Special Article
Cost-Effectiveness of HIV Treatment in Resource-Poor Settings — The Case of Côte d'Ivoire
N Engl J Med 2006; 355:1141-1153September 14, 2006
- Abstract
Background
As antiretroviral therapy is increasingly used in settings with limited resources, key questions about the timing of treatment and use of diagnostic tests to guide clinical decisions must be addressed.
Methods
We assessed the cost-effectiveness of treatment strategies for a cohort of adults in Côte d'Ivoire who were infected with the human immunodeficiency virus (HIV) (mean age, 33 years; CD4 cell count, 331 per cubic millimeter; HIV RNA level, 5.3 log copies per milliliter). Using a computer-based simulation model that incorporates the CD4 cell count and HIV RNA level as predictors of disease progression, we compared the long-term clinical and economic outcomes associated with no treatment, trimethoprim–sulfamethoxazole prophylaxis alone, antiretroviral therapy alone, and prophylaxis with antiretroviral therapy.
Results
Undiscounted gains in life expectancy ranged from 10.7 months with antiretroviral therapy and prophylaxis initiated on the basis of clinical criteria to 45.9 months with antiretroviral therapy and prophylaxis initiated on the basis of CD4 testing and clinical criteria, as compared with trimethoprim–sulfamethoxazole prophylaxis alone. The incremental cost per year of life gained was $240 (in 2002 U.S. dollars) for prophylaxis alone, $620 for antiretroviral therapy and prophylaxis without CD4 testing, and $1,180 for antiretroviral therapy and prophylaxis with CD4 testing, each compared with the next least expensive strategy. None of the strategies that used antiretroviral therapy alone were as cost-effective as those that also used trimethoprim–sulfamethoxazole prophylaxis. Life expectancy was increased by 30% with use of a second line of antiretroviral therapy after failure of the first-line regimen.
Conclusions
A strategy of trimethoprim–sulfamethoxazole prophylaxis and antiretroviral therapy, with the use of clinical criteria alone or in combination with CD4 testing to guide the timing of treatment, is an economically attractive health investment in settings with limited resources.
Media in This Article
Figure 1
Internal Validity of the Model.Figure 2
Cost-Effectiveness of Treatment Strategies.Article Activity
- Article
The use of potent antiretroviral therapy has transformed the epidemic of the acquired immunodeficiency syndrome (AIDS) in populations with access to these drugs.1 In settings with limited resources, where the provision of antiretroviral therapy was previously thought to be technically impossible, combination regimens have been found to have short-term efficacy similar to that in developed countries.2-8 The dramatic rise in global funding for the human immunodeficiency virus (HIV) and AIDS,9,10 together with reduced drug costs,11-14 improves the feasibility of providing antiretroviral therapy in settings with limited resources.
As part of the “3 by 5” initiative to distribute antiretroviral treatment to 3 million people in 50 developing countries by the end of 2005, the World Health Organization (WHO) proposed guidelines that incorporate evidence from clinical trials and observational studies of the efficacy and toxicity of antiretroviral therapy.15 In addition to the relative efficacy, feasibility, and affordability of various treatment regimens, clinical guidance for treating patients with HIV infection requires consideration of criteria for initiating antiretroviral therapy, the relative performance and costs of diagnostic tests, and coordination with other treatment options, such as prophylaxis against opportunistic disease.
When the complexity of a clinical problem involves competing choices and the information required for some components of the decision is incomplete, decision-analysis methods offer a systematic approach to synthesizing existing data and quantifying the trade-offs for alternative options. Capitalizing on the availability of primary data from Côte d'Ivoire2 and a recent analysis of the costs of trimethoprim–sulfamethoxazole prophylaxis,16 we conducted a decision analysis to estimate the clinical and economic outcomes associated with different treatment strategies in adults infected with HIV type 1 (HIV-1) in Côte d'Ivoire, a setting with limited resources.
Methods
Overview
We modified a previously published model17 to simulate the natural history of HIV infection in patients in Côte d'Ivoire and to project the short- and long-term outcomes associated with trimethoprim–sulfamethoxazole prophylaxis alone, antiretroviral therapy alone, and trimethoprim–sulfamethoxazole prophylaxis with antiretroviral therapy. We compared 22 strategies in which thresholds for initiating and discontinuing a single line of antiretroviral therapy were based on clinical criteria alone or on both the CD4 cell count and clinical criteria. The comparative performance of various strategies was assessed with the use of incremental cost-effectiveness ratios, in 2002 U.S. dollars per year of life gained. We calculated the incremental cost-effectiveness ratio, defined as the additional cost of a strategy, divided by its additional clinical benefit, as compared with the next least expensive strategy. We excluded strategies with higher costs and lower benefits than other options and those with higher incremental cost-effectiveness ratios than other more effective options.18 We adopted a modified societal perspective, in that the costs of patients' time and travel were not included, and future costs and benefits were discounted at 3% per year.18
Model
We used first-order Monte Carlo simulation in which disease progression in an individual patient is characterized as a sequence of monthly transitions between health states.19 Health states in the model, descriptive of each patient's underlying true health, were defined by current and maximum HIV RNA levels, current and nadir CD4 cell counts, and current and prior opportunistic diseases. Individual characteristics (age, sex, CD4 cell count, and HIV RNA level) were randomly drawn from a specified distribution of patients similar to those enrolled in the placebo group of the Agence Nationale de Recherches sur le SIDA (ANRS) 059 trial, a randomized, controlled trial of trimethoprim–sulfamethoxazole prophylaxis in Abidjan, Côte d'Ivoire20: median age, 33 years; 40% men; and baseline CD4 cell count, 331 cells per cubic millimeter. Each patient's lifetime clinical course was tracked, with all clinical events and accrued costs tallied. One million patients were simulated, one at a time, in order to provide stable estimates of long-term outcomes for each strategy.
Disease progression was modeled as a function of both the HIV RNA level and the CD4 cell count.17,21,22 Opportunistic diseases were divided into 11 groups and categorized as severe or mild.16,23 Incidence rates of opportunistic diseases and AIDS-related death were modeled as a function of the CD4 cell count and the presence or absence of a history of opportunistic infection. Successful HIV RNA suppression after antiretroviral therapy resulted in a rise in the CD4 cell count and a corresponding reduction in the risks of opportunistic disease and death. Virologic failure was defined in the model as a 0.5-log increase in the HIV RNA level in 2 consecutive months while the patient was receiving antiretroviral therapy, after which the CD4 cell count stayed constant for 1 year before declining at a monthly rate that depended on the viral load. Although the model updated CD4 cell counts and HIV RNA levels monthly and determined disease progression on the basis of these values, we assumed that clinical decisions were based on less frequent CD4 testing and clinical assessments (every 6 to 12 months) (see the Supplementary Appendix, available with the full text of this article at www.nejm.org).
Several assumptions were necessary because of uncertainty about the efficacy of antiretroviral therapy; the rationale for our choices for the base case is described in the Supplementary Appendix.24-27 We conservatively assumed that after 10 years, patients no longer had virologic improvement; that after virologic failure, there was a delay of 12 months before the CD4 cell count started to decline; and that in patients with a CD4 cell count of 50 per cubic millimeter or higher, antiretroviral therapy had an independent effect in reducing the incidence of opportunistic disease and mortality from AIDS.23,28
Simulated Strategies
Antiretroviral therapy was initiated on the basis of a defined number of specific opportunistic diseases, the results of a CD4 test, or both. When CD4 testing was available, we assumed that antiretroviral therapy was started in patients with a CD4 count of less than 200 per cubic millimeter; a CD4 count of 200 to 350 per cubic millimeter with severe malaria, a severe bacterial infection, a severe fungal infection, tuberculosis, isosporiasis, cerebral toxoplasmosis, nontuberculous mycobacteriosis, or another severe illness; or a CD4 cell count of more than 350 per cubic millimeter and a severe opportunistic disease other than malaria, bacterial infection, or tuberculosis. Antiretroviral therapy was discontinued, or second-line therapy was instituted, on the basis of an observed 50% or 90% decrease from the peak CD4 cell count during treatment. When CD4 testing was unavailable, we assumed that antiretroviral therapy was initiated if either one or two severe opportunistic diseases developed and was discontinued (or switched to second-line therapy) if there was clinical failure, defined as a specified number of severe opportunistic diseases (one, three, or five). To ensure adequate time for the immunologic benefits of antiretroviral therapy to be realized,29 opportunistic diseases diagnosed during the first 6 months of antiretroviral therapy were not considered as criteria for discontinuation of treatment.
We conservatively assumed that only a single antiretroviral regimen was available, although second-line therapy was evaluated in a secondary analysis. We also made the following six assumptions: trimethoprim–sulfamethoxazole prophylaxis was initiated when the CD4 cell count was less than 500 per cubic millimeter or after any opportunistic disease; if CD4 testing was not available, routine clinic visits occurred every 12 months; after an opportunistic disease or during treatment with trimethoprim–sulfamethoxazole and antiretroviral therapy, visits occurred every 6 months; if CD4 testing was available, clinic visits and CD4 testing occurred every 6 months; treatment was provided for opportunistic diseases, with the exception of Kaposi's sarcoma, lymphoma, invasive herpesvirus infection, and cytomegalovirus infection; and lifelong maintenance therapy was provided for pneumocystis pneumonia and isosporiasis but not for toxoplasmosis or nontuberculous mycobacteriosis.
Baseline Estimates for Model Variables
Baseline estimates for selected variables, as derived from published studies, are shown in Table 1Table 1
Baseline Estimates for Selected Model Variables..2,3,5-8,11-14,16,20-23,28,30-34 Additional details are provided in the Supplementary Appendix. Estimates of the initial HIV RNA distribution, efficacy of antiretroviral therapy, and drug toxicity were obtained from the ANRS 1203 study, a continuation of the ANRS 059 study of trimethoprim–sulfamethoxazole prophylaxis in Abidjan, Côte d'Ivoire.2 Estimates of the efficacy of antiretroviral therapy implicitly reflect the rate of adherence in the trial from which the data are drawn; we therefore conducted a sensitivity analysis based on estimates from a literature review.3,5-8,35 Estimates for the incidence of opportunistic diseases and death were based on data from the placebo group in the ANRS 059 study and estimated as functions of four CD4 strata (≤50, 51 to 200, 201 to 500, and >500 cells per cubic millimeter) as previously described.17,20,23,33 Mortality rates from causes other than HIV infection were based on data specific for Côte d'Ivoire.36 Direct medical costs of HIV-related care included the costs of hospitalizations, outpatient consultations, laboratory tests, clinical procedures, and medications.20 Costs were estimated for the treatment of opportunistic diseases and for long-term care (termed “routine care costs”) in patients with different CD4 cell counts, as previously described (see the Supplementary Appendix).16 Costs were inflated to 2002 price levels with adjustment according to the gross domestic product (GDP) for Côte d'Ivoire, and costs in local currency were converted to U.S. dollars on the basis of prevailing exchange rates.37,38
The study was approved by the institutional review boards at the participating institutions. The requirement for informed consent was waived because our study involved analysis of secondary data.
Results
Model Validation
Figure 1Figure 1
Internal Validity of the Model. shows the model-based estimates of opportunistic diseases as compared with data from the ANRS 059 trial.20 Projected model outcomes were generally within 10 to 15% of reported trial results.Base-Case Analysis
Figure 2Figure 2
Cost-Effectiveness of Treatment Strategies. shows the relationship between the total lifetime costs and discounted life expectancy for all 22 treatment strategies assessed. Strategies involving both antiretroviral therapy and trimethoprim–sulfamethoxazole prophylaxis were consistently more effective and more cost-effective than those involving antiretroviral therapy alone. Strategies based on CD4 measurements and clinical criteria for initiating and discontinuing antiretroviral therapy were always more effective than strategies based on clinical criteria alone.Table 2Table 2
Clinical Benefits and Cost-Effectiveness of Alternative Treatment Strategies. shows the costs, life expectancy, and incremental cost-effectiveness ratios associated with the six most efficient strategies. Trimethoprim–sulfamethoxazole alone increased undiscounted life expectancy by 1.6 months and cost $240 per year of life gained, as compared with no treatment. The incremental benefit of using antiretroviral therapy in addition to prophylaxis ranged from 10.7 to 45.9 undiscounted months, depending on the criteria for initiating and discontinuing antiretroviral therapy. The most effective strategy used CD4 testing and provided a gain of 14.0 months in life expectancy, at a cost of $1,180 per year of life gained, as compared with strategies relying on clinical information alone.Sensitivity Analysis
Figure 3Figure 3
Sensitivity Analysis of Potentially Important Model Variables. shows how the incremental cost-effectiveness ratio for the most effective strategy, antiretroviral therapy and prophylaxis with the use of CD4 testing, varied with changes in selected variables. The results were most sensitive to changes in the costs of routine care, antiretroviral therapy, and CD4 tests and were less sensitive to plausible changes in the efficacy of antiretroviral therapy and in the costs of treatment for opportunistic diseases.The most influential of the base-case assumptions was that of an independent effect of antiretroviral therapy on the incidence of opportunistic disease and mortality from AIDS among patients with a CD4 cell count of 50 per cubic millimeter or higher.17,20,23,33 For the most effective strategy (antiretroviral therapy and prophylaxis with the use of CD4 testing), gains in life expectancy over that associated with no treatment were approximately 50% lower and incremental cost-effectiveness ratios were twice those for the base case when we assumed no independent effect of antiretroviral therapy. Other assumptions (e.g., a delayed decline in the CD4 cell count after virologic failure and the enduring efficacy of antiretroviral therapy) similarly influenced both life-expectancy gains and costs, and thus had minimal influence on cost-effectiveness (see the Supplementary Appendix).
With access to second-line antiretroviral therapy, life expectancy improved by 10 months (approximately 30%), lifetime costs increased by $1,080, and the incremental cost-effectiveness ratio was $1,300 per year of life gained as compared with the most effective first-line antiretroviral strategy. Results of sensitivity analyses that included first- and second-line treatment were similar to those in the base case (see the Supplementary Appendix).
Table 3Table 3
Sensitivity Analysis of Opportunistic Diseases (ODs) Included in the Clinical Criteria for Starting Antiretroviral Therapy (ART). shows the average CD4 cell count at which antiretroviral therapy was initiated if CD4 testing was not available, as well as the incremental gains in life expectancy as compared with no treatment, for three strategies with different clinical criteria for initiating treatment. In the base case, the average CD4 cell count at the initiation of antiretroviral therapy was higher with the more lenient criterion of one opportunistic disease than with the stricter criterion of two opportunistic diseases (255 vs. 189 per cubic millimeter), when CD4 testing was not available. With the most effective strategy, involving the use of both clinical criteria and CD4 test information to guide the initiation of antiretroviral therapy, the average CD4 cell count at which treatment was initiated was 231 per cubic millimeter, reflecting the most efficient targeting of patients likely to benefit from therapy.For all three strategies, as the set of opportunistic diseases used as criteria for the initiation of antiretroviral therapy was expanded incrementally to include severe malaria, tuberculosis, and severe bacterial disease, the average CD4 cell count at which treatment was initiated increased (Table 3). With the most inclusive set of opportunistic diseases, the incremental increases in life expectancy were greatest for strategies that rely solely on clinical criteria.
Discussion
Several studies have addressed economic issues related to the prevention and treatment of HIV infection in developing countries,39-45 but few analyses have compared different strategies for initiating antiretroviral therapy by quantifying their clinical benefits for individual patients. When added to trimethoprim–sulfamethoxazole prophylaxis, antiretroviral therapy with the use of CD4 testing provided a gain in life expectancy of nearly 4 years. For strategies relying only on clinical criteria, the initiation of antiretroviral therapy after the first severe opportunistic disease provided nearly 1 year of additional life expectancy, as compared with initiation of treatment after two opportunistic diseases. Furthermore, if only a single antiretroviral regimen is available, delaying its discontinuation until three or more opportunistic diseases occur provides substantial clinical benefits. The added value of CD4 testing to guide decisions about the timing of treatment translated into a gain in life expectancy of more than 1 year, as compared with the most effective strategy relying solely on clinical information. These survival gains are similar to, or exceed, those associated with most other treatment interventions.46
There is no universal definition of a threshold ratio above which an intervention would not be considered cost-effective. Some have suggested that interventions with cost-effectiveness ratios less than the per capita GDP for a given country ($708 in Côte d'Ivoire) be considered “very cost-effective,” and less than three times the per capita GDP ($2,124 in Côte d'Ivoire) be considered “cost-effective.”47,48 In the absence of available CD4 testing, providing trimethoprim–sulfamethoxazole prophylaxis and antiretroviral therapy according to the earliest initiation and latest discontinuation criteria would be very cost-effective, and the most effective strategy — using both CD4 testing and clinical criteria to guide decisions about the timing of treatment — would also be cost-effective; these approaches are consistent with the WHO guidelines.
This analysis has several limitations. We focused on survival gains, since there are limited data on disability or quality-of-life weights that are suitable for health states in our model. In addition, data were combined from multiple sources. Some cost variables were extrapolated from a clinical trial, although we omitted costs of protocol-driven procedures that are unlikely to be available in low-income settings.32 We did not include lost productivity costs associated with AIDS, but if we had, antiretroviral therapy and trimethoprim–sulfamethoxazole prophylaxis would have been even more cost-effective. Since we allowed for variation in CD4 measurements but did not explicitly model errors in clinical information, we may have unfairly biased the analysis against CD4 testing.
To make the results most relevant to real-world decisions, we focused on a narrow subgroup of questions about the most effective strategies for using clinical criteria with or without CD4 testing to guide the initiation and discontinuation of antiretroviral therapy when only a single line of therapy is available. The results for two lines of therapy are similar. A dynamic transmission model would be needed to address questions at the population level about the relative cost-effectiveness of both prevention and treatment strategies. The growing consensus, however, is that both prevention and treatment are critical to the control of HIV infection in developing countries.44,45,49 Although the issue of HIV screening is beyond the scope of this report, improved screening and linkage to care would allow a larger segment of the HIV-infected population to benefit from antiretroviral therapy. The benefits of trimethoprim–sulfamethoxazole prophylaxis reported for HIV-infected patients in Côte d'Ivoire should be extrapolated with caution to sub-Saharan African countries with a high prevalence of resistance to trimethoprim–sulfamethoxazole,50,51 although benefits have been shown even in these areas.52-55
Finally, this analysis focuses on HIV-1, not HIV-2. In areas with high rates of HIV-2 infection, additional issues should be considered, such as the optimal choice of a first-line antiretroviral regimen in the setting of resistance to nonnucleoside reverse-transcriptase inhibitors.52,53
Cost-effectiveness is only one consideration in the allocation of scarce resources.49,56,57 There may be differences in the availability of strategies, and the selection of a strategy may be based on considerations of infrastructure, equity, qualitative attributes, nonmonetary constraints, or synergy with other high-priority initiatives.49,56-58 Strategies identified as cost-effective may be unaffordable in the poorest countries without assistance. The results of this analysis may be used, however, to motivate the global community to direct resources toward investments that have the greatest promise of providing gains in health. Better data from treatment-rollout programs — data on efficacy, toxicity, direct medical and programmatic costs (including costs of reducing wastage and scaling up) — should be incorporated when available.59 This is particularly important because nonmedical costs have been found to account for a substantial proportion of the total costs of interventions in other diseases.60
Our results show that a single regimen of antiretroviral therapy combined with trimethoprim–sulfamethoxazole prophylaxis affords major survival benefits. Adding second-line regimens will increase survival further. It is always more effective and cost-effective to use trimethoprim–sulfamethoxazole in combination with an antiretroviral regimen. Approaches guided by CD4 testing, although more costly than those based on clinical information alone, are substantially more effective in terms of survival and are a promising public health investment.
Supported by grants from the National Institute of Allergy and Infectious Diseases (RO1-AI058736, K23-AI01794, K24-AI062476, and K25-AI50436), the French Agence Nationale de Recherches sur le SIDA (1286), and the Centers for Disease Control and Prevention (Cooperative Agreements U64/CCU 114927 and U64/CCU 119525).
Dr. Holmes was a faculty member at Harvard Medical School when the study was designed, performed, submitted, and accepted for publication. As of July 1, 2006, he is an employee of Gilead Sciences and reports owning equity in that entity. No other potential conflict of interest relevant to this article was reported.
We are indebted to the entire Global AIDS Policy model team and investigators in Côte d'Ivoire, including Siaka Touré, Catherine Seyler, Eugène Messou, and Thérèse N'Dri-Yoman (Programme PACCI, Abidjan) for their contributions; to N. Kumarasamy, J. Anitha Cecelia, and A.K. Ganesh (Y.R. Gaitonde Centre for AIDS Research and Education, Chennai, India); to R. Wood (University of Cape Town, Cape Town, South Africa); to G. Gray, J. McIntyre, N.A. Martinson, and L. Mohapi (Perinatal HIV Research Unit, WITS Health Consortium, Johannesburg, South Africa); to M. Lipsitch, J. Sevilla, and G.R. Seage III (Harvard School of Public Health, Boston); to T. Flanigan and K. Mayer (Miriam Hospital, Providence, Rhode Island); to A.D. Paltiel (Yale University, New Haven, Connecticut); to M. Bender, Z. Lu, B. Wang, N. Divi, L. Wolf, and C. Scott (Massachusetts General Hospital, Boston); and to Steven Sweet and Hong Zhang (Massachusetts General Hospital, Boston) for outstanding technical and computer-programming assistance.
Source Information
From the Harvard School of Public Health, Boston (S.J.G., M.C.W., A.K., K.A.F.); Service Universitaire des Maladies Infectieuses et du Voyageur, Centre Hospitalier de Tourcoing, EA 2694, Faculté de Médecine de Lille, and Laboratoire de Recherches Économiques et Sociales, Centre National de la Recherche Scientifique Unité de Recherche Associée 362, Lille — all in France (Y.Y.); Boston University School of Public Health, Boston (E.L., K.A.F.); INSERM Unité 593, Bordeaux, France, and Programme PAC-CI, Abidjan, Côte d'Ivoire (X.A.); Massachusetts General Hospital and Harvard Medical School, Boston (R.P.W., H.E.H., C.H., K.A.F.); the Section of Decision Science and Clinical Systems Modeling, University of Pittsburgh School of Medicine, Pittsburgh (H.E.H.); and the Centers for Disease Control and Prevention, Atlanta (J.E.K.).
Address reprint requests to Dr. Goldie at the Department of Health Policy and Management, Program in Health Decision Science, Harvard School of Public Health, 718 Huntington Ave., 2nd Fl., Boston, MA 02115, or at sue_goldie@harvard.edu.
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