Diagnostic Tools Initiative (DTI)

Prove It Studies | Virtual Implementation of New Diagnostic Tools | PIGRA and Childhood Tuberculosis

When TREAT TB began in 2008, the World Health Organization (WHO) had just recommended number of promising new diagnostic tools. However, much of the information presented to WHO guideline committees focused on the performance (accuracy) of new tools with limited information on laboratory infrastructure and needs. To be able to decide whether a tool is suited for a particular context and what infrastructure is needed for its implementation and scale-up, more complex research was needed because tools perform differently in different epidemiological settings and within various patient risk groups. Above all, it was clear that patient access to diagnosis and related costs significantly impact whether a new tool will truly be able to bring the anticipated change in outcomes.

Between 2009 and 2015, TREAT TB and its partners conducted several important studies that clarified the use of these new diagnostic tools in diverse, real-world settings including the PROVE IT studies, Virtual Implementation, and the Interferon-gamma release assays and childhood tuberculosis systematic review and meta-analysis (PIGRA) among others. Please see below for a description of these studies and links to the publications that emerged from this research.

PROVE IT Studies

Policy Relevant Outcomes from Validating Evidence on ImpacT

















Virtual Implementation of New Diagnostic Tools













Interferon-gamma Release Assays and Childhood Tuberculosis

Systematic Review and Meta-analysis (PIGRA)


Confirming the effectiveness and applicability of diagnostic tools used to diagnose multi-drug resistant tuberculosis (MDR-TB) in various settings was a top priority when the Diagnostic Tools Initiative began. Line Probe Assays (LPAs) were recommended by WHO for the rapid detection of drug-resistant TB. LPAs can provide results in as little as 1-3 days between sample collection and delivery of test results. The PROVE IT LPA study evaluated the performance of LPAs within different health systems through field evaluation studies conducted in Brazil, Russia, and South Africa. The goals of these studies was to identify the number of additional cases of drug-resistant TB discovered through the use of LPA technology as opposed to older techniques, to assess how diagnosis through the use of LPAs leads to patients being placed on appropriate treatment, to determine how LPAs affect the transmission of drug resistant TB, what types of patients benefit most from the introduction of LPA technology, and what laboratory measures are needed to optimize the use of LPAs in various settings. The preliminary results of these studies were presented at the 2013 World Conference on Lung Health and provided policy-makers with additional information on how to integrate LPA technology into the health systems within high-burden countries.

Research Partners:
Liverpool School of Tropical Medicine, Rede-TB (Brazil), Desmond Tutu TB Centre/Stellenbosch University (South Africa), Northern State Medical University (Russia)

PROVE IT South Africa – Published. Naidoo P, du Toit E, Dunbar R, Lombard C, Caldwell J, et al. (2014) A Comparison of Multidrug-Resistant Tuberculosis Treatment Commencement Times in MDRTBPlus Line Probe Assay and Xpert® MTB/RIF-Based Algorithms in a Routine Operational Setting in Cape Town. PLoS ONE 9(7): e103328

PROVE IT Brazil – Pending publication.

PROVE IT Russia – Pending publication.


Virtual implementation of new diagnostic is a comprehensive modeling approach that offers improved guidance to managers and policy‐makers in high‐burden countries regarding the packages of diagnostic tools most suitable for their epidemiological settings as well as the health system requirements associated with the adoption and implementation of these tools on a large scale. Using computer models that are representative of the behavior of real-life systems (e.g., patient pathways, laboratory infrastructure, sample flow, and the epidemiology of TB and HIV), virtual implementation is used to address the “what if?” questions regarding a new tool. This is carried out by predictive experimentation, i.e. by adding new tools in different steps of a pathway, for different groups of patients. These experiments were a way of performing operational research, projecting impacts and effectiveness of changes to the current practice in a virtual world without risk or disturbance to the real-life system.

In addition to publication in a peer-reviewed journal, the results of this activity were presented at the 2012 World Conference on Lung Health and other major global health events.

Research Partners:
Liverpool School of Tropical Medicine, National Taiwan University College of Public Health, Brigham & Women’s Hospital/Harvard School of Public Health

Published. Lin H-H, Langley I, Mwenda R, Doulla B, Egwaga S, Millington K, Mann G, Murray M, Squire SB, and Cohen T. A modelling framework to support the selection and implementation of new tuberculosis diagnostic tools. Int J Tuberc Lung Dis 2011; 15(8):996- 1004.

Children infected with Mycobacterium tuberculosis have significant risk of developing TB and can benefit from preventative therapy. To assess the value of interferon-gamma release assays (IGRAs) and the tuberculin skin test (TST) in the diagnosis of TB infection and disease in children, TREAT TB and its partners systematically analyzed 33 studies assessing commercial IGRAs. This assessment found that TST and IGRAs have similar accuracy for the detection of TB infection or the diagnosis of the disease in children and called for a rigorous, standardized approach to evaluate TB diagnostic tests in children.

Research Partners:
McGill University, Montreal Chest Institute, Case Western Reserve University, Desmond Tutu TB Centre/Stellenbosch University

Published. Mandalakas A, Detjen A, Hesseling A, Benedetti A and Menzies D. Interferon-gamma release assays and childhood tuberculosis: systematic review and meta-analysis. Int J Tuberc Lung Dis 2011; 15(8): 1018-1032.