The “3 by 5” initiative, launched by the World Health Organization (WHO) and the Joint United Nations Program on HIV/AIDS (UNAIDS) in December 2003, was designed to mobilize the world to provide highly active antiretroviral therapy (HAART) to 3 million people in low- and middle-income countries by the end of 2005.1,2 Although this goal was not fully reached, the plan resulted in a large increase in HAART programs throughout the developing world. As HAART programs gain momentum in resource-limited settings, questions remain about the best way to select patients to receive HAART and to monitor these patients.
The WHO “3 by 5” guidelines recommend initiating HAART for patients with CD4 counts ≤200 cells/mm3 or for patients with AIDS (defined as WHO stage IV HIV disease) and monitoring treatment with CD4 cell counts every 6 months.3 The WHO guidelines differ from many developed world guidelines, such as those prepared by the United States Department of Health and Human Services (US DHHS),4 which recommend initiating HAART at a CD4 count of ≤350 cells/mm3, at a viral load of >100,000 copies/mL, or for patients with AIDS and monitoring with CD4 cell counts and viral load every 3 months.
There are a number of arguments for starting HAART earlier or later in the course of the disease. Arguments in favor of starting HAART earlier include preventing opportunistic infections, decreasing constitutional symptoms and thereby improving quality of life, preserving HIV-specific cellular responses, and reducing disease progression and associated viral virulence. Arguments for starting HAART later include preventing short- and long-term toxicity, decreasing the development of resistant strains attributable to poor adherence to complex HAART regimens, and preserving the possibility of treating with novel and more effective future therapies in treatment-naive patients.5
To our knowledge, no studies have comprehensively compared the health and economic impact of implementing developed versus developing world guidelines in the developing world. We selected US DHHS guidelines for use in this model as an example of developed world guidelines. Therefore, the objective of our analysis was to evaluate the cost-effectiveness of initiating and monitoring treatment in the developing world according to US DHHS versus WHO guidelines. We also sought to assess how incorporating the costs and benefits associated with reductions in HIV transmission would influence the cost-effectiveness of HAART programs in developing countries.
Background and Objectives
We developed a lifetime Markov model of direct and indirect costs, quality of life, survival, and transmission to sexual contacts associated with treating patients with HIV according to US DHHS versus WHO guidelines. We adhered to the recommendations of the Panel on Cost-Effectiveness in Health and Medicine for conducting cost-effectiveness analyses.6
We developed a lifetime Markov Monte Carlo simulation model (TreeAge Pro 2005, release 1.5; TreeAge Software, Williamstown, MA). The Markov model contains 38 mutually exclusive health states defined by patients' current viral loads, CD4 cell counts, and treatment status (Fig. 1). Transition probabilities determine which health states patients transition to during each cycle of the model. Whenever possible, we based our probability estimates on high-quality published studies (Table 1). When published data were not available, we relied on the judgment of clinical experts and tested a range of values in sensitivity analyses. We analyzed the model using a Monte Carlo simulation with 10,000 patients.
The target population for our analysis was HIV-positive adults in South Africa. The age at which patients entered our model (26 years of age) and the initial distribution of CD4 cell counts and viral loads were based on data from a community-based treatment-naive population of HIV-positive patients in South Africa (Dr. Bertran Auvert, personal communication, September 2005).7 We chose South Africa for our base case because it has a larger total number of HIV-positive individuals than any other country.30 South Africa is considered an upper-middle-income economy; as such, it is comparable to other upper-middle-income countries like Botswana, Malaysia, Mexico, and the Russian Federation.38
HIV Disease Progression
Risk of disease progression was based on patients' viral load and CD4 cell counts. For patients with an unsuppressed viral load, CD4 cell counts declined based on the viral load level.12,13 We used natural history data to estimate the rates of disease progression without therapy.8,13,17-21 As the patients' viral load or CD4 cell count changed, so did their risk of AIDS or death (see Table 1). Patients could die during any cycle of the model from non-AIDS-related causes.39 We assumed that all HIV-related deaths occurred among individuals with AIDS.
Treatment of HIV Infection
Patients in the WHO group who enter the model with a CD4 count ≤200 cells/mm3 or WHO stage III or IV disease begin HAART immediately. The remaining patients initiate HAART once their CD4 count drops to ≤200 cells/mm3 or they develop AIDS. Patients in the US group who enter the model with a CD4 count ≤350 cells/mm3, a viral load >100,000 copies/mL, or WHO stage III or IV disease begin HAART immediately. The remaining patients initiate HAART once their CD4 count drops to ≤350 cells/mm3, their viral load increases to >100,000 copies/mL, or they develop AIDS.
Patients were able to receive first-line and second-line HAART. As recommended in the WHO guidelines, first-line HAART consisted of stavudine, lamivudine, and nevirapine (for women) or efavirenz (for men). Second-line HAART consisted of tenofovir, didanosine, and ritonavir-boosted lopinavir. Patients in the WHO and US arms of the model received the same drug regimens.
After starting a HAART regimen, patients in whom virologic replication was suppressed had an increase in their CD4 cell count (see Table 1). Each month, patients with virologic suppression could have continued virologic suppression, treatment-related toxicity, or virologic rebound. CD4 cell counts declined in patients experiencing virologic rebound. Patients who had drug-related toxicity switched to an alternate WHO-recommended antiretroviral regimen.
Patients in the WHO group with incompletely suppressed viral loads could be identified when their CD4 cell counts were tested every 6 months. Based on WHO guidelines, these patients were switched to a second-line regimen if their CD4 count fell to lower than 50% of its peak value or to <200 cells/mm3. In the US group, treatment failure was identified when patients' viral load was tested every 3 months. These patients were switched to a second-line regimen if their viral load was >400 copies/mL. Patients in both groups also changed HAART regimens if they developed AIDS. We assumed that the probability of achieving a successful virologic response decreased after virologic failure but not after changing antiretrovirals because of toxicity (see Table 1).13,14,40
If resistance developed to second-line HAART, we assumed that only partial virologic suppression was possible.13 We assumed that this partial suppression was sustained until patients discontinued HAART because of the development of toxicity or death. All patients received opportunistic infection prophylaxis when appropriate.
Transmission of HIV
Sexual transmission of HIV depended on the infected patient's gender, number of sexual partners, and viral load (see Table 1). We assumed that everyone in the population was heterosexual and used HIV transmission rates from a predominantly heterosexual population in Africa.9 We assumed that reductions in viral load reduced transmission (see Table 1). As their CD4 cell counts decreased, newly infected patients initiated HAART based on the US or WHO guidelines, depending on whether the person who infected them was in the US or WHO arm of the model, respectively. To incorporate the impact of the newly infected partners' HIV disease on the overall cost-effectiveness in the model, the newly-infected partners' life expectancy and lifetime costs were attributed to the index patients who infected them.
Quality of Life
HIV/AIDS can markedly affect a patient's quality of life. Accordingly, we incorporated adjustments for quality of life in our analysis (see Table 1). In the absence of quality-of-life data for South African HIV/AIDS patients across all CD4 cell counts, we used quality-of-life data for HIV/AIDS patients in the United States.13 The utilities were similar to those collected for patients with CD4 counts <200 cells/mm3 at baseline in South Africa, however.32
We assumed a societal perspective and, as such, included direct costs of medical care and indirect costs resulting from HIV/AIDS morbidity (incorporated as utilities) in the base-case analysis. Resource utilization was based on WHO and South African treatment guidelines.3,34 Additional resource utilization and unit cost data were obtained from a South African cost-effectiveness study.32 South African rand costs were adjusted for inflation to 2005 values using the implicit gross domestic product (GDP) price deflator and were converted to US dollars.37,41
In a separate analysis, we included the indirect costs resulting from early mortality attributable to HIV/AIDS for index patients and their sexual partners. For the index patients, we identified the average age at which patients in the WHO and US groups died. South African GDP per capita was used to assign a monetary value to the years of life lost in the WHO group compared with the US group.42 GDP per capita was calculated across the lifetimes of the index patients and their sexual partners based on projected real GDP growth rates and was discounted at 3%.41,43,44
For newly infected sexual partners of the index patients, we first determined the number of HIV transmissions that would be prevented by treating patients according to US versus WHO guidelines. We then determined the difference in life expectancy between patients treated according to WHO guidelines and the general population in South Africa.45 The additional years of life saved for patients who did not contract HIV were valued using GDP per capita.
Role of Funding Source
Roche Molecular Systems was not involved in the design, conduct, or writing of this manuscript. The authors had full editorial independence, including the decision about whether to publish the manuscript.
Most Monte Carlo analyses are based on a simulation cohort of 10,000 patients. We assessed the margin of error in the base-case analysis using a sequence of 10 simulations with 10,000 patients each. Across the 10 simulations, the average lifetime cost per patient changed by <0.6% and the average life expectancy changed by <0.4%. The margin of error in analyzing a group of 100,000 versus 10,000 patients was <0.5% for effectiveness and cost per patient.
Benefit of Treating and Monitoring According to US Department of Health and Human Services Versus World Health Organization Guidelines
When we considered only the benefit to index patients, we found that initiating HAART at a CD4 count of 350 cells/mm3 as opposed to 200 cells/mm3, incorporating viral load testing, and testing more frequently increased life expectancy by 1.89 years, or 1.83 quality-adjusted life-years (QALYs), at an incremental lifetime cost of $9725, for an incremental cost-effectiveness ratio (ICER) of $5314 per QALY (Table 2).
Impact of US Department of Health and Human Services Guidelines on Reduced Transmission of HIV
We then determined the impact on the transmission of HIV of treating patients earlier in the course of their disease (CD4 count ≤350 cells/mm3 instead of CD4 count ≤200 cells/mm3) and treating patients with a high viral load (>100,000 copies/mL) regardless of CD4 cell count. Treating all HIV-infected adults in South Africa (assuming a population of 5.1 million30) according to US versus WHO guidelines would reduce the number of HIV transmissions to sexual partners by 137,000 over the lifetime of the currently infected patients. Of this overall decrease, 35% of the reduction in transmission is attributable to initiating treatment at a higher CD4 count (CD4 count ≤350 cells/mm3 vs. CD4 count ≤200 cells/mm3), 60% is attributable to inclusion of viral load testing (treating patients with viral load of >100,000 copies/mL and testing viral load every 6 months), and 5% is attributable to more frequent testing (every 3 months vs. every 6 months).
Incorporating the impact of transmission to partners, we estimated that treating patients according to US versus WHO guidelines cost $11,867 more and increased life expectancy by 2.58 years, or 3.00 QALYs, for an incremental cost-effectiveness of $3956 per QALY (see Table 2). Seventy percent of the differential increase in life expectancy was attributable to initiating treatment at a higher CD4 count, 28% to inclusion of viral load testing, and 2% to more frequent testing (Table 3). Looked at separately, the ICERs of each of these 3 strategies were $1168, $7860, and $41,286, respectively (see Table 3).
Over a 5-year period, treating and monitoring all adult HIV-positive patients in South Africa according to US versus WHO guidelines would increase direct costs by $14 billion but result in approximately 370,000 fewer deaths, 1 million fewer new AIDS cases, and 290,000 fewer new cases of HIV.
We conducted a separate analysis to determine the indirect costs resulting from early mortality attributable to HIV/AIDS for the index patients and the partners to whom they transmitted HIV. The incremental indirect cost per patient was $16,778 for the index patient and $101,265 for their sexual partners. The indirect cost for the sexual partners is considerably higher because of the expectation that the GDP net of inflation is going to rise rapidly over the next 50 years in South Africa.44
For South Africa, based on our modeling assumptions, treating all HIV-positive patients according to US versus WHO guidelines would result in cost savings of $39.4 billion over the lifetime of all currently infected adult patients and the partners to whom they transmit HIV (approximately 38 years). Increased direct medical costs of $60.5 billion would be more than offset by indirect cost savings of $99.9 billion across the index patients and their sexual partners.
We conducted 1-way sensitivity analyses to determine the effect of individual parameters in the model on the stability of the cost-effectiveness results. The results are presented as a Tornado diagram (Fig. 2). The ICER comparing the US versus WHO guidelines is displayed on the horizontal axis, with each bar representing the range of ICERs generated by varying the related variable. A wide bar indicates that the associated variable has a large effect on the results of the model. The bars are arranged in order, with the widest bar (the most critical uncertainty) at the top.
Results were most sensitive to clinical variables, such as the effect of changes in CD4 cell count on the risk of progression to AIDS and the probability of virologic rebound, as well as to the quality-of-life variables. The major cost variables to which the model was sensitive were the cost of viral load testing and the cost of second-line and alternate second-line HAART regimens (see Fig. 2). Results were also sensitive to the discount rate.
The transmission variables to which the model was most sensitive included the effect of changes in viral load on the risk of transmission, the viral load level at which baseline transmission occurred, the annual rates of transmission, and the number of susceptible partners at risk.
We also conducted a multiway sensitivity analysis on the 3 variables the model was most sensitive to: impact of CD4 cell count on progression to AIDS, quality of life, and the cost of measuring viral load. ICERs ranged from $3100 to $10,700, indicating that the results remained cost-effective in multiway sensitivity analysis, even when the values for all 3 variables were at the upper end of the sensitivity range.
In addition to the devastating impact of HIV/AIDS on affected individuals and their families, the impact on the economies of sub-Saharan Africa has been staggering. A recent South African study claimed that by the year 2010, the national GDP could be lower by 17% because of HIV/AIDS.46 Our base-case analysis, which included the impact of treatment on transmission of HIV but did not include indirect costs, demonstrated that treating patients according to US guidelines costs $3956 for every QALY saved. When the indirect costs attributable to early mortality from HIV/AIDS are considered, our model results indicate that treating HIV-positive patients according to US guidelines may be cost-saving. In South Africa, treating all adult HIV-positive patients according to US guidelines could result in net savings to the economy of $40 billion over the next 38 years.
According to the Commission on Macroeconomics and Health,47 interventions with an ICER that is between 1 and 3 times the GDP per capita are considered cost-effective. Interventions with an ICER that is less than the GDP per capita are considered “very cost-effective”.48 Our base-case result of $3956 per QALY (not including indirect costs) is less than the GDP per capita for South Africa ($4900 in 200549); as such, it is considered a highly cost-effective result. Throughout the sensitivity analyses, the cost per QALY for the US guidelines group remained less than $10,700, indicating that the results are cost-effective across all ranges of the parameters that we tested. Furthermore, for the base-case model, including transmission, we isolated the incremental cost-effectiveness of the individual components of the developed world versus developing world guidelines. Based on this analysis, the strategy of treating patients with CD4 counts ≤350 cells/mm3 was found to be highly cost-effective and using viral load testing was found to be cost-effective.
The WHO “3 by 5” program is a major advance in the treatment of HIV/AIDS in the developing world, and significant progress has been made to date. Despite this progress, there are a number of potentially significant benefits to treating patients in the developing world according to developed world (eg, US) guidelines. First, by initiating treatment at a higher CD4 cell count, people can begin life-saving HAART before severe immunologic damage occurs. Patients initiating HAART at lower CD4 cell counts are less likely to respond to treatment, more likely to progress to AIDS and die,50-52 and more likely to experience immune reconstitution inflammatory syndrome (IRIS).53 A recent cohort study in South Africa found that 24% of deaths while on HAART were attributable to IRIS.51 Second, by initiating treatment for patients with viral loads >100,000 copies/mL regardless of CD4 cell count, patients who are likely to progress most quickly to AIDS and die instead initiate HAART before their CD4 cell counts decline significantly.54 Third, using the viral load to monitor patients on HAART allows clinicians to identify treatment failure more quickly and to switch antiretroviral therapy, slowing disease progression, improving treatment outcomes, and reducing transmission of HIV. Finally, patients with viral loads >100,000 copies/mL are much more likely to transmit HIV than patients with lower viral loads.9 Implementation of WHO guidelines, with the exclusion of viral load testing, does not specifically target this group of highly efficient transmitters of HIV. Treating this group of efficient transmitters would reduce the HIV transmission rates as a result of the decreased viral load during HAART.55 Other techniques that have been proven to decrease HIV transmission (eg, education, voluntary counseling and testing programs) are also critically important. All these approaches improve the chances of gaining control of this epidemic.
Our results are similar to those of recently published studies that evaluated the cost-effectiveness of early versus late initiation of antiretrovirals in South Africa.56,57 Bachmann56 found that initiating HAART at a CD4 count of 350 versus 200 cells/mm3 increased life expectancy by 2.3 years compared with 1.9 years in our model. His incremental costs were significantly higher than ours ($8936 vs. $2350 in our model), resulting in a somewhat higher ICER ($3885 per life-year gained versus $1270 in our model [calculated using Bachmann's assumptions]). Badri et al57 found a similar increase in life expectancy (2.2 years) but had a somewhat lower incremental cost ($1650) and ICER ($766 per life year saved). The difference in cost between our study and Bachmann's is most likely attributable to the inclusion of overhead in his cost estimates (because this study was conducted from the perspective of a tax-funded health service) and higher drug prices.
Our finding that treating patients with a high viral load (>100,000 copies/mL) increased life expectancy compared with treating patients based on CD4 cell counts alone is consistent with a recent analysis by Bogaards et al.58 In a community-based setting in sub-Saharan Africa, selecting patients for treatment based on plasma viral load in addition to CD4 cell counts was the most efficient treatment initiation strategy and resulted in a larger reduction in the 1-year AIDS incidence rate than selecting based on CD4 cell counts alone.
Our analysis has several limitations. First, an inherent limitation of modeling is that data were combined from multiple sources with varied study designs. We obtained data from the best available published literature, however, and where such data were not available, we based any assumptions on the advice of clinical experts and varied all estimates widely in sensitivity analyses. Second, there were a few parameters for which data from the developing world were not available, and we had to rely on data from the developed world or make simplifying assumptions. We limited the use of developed world data to natural history or clinical parameters that are thought to be similar across countries.
Third, with regard to indirect costs, it is possible that AIDS patients receiving HAART are sicker than the average adult (working or nonworking and HIV-positive or HIV-negative) and that, as a result, GDP per capita may not be an entirely accurate reflection of incremental productivity in this group. Fourth, it is possible that the real GDP in South Africa may grow more slowly than predicted, although we took our estimate from an authoritative source.44 Fifth, our analysis was limited to sexual transmission of HIV and did not incorporate the impact of mother-to-child transmission (MTCT). MTCT is an important problem, but the guidelines for treating pregnant women and their infants are quite different than the overall WHO “3 by 5” guidelines and warrant a separate analysis. Sixth, we have assumed that the infrastructure to conduct CD4 cell count and viral load testing exists in South Africa. This seems reasonable, because real-time viral load and CD4 cell count testing are available at all government sites in the HIV treatment program, which encompasses approximately 260 treatment sites and more than 30 laboratories (Dr. Ian Sanne, personal communication, May 10, 2007). Finally, regardless of the finding that treating patients according to developed world guidelines is highly cost-effective in the short-term and cost-saving in the long-term, some governments may not be able to afford to follow these guidelines without outside financial assistance. However, the main goal of cost-effectiveness analyses is to highlight interventions that result in the greatest clinical gain for a given investment of resources. In that context, we have demonstrated that treating HIV patients according to developed world guidelines should be prioritized highly from the perspectives of both cost and effectiveness.
Our analysis indicated that treating patients in the developing world according to developed world (US DHHS) guidelines is more effective than treating patients according to developing world (WHO) guidelines. Including the impact of HIV/AIDS on direct and indirect costs, this approach is cost-saving for the economy as a whole. Even without the inclusion of indirect costs, the cost-effectiveness of this approach is well within the range of cost-effective interventions outlined by the Commission on Macroeconomics and Health. These findings suggest that in many upper- to middle-income countries in the developing world, treating patients according to developed world guidelines can provide important health benefits for a reasonable investment in health care resources.
The authors are grateful to Bertran Auvert for providing data on the distribution of CD4 cell counts and viral load levels among patients in South Africa. They also thank Nicholas Hellmann for clinical advice and comments on the paper and Gillian Sanders for advice on HIV cost-effectiveness modeling and her comments on the paper.
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