-1-infected individuals with virological failure to all three antiretroviral-drug classes. Lancet
40. Deeks SG, Wrin T, Liegler T, et al. Virologic and immunologic consequences of discontinuing combination antiretroviral-drug therapy in HIV-infected patients with detectable viremia. N Engl J Med
41. Munderi P, DART. Safety of nevirapine compared to abacavir on a background of zidovudine/lamivudine as first-line antiretroviral therapy: a randomized double-blind trial. Presented at: Conference on Retroviruses and Opportunistic Infections; February 5-8, 2006; Denver, CO.
42. Amoroso A. ART-associated toxicities leading to a switch in medication: experience in Uganda, Kenya, and Zambia. Presented at: Conference on Retroviruses and Opportunistic Infections; February 25-28, 2007; Los Angeles, CA.
43. Badri M, Maartens G, Mandalia S, et al. Cost-effectiveness of highly active antiretroviral therapy in South Africa. PLoS Med
44. Govender V, McIntyre D, Grimwood A, et al. The Costs and Perceived Quality of Care for People Living with HIV/AIDS in the Western Cape Province in South Africa. Bethesda, MD: Partnerships for Health Reform; 2000.
45. Cleary SM, McIntyre D, Boulle AM. The cost-effectiveness of Antiretroviral Treatment in Khayelitsha, South Africa - a primary data analysis. Cost Eff Resour Alloc. 2006;4:1-14.
46. Thomas LS, Manning A, Holmes CB, et al. Comparative costs of inpatient care for HIV-infected and uninfected children and adults in Soweto, South Africa. J Acquir Immune Defic Syndr
47. Medicines Sans Frontiers. Untangling the web of price reductions: a pricing guide for the purchase of ARVs for developing countries. 2007. Available at http://www.accessmed-msf.org
. Accessed November 26, 2008.
49. Rewari B, Rajasekaran S, Deshpande A, et al. Evaluating patients for second-line ART in India: confirmation of virologic failure prevents unnecessary treatment switches. Presented at: Conference on Retroviruses and Opportunistic Infections; February 8-11, 2009; Montreal, Canada.
50. Zijenah LS, Kadzirange G, Madzime S, et al. Affordable flow cytometry for enumeration of absolute CD4+ T-lymphocytes to identify subtype C HIV-1 infected adults requiring antiretroviral therapy (ART) and monitoring response to ART in a resource-limited setting. J Transl Med. 2006;4:33-39.
51. Rouet F, Rouzioux C. HIV-1 viral load testing cost in developing countries: what's new? Expert Rev Mol Diagn
52. Calmy AA, Ford NN, Hirschel BB, et al. HIV viral load monitoring in resource-limited regions: optional or necessary? Clin Infect Dis
53. Kitahata M, SJ G, RD M, et al. Initiating rather than deferring HAART at a CD4+ count between 351-500 cells/mm3
is associated with improved survival. Presented at: ICAAC/IDSA; February 3-6, 2008; Washington, DC.
54. Walensky RP, Wolf L, Wood R, et al. When to Start ART-a policy evaluation while awaiting trial results: South Africa. Presented at: Conference on Retroviruses and Opportunistic Infections; February 8-11, 2009; Montreal, Canada.
55. Stevens G, Rekhviashvili N, Scott LE, et al. Evaluation of two commercially available, inexpensive alternative assays used for assessing viral load in a cohort of human immunodeficiency virus type 1 subtype C-infected patients from South Africa. J clin Microbiol
56. Briggs A, Fenn P. Confidence intervals or surfaces? Uncertainty on the cost-effectiveness plane. Health Econ
57. Badri M, Lawn SD, Wood R. Utility of CD4 cell counts for early prediction of virological failure during antiretroviral therapy in a resource-limited setting. BMC Infect Dis
58. Keiser O, IeDEA, ART-LINC. Switching to second-line ART, and mortality in resource-limited settings: collaborative analysis of treatment programs in Africa, Asia, and Latin America. Presented at: Conference on Retroviruses and Opportunistic Infections; February 8-11, 2009; Montreal, Canada.
59. World Health Report: Reducing Risk, Promoting Healthy Life
. Geneva, Switzerland: World Health Organization; 2002.
60. Macroeconomics and Health: Investing in Health for Economic Development. Report of the Commission on Macroeconomics and Health
. Geneva, Switzerland: World Health Organization; 2001.
61. Etiebet M, Gebi U, Shepherd J, et al. Performance of WHO immunological criteria for determining virological treatment failure in low-resource Settings. Presented at: Conference on Retroviruses and Opportunistic Infections; 2009; Montreal, Canada.