The authors have declared that no competing interests exist.
Conceived and designed the experiments: JE LS NB ME OK. Performed the experiments: JE LS NB. Wrote the paper: JE LS NB ME OK.
The cost-effectiveness of routine viral load (VL) monitoring of HIV-infected patients on antiretroviral therapy (ART) depends on various factors that differ between settings and across time. Low-cost point-of-care (POC) tests for VL are in development and may make routine VL monitoring affordable in resource-limited settings. We developed a software tool to study the cost-effectiveness of switching to second-line ART with different monitoring strategies, and focused on POC-VL monitoring.
We used a mathematical model to simulate cohorts of patients from start of ART until death. We modeled 13 strategies (no 2nd-line, clinical, CD4 (with or without targeted VL), POC-VL, and laboratory-based VL monitoring, with different frequencies). We included a scenario with identical failure rates across strategies, and one in which routine VL monitoring reduces the risk of failure. We compared lifetime costs and averted disability-adjusted life-years (DALYs). We calculated incremental cost-effectiveness ratios (ICER). We developed an Excel tool to update the results of the model for varying unit costs and cohort characteristics, and conducted several sensitivity analyses varying the input costs.
Introducing 2nd-line ART had an ICER of US$1651-1766/DALY averted. Compared with clinical monitoring, the ICER of CD4 monitoring was US$1896-US$5488/DALY averted and VL monitoring US$951-US$5813/DALY averted. We found no difference between POC- and laboratory-based VL monitoring, except for the highest measurement frequency (every 6 months), where laboratory-based testing was more effective. Targeted VL monitoring was on the cost-effectiveness frontier only if the difference between 1st- and 2nd-line costs remained large, and if we assumed that routine VL monitoring does not prevent failure.
Compared with the less expensive strategies, the cost-effectiveness of routine VL monitoring essentially depends on the cost of 2nd-line ART. Our Excel tool is useful for determining optimal monitoring strategies for specific settings, with specific sex-and age-distributions and unit costs.
The latest World Health Organization (WHO) guidelines recommend routine viral load monitoring for HIV-infected patients on antiretroviral therapy (ART) in resource-limited settings [
We have shown that monitoring VL with a qualitative, moderately-sensitive POC VL test benefits the patient, and may be cost-effective compared with CD4 or clinical monitoring [
The feasibility and cost-effectiveness of VL monitoring with a qualitative POC test depends on many factors, and these can vary substantially among settings. We built on our earlier mathematical simulation model of patients on ART, to create a flexible and user-friendly tool that allows users to evaluate the cost-effectiveness of a wide range of monitoring and switching strategies with varying assumptions.
We modeled cohorts of HIV-infected patients from ART initiation until death. We used three indicators to define the progression of HIV infection qualitatively: virological; immunological; and, clinical. All three indicators have two possible values: normal and failing. We assumed that all patients started in the normal virological, immunological and clinical stages. This represents the successful introduction of ART: VL decreases rapidly to undetectable values and remains suppressed; CD4 cell count increases; and, the patient will have no more clinical (WHO stage 3 or 4 defining) symptoms. The patient can proceed to virological failure at any time: this represents a rebound in VL to a detectable value of >1000 copies/ml (or, at the early stages of treatment, the failure to suppress). Progression to immunological failure represents a CD4 cell count decline to a level that meets the WHO immunological criteria of treatment failure [
Panel A shows the progression of the patient’s treatment regimen and observed failure status. Within each compartment of panel A, the patient will proceed according to the underlying treatment progression shown in Panel B. The type of failure that can be detected depends on the monitoring strategy. After switching to second-line therapy, the patient will start either in the successful ART compartment (if he/she had no or concordant immunological/clinical failure) or in the clinical and/or immunological failure compartment (if he/she had a discordant failure of the corresponding type). See the main text for definitions of concordant and discordant failures.
Outcome | Source | Statistical model | Starting | Value (95% CI) | Dimension | Risk |
---|---|---|---|---|---|---|
(a) First-line ART; second-line ART with immediate switch | Cohorts | Parametric Weibull | 3 months from ART start/switch | 0.57 (0.52–0.63) | Shape | 3.4% fail by 1 year after ART start |
2.75 (2.29–3.31) | Scale (100 years) | |||||
(b)Resistance penalty | [ |
n/a | 0.05 (0.00–0.20) | Decrease in efficacy | n/a | |
(a) After virologic failure | Cohorts | Parametric exponential | Virological failure | 0.08 (0.06–0.10) | Rate (years-1) | 7.6% fail by 1 year after virologic failure |
(b) Independently of virologic failure | Cohorts | Parametric Weibull | 3 months from ART start | 0.22 (0.20–0.25) | Shape | 3.0% fail by 1 year after ART start |
5.46 (3.14–9.51) | Scale (106 years) | |||||
(a)Without virologic or immunologic failure | [ |
Parametric exponential | ART start | 0.004 | Rate (years-§) | 0.4% fail by 1 year after ART start |
(b) Extra hazard after immunologic failure | [ |
Cox regression | Immunologic failure | 3.3 | HR, constant over time | n/a |
(c) Extra hazard after virologic failure | [ |
Cox regression | Virologic failure | 2 | HR, constant over time | n/a |
(a) Observed mortality | Cohorts | No specific model (competing risk analysis) | ART start | n/a | 6.5% die by 1 year after ART start | |
(b) Observed LTFU | Cohorts | No specific model (competing risk analysis) | ART start | n/a | 4.0% lost by 1 year after ART start | |
(c) Mortality among LTFU | Analysis 4b, [ |
No specific model (theoretical calculation) | n/a | n/a | n/a | |
(d) HIV-related mortality | Analyses 4a-4c | Theoretical calculation, double Weibull |
ART start | 0.88 (0.88–0.90) | Shape 1 | 8.8% die by 1 year after ART start |
0.35 (0.32–0.39) | Scale 1 (years) | |||||
1.00 (1.00–1.00) | Shape 2 | |||||
64.60 (54.52–76.55) | Scale 2 (years) | |||||
0.08 (0.08–0.08) | Weight (1st component) | |||||
(a) Extra hazard after clinical failure | assumption | Cox regression | Clinical failure | 2 | HR, constant over time | n/a |
(b) Extra hazard after immunologic failure | Cohorts | Cox regression | Immunologic failure | 1.76 (1.16–2.68) | HR, constant over time | n/a |
(c) Extra hazard after virologic failure | Cohorts | Cox regression | Virologic failure | 1.26 (0.86–1.85) | HR, constant over time | n/a |
The hazard of virological failure (1a) is applied for first-line ART as such, and for second-line ART together with a resistance penalty factor (1b) which depends on the time spent on failing first-line ART. Immunological failure can happen through two independent hazard functions: the other is applied only to patients on virologically failing first- or second-line ART (2a), the other for all patients irrespective of the virological status or ART regimen (2b). For clinical failure, the hazard function (3a) is used as such for patients without virological and immunological failures, and the hazard ratios (3b, 3c) are applied for patients with the corresponding failures. HIV-related mortality is calculated from a competing risk analysis of observed mortality (4a) and loss to follow-up (4b) as well as the expected mortality among lost patients (4c). The parametric hazard function for mortality (4d) is used as such for patients without virological, immunological or clinical treatment failure, and the hazard ratios (4e, 4f, 4g) are applied to patients with the corresponding failures.
CI, confidence interval; ART, antiretroviral therapy; HR, hazard ratio; LTFU, loss to follow-up; n/a, not applicable
* Relative decrease in second-line efficacy per year spent on failing first-line ART
** Observed mortality and LTFU rates on successful first-line ART were calculated from the data and used, together with background mortality and expected mortality among patients LTFU, to calculate the corrected HIV-related mortality for the cohort
*** Weighted sum of two Weibull distributions
The failing virological, immunological and clinical stages will persist, unless the patient switches to a 2nd-line ART regimen. When the patient switches, the failing virological as well as concordant failing immunological and clinical stages will return to normal. Failing discordant immunological and clinical stages will remain failing after switching. During 2nd-line therapy, the patient is again at risk of proceeding to the failing virological, immunological or clinical stage. The parameterization was the same as for 1st-line therapy, except that the risk of proceeding to virological failure was scaled up with a resistance penalty factor, which depended on the time the patient spent on virologically failing 1st-line therapy.
Mortality consists of two components: HIV-free background mortality and HIV-related mortality. We assumed the risk of mortality increased for patients in the failing virological, immunological or clinical stage. Although we assumed that all patients are retained in care from ART initiation until death, we accounted for the expected high mortality among patients lost to follow-up when we estimated the HIV-related mortality rates [
We considered a total of 13 monitoring and switching strategies (
Strategy | Visits | CD4 tests | VL tests | Switching criteria |
---|---|---|---|---|
1.1 No 2nd-line ART | every 3 months |
no | no | no |
2.1 Clinical monitoring | every 3 months | no | no | WHO clinical criteria |
3.1 Irregular CD4 monitoring 6m | every 3 months |
every 6 months |
no | 2x WHO immunological criteria |
3.2 CD4 monitoring 24m | every 3 months |
every 24 months | no | 2x WHO immunological criteria |
3.3 CD4 monitoring 12m | every 3 months |
every 12 months | no | 2x WHO immunological criteria |
3.4 CD4 monitoring 6m | every 3 months |
every 6 months | no | 2x WHO immunological criteria |
3.5 CD4 6m + tVL monitoring | every 3 months |
every 6 months | POC after CD4 failure | WHO immunological criteria + 2x VL≥5000 copies/ml |
4.1 POC-VL monitoring 24m | every 3 months |
no | POC every 24 months | 2x VL≥5000 copies/ml |
4.2 POC-VL monitoring 12m | every 3 months |
no | POC every 12 months | 2x VL≥5000 copies/ml |
4.3 POC-VL monitoring 6m | every 3 months |
no | POC every 6 months | 2x VL≥5000 copies/ml |
5.1 Lab VL monitoring 24m | every 3 months |
every 24 months |
Lab every 24 months | 2x VL≥1000 copies/ml |
5.2 Lab VL monitoring 12m | every 3 months |
every 12 months |
Lab every 12 months | 2x VL≥1000 copies/ml |
5.3 Lab VL monitoring 6m | every 3 months |
every 6 months |
Lab every 6 months | 2x VL≥1000 copies/ml |
ART, antiretroviral therapy; VL, viral load; tVL, targeted viral load; m, monthly; POC, point-of-care; Lab, laboratory-based
POC VL tests are assumed to be qualitative with a detection limit of 5000 copies/ml; lab-VL tests are assumed to be fully quantitative
2x = second confirmatory measurement 3 months after first observation needed
* The information from these visits/tests is not used to decide about switching to second-line
** The probability of having a test is 50%
We modeled two scenarios. In
The model was constructed in three steps. In the first step, we simulated cohorts of 100,000 patients for all 13 monitoring and switching strategies, without baseline characteristics or background mortality. We implemented the model using ‘gems’, an R package for generalized multistate simulation models [
In the second step, these cohorts were updated to account for differences in background mortality. Thirty-two copies of each of the 20 cohorts were created, for the different sexes (male and female), baseline age groups (15–24, 25–34, 35–44 and 45–54 years), and four different scenarios of background mortality. In the first three scenarios, the background mortality rates represent the overall HIV-free mortality in the general populations of Malawi and Zimbabwe [
In the third step, we analyzed the outcomes of interest. The main outcomes were disability-adjusted life-years (DALY) lost to HIV, total cost, and cost-effectiveness ratios of the intervention compared to current practice as well as to the next less expensive strategy (incremental cost-effectiveness ratio, ICER). Definitions are given in
We developed an Excel spreadsheet tool to adapt the model outputs to specific scenarios. The Excel table contains all outputs of the simulation and presents the results according to the scenario defined by the user. The user can vary the following input variables continuously (
Input | Value for main analysis | Values for sensitivity analyses | Values included in the Excel tool |
---|---|---|---|
Visit | Not included | Not included | 0 to infinity |
CD4 test | US$ 5 | US$ 2 | 0 to infinity |
POC viral load test | US$ 10 | US$ 5, US$ 7, US$ 15 | 0 to infinity |
Laboratory viral load test | US$ 10 | US$ 5, US$ 7, US$ 15 | 0 to infinity |
1st-line ART per year | US$ 99 | US$ 55, US$ 128 | 0 to infinity |
2nd-line ART per year | US$ 280 | US$ 140, US$ 210, US$ 350 | 0 to infinity |
Asymptomatic HIV | 0.135 | - | 0 to 1 |
Symptomatic HIV | 0.369 | - | 0 to 1 |
Size of cohort | 1 | - | 1 to infinity |
Annual discounting rate | 3% | 0% | 0%, 1%, 2%, 3%, 4%, 5% |
Proportion of each age-gender group | 1% M15–24; 8% F15–24; 12% M25–34; 35% F25–34; 14% M35–45; 18% F35–44; 6% M45–54; 6% F45–54 | - | 0 to 100% in any group, summing to 100% |
HIV-unrelated mortality | ASSA2008 (Africans in Western Cape) | - | GBD Malawi; GBD Zimbabwe; ASSA2008 Africans in Western Cape; 75 years |
Virological failure rate in strategies without viral load monitoring (1.1–3.5) | Identical to strategies 4.1–5.3 (see |
Twice as high compared to strategies 4.1–5.3 |
Identical or twice as high compared to strategies 4.1–5.3 |
ART, antiretroviral therapy; US$, US dollar; M, male; F, female; GBD, Global Burden of Disease study; ASSA2008, ASSA2008 model
* Every simulated patient dies at the age of 75 if the effect of HIV is not accounted for
** This analysis is presented as the second main analysis, not a sensitivity analysis
We present the results in this manuscript for a set of input parameters (
The Excel tool is presented in
In the absence of 2nd-line and monitoring, the average lifetime cost of ART was US$1419 per patient (
Strategy | No 2nd-l. | Clinical | CD4 monitoring | POC-VL monitoring | Lab-VL monitoring | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Life-years | |||||||||||||
Healthy life-years left | 19.5 | 19.5 | 19.5 | 19.5 | 19.5 | 19.5 | 19.5 | 19.5 | 19.5 | 19.5 | 19.5 | 19.5 | 19.5 |
Life-years on 1st-line ART | 14.3 | 13.7 | 13.3 | 13.3 | 13.2 | 13.2 | 13.6 | 12.7 | 12.7 | 12.5 | 12.8 | 12.7 | 12.7 |
Life-years on 2nd-line ART | 0.0 | 0.8 | 1.3 | 1.2 | 1.3 | 1.3 | 0.9 | 1.8 | 1.9 | 2.0 | 1.8 | 1.9 | 1.9 |
Life-years without symptoms | 13.6 | 13.8 | 13.8 | 13.8 | 13.8 | 13.8 | 13.8 | 13.9 | 13.9 | 13.9 | 13.9 | 13.9 | 13.9 |
Life-years with symptoms | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 |
Life-years lost to HIV | 5.2 | 5.1 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 4.9 | 5.0 | 5.0 | 5.0 | 4.9 |
Disability-weighted life-years | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 |
Cost of 1st-line ART | 1419 | 1353 | 1313 | 1313 | 1306 | 1304 | 1347 | 1261 | 1254 | 1238 | 1263 | 1256 | 1255 |
Cost of 2nd-line ART | 0 | 211 | 351 | 341 | 363 | 365 | 259 | 507 | 534 | 563 | 499 | 521 | 530 |
Cost of diagnostic tests | 0 | 0 | 71 | 36 | 72 | 143 | 156 | 73 | 147 | 292 | 107 | 216 | 431 |
Please see
POC-VL, point-of-care viral load; lab-VL, laboratory-based viral load; ART, antiretroviral therapy; DALY, disability-adjusted life-year; CER, cost-effectiveness ratio; ICER, incremental cost-effectiveness ratio; l/e, least expensive and least effective strategy; w/d, weakly dominated; s/d, strongly dominated.
No 2nd-line (1.1), clinical monitoring (2.1), irregular CD4 monitoring every 6 months (3.1), CD4 monitoring every 6 months with targeted VL monitoring (3.5), and POC VL monitoring every 12 months (4.2) were on the cost-effectiveness frontier (
Panel A presents Scenario A (failure rate identical in all monitoring strategies). Panel B presents Scenario B (failure rate twice as high in strategies without compared to strategies with routine viral load monitoring). Cost and DALYs averted are presented per one patient for the duration of ART. Please see
When only 1st-line ART was available (1.1), the average lifetime cost of ART was US$1401 per patient (
Strategy | No 2nd-l. | Clinical | CD4 monitoring | POC-VL monitoring | Lab-VL monitoring | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Healthy life-years left | 19.5 | 19.5 | 19.5 | 19.5 | 19.5 | 19.5 | 19.5 | 19.5 | 19.5 | 19.5 | 19.5 | 19.5 | 19.5 |
Life-years on 1st-line ART | 14.2 | 13.3 | 12.5 | 12.5 | 12.4 | 12.4 | 12.8 | 12.7 | 12.7 | 12.5 | 12.8 | 12.7 | 12.7 |
Life-years on 2nd-line ART | 0.0 | 0.9 | 1.9 | 1.9 | 2.0 | 2.0 | 1.7 | 1.8 | 1.9 | 2.0 | 1.8 | 1.9 | 1.9 |
Life-years without symptoms | 13.3 | 13.5 | 13.6 | 13.6 | 13.7 | 13.7 | 13.7 | 13.9 | 13.9 | 13.9 | 13.9 | 13.9 | 13.9 |
Life-years with symptoms | 0.8 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 |
Life-years lost to HIV | 5.4 | 5.3 | 5.2 | 5.1 | 5.1 | 5.1 | 5.1 | 5.0 | 4.9 | 5.0 | 5.0 | 5.0 | 4.9 |
Disability-weighted life-years | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 |
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Cost of 1st-line ART | 1401 | 1320 | 1234 | 1237 | 1228 | 1228 | 1263 | 1261 | 1254 | 1238 | 1263 | 1256 | 1255 |
Cost of 2nd-line ART | 0 | 249 | 527 | 521 | 547 | 558 | 466 | 507 | 534 | 563 | 499 | 521 | 530 |
Cost of diagnostic tests | 0 | 0 | 70 | 36 | 72 | 143 | 156 | 73 | 147 | 292 | 107 | 216 | 431 |
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Please see
POC-VL, point-of-care viral load; lab-VL, laboratory-based viral load; ART, antiretroviral therapy; DALY, disability-adjusted life-year; CER, cost-effectiveness ratio; ICER, incremental cost-effectiveness ratio; l/e, least expensive and least effective strategy; w/d, weakly dominated; s/d, strongly dominated.
Except for the no 2nd-line strategy (1.1), only POC VL monitoring every 24 (4.1) and 12 months (4.2) were on the cost-effectiveness frontier (
The results of the sensitivity analyses are presented in detail in
Analysis | Varied input value | Cost-effectiveness ratio (US$/DALY averted) | |
---|---|---|---|
Scenario A | Scenario B | ||
Main | 2723 | 1180 | |
VL1 | Cost of VL test: US$7 | 2400 | 1037 |
VL2 | Cost of VL test: US$5 | 2184 | 942 |
VL3 | Cost of VL test: US$15 | 3262 | 1417 |
FL1 | Cost of 1st-line ART: US$55/year | 3045 | 1275 |
FL2 | Cost of 1st-line ART: US$128/year | 2511 | 1117 |
SL1 | Cost of 2nd-line ART: US$210/year | 2131 | 950 |
SL2 | Cost of 2nd-line ART: US$140/year | 1539 | 720 |
SL3 | Cost of 2nd-line ART: US$350/year | 3315 | 1409 |
DI1 | No discounting | 2015 | 892 |
All performed sensitivity analyses are listed in
We simulated cohorts of patients who initiated ART under 13 different ART monitoring and switching strategies. VL monitoring was slightly more effective than CD4 monitoring (in particular if we assumed that VL monitoring also reduces the risk of treatment failure). CD4 monitoring was more effective than clinical monitoring, and clinical monitoring was more effective than 1st-line ART only. However, differences in the effectiveness of any two strategies were all below 1 DALY per patient. We observed no clear difference between monitoring strategies that measured at different intervals, or between VL monitoring that used fully quantitative, highly specific and sensitive laboratory tests or those that used a qualitative POC test. The cost-effectiveness of POC VL monitoring clearly improved if we reduced the gap between prices of 1st- and 2nd-line ART (
Across our analyses, 12-month routine POC VL monitoring was on the cost-effectiveness frontier. The cost-effectiveness ratio of this strategy compared to clinical monitoring varied between US$700 and US$3300 per DALY averted. Cost-effectiveness improved if we assumed that VL monitoring reduces the risk of failure, and when the price of 2nd-line ART and 1st-line ART were close. The cost-effectiveness ratio of US$700 per DALY averted was reached if 2nd-line costs were reduced to minimum and we assumed that routine VL monitoring prevents failure. If we define a cost-effective intervention as having a cost-effectiveness ratio of less than 3 times the local per-capita gross domestic product, a cost-effectiveness ratio of US$700 per DALY averted can be considered cost-effective in any country [
The cost-effectiveness of targeted VL monitoring varied changed with the input parameters. We found that if the price of 2nd-line ART is considerably higher than for 1st-line ART, and if we assume no additional benefits for routine VL monitoring, it may be cost-effective to conduct routine CD4 monitoring with targeted VL testing. This strategy uses routine CD4 tests to detect patients who may be on a failing 1st-line regimen. Patients are then given VL tests to confirm their status. This strategy reduces the total costs by restricting use of 2nd-line ART to patients who need it most (those with low CD4 cell counts and a high risk of mortality), and by not switching patients with suppressed VLs. However, if we assume that routine VL monitoring can also reduce the risk of failure, for example, or if the cost difference between the regimens is small, routine VL monitoring is preferable.
Optimal monitoring strategy has been investigated in a number of mathematical modeling studies. Walensky
Our analysis has several limitations. First, we only included time on ART. Diagnostic tests, and, in particular, CD4 cell counts, are usually recommended for monitoring patients before ART is initiated. If assessment of ART eligibility continues to depend on CD4 cell counts, CD4 monitoring will continue to be important. However, there is a growing tendency towards simpler rules for ART initiation, such as “Option B+”, in which all pregnant and breastfeeding women start lifelong ART [
We did not include loss to follow-up (LTFU). High rates of LTFU are a serious problem in most ART programs in resource-limited settings [
We did not model onward transmission. VL monitoring reduces the time the patient spends on a failing regimen. VL monitoring can prevent about 30% of transmissions from patients on treatment [
We assumed that laboratory-based VL monitoring, together with CD4 tests, is 100% sensitive and specific to detect true treatment failure. Although this is a simplifying assumption, we wanted to include two distinct types of viral load test: one of these is as accurate as possible, and one has a lower sensitivity and specificity. The POC tests were assumed to be neither fully sensitive nor specific to detect true treatment failure. The users of the Excel tool can therefore choose either laboratory-based or POC viral load monitoring, depending on the diagnostic capacities of the viral load test they want to investigate. We also assumed CD4 tests are fully sensitive and specific. But since CD4 cell count poorly predicts true treatment failure, this assumption should not have much effect on the results.
The Excel tool allows users to vary input costs and several other input parameters. However, parameters related to disease progression cannot be changed in the Excel tool. Most key parameters for HIV progression are based on two large ART cohorts from the Cape Town area: Gugulethu and Khayelitsha. The results of the model reflect the characteristics of these routine ART programs, typical of southern Africa: the majority of patients are women, and most patients start ART with low CD4 cell counts and advanced clinical symptoms. We believe the results of our model can be generalized widely for ART programs in sub-Saharan Africa. However, there are also important differences between the cohorts we drew on, and others: the Cape Town cohorts had access to frequent laboratory measurements, and there is a continuous tendency to start ART earlier. Our model may not be able to catch all site-level characteristics of the different settings.
POC VL testing appears to be a promising alternative for routinely monitoring ART in resource-limited settings, especially if we assume that viral load monitoring can prevent treatment failure by improving adherence, and that the gap between prices of 1st- and 2nd-line ART can be decreased. Under these conditions, VL monitoring every 12 or 24 months, with an affordable qualitative test, does not considerably increase the costs above those of CD4 monitoring. POC VL testing may offer the same benefit as frequently monitoring VL with a quantitative test. Routine VL monitoring may also have benefits beyond more accurate detection of treatment failure; it may for example be able to prevent treatment failure by improved adherence. Targeted VL monitoring, based on routine CD4 monitoring, may be an option if these potential benefits are small, if 2nd-line ART remains substantially more expensive than 1st-line ART, and if it is expected that 2nd-line ART cannot be provided for everyone failing virologically. Special attention should be paid to the price of 2nd-line ART, which we expect will play a more substantial role than the price of the diagnostic tests. To make routine viral load monitoring cost-effective, efforts must be made to drop the costs of 2nd-line antiretroviral drugs. The optimal monitoring and switching strategy for each setting depends substantially on factors such as the unit costs. Our Excel tool allows researchers and policy-makers to vary important cost and population structure parameters and may be a valuable tool for developing local monitoring guidelines.
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We thank Cindy Zahnd for helpful comments and suggestions and Kali Tal for editing the manuscript.