title: Modeling of novel diagnostic strategies for active tuberculosis - a systematic review: current practices and recommendations.
abstract: BACKGROUND
  The field of diagnostics for active tuberculosis (TB) is rapidly developing. TB diagnostic modeling can help to inform policy makers and support complicated decisions on diagnostic strategy, with important budgetary implications. Demand for TB diagnostic modeling is likely to increase, and an evaluation of current practice is important. We aimed to systematically review all studies employing mathematical modeling to evaluate cost-effectiveness or epidemiological impact of novel diagnostic strategies for active TB.
  
  
  METHODS
  Pubmed, personal libraries and reference lists were searched to identify eligible papers. We extracted data on a wide variety of model structure, parameter choices, sensitivity analyses and study conclusions, which were discussed during a meeting of content experts.
  
  
  CONCLUSIONS
  From 5619 records a total of 36 papers were included in the analysis. Sixteen papers included population impact/transmission modeling, 5 were health systems models, and 24 included estimates of cost-effectiveness. Transmission and health systems models included specific structure to explore the importance of the diagnostic pathway (n = 4), key determinants of diagnostic delay (n = 5), operational context (n = 5), and the pre-diagnostic infectious period (n = 1). The majority of models implemented sensitivity analysis, although only 18 studies described multi-way sensitivity analysis of more than 2 parameters simultaneously. Among the models used to make cost-effectiveness estimates, most frequent diagnostic assays studied included Xpert MTB/RIF (n = 7), and alternative nucleic acid amplification tests (NAATs) (n = 4). Most (n = 16) of the cost-effectiveness models compared new assays to an existing baseline and generated an incremental cost-effectiveness ratio (ICER).
  
  
  CONCLUSIONS
  Although models have addressed a small number of important issues, many decisions regarding implementation of TB diagnostics are being made without the full benefits of insight from mathematical models. Further models are needed that address a wider array of diagnostic and epidemiological settings, that explore the inherent uncertainty of models and that include additional epidemiological data on transmission implications of false-negative diagnosis and the pre-diagnostic period.
authors.affiliation: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
authors.forename: Alice
authors.initials: A
authors.lastname: Zwerling
authors.affiliation: TB Modelling Group, TB Centre, London School of Hygiene and Tropical Medicine, London, United Kingdom.
authors.forename: Richard G
authors.initials: RG
authors.lastname: White
authors.affiliation: Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, United Kingdom.
authors.forename: Anna
authors.initials: A
authors.lastname: Vassall
authors.affiliation: Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America.
authors.forename: Ted
authors.initials: T
authors.lastname: Cohen
authors.affiliation: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
authors.forename: David W
authors.initials: DW
authors.lastname: Dowdy
authors.affiliation: TB Modelling Group, TB Centre, London School of Hygiene and Tropical Medicine, London, United Kingdom.
authors.forename: Rein M G J
authors.initials: RM
authors.lastname: Houben
mesh_terms: D003362:Cost-Benefit Analysis; D019937:Diagnostic Techniques and Procedures / Q000191:economics; D006287:Health Planning Guidelines; D006801:Humans; D008962:Models, Theoretical; D010818:Practice Patterns, Physicians'; D014397:Tuberculosis, Pulmonary / Q000175:diagnosis*; D035501:Uncertainty