The value of point-of-care CD4+ and laboratory viral load in tailoring antiretroviral therapy monitoring strategies to resource limitations : AIDS

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The value of point-of-care CD4+ and laboratory viral load in tailoring antiretroviral therapy monitoring strategies to resource limitations

Hyle, Emily P.a,b; Jani, Ilesh V.c; Rosettie, Katherine L.a,d; Wood, Robine; Osher, Benjamina,d; Resch, Stephenf; Pei, Pamela P.a,d; Maggiore, Paolog; Freedberg, Kenneth A.a,b,d,h,f; Peter, Trevori; Parker, Robert A.a,j,h; Walensky, Rochelle P.a,b,d,h

Author Information
AIDS 31(15):p 2135-2145, September 24, 2017. | DOI: 10.1097/QAD.0000000000001586



For the millions worldwide on antiretroviral therapy (ART), the WHO recommends monitoring for ART failure [1]. ART failure results from poor ART adherence and/or virologic resistance, which can emerge in the setting of partially effective ART [2]. Three strategies are used to evaluate for ART failure: clinical, immunologic (CD4+), or virologic (viral load) monitoring. When access to laboratory testing is unavailable or unreliable, clinicians still depend on clinical monitoring alone for disease progression [3]. Immunologic monitoring with CD4+ tests is used in settings where laboratory services are available, but virologic testing is not. Virologic monitoring, used in all developed countries, is preferred given its high sensitivity and specificity to diagnose ART failure, but its use has been restricted in resource-limited settings because of lack of available infrastructure, equipment, technical expertise, and cost [1].

POC-CD4+ tests are now available and most often deployed in settings with insufficient access to laboratory infrastructure; they are in use in 30 countries throughout sub-Saharan Africa [4]. In Mozambique, such technology is already available in rural settings to determine ART eligibility for newly diagnosed people living with HIV (PLWH) [5] and has been shown to be cost-effective [6]. Given available POC-CD4+ in rural clinics, it is logical to consider extending its use for ART monitoring.

The value of POC-CD4+ is less clear in settings with access to laboratory services. POC-CD4+ could provide additional benefit in ART monitoring because it expedites clinical decision-making by reducing the turnaround time for test results and the number of lost tests [7]. However, in comparison to viral load, which is more accurate and increasingly available, POC-CD4+ might not be worth additional investment.

Using a modeling approach, we investigate whether POC-CD4+ for ART monitoring could improve clinical outcomes and be economically efficient in rural or urban settings compared with current standards of care in Mozambique and compared with viral load in urban settings.


Analytic overview

We use the Cost-Effectiveness of Preventing AIDS Complications-International model to examine the clinical impact, cost, and cost-effectiveness of monitoring for ART failure in PLWH in Mozambique. These monitoring strategies differ in terms of the performance characteristics of the tests used to detect ART failure (i.e. bias and random error), time delay from observed ART failure until clinical decision-making, test frequency, and costs.

We specifically consider the following implementation strategies in two different settings. In the rural setting, we assume no laboratory infrastructure exists, so a clinical ART monitoring strategy (CLIN) is the standard of care; we incrementally compare the addition of annual POC-CD4+ to CLIN, assuming a single platform Alere Pima (Waltham, Massachusetts, USA) POC-CD4+ technology is in place and available. We next examine an urban setting with established access to centralized laboratory services; here, biannual laboratory CD4+ (LAB-CD4+) is the standard of care, and annual viral load is the proposed goal [8]. We investigate the potential benefits of replacing LAB-CD4+ with either biannual POC-CD4+ or annual viral load. Because monitoring frequency differs by setting, a subscript indicates test frequency (e.g. POC-CD412 denotes monitoring every 12 months with POC-CD4+).

We project the clinical (life expectancy, time on failed ART) and economic outcomes [per person lifetime costs (2014US$)] for these strategies, from which we calculate incremental cost-effectiveness ratios (ICERs, Δ$/Δlife expectancy) with 3% annual discounting. We use the modified societal perspective, including all direct medical costs incurred by different funders but excluding indirect costs, such as lost economic productivity [9]. We consider monitoring strategies to be ‘cost-effective’ if their ICERs are less than or equal $620/YLS [10], the Mozambique 2014 annual per capita gross domestic product [11].

Model structure

The Cost-Effectiveness of Preventing AIDS Complications-International model is a previously published Monte Carlo simulation model of HIV disease and treatment [6,12]. Simulated patients draw from initial distributions of age, sex, CD4+, and viral load populated from clinical trials and cohort data representative of a Mozambique population initiating ART [13]. In the first month of simulation, a hypothetical cohort of ART-eligible PLWH enters HIV care to initiate ART. Patients can die from acute HIV-associated events, chronic HIV disease, or non-HIV-associated causes.

Clinical care

Simulated PLWH attend clinic and are prescribed ART, which can be effective [i.e. leading to virologic suppression (<50 copies/ml) and rising CD4+ cell counts] or not (i.e. detectable viral load and declining CD4+ cell counts). Patients can be lost to follow-up and subsequently return to care (Appendix; Table SDC1,

ART failure and monitoring

The model distinguishes between true and observed ART failure. ‘True’ ART failure occurs when a patient's viral load rises despite being prescribed ART. This modeled biologic truth is only clinically actionable if there is also ‘observed’ ART failure, in which a test and/or documented clinical event detects ART failure.

We define ART monitoring as any strategy used to detect observed ART failure [1]. CLIN detects ART failure if patients develop an opportunistic infection, usually because of CD4+ decline. Immunologic monitoring (i.e. LAB-CD4+ or POC-CD4+) detects true ART failure only after sufficient CD4+ decline following virologic rebound. Because CD4+ tests are subject to bias and random error, the observed CD4+ test result differs from the true in-vivo CD4+ cell count (Appendix; Table SDC2, [6]. Immunologic monitoring and CLIN, therefore, detect both false positives (i.e. observed ART failure without true ART failure) and false negatives (i.e. true ART failure that is not observed). Viral load provides the earliest and most accurate diagnosis after true ART failure because it directly detects the virus.

Clinical management of ART failure

Upon observed first-line ART failure, patients undergo adherence counseling with an opportunity for first-line ‘resuppression.’ If observed failure is detected again, patients switch to second-line ART. To account for real-life differences in test result availability and access to second-line ART, a strategy-specific time delay occurs between the procurement of the test sample diagnosing ART failure and clinical decision-making. Patients with observed failure on second-line ART despite another adherence intervention continue on second-line ART until death without additional monitoring.

Input parameters

Cohort characteristics and clinical care

Simulated patients have a median CD4+ cell count of 166 cells/μl (interquartile range 78–226 cells/μl) [13], and 79% achieve virologic suppression at 6 months of treatment (Table 1; Table SDC1, [14]. We incorporate loss to follow-up rates from sub-Saharan Africa (Appendix; Table SDC1,

Table 1:
Base case input parameters for an analysis of antiretroviral therapy monitoring in Mozambique.

Definition of observed antiretroviral therapy failure

Observed ART failure is not diagnosed during the first year of ART [8]. CLIN detects observed ART failure in patients who experience a WHO stage III or IV opportunistic infection. Immunologic and virologic monitoring detect observed ART failure according to Mozambique national guidelines; if opportunistic infections occur, patients are then tested by the strategy-specific tests to confirm ART failure (Table 1, [15–19]) [8].

Test characteristics and confirmatory tests

We derive bias and random error for both types of CD4+ test from the published literature (Table SDC2, [20]. We consider viral load to have no bias or random error (Table 1). If the first strategy-specific test meets criteria for observed ART failure, a second confirmatory test is performed the following month (CD4+) or 3 months later (viral load); observed ART failure is diagnosed only when both test results meet ART failure criteria [8].


CLIN adds no additional costs because the costs of detecting and treating opportunistic infections are included in HIV clinical care [16]. LAB-CD4+, POC-CD4+, and HIV RNA tests cost US$11, US$13, and US$20/test, respectively; we incorporate start-up costs for laboratory infrastructure and personnel training for HIV RNA tests as these are new technologies in Mozambique (Appendix; Table SDC3, [17,18]. All costs are from 2014 (Appendix).

Clinical management of antiretroviral therapy failure: adherence intervention

When CLIN detects ART failure, patients immediately receive an adherence intervention. In the other strategies, a confirmatory test is needed to finalize the ART failure diagnosis. When POC-CD4+ is used, adherence counseling occurs when the confirmatory test is performed; there is a delay with LAB-CD4+ or viral load because of transport, processing time, and the potential for lost samples/results (Table 1).

Switch to second-line antiretroviral therapy

The time delay before switching ART represents the time to receive test results, achieve centralized committee approval, and transport second-line ART to the clinic for dispensing [21]. Estimates from sub-Saharan Africa range from 5 to 20 months [19]. Because laboratory-based strategies require additional time for specimen transport, we included a 3 month longer time delay for LAB-CD4+ and viral load (14 months) than for CLIN and POC-CD4+ (11 months).

Performance characteristics of antiretroviral therapy monitoring strategies

We use model output to quantify the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of each ART monitoring strategy to detect ART failure. For instance, the sensitivity of each ART monitoring strategy is the number of patients correctly detected with observed ART failure (i.e. true positives) among all patients with true ART failure (Fig. SDC1,

Sensitivity analyses

We examine the impact of plausible ranges of key parameters in one and multiway deterministic sensitivity analyses. POC-CD4+ test costs are sensitive to the numbers of tests performed per machine. We evaluate the impact of POC-CD4+ test costs ranging from $9.72/test (20 tests/day) to $210/test (16 tests/year); these machine volumes are consistent with current use in Mozambique. Longer time delays can result from downtime of POC-CD4+ machines, stockouts of consumables, or when the number of daily tests exceeds the machine's capacity (i.e. >20 tests/daily). We perform two-way sensitivity analysis on POC-CD4+ test cost and time delay to investigate the impact of POC-CD4+ utilization (i.e. decreased per test costs because of more tests/day and increased time delay when the number of daily tests exceeds the machine's capacity). To examine the impact of improved transport to laboratory diagnostic hubs, we perform two-way sensitivity analysis on decreased time delay and increased cost for LAB-CD4+ and viral load. We also perform probabilistic sensitivity analysis to investigate the impact of uncertainty surrounding data estimates of the five monitoring-specific input parameters (Table SDC4,

Budget impact analysis

To investigate the affordability of ART monitoring strategies in Mozambique to its government and donors, we examine the costs associated with implementing POC-CD412 in a rural setting and POC-CD46 or viral load12 in an urban setting. In Mozambique, 668 100 people are diagnosed with HIV and on ART in 2015, of whom 1% are on second-line ART; we assume 20% live in a rural setting without access to laboratory services, and 80% live in a setting with laboratory services. We anticipate that 980 000 patients will initiate ART by 2025 to achieve 80% coverage as per President's Emergency Plan for AIDS Relief (PEPFAR) projections (Table SDC5, [22]. We include strategy-specific monitoring costs, as well as ART and routine care costs associated with guideline-concordant care in Mozambique [8]. We examine undiscounted costs over a 10-year time horizon.


Rural setting

Base case

The sensitivity to detect ART failure is 1.4% with CLIN, increasing to 34.6% with POC-CD412 (Table 2, top left). The PPV and NPV of CLIN are 70.7 and 69.5%, increasing to 93.8 and 83.5% for POC-CD412 (Fig. SDC1,

Table 2:
Base case results for an analysis of antiretroviral therapy monitoring in Mozambique.

For PLWH initiating ART, the undiscounted (discounted) life expectancy monitored with CLIN is 17.1 (11.5) years, which increases to 19.9 (12.8) years with POC-CD412 (Table 2, top right). Discounted lifetime costs are US$2360 for CLIN and increase to US$3000 with POC-CD412, resulting in a cost-effective ICER, US$480/YLS.

In CLIN, PLWH spend 10.5 years suppressed on first-line ART, which increases to 12.3 years with POC-CD412 (Fig. 1, top). PLWH spend 4.0 years taking failed first-line ART in CLIN, which is reduced by 1.1 years with POC-CD412. More patients (29.4%) are transitioned to second-line ART using POC-CD412 monitoring than with CLIN (12.0%).

Fig. 1:
Mean time spent on suppressed and failed ART.Mean per person years spent suppressed (dark blue) and failed (red) on first-line ART, and suppressed (light blue) and failed (orange) second-line ART for the rural setting (CLIN and POC-CD412, top) and urban setting (LAB-CD46, POC-CD46, and VL12, bottom). Strategies do not sum to total life expectancy since time spent lost to follow-up is not included. The subscript indicates test frequency (e.g. POC-CD412 denotes monitoring every 12 months with POC-CD4+). ART, antiretroviral therapy; CLIN, clinical ART monitoring strategy; LAB- CD4+ ART monitoring strategy; POC-CD4+ ART monitoring strategy.

One-way sensitivity analyses

POC-CD412 remains clinically preferred but no longer cost-effective if test bias is less than −30% (>6× base case) or random error is more than 28% (>1.4× base case). When operating at capacity (i.e. 20 tests/day at $9.72/test), POC-CD412 is cost-effective; only when POC-CD4+ costs exceed $27/test (i.e. 20 tests/month) is POC-CD412 no longer cost-effective. When time delays are prolonged prior to adherence intervention and/or ART switch to second line, the cost-effectiveness of POC-CD412 compared to CLIN is minimally affected (ICERs, $470–480/YLS). Without a confirmatory CD4+ test, the ICER of POC-CD412 rises compared with CLIN ($860/YLS). With more frequent testing, POC-CD4+ is less economically efficient (e.g. ICER, $750/YLS with POC-CD46).

Multiway and probabilistic sensitivity analysis

When we simultaneously vary POC-CD412 bias, random error, and cost, POC-CD412 remains cost-effective compared to CLIN for the POC-CD4+ random error and bias reported in all but one published study (Fig. 2). In sensitivity analysis regarding POC-CD4+ capacity, POC-CD412 is most cost-effective when used at maximum capacity (ICER, $440/YLS) but remains cost-effective even at modest capacity (i.e. 180 tests/year). When test volumes overwhelm machine capacity, POC-CD412 remains cost-effective compared with CLIN but with reduced clinical benefit. In probabilistic sensitivity analysis (PSA), POC-CD412 is cost-effective in 86.1% of simulations at a willingness to pay threshold of $620/YLS (Fig. SDC2A,

Fig. 2:
Heat maps of the ICER of POC-CD412 relative to CLIN.Heat maps of multiway sensitivity analysis in the rural setting display the ICER of POC-CD412 relative to CLIN. Each panel shows results using different costs for POC-CD4+ tests; POC-CD412 random error. POC-CD412 random error increases left to right along the horizontal axes, and POC-CD412 bias becomes more negative down the vertical axes. The POC-CD412 base case value (from a POC-CD4+ meta-analysis) is marked with an X. Other published estimates of POC-CD4+ test bias and random error are marked with other symbols. GDP, per capita gross domestic product; ICER, incremental cost-effectiveness ratio; LAB, laboratory; POC, point-of-care. Adapted with permission [20] , [23] , [24] , [25], and [26].

Urban setting

Base case

In the urban setting, the sensitivity to detect ART failure is: 23.7% (LAB-CD46), 24.9% (POC-CD46), and 89.0% (viral load12) (Table 2, bottom left). The PPV of each ART monitoring strategy is: 92.2% (LAB-CD46), 85.8% (POC-CD46), and 100.0% (viral load12). The NPV is: 83.9% (LAB-CD46), 84.3% (POC-CD46), and 98.4% (viral load12).

We project a life expectancy of 19.8 years for PLWH monitored with LAB-CD46 or POC-CD46, which increases to 20.4 years with viral load12 (Table 2, bottom right). Discounted life expectancies for LAB-CD46, POC-CD46, and viral load12 are 12.7, 12.8, and 13.0 years. Discounted per person lifetime costs increase from US$3120 (LAB-CD46) to US$3250 (viral load12) to US$3380 (POC-CD46). POC-CD46 confers fewer life years and higher costs compared with viral load12; viral load12 is cost-effective compared with LAB-CD46 (ICER, US$440/YLS).

In LAB-CD46, PLWH spend 11.7 years suppressed and 2.8 years failing on first-line ART, which decreases to 10.6 years and 2.5 years with POC-CD46, respectively (Fig. 1, bottom). PLWH monitored with viral load12 spend 13.6 years suppressed on first-line ART and only 1.7 years on failed first-line ART. Initiation of second-line ART varies from 32.1% (LAB-CD46) to 41.2% (POC-CD46) and is lowest when viral load12 is used for ART monitoring (30.9%).

One-way sensitivity analyses

POC-CD46 remains dominated (i.e. less effective, higher costs) by viral load12 across a wide range of parameters (Appendix). When LAB-CD4+ test random error is reduced or when monitoring is less frequent, clinical outcomes improve in LAB-CD4+ so that viral load12 is less cost-effective in comparison (ICERs, US$500–960/YLS). When more patients suppress with ART or resuppress after adherence interventions, viral load12 monitoring provides fewer clinical benefits in comparison with LAB-CD412. Among populations with higher CD4+ cell counts at ART initiation (≥350 cells/μl), the clinical benefits of viral load12 are greater and the ICER is lower ($370/YLS). Viral load12 is no longer cost-effective when viral load costs more than $24/test.

Multiway and probabilistic sensitivity analysis

We simultaneously vary the time delay for viral load12 and the probability of re-suppression for all ART monitoring strategies; we then compare viral load12 with LAB-CD46 because POC-CD46 is dominated. Increased viral load12 time delays reduces its clinical benefit (i.e. more months spent on failing ART); clinical benefits of viral load12 in comparison with LAB-CD46 wane as resuppression efficacy rises in both strategies (Fig. SDC3, Reducing transport time for laboratory-based strategies could improve clinical outcomes and be cost-effective; POC-CD46 is only preferred when operating at capacity (i.e. $9.72/test) and laboratory-based strategies are twice their current test costs (Table SDC7, In PSA, viral load12 is the preferred strategy 68.9% of the time at the willingness to pay threshold threshold of $620/YLS (Fig. SDC2b,

Budget impact analysis

Using model output, we project costs of $153.4 million/year for guideline-concordant HIV care in 2015; PEPFAR estimates costs of $159.7 million/year, comprising approximately 90% from international donors and ∼10% from the Mozambique Ministry of Health [22]. We estimate costs of guideline-concordant HIV care with CLIN are $433.7 million over 10 years; POC-CD412 would cost $60.1 million more (13.8% of CLIN budget). In settings with laboratory services, we project that guideline-concordant care with LAB-CD46 will cost $2.0 billion over 10 years; viral load12 would cost $151.7 million more (7.5% of LAB-CD46 budget). In both settings, improving ART monitoring decreases first-line ART costs but increases second-line ART costs and adds monitoring costs (Fig. 3).

Fig. 3:
Budget impact analysis over 10 years for rural and urban settings.Budget impact analysis over a 10-year time horizon for the rural (CLIN and POC-CD412) and urban (LAB-CD46, POC-CD46, and viral load12) settings. Cumulative costs (2014 US$, millions) are on the vertical axis. ART, antiretroviral therapy; CLIN, clinical ART monitoring strategy; LAB, laboratory; POC, point-of-care.


Using POC-CD4+ to monitor for ART failure could improve clinical outcomes and be cost-effective in settings without access to laboratory services. Where laboratory services are already available, however, viral load offers the greatest clinical benefits and is cost-effective compared with LAB-CD4+, given recently reduced costs for HIV RNA tests in Mozambique.

In rural settings without laboratory services, POC-CD4+ monitoring improves outcomes at good value. Over 10 years, POC-CD412 would cost an additional 13.8% of the CLIN budget, in return for adding 2.8 years of life (16.6% of CLIN life expectancy). With POC-CD4+, fewer PLWH are inappropriately moved to second-line ART when they are not failing first-line ART, and fewer PLWH truly failing first-line ART are maintained on it. POC-CD412 remains economically efficient even with less favorable operating characteristics or higher test costs, which can occur when POC technology is used by less well trained staff [23] or less frequently [27]. More frequent monitoring offers minimal additional clinical benefit; its lower PPV results in more patients incorrectly diagnosed with ART failure and unnecessarily started on more expensive second-line ART.

In settings with existing laboratory services, POC-CD4+ for ART monitoring is not beneficial, especially when opportunities exist for further investment in viral load. When compared with LAB-CD4+, the more expensive POC-CD4+ results in more false positive results and more unnecessary switches to costly second-line ART. Although POC-CD4+ allows clinicians to receive more rapid test results, the impact of expedited clinical decision-making regarding ART failure has less clinical benefit than might be anticipated. In contrast to newly diagnosed PLWH who frequently do not link to care if they do not learn the results of their CD4+ test [13,28], PLWH in care and on ART can be retested at the next clinical visit if laboratory-based test results have been lost, unless they become lost to follow-up.

ART monitoring provides value only if it improves clinical care. Our findings support other modeling studies, underscoring that investments in ART monitoring strategies can offer good value in very resource-limited settings, if opportunities are available to implement adherence interventions or second-line ART. With severely constrained budgets, however, expanding access to ART is a more efficient use of funds [16,29]. If ART suppression or resuppression rates are high (i.e. ART failure is less common), the value of more accurate but more costly monitoring strategies, such as viral load, is reduced [30]. In Mozambique, 19–24% of patients on first-line ART have evidence of virologic failure [31,32], similar to other settings in sub-Saharan Africa [33,34]; while scale-up of ART coverage continues, investment in longitudinal care is essential to maintain virologic suppression, gain long-term benefits of ART, and reduce transmissions and deaths.

Findings from our budget impact analysis highlight the cost tradeoffs with different ART monitoring strategies. At 10 years, we project viral load12 would add $72.5 million in monitoring costs but would save $37.9 million in costs of first-line ART prescribed to patients failing it. The $151.7 million total increase in costs is largely because of an increase of $119.8 million in second-line ART costs for patients failing first-line ART despite an adherence intervention. If second-line ART costs decline, viral load will become more affordable. With improved ART monitoring and access to suppressive second-line ART, these patients would no longer be left on failing first-line ART, which can contribute to the development of increased viral resistance and more HIV transmissions [2].

Our analysis includes several assumptions and limitations. We assume that the viral load strategy includes no CD4+ monitoring tests [35], which would increase costs and reduce its cost-effectiveness. Although our analysis does not formally include HIV transmissions or the development of resistance, we assess time on failed ART as a proxy for these outcomes. Increased time on failed ART, as occurs with CLIN or CD4+ compared to viral load, will result in more transmissions, more virologic resistance, and further increases the value of viral load compared to other strategies. We do not include additional start-up costs of POC-CD4+ in urban settings where they are not currently in use; however, sensitivity analyses on POC-CD4+ costs demonstrate the impact of a more costly POC-CD4+ test. If POC-CD4+ technology has greater throughput/capacity or is combined with additional tests relevant to HIV, then additional benefits or efficiencies could exist that our analysis would not capture. We did not include POC viral load in our analysis because it is not yet commercially available and its test characteristics and costs are not clearly described. Finally, we used the Mozambique per capita gross domestic product as a benchmark for cost-effectiveness and as a familiar reference point. Concerns have been raised about the use of ‘demand-side’ thresholds [36], although this benchmark is supported by theory and frequently cited [37]. Additionally, ‘supply-side’ cost-effectiveness thresholds derived from current health spending profiles are not readily available in resource-limited settings. Criteria for resource allocation decisions are further complicated in settings like Mozambique, where international donors finance more than 90% of the HIV program [38].

National ART programs provide services in a diversity of settings, some with better access to laboratory infrastructure than others [5]. In rural communities, which already have access to POC-CD4+, our results support using POC-CD4+ for ART monitoring with ongoing attention towards further scale-up of laboratory services, including viral load. In settings where laboratory services are already available, POC-CD4+ does not offer clinical or economic benefits compared with LAB-CD4+ for ART monitoring. Viral load will improve clinical outcomes and be cost-effective and is worth further investment.


Conceptualization: E.P.H., I.V.J., R.P.W.

Data curation: E.P.H., I.V.J., K.L.R., R.W., B.O., R.A.P., P.M.

Formal analysis: E.P.H., K.L.R., B.O., S.R., P.P.P., R.A.P., R.P.W.

Funding acquisition: E.P.H., K.A.F., R.P.W.

Investigation: E.P.H., I.V.J., K.L.R., B.O., T.P., R.P.W.

Methodology: E.P.H., S.R., P.P.P., K.A.F., R.A.P., R.P.W.

Project administration: E.P.H., K.L.R., B.O., R.P.W.

Resources: P.P.P., K.A.F., R.A.P., R.P.W.

Software: K.L.R., B.O., P.P.P., R.A.P.

Supervision: E.P.H., P.P.P., R.A.P., R.P.W.

Validation: E.P.H., K.L.R., B.O., S.R., P.P.P., R.A.P., R.P.W.

Visualization: E.P.H., B.O., R.P.W.

Writing original draft: E.P.H.

Writing reviewing and editing: E.P.H., I.V.J., K.L.R., R.W., B.O., S.R., P.P.P., P.M., K.A.F., T.P., R.A.P., R.P.W.

The work was supported by the National Institutes of Health (R01AI058736; R37AI093269; K01HL123349, P30AI069354), UNITAID (2017–01-UCPOC2b), and by the Steve and Deborah Gorlin MGH Research Scholars Award (R.P.W.). The content is solely the responsibility of the authors, and the study's findings and conclusions do not necessarily represent the official views of the NIH.

E.P.H. is a coauthor at

Conflicts of interest

There are no conflicts of interest.


1. World Health Organization. Consolidated guidelines on HIV prevention, diagnosis, treatment and care for key populations 2016. Available from: [Accessed 15 June 2017].
2. TenoRes Study Group. Global epidemiology of drug resistance after failure of WHO recommended first-line regimens for adult HIV-1 infection: a multicentre retrospective cohort study. Lancet Infect Dis 2016; 16:565–575.
3. Laurent C, Kouanfack C, Laborde-Balen G, Aghokeng AF, Mbougua JB, Boyer S, et al. Monitoring of HIV viral loads, CD4 cell counts, and clinical assessments versus clinical monitoring alone for antiretroviral therapy in rural district hospitals in Cameroon (Stratall ANRS 12110/ESTHER): a randomised noninferiority trial. Lancet Infect Dis 2011; 11:825–833.
4. Alere. Studies & Implementation 2017. Available from: [Accessed 14 June 2017].
5. UNAIDS. Resposta global à SIDA relatório do progresso, 2016 Moçambique United Nations; 2016. Available from: [Accessed 15 June 2017].
6. Hyle EP, Jani IV, Lehe J, Su AE, Wood R, Quevedo J, et al. The clinical and economic impact of point-of-care CD4 testing in Mozambique and other resource-limited settings: a cost-effectiveness analysis. PLoS Med 2014; 11:e1001725.
7. Drain PK, Hyle EP, Noubary F, Freedberg KA, Wilson D, Bishai WR, et al. Diagnostic point-of-care tests in resource-limited settings. Lancet Infect Dis 2014; 14:239–249.
8. Mozambique Ministry of Health. Guia de tratamento antiretroviral e infecções oportunistas no adulto, criança, adolescente e grávida 2016. Available from: [Accessed 14 June 2017].
9. Sanders GD, Neumann PJ, Basu A, Brock DW, Feeny D, Krahn M, et al. Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: second panel on cost-effectiveness in health and medicine. JAMA 2016; 316:1093–1103.
10. World Health Organization. Macroeconomics and health: Investing in health for economic development. Report of the Commission on Macroeconomics and Health Geneva: World Health Organization; 2001. Available from: Available: [Accessed 10 June 2017].
11. The World Bank. 2017. Available from: [Accessed 14 June 2017].
12. Walensky RP, Ross EL, Kumarasamy N, Wood R, Noubary F, Paltiel AD, et al. Cost-effectiveness of HIV treatment as prevention in serodiscordant couples. N Engl J Med 2013; 369:1715–1725.
13. Jani IV, Sitoe NE, Alfai ER, Chongo PL, Quevedo JI, Rocha BM, et al. Effect of point-of-care CD4 cell count tests on retention of patients and rates of antiretroviral therapy initiation in primary health clinics: an observational cohort study. Lancet 2011; 378:1572–1579.
14. Messou E, Chaix ML, Gabillard D, Minga A, Losina E, Yapo V, et al. Association between medication possession ratio, virologic failure and drug resistance in HIV-1-infected adults on antiretroviral therapy in Côte d’Ivoire. J Acquir Immune Defic Syndr 2011; 56:356–364.
15. Jobanputra K, Parker LA, Azih C, Okello V, Maphalala G, Kershberger B, et al. Factors associated with virological failure and suppression after enhanced adherence counselling, in children, adolescents and adults on antiretroviral therapy for HIV in Swaziland. PLoS One 2015; 10:e0116144.
16. Korenromp EL, Gobet B, Fazito E, Lara J, Bollinger L, Stover J. Impact and cost of the HIV/AIDS national strategic plan for Mozambique, 2015-2019: projections with the spectrum/goals model. PLoS One 2015; 10:e0142908.
17. Roche Molecular Diagnostics. Global Access Program 2016. Available from: [Accessed 15 June 2017].
18. Jani I, Sitoe N, Lehe J, Peter T. Cost comparison of point-of-care and laboratory CD4 testing in resource-limited settings [abstract]. 6th International AIDS Society Conference on HIV Pathogenesis and Treatment; 2011; Rome, Italy
19. Keiser O, Tweya H, Boulle A, Braitstein P, Schecter M, Brinkhof MW, et al. Switching to second-line antiretroviral therapy in resource-limited settings: comparison of programmes with and without viral load monitoring. AIDS 2009; 23:1867–1874.
20. Scott LE, Campbell J, Westerman L, Kestens L, Vojnov L, Kohastsu L, et al. A meta-analysis of the performance of the PimaTM CD4 for point of care testing. BMC Med 2015; 13:168.
21. Moon TD, Burlison JR, Sidat M, Pires P, Silva W, Solis M, et al. Lessons learned while implementing an HIV/AIDS care and treatment program in rural Mozambique. Retrovirology (Auckl) 2010; 3:1–14.
22. Mozambique Country Operational Plan (COP) 2016 Strategic Direction summary Washington, D.C.: PEPFAR; 2016. Available from: [Accessed 14 June 2017].
23. Glencross DK, Coetzee LM, Faal M, Masango M, Stevens WS, Venter WF, et al. Performance evaluation of the PimaTM point-of-care CD4 analyser using capillary blood sampling in field tests in South Africa. J Int AIDS Soc 2012; 15:3.
24. Diaw PA, Daneau G, Coly AA, Ndiaye BP, Wade D, Camara M, et al. Multisite evaluation of a point-of-care instrument for CD4(+) T-cell enumeration using venous and finger-prick blood: the PIMA CD4. J Acquir Immune Defic Syndr 2011; 58:e103–e111.
25. Jani IV, Sitoe NE, Chongo PL, Alfai ER, Quevedo JI, Tobaiwa O, et al. Accurate CD4 T-cell enumeration and antiretroviral drug toxicity monitoring in primary healthcare clinics using point-of-care testing. AIDS 2011; 25:807–812.
26. Mtapuri-Zinyowera S, Chideme M, Mangwanya D, Mugurungi O, Gudukeya S, Hatzold K, et al. Evaluation of the PIMA point-of-care CD4 analyzer in VCT clinics in Zimbabwe. J Acquir Immune Defic Syndr 2010; 55:1–7.
27. Larson B, Schnippel K, Ndibongo B, Long L, Fox MP, Rosen S. How to estimate the cost of point-of-care CD4 testing in program settings: an example using the Alere Pima Analyzer in South Africa. PLoS One 2012; 7:e35444.
28. Rosen S, Maskew M, Fox MP, Nyoni C, Mongwenyana C, Malete G, et al. Initiating antiretroviral therapy for HIV at a patient's first clinic visit: the RapIT Randomized Controlled Trial. PLoS Med 2016; 13:e1002015.
29. Keebler D, Revill P, Braithwaite S, Phillips A, Blaser N, Borquez A, et al. Cost-effectiveness of different strategies to monitor adults on antiretroviral treatment: a combined analysis of three mathematical models. Lancet Glob Health 2014; 2:e35–43.
30. Phillips A, Shroufi A, Vojnov L, Cohn J, Roberts T, Ellman T, et al. Working Group on Modelling of Antiretroviral Therapy Monitoring Strategies in Sub-Saharan Africa. Sustainable HIV treatment in Africa through viral-load-informed differentiated care. Nature 2015; 528:S68–S76.
31. Ruperez M, Pou C, Maculuve S, Cedeno S, Luis L, Rodriguez J, et al. Determinants of virological failure and antiretroviral drug resistance in Mozambique. J Antimicrob Chemother 2015; 70:2639–2647.
32. Tilghman M, Tsai D, Buene TP, Tomas M, Amade S, Gehlbach D, et al. Pooled nucleic acid testing to detect antiretroviral treatment failure in HIV-infected patients in Mozambique. J Acquir Immune Defic Syndr 2015; 70:256–261.
33. Dolling DI, Goodall RL, Chirara M, Hakim J, Nkurunziza P, Munderi P, et al. The virological durability of first-line ART among HIV-positive adult patients in resource limited settings without virological monitoring: a retrospective analysis of DART trial data. BMC Infect Dis 2017; 17:160.
34. Castelnuovo B, Kiragga A, Musaazi J, Sempa J, Mubiru F, Wanyama J, et al. Outcomes in a cohort of patients started on antiretroviral treatment and followed up for a decade in an urban clinic in Uganda. PLoS One 2015; 10:e0142722.
35. Ford N, Stinson K, Gale H, Mills EJ, Stevens W, Perez Gonzalez M, et al. CD4 changes among virologically suppressed patients on antiretroviral therapy: a systematic review and meta-analysis. J Int AIDS Soc 2015; 18:20061.
36. Woods B, Revill P, Sculpher M, Claxton K. Country-level cost-effectiveness thresholds: initial estimates and the need for further research. Value Health 2016; 19:929–935.
37. Robinson LA, Hammitt JK, Chang AY, Resch S. Understanding and improving the one and three times GDP per capita cost-effectiveness thresholds. Health Policy Plan 2016; 32:141–145.
38. Resch S, Ryckman T, Hecht R. Funding AIDS programmes in the era of shared responsibility: an analysis of domestic spending in 12 low-income and middle-income countries. Lancet Glob Health 2015; 3:e52–61.

sub-Saharan Africa; antiretroviral therapy; highly active/economics; HIV; point-of-care; viral load

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