The authors have declared that no competing interests exist.
Conceived and designed the experiments: VvW VC MRJ SB ANP. Performed the experiments: VvW. Analyzed the data: VvW VC MRJ SB AM DP JL ANP. Wrote the paper: VvW ANP. N/A.
The most recent World Health Organization (WHO) antiretroviral treatment guidelines recommend the inclusion of zidovudine (ZDV) or tenofovir (TDF) in first-line therapy. We conducted a cost-effectiveness analysis with emphasis on emerging patterns of drug resistance upon treatment failure and their impact on second-line therapy.
We used a stochastic simulation of a generalized HIV-1 epidemic in sub-Saharan Africa to compare two strategies for first-line combination antiretroviral treatment including lamivudine, nevirapine and either ZDV or TDF. Model input parameters were derived from literature and, for the simulation of resistance pathways, estimated from drug resistance data obtained after first-line treatment failure in settings without virological monitoring. Treatment failure and cost effectiveness were determined based on WHO definitions. Two scenarios with optimistic (no emergence; base) and pessimistic (extensive emergence) assumptions regarding occurrence of multidrug resistance patterns were tested.
In the base scenario, cumulative proportions of treatment failure according to WHO criteria were higher among first-line ZDV users (median after six years 36% [95% simulation interval 32%; 39%]) compared with first-line TDF users (31% [29%; 33%]). Consequently, a higher proportion initiated second-line therapy (including lamivudine, boosted protease inhibitors and either ZDV or TDF) in the first-line ZDV user group 34% [31%; 37%] relative to first-line TDF users (30% [27%; 32%]). At the time of second-line initiation, a higher proportion (16%) of first-line ZDV users harboured TDF-resistant HIV compared with ZDV-resistant viruses among first-line TDF users (0% and 6% in base and pessimistic scenarios, respectively). In the base scenario, the incremental cost effectiveness ratio with respect to quality adjusted life years (QALY) was US$83 when TDF instead of ZDV was used in first-line therapy (pessimistic scenario: US$ 315), which was below the WHO threshold for high cost effectiveness (US$ 2154).
Using TDF instead of ZDV in first-line treatment in resource-limited settings is very cost-effective and likely to better preserve future treatment options in absence of virological monitoring.
The public health approach for combination antiretroviral therapy (cART) in resource-limited settings includes the use of one standard first-line and one standard second-line regimen
One particular concern regarding the widespread use of TDF in settings without virological monitoring is the potential for development of extensive nucleoside and nucleotide analogue cross-resistance via the emergence of the reverse transcriptase mutation K65R, and possibly also multidrug resistance patterns such as Q151M, although the latter has not been detected in well-controlled clinical trials in resource-rich settings
Previous cost effectiveness analyses have already pointed towards better clinical outcomes of TDF use compared with other NRTIs in industrialized
The model presented here corresponds to the version described extensively in
A typical simulation run, which is influenced by many random elements, shows the following characteristics: starting in 1989, the population of approximately 25 000 uninfected persons initially contains about 5 HIV infected individuals. The epidemic starts to spread via individuals who acquire HIV through heterosexual contacts with HIV-1 infected short or long term partners. The probability of transmission of HIV depends on whether the partner is undergoing primary infection, on the partner’s HIV-RNA viral load (obtained by sampling from the distribution of viral load levels found in partnerships formed by HIV-infected people, accounting for gender and age), on the subject’s gender and on the presence of other sexually transmitted infections. Each HIV-infected individual experiences HIV RNA levels, CD4 declines and mortality rates that correspond to their specific age and gender, health status with respect to co-morbidities, and to antiretroviral treatment exposure. We assumed that cART became available in 2007 (corresponding to the first availability of TDF in national and regional treatment programs
Selected model input parameters are shown in
Rates per 3 months | Value for sensitivity analysis | Source | |
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Zidovudine (ZDV) | |||
nausea* | 0.1 | own estimate | |
lipodystrophy | 0.015 | own estimate | |
anemia* | 0.03 | ||
Headache* | 0.1 | own estimate | |
lactic acidosis | 0.001 | own estimate | |
Tenofovir (TDF) | |||
Nephrotoxicity | 0.01 | ||
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Adherence benefit of TDF over ZDV | 0.03 | 0 |
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worse adherence if drug related toxicities | 0.1 |
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Emergence of NNRTI mutations in presence of | |||
Detectable HIV RNA<500 copies/mL | 0.4 | own estimate | |
Detectable HIV RNA >500 copies/mL | 0.95 | own estimate | |
Emergence of M184V mutations in presence of | |||
Detectable HIV RNA<500 copies/mL | 0.4 | own estimate | |
Detectable HIV RNA >500 copies/mL | 0.9 | own estimate | |
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Probability for switch if treatment failure detected | 0.8 | 0.1 |
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The setting of the main analysis consists of an HIV-infected population, in which ART has not previously been used. HIV diagnosis became available in 2003 and is either made by voluntary testing of a fixed rate of the population (7.5% chance every three months) or triggered by AIDS defining conditions. CD4 count determinations are performed every 6 months in diagnosed individuals, and if measured CD4 levels drop<200 cells/mm3 or a WHO stage 4 event has been diagnosed, ART is initiated.
The definition for treatment failure is based on clinical (new or recurrent WHO stage 4 condition), specific WHO clinical stage 3 conditions (e.g. pulmonary tuberculosis) and immunological criteria (fall of CD4 count to baseline or below, or 50% fall from on-treatment peak value, or persistent CD4 levels below 100 cells/mm3) and CD4 cell measurements occur on a 6-monthly basis. It is also assumed that virological testing is not available and that switching to second-line ART occurs almost immediately after detection of treatment failure at a rate of 80% per 3 months, unless an individual is lost to follow-up.
Second-line ART consists of LPV/r, 3TC, and either ZDV or TDF, whichever has not been used in first-line treatment already. In this hypothetical setting with only two lines of treatment available, individuals who fail second-line therapy remain on the failing regimen.
Clinical (AIDS-defining conditions, mortality) and treatment outcomes (CD4 cell gain, rates of viral suppression, and treatment failure based on WHO criteria) of the simulation are presented as medians [2.5th–97.5th percentiles] from the distribution of point estimates from all simulation runs per analysis (n = 100). Unless stated otherwise, treatment outcomes were estimated on an intent-to-continue treatment basis by the Kaplan-Meier method, meaning that study outcomes were still attributed to the respective first-line strategy in spite of possible switches to second-line therapy.
Main cost-effectiveness outcomes are average costs (per treated individual) and cumulative costs accrued by year 2022 (15 years after cART became available) for antiretroviral treatment and expenses for management of TB or HIV-related morbidity. In addition, cumulative person-years and quality adjusted life-years (QALY) lived from ART start to death or until 2022, whichever came first, are compared between treatment strategies. On the basis of estimates from
Costs for cART were derived from the Clinton Foundation price list of November 2010
The simulation modelled the emergence of thymidine analogue mutations (TAM), K65R, and Q151M on failing cART. To obtain estimates of rates and order of mutation accumulation of K65R and Q151M, descriptive statistical analyses of six publicly available data sets with genotypic drug resistance test (GRT) data of non-B subtype viruses from Sub-Saharan African settings were performed
The transition probabilities given next to arrows are per 3 months spent on a failing treatment with an (unmeasured) HIV RNA >500 copies/mL. Due to scarcity of resistance data of failing tenofovir regimens from developing settings two separate pathways were tested in the simulation. The base scenario (1B) was derived from a limited set of sequences from tenofovir failures and does not include the multidrug resistance pattern Q151M. The pessimistic scenario (1C) is based on estimations from sequences obtained after virological failure with stavudine and allows for extensive multidrug (i.e. Q151M) resistance emergence. Also note that the multidrug resistance patterns in the zidovudine pathway were not observed in the data (enframed by dashed lines), but were assumed to occur at low frequency. Abbreviations: ZDV, Zidovudine; TDF, tenofovir; S, susceptible; I, intermediate resistant; R, fully resistant.
We constructed mutagenic trees by grouping the GRTs according to mutation patterns with respect to TAM, K65R, and Q151M, and by assuming a specific order for the emergence of these mutations (in
The estimated 3-month incidence rates for NNRTI and 3TC mutations, which were also derived from genotypic data, are displayed in
The effect of considering LPV/r worth only 1 drug, of immediate or delayed switching after detection of first-line treatment failure or of the assumed adherence benefit of TDF use on outcomes (and treatment costs in particular), and combinations of these parameters, were subjected to sensitivity analyses by re-running the simulation using predefined parameter values (
Moreover, simulations were repeated in alternative settings, which included the availability of viral load monitoring, the effect of substituting drug components due to toxic side effects, and by introducing ART into a setting where transmitted drug resistance from D4T/3TC/nevirapine was present (for details see
A total of 605 genotypic sequences obtained after first line treatment failure with either 3TC+ZDV+NNRTI (n = 133), 3TC+TDF+NNRTI (n = 24) or 3TC+D4T+NNRTI (n = 472) were analyzed. Given the small number of individuals who have received TDF in first-line treatment, rates of K65R and Q151M mutation emergence were also estimated from the D4T data and applied to a separate simulation representing an alternate, “pessimistic” scenario. The distribution of viral subtypes was as follows: C 53% (n = 320); G 16% (n = 97); CRF02_AG 14% (n = 83); and a variety of other non-B subtypes occurring at <4%.
Probabilities for the emergence of resistance upon clinical or immunological treatment failure were calculated as the percentage of genotypic resistance tests showing a specific mutation pattern and are displayed in
Out of a population of 4346 [4075; 4618] simulated HIV infected individuals, 2012 [1065; 2501] and 2045 [1536; 2517] individuals ever initiated ART with ZDV or TDF between the years 2007 and 2022 (end of simulation), respectively. The median age at time of cART initiation was 43 years, and 52% were women, irrespective of treatment strategy group. The median follow-up time after initiation of first-line therapy was 6 years. At time of therapy initiation, median values [interquartile range] of HIV RNA measurements were 5.09 [5.06; 5.12] log10 copies/mL, and median [interquartile] CD4 count measurements reached 140 [133; 147] cells/microliter, irrespective of treatment group. Around 7%
Differences in first-line therapy outcomes were predicted with respect to CD4 cell count recovery, with a gain of 102 cells/microliter [97; 113] within 1 year in the ZDV group and gains of 114 cells/microliter [107; 121] (base scenario) and 107 cells/microliter [102; 112] (pessimistic scenario) in the TDF group, respectively. Intent to treat viral suppression rates below 50 copies\mL after 1 year were estimated at 64% [62; 67] among ZDV starters and at 68% [66; 71] (base scenario) and 66% [63; 68] (pessimistic scenario), respectively, in the TDF group.
As shown in
For individuals starting with TDF, resistance emergence was modelled by two different scenarios (also see
A higher proportion of ZDV starters was predicted to have initiated LPV/r-based second-line therapy within 6 years after antiretroviral treatment initiation (33.9% [30.7; 36.6], median n = 602) when compared with the group of TDF starters (base scenario: 29.9% [27.2; 31.6], median n = 547; pessimistic scenario: 33.0% [29.8; 34.9], median n = 597). Among individuals from the ZDV group who initiated second-line therapy, TAMs were predicted to be present in 28.4% [23.2; 32.5], and none in the two TDF groups. Among individuals who started TDF as first-line therapy, the predicted proportion of K65R was almost 9-fold higher in the base scenario (43.4% [38.8; 48.4]) compared with the pessimistic scenario (4.7% [3.3; 6.7]). In contrast, while there were no Q151M mutations emerging in the base scenario, the prevalence of Q151M was estimated at 5.9% [3.7; 8.3] in the pessimistic scenario. With respect to NNRTI mutations (56–57%) and M184V (62–63%), the simulation yielded almost identical estimates across the three groups (not shown).
Next, we analyzed the potential activity of second-line regimens against a background of different resistance mutations patterns. Previous studies have demonstrated that ritonavir-boosted PIs such as LPV/r have a very high potency to inhibit viral replication and are very robust to the emergence of drug resistance
Setting | ZDV first | TDF first, base scenario | TDF first, pessimistic scenario |
Base | |||
<2.75 active drugs | 16.2 [12.3; 19.3] | 0 [0; 0.2] | 5.9 [3.7; 8.3] |
<2 active drugs | 0.7 [0; 1.3] | 0 | 5.0 [3.3; 7.8] |
Transmitted Resistance | |||
<2.75 active drugs | 16.3 [13.3; 20.6] | 0.2 [0; 0.7] | 6.2 [3.6; 8.9] |
<2 active drugs | 0.8 [0; 1.3] | 0 | 5.4 [3.0; 7.6] |
Virological Monitoring | |||
<2.75 active drugs | 6.3 [4.8; 8.9] | 0 [0; 0.1] | 1.7 [1.0; 3.0] |
<2 active drugs | 0 [0; 0.5] | 0 | 1.6 [1.0; 2.9] |
Switches allowed | |||
<2.75 active drugs | 14.3 [11.2; 18.8] | 1.4 [0.4; 2.7] | 6.5 [4.4; 8.9] |
<2 active drugs | 0.9 [0; 2.3] | 0.8 [0.2; 1.7] | 5.6 [3.1; 8.5] |
For the cost effectiveness analysis, the cumulative treatment costs accrued after therapy start until death or the year 2022 (whichever came first) were compared (
Scenario | ZDV first, basescenario | TDF first, basescenario | ZDV first, pessimisticscenario | TDF first, pessimisticscenario |
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Treatment costs | 1058 [904; 1145] | 1070 [994; 1140] | 1072 [995; 1148] | 1102 [997; 1182] |
HIV-morbidity related costs | 160 [136; 170] | 148 [135; 159] | 160 [145; 170] | 151 [136; 162] |
Total costs | 1217 [1040; 1314] | 1219 [1137; 1292] | 1232 [1143; 1314] | 1252 [1138; 1338] |
Life years lived | 4.811 [4.290; 5.100] | 4.936 [4.721; 5.172] | 4.856 [4.642; 5.062] | 4.910 [4.678; 5.125] |
QALYs lived | 3.719 [3.317; 3.944] | 3.869 [3.701; 4.054] | 3.753 [3.588; 3.917] | 3.846 [3.663; 4.015] |
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life years lived | 99 | 540 | ||
QALYs lived | 83 | 315 | ||
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life years lived | 11 | 377 | ||
QALYs lived | 9 | 220 | ||
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Treatment costs | 1076 [980; 1162] | 1065 [827; 1135] | 1070 [998; 1154] | 1103 [950; 1177] |
HIV-morbidity related costs | 161 [151; 175] | 149 [120; 159] | 161 [145; 174] | 152 [134; 162] |
Total costs | 1237 [1131; 1327] | 1213 [948; 1289] | 1231 [1146; 1326] | 1255 [1085; 1339] |
Life years lived | 4.856 [4.519; 5.155] | 4.895 [4.139; 5.161] | 4.832 [4.551; 5.113] | 4.891 [4.411; 5.129] |
QALYs lived | 3.754 [3.492; 3.986] | 3.836 [3.244; 4.046] | 3.735 [3.518; 3.955] | 3.830 [3.454; 4.016] |
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life years lived | dominant |
551 | ||
QALYs lived | dominant |
339 | ||
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life years lived | dominant |
405 | ||
QALYs lived | dominant |
250 | ||
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Treatment costs | 1102 [1011; 1170] | 1073 [918; 1152] | 1112 [997; 1187] | 1134 [1056; 1209] |
HIV-morbidity related costs | 150 [132; 161] | 138 [113; 150] | 149 [136; 161] | 142 [132; 150] |
Total costs | 1252 [1151; 1325] | 1211 [1031; 1295] | 1261 [1135; 1339] | 1277 [1188; 1355] |
Life years lived | 4.910 [4.558; 5.174] | 4.977 [4.406; 5.243] | 4.929 [4.576; 5.178] | 4.995 [4.736; 5.241] |
QALYs lived | 3.800 [3.530; 4.001] | 3.905 [3.456; 4.112] | 3.815 [3.544; 4.008] | 3.915 [3.712; 4.109] |
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life years lived | dominant |
348 | ||
QALYs lived | dominant |
229 | ||
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life years lived | dominant |
243 | ||
QALYs lived | dominant |
160 | ||
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Treatment costs | 1012 [952; 1080] | 1021 [937; 1085] | 1027 [962; 1087] | 1054 [945; 1122] |
HIV-morbidity related costs | 164 [148; 175] | 154 [139; 167] | 163 [156; 172] | 155 [144; 165] |
Total costs | 1176 [1104; 1253] | 1175 [1084; 1244] | 1190 [1126; 1250] | 1209 [1091; 1287] |
Life years lived | 4.803 [4.543; 5.029] | 4.908 [4.605; 5.126] | 4.855 [4.662; 5.072] | 4.884 [4.445; 5.128] |
QALYs lived | 3.723 [3.524; 3.899] | 3.843 [3.606; 4.016] | 3.763 [3.611; 3.935] | 3.822 [3.477; 4.012] |
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life years lived | 86 | 937 | ||
QALYs lived | 75 | 453 | ||
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life years lived | dominant |
663 | ||
QALYs lived | dominant |
321 |
Footnotes:
All costs are in US$.
TDF dominant over ZDV because of lower costs and higher QALYs.
Regarding morbidity and life-years lived as outcomes, the three strategies seemed to be comparable: the six year Kaplan-Meier estimates for mortality were 25.9% [22.1; 28.4] for the ZDV group and 23.3% [20.8; 25.9] (base scenario) and 23.6% [21.5; 25.4] (pessimistic scenario) for the TDF groups. The mean number of discounted life years lived since therapy start until death or 2022 per individual looked similar across the three groups (4.9 years,
The connected dots refer to one set of comparisons between first-line tenofovir (TDF; coloured dots) or zidovudine (ZDV; black dots) using different assumptions regarding scenarios of TDF resistance emergence (red dots: base scenario; blue dots: pessimistic scenario involving the emergence of the Q151M multidrug-resistance complex; see
Next, we assessed the robustness of model outcomes in different settings (presence of transmitted resistance, virological monitoring available, drug switches due to drug toxicities allowed; see
Furthermore, the sensitivity of results to specific parameter values was explored (
Scenario/Sensitivity analysis | Base Scenario | Pessimistic Scenario | ||||
Uncertaintybounds ofICERestimates | % TDF strategy dominant | % ICERestimates< WHOthreshold | Uncertaintybounds of ICERestimates | % TDF strategy dominant | % ICERestimates< WHOthreshold | |
Base | [−2186; 3269] | 27% | 97% | [−2439; 2729] | 9% | 96% |
Transmitted Resistance | [−3327; 3353] | 32% | 95% | [−1347; 2874] | 10% | 97% |
Virological Monitoring | [−5970; 3747] | 47% | 96% | [−1576; 2683] | 14% | 97% |
Switches allowed | [−1512; 3132] | 23% | 96% | [−2761; 2676] | 6% | 97% |
LPV worth only 1 drug instead of 1.5 | [−2222; 1901] | 31% | 98% | [−4490; 2133] | 19% | 98% |
Median time to switch 22 months (i.e.3-month switch probability of 10%) | [−5111; 4950] | 32% | 95% | [−2981; 3111] | 28% | 96% |
No additional adherence benefit for TDF | [−766; 1807] | 5% | 98% | [−1240; 2287] | 8% | 96% |
Median time to switch 22 months & noadditional adherence benefit for TDF | [−1558; 2406] | 13% | 97% | [−5426; 5132] | 30% | 95% |
Results were obtained by repeatedly drawing one simulation with TDF as the initial strategy and one simulation with zidovudine (ZDV) in the initial treatment. From this pair of simulations the incremental cost effectiveness ratio was calculated. Dominance was defined by lower costs and higher quality adjusted life year estimates for a specific treatment. By repeating this process 1000 times we obtained an estimate for how frequently the TDF strategy was dominant. Analogous calculations were performed to check how often the ICER estimates were below the WHO threshold for high cost effectiveness (annual per capita gross domestic product of US$ 2154). Uncertainty bounds reflect ranges that include 95% of all ICER estimates.
Abbreviations: ICER, incremental cost effectiveness ratio; TDF, tenofovir; WHO, World Health Organization.
By using an established stochastic simulation of HIV disease progression and therapy we have explored the impact of using different NRTI drugs (namely ZDV versus TDF) on resistance emergence and its consequences in terms of response to available second-line regimens and associated costs. Owing to uncertainty with respect to the influence of prolonged exposure to failing regimens and the effect of non-B subtype infection on NRTI-cross resistance we tested two pathways for resistance emergence while receiving TDF therapy. The base scenario assumed a rapid and frequent emergence of the TDF signature mutation K65R but only a very limited degree of NRTI cross-resistance. The second, pessimistic scenario was derived from analyses of genotypic resistance tests performed after failure of first-line combination treatment with D4T and was characterized by a more limited emergence of K65R, but a considerable risk for NRTI-cross resistance by the emergence of Q151M.
Our analyses suggest that first-line TDF use is a cost-effective treatment strategy compared with first-line ZDV use when considering quality adjusted life years as outcome, although dominance of the TDF strategy was only observed in 11% to 46% of comparisons (
Depending on the actual rate of NRTI multidrug resistance emergence, first-line TDF use may increase emergence of extensively NRTI class-resistant HIV by 8.5-fold (17/1000 first-line TDF users in the pessimistic scenario compared with 2/1000 first-line ZDV users). Observational studies have reported associations of K65R mutations with Q151M, possibly pointing towards a co-selection of these mutations
Some limitations should be noted about this study. Like any model, our simulation involves simplifications of reality and is based on assumptions regarding input parameters. In particular, given the lack of real data we had to make assumptions regarding rates and extent of drug resistance following immunological failures in resource-limited settings, as shown in
In summary, taking into account the possibility of more extensive drug resistance or possible long term renal toxicity by TDF use we conclude that first-line TDF use is likely to be a very cost-effective treatment strategy in resource-limited settings even in the absence of virological monitoring, because of the better tolerability and the small cost difference.
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The authors wish to thank the two anonymous reviewers for their constructive comments.