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Lancet Infect Dis. 2018 Aug;18(8):e228-e238. doi: 10.1016/S1473-3099(18)30134-8. Epub 2018 Apr 10.

Progression from latent infection to active disease in dynamic tuberculosis transmission models: a systematic review of the validity of modelling assumptions.

Author information

1
Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, MA, USA; Center for Health Decision Science, Harvard TH Chan School of Public Health, Boston, MA, USA. Electronic address: nmenzies@hsph.harvard.edu.
2
Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, MA, USA.
3
Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA.
4
Division of TB Elimination, US Centers for Disease Control and Prevention, Atlanta, GA, USA.
5
MRC Centre for Outbreak Analysis and Modelling and NIHR Health Protection Research Unit in Modelling Methodology, Imperial College London, London, UK; Modelling and Economics Unit, National Infection Service, Public Health England, London, UK.
6
Institute for Global Health, University College London, London, UK.
7
Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, MA, USA; Center for Health Policy and Center for Primary Care and Outcomes Research, Stanford University, Stanford, CA, USA.

Erratum in

Abstract

Mathematical modelling is commonly used to evaluate infectious disease control policy and is influential in shaping policy and budgets. Mathematical models necessarily make assumptions about disease natural history and, if these assumptions are not valid, the results of these studies can be biased. We did a systematic review of published tuberculosis transmission models to assess the validity of assumptions about progression to active disease after initial infection (PROSPERO ID CRD42016030009). We searched PubMed, Web of Science, Embase, Biosis, and Cochrane Library, and included studies from the earliest available date (Jan 1, 1962) to Aug 31, 2017. We identified 312 studies that met inclusion criteria. Predicted tuberculosis incidence varied widely across studies for each risk factor investigated. For population groups with no individual risk factors, annual incidence varied by several orders of magnitude, and 20-year cumulative incidence ranged from close to 0% to 100%. A substantial proportion of modelled results were inconsistent with empirical evidence: for 10-year cumulative incidence, 40% of modelled results were more than double or less than half the empirical estimates. These results demonstrate substantial disagreement between modelling studies on a central feature of tuberculosis natural history. Greater attention to reproducing known features of epidemiology would strengthen future tuberculosis modelling studies, and readers of modelling studies are recommended to assess how well those studies demonstrate their validity.

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