Differentiated Human Immunodeficiency Virus RNA Monitoring in Resource-Limited Settings: An Economic Analysis

Making Smarter Decisions Faster: Systems Engineering to Improve the Global Public Health Response to HIV

PURPOSE OF REVIEW

This review offers an operational definition of systems engineering (SE) as applied to public health, reviews applications of SE in the field of HIV, and identifies opportunities and challenges of broader application of SE in global health.

RECENT FINDINGS

SE involves the deliberate sequencing of three steps: diagnosing a problem, evaluating options using modeling or optimization, and providing actionable recommendations. SE includes diverse tools (from process improvement to mathematical modeling) applied to decisions at various levels (from local staffing decisions to planning national-level roll-out of new interventions). Contextual factors are crucial to effective decision-making, but there are gaps in understanding global decision-making processes. Integrating SE into pre-service training and translating SE tools to be more accessible could increase utilization of SE approaches in global health. SE is a promising, but under-recognized approach to improve public health response to HIV globally.

The role of HIV viral load in mathematical models of HIV transmission and treatment: a review

Introduction

HIV viral load (VL) is accepted as a key biomarker in HIV transmission and pathogenesis. This paper presents a review of the role of VL testing in mathematical models for HIV prevention and treatment.

Methods

A search for simulation models of HIV was conducted in PubMed, yielding a total of 1210 studies. Publications before the year 2000, studies involving animals and analyses that did not use mathematical simulations were excluded. The full text of eligible articles was sourced and information about the intervention and population being modelled, type of modelling approach and disease monitoring strategy was extracted.

Results and discussion

A total of 279 studies related to HIV simulation models were included in the review, though only 17 (6%) included consideration of VL or VL testing and were evaluated in detail. Within the studies that included assessment of VL, routine monitoring was the focus, and usually in comparison to alternate monitoring strategies such as clinical or CD4 count-based monitoring. The majority of remaining models focus on the impact or delivery of antiretroviral therapy (n=68; 27%), pre-exposure prophylaxis (n=28; 11%) and/or HIV testing (n=24; 9%) on population estimates of HIV epidemiology and exclude consideration of VL. Few studies investigate or compare alternate VL monitoring frequencies, and only a small number of studies overall (3%) include consideration of vulnerable population groups such as pregnant women or infants.

Conclusions

There are very few simulations of HIV treatment or prevention that include VL measures, despite VL being recognised as the key determinant of both transmission and treatment outcomes. With growing emphasis on VL monitoring as key tool for population-level HIV control, there is a clear need for simulations of HIV epidemiology based on VL.

Point-of-Care HIV Viral Load Testing: an Essential Tool for a Sustainable Global HIV/AIDS Response

The global public health community has set ambitious treatment targets to end the HIV/AIDS pandemic. With the notable absence of a cure, the goal of HIV treatment is to achieve sustained suppression of an HIV viral load, which allows for immunological recovery and reduces the risk of onward HIV transmission. Monitoring HIV viral load in people living with HIV is therefore central to maintaining effective individual antiretroviral therapy as well as monitoring progress toward achieving population targets for viral suppression. The capacity for laboratory-based HIV viral load testing has increased rapidly in low- and middle-income countries, but implementation of universal viral load monitoring is still hindered by several barriers and delays. New devices for point-of-care HIV viral load testing may be used near patients to improve HIV management by reducing the turnaround time for clinical test results. The implementation of near-patient testing using these new and emerging technologies may be an essential tool for ensuring a sustainable response that will ultimately enable an end to the HIV/AIDS pandemic. In this report, we review the current and emerging technology, the evidence for decentralized viral load monitoring by non-laboratory health care workers, and the additional considerations for expanding point-of-care HIV viral load testing.

Optimal timing of viral load monitoring during pregnancy to predict viraemia at delivery in HIV-infected women initiating ART in South Africa: a simulation study

Introduction

HIV viral load (VL) monitoring is a central tool to evaluate ART effectiveness and transmission risk. There is a global movement to expand VL monitoring following recent recommendations from the World Health Organization (WHO), but there has been little research into VL monitoring in pregnant women. We investigated one important question in this area: when and how frequently VL should be monitored in women initiating ART during pregnancy to predict VL at the time of delivery in a simulated South African population.

Methods

We developed a mathematical model simulating VL from conception through delivery using VL data from the Maternal and Child Health – Antiretroviral Therapy (MCH‐ART) cohort. VL was modelled based on three major compartments: pre‐ART VL, viral decay immediately after ART initiation and viral maintenance (including viral suppression and viraemic episodes). Using this simulation, we examined the performance of various VL monitoring schema in predicting elevated VL at delivery.

Results and discussion

If WHO guidelines for non‐pregnant adults were used, the majority of HIV‐infected pregnant women (69%) would not receive a VL test during pregnancy. Most models that based VL monitoring in pregnancy on the time elapsed since ART initiation (regardless of gestation) performed poorly (sensitivity <50%); models that based VL measures in pregnancy on the woman's gestation (regardless of time on ART) appeared to perform better overall (sensitivity >60%). Across all permutations, inclusion of pre‐ART VL values had a negligible impact on predictive performance (improving test sensitivity and specificity <6%). Performance of VL monitoring in predicting VL at delivery generally improved at later gestations, with the best performing option a single VL measure at 36 weeks’ gestation.

Conclusions

Development and evaluation of a novel simulation model suggests that strategies to measure VL relative to gestational age may be more useful than strategies relative to duration on ART, in women initiating ART during pregnancy, supporting better integration of maternal and HIV health services. Testing turnaround times require careful consideration, and point‐of‐care VL testing may be the best approach for measuring VL at delivery. Broadening the scope of this simulation model in the light of current scale up of VL monitoring in high burden countries is important.

Cost-effectiveness of routine viral load monitoring in low- and middle-income countries: a systematic review

INTRODUCTION

Routine viral load monitoring for HIV-1 management of persons on antiretroviral therapy (ART) has been recommended by the World Health Organization (WHO) to identify treatment failure. However, viral load testing represents a substantial cost in resource constrained health care systems. The central challenge is whether and how viral load monitoring may be delivered such that it maximizes health gains across the population for the costs incurred. We hypothesized that key features of program design and delivery costs drive the cost-effectiveness of viral load monitoring within programs.

METHODS

We conducted a systematic review of studies on the cost-effectiveness of viral load monitoring in low- and middle-income countries (LMICs). We followed the Cochrane Collaboration guidelines and the PRISMA reporting guidelines.

RESULTS AND DISCUSSION

We identified 18 studies that evaluated the cost-effectiveness of viral load monitoring in HIV treatment programs. Overall, we identified three key factors that make it more likely for viral load monitoring to be cost-effective: 1) Use of effective, lower cost approaches to viral load monitoring (e.g. use of dried blood spots); 2) Ensuring the pathway to health improvement is established and that viral load results are acted upon; and 3) Viral load results are used to simplify HIV care in patients with viral suppression (i.e. differentiated care, with fewer clinic visits and longer prescriptions). Within the context of differentiated care, viral load monitoring has the potential to double the health gains and be cost saving compared to the current standard (CD4 monitoring).

CONCLUSIONS

The cost-effectiveness of viral load monitoring critically depends on how it is delivered and the program context. Viral load monitoring as part of differentiated HIV care is likely to be cost-effective. Viral load monitoring in differentiated care programs provides evidence that reduced clinical engagement, where appropriate, is not impacting health outcomes. Introducing viral load monitoring without differentiated care is unlikely to be cost-effective in most settings and results in lost opportunity for health gains through alternative uses of limited resources. As countries scale up differentiated care programs, data on viral suppression outcomes and costs should be collected to evaluate the on-going cost-effectiveness of viral load monitoring as utilized in practice.