TB disease has been known by various names for thousands of years, and has of late been described in exquisite biological detail. Yet we still struggle to reliably answer the question: Does a particular person have ‘active’ TB? The available diagnostic tests have several limitations and perform poorly especially in developing countries where they are most needed. We need new point of care diagnostic tests, be able to accurately distinguish between TB infection and TB disease and have tests which accurately predict cure.
The introduction and scale-up of new tools for the diagnosis of tuberculosis (TB) has the potential to make a huge difference to the lives of millions of people. To realise these benefits and make the best decisions, policy makers need answers to many difficult questions about which new tools to implement and where in the diagnostic algorithm to apply them cost effectively. Here we explore virtual implementation as a tool to predict the health system, patient, and community impacts of alternative diagnostics and diagnostic algorithms for TB, in order to facilitate context specific decisions on scale-up. Virtual implementation is an approach that can model the impacts of implementation of a new diagnostics by taking data from the context being considered alongside data from contexts where the new technology has been implemented (probably as a trial).
Initiation of antiretroviral treatment (ART) is guided by a CD4 count and the current WHO guidelines recommend a CD4 count of 350 cells/mm3 as the threshold. In resource poor settings, traditional flow cytometric CD4 counting facilities are not widely available due to high costs and the infrastructure required. In these cases they rely on clinical Read More