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Int J Tuberc Lung Dis. 2014 Sep;18(9):1012-8. doi: 10.5588/ijtld.13.0851.

Impact and cost-effectiveness of current and future tuberculosis diagnostics: the contribution of modelling.

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Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Department of Infectious Disease Epidemiology and TB Modelling Group, London School of Hygiene & Tropical Medicine, London, UK.
Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA.
Department of Epidemiology and Biostatistics & McGill International TB Centre, McGill University, Montreal, Quebec, Canada.
Department of Global Health and Amsterdam Institute for Global Health and Development, Academic Medical Center, Amsterdam, The Netherlands.
SAME Modelling and Economics, Department of Global Health and Development, London School of Hygiene & Tropical Medicine, London, UK.
Center for Health Decision Science, Harvard School of Public Health, Boston, Massachusetts, USA.
Department of Clinical Sciences and Centre for Applied Health Research & Delivery, Liverpool School of Tropical Medicine, Liverpool, UK.


The landscape of diagnostic testing for tuberculosis (TB) is changing rapidly, and stakeholders need urgent guidance on how to develop, deploy and optimize TB diagnostics in a way that maximizes impact and makes best use of available resources. When decisions must be made with only incomplete or preliminary data available, modelling is a useful tool for providing such guidance. Following a meeting of modelers and other key stakeholders organized by the TB Modelling and Analysis Consortium, we propose a conceptual framework for positioning models of TB diagnostics. We use that framework to describe modelling priorities in four key areas: Xpert(®) MTB/RIF scale-up, target product profiles for novel assays, drug susceptibility testing to support new drug regimens, and the improvement of future TB diagnostic models. If we are to maximize the impact and cost-effectiveness of TB diagnostics, these modelling priorities should figure prominently as targets for future research.

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