title: Impact and cost-effectiveness of current and future tuberculosis diagnostics: the contribution of modelling.
abstract: 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.
authors.affiliation: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
authors.forename: D W
authors.initials: DW
authors.lastname: Dowdy
authors.affiliation: Department of Infectious Disease Epidemiology and TB Modelling Group, London School of Hygiene & Tropical Medicine, London, UK.
authors.forename: R
authors.initials: R
authors.lastname: Houben
authors.affiliation: Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA.
authors.forename: T
authors.initials: T
authors.lastname: Cohen
authors.affiliation: Department of Epidemiology and Biostatistics & McGill International TB Centre, McGill University, Montreal, Quebec, Canada.
authors.forename: M
authors.initials: M
authors.lastname: Pai
authors.affiliation: Department of Global Health and Amsterdam Institute for Global Health and Development, Academic Medical Center, Amsterdam, The Netherlands.
authors.forename: F
authors.initials: F
authors.lastname: Cobelens
authors.affiliation: SAME Modelling and Economics, Department of Global Health and Development, London School of Hygiene & Tropical Medicine, London, UK.
authors.forename: A
authors.initials: A
authors.lastname: Vassall
authors.affiliation: Center for Health Decision Science, Harvard School of Public Health, Boston, Massachusetts, USA.
authors.forename: N A
authors.initials: NA
authors.lastname: Menzies
authors.affiliation: Department of Global Health and Amsterdam Institute for Global Health and Development, Academic Medical Center, Amsterdam, The Netherlands.
authors.forename: G B
authors.initials: GB
authors.lastname: Gomez
authors.affiliation: Department of Clinical Sciences and Centre for Applied Health Research & Delivery, Liverpool School of Tropical Medicine, Liverpool, UK.
authors.forename: I
authors.initials: I
authors.lastname: Langley
authors.affiliation: Department of Clinical Sciences and Centre for Applied Health Research & Delivery, Liverpool School of Tropical Medicine, Liverpool, UK.
authors.forename: S B
authors.initials: SB
authors.lastname: Squire
authors.affiliation: Department of Infectious Disease Epidemiology and TB Modelling Group, London School of Hygiene & Tropical Medicine, London, UK.
authors.forename: R
authors.initials: R
authors.lastname: White
mesh_terms: D000995:Antitubercular Agents / Q000627:therapeutic use; D001431:Bacteriological Techniques / Q000191:economics* / Q000592:standards; D035843:Biomedical Research / Q000191:economics; D003362:Cost-Benefit Analysis; D017048:Health Care Costs; D006292:Health Priorities / Q000191:economics; D006801:Humans; D008826:Microbial Sensitivity Tests / Q000191:economics; D018803:Models, Economic; D017410:Practice Guidelines as Topic; D011237:Predictive Value of Tests; D011379:Prognosis; D014376:Tuberculosis / Q000175:diagnosis* / Q000188:drug therapy / Q000191:economics / Q000382:microbiology
chemical_list: D000995:Antitubercular Agents