Journal Article > ResearchFull Text
Nature. 2015 December 1; Volume 528 (Issue 7580); S68-S76.; DOI:10.1038/nature16046
Phillips AN, Shroufi A, Vojnov L, Cohn J, Roberts TR, et al.
Nature. 2015 December 1; Volume 528 (Issue 7580); S68-S76.; DOI:10.1038/nature16046
There are inefficiencies in current approaches to monitoring patients on antiretroviral therapy in sub-Saharan Africa. Patients typically attend clinics every 1 to 3 months for clinical assessment. The clinic costs are comparable with the costs of the drugs themselves and CD4 counts are measured every 6 months, but patients are rarely switched to second-line therapies. To ensure sustainability of treatment programmes, a transition to more cost-effective delivery of antiretroviral therapy is needed. In contrast to the CD4 count, measurement of the level of HIV RNA in plasma (the viral load) provides a direct measure of the current treatment effect. Viral-load-informed differentiated care is a means of tailoring care so that those with suppressed viral load visit the clinic less frequently and attention is focussed on those with unsuppressed viral load to promote adherence and timely switching to a second-line regimen. The most feasible approach to measuring viral load in many countries is to collect dried blood spot samples for testing in regional laboratories; however, there have been concerns over the sensitivity and specificity of this approach to define treatment failure and the delay in returning results to the clinic. We use modelling to synthesize evidence and evaluate the cost-effectiveness of viral-load-informed differentiated care, accounting for limitations of dried blood sample testing. We find that viral-load-informed differentiated care using dried blood sample testing is cost-effective and is a recommended strategy for patient monitoring, although further empirical evidence as the approach is rolled out would be of value. We also explore the potential benefits of point-of-care viral load tests that may become available in the future.
Journal Article > CommentaryFull Text
Lancet Infect Dis. 2016 October 20; Volume 17 (Issue 1); e26-e29.; DOI:10.1016/S1473-3099(16)30212-2
Peter T, Ellenberger D, Kim AA, Boeras D, Messele T, et al.
Lancet Infect Dis. 2016 October 20; Volume 17 (Issue 1); e26-e29.; DOI:10.1016/S1473-3099(16)30212-2
Scaling up access to HIV viral load testing for individuals undergoing antiretroviral therapy in low-resource settings is a global health priority, as emphasised by research showing the benefits of suppressed viral load for the individual and the whole population. Historically, large-scale diagnostic test implementation has been slow and incomplete because of service delivery and other challenges. Building on lessons from the past, in this Personal View we propose a new framework to accelerate viral load scale-up and ensure equitable access to this essential test. The framework includes the following steps: (1) ensuring adequate financial investment in scaling up this test; (2) achieving pricing agreements and consolidating procurement to lower prices of the test; (3) strengthening functional tiered laboratory networks and systems to expand access to reliable, high-quality testing across countries; (4) strengthening national leadership, with prioritisation of laboratory services; and (5) demand creation and uptake of test results by clinicians, nurses, and patients, which will be vital in ensuring viral load tests are appropriately used to improve the quality of care. The use of dried blood spots to stabilise and ship samples from clinics to laboratories, and the use of point-of-care diagnostic tests, will also be important for ensuring access, especially in settings with reduced laboratory capacity. For countries that have just started to scale up viral load testing, lessons can be learnt from countries such as Botswana, Brazil, South Africa, and Thailand, which have already established viral load programmes. This framework might be useful for guiding the implementation of viral load with the aim of achieving the new global HIV 90-90-90 goals by 2020.
Journal Article > ResearchFull Text
BMJ Glob Health. 2020 February 28; Volume 5 (Issue 2); e002067.; DOI:10.1136/bmjgh-2019-002067
Pelle KG, Rambaud-Althaus C, d’Acremont V, Moran G, Sampath R, et al.
BMJ Glob Health. 2020 February 28; Volume 5 (Issue 2); e002067.; DOI:10.1136/bmjgh-2019-002067
Health workers in low-resource settings often lack the support and tools to follow evidence-based clinical recommendations for diagnosing, treating and managing sick patients. Digital technologies, by combining patient health information and point-of-care diagnostics with evidence-based clinical protocols, can help improve the quality of care and the rational use of resources, and save patient lives. A growing number of electronic clinical decision support algorithms (CDSAs) on mobile devices are being developed and piloted without evidence of safety or impact. Here, we present a target product profile (TPP) for CDSAs aimed at guiding preventive or curative consultations in low-resource settings. This document will help align developer and implementer processes and product specifications with the needs of end users, in terms of quality, safety, performance and operational functionality. To identify the characteristics of CDSAs, a multidisciplinary group of experts (academia, industry and policy makers) with expertise in diagnostic and CDSA development and implementation in low-income and middle-income countries were convened to discuss a draft TPP. The TPP was finalised through a Delphi process to facilitate consensus building. An agreement greater than 75% was reached for all 40 TPP characteristics. In general, experts were in overwhelming agreement that, given that CDSAs provide patient management recommendations, the underlying clinical algorithms should be human-interpretable and evidence-based. Whenever possible, the algorithm’s patient management output should take into account pretest disease probabilities and likelihood ratios of clinical and diagnostic predictors. In addition, validation processes should at a minimum show that CDSAs are implementing faithfully the evidence they are based on, and ideally the impact on patient health outcomes. In terms of operational needs, CDSAs should be designed to fit within clinic workflows and function in connectivity-challenged and high-volume settings. Data collected through the tool should conform to local patient privacy regulations and international data standards.