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

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.

Modelling the impact of tuberculosis preventive therapy: the importance of disease progression assumptions

“…Both Menzies et al [6] and Ragonnet et al [5] showed that models 1 and 2 gave equally good fit to the available data on the cumulative incidence of TB following infection and it was not possible to distinguish a "best" model. Both studies also found model 3 gave a significantly worse fit, but as this structure is employed in a large proportion of published modelling studies (approximately 50% based on the literature review in [6]) we included it in our analysis. Figure 1 shows the different model structures incorporated into a simple dynamic transmission model of TB.…”

“…We compare 3 model structures for progression from infection to disease that are commonly used in TB modelling studies. The systematic literature review reported in Menzies et al [6] found that these 3 structures accounted for approximately 70% of all published TB models. Both Menzies et al [6] and Ragonnet et al [5] showed that models 1 and 2 gave equally good fit to the available data on the cumulative incidence of TB following infection and it was not possible to distinguish a "best" model.…”

“…The systematic literature review reported in Menzies et al [6] found that these 3 structures accounted for approximately 70% of all published TB models. Both Menzies et al [6] and Ragonnet et al [5] showed that models 1 and 2 gave equally good fit to the available data on the cumulative incidence of TB following infection and it was not possible to distinguish a "best" model. Both studies also found model 3 gave a significantly worse fit, but as this structure is employed in a large proportion of published modelling studies (approximately 50% based on the literature review in [6]) we included it in our analysis.…”

“…The analysis in Ragonnet et al [5] and Menzies et al [6] compared the various model structures to different data sets and therefore identified different best-fitting parameters. We compare the impact of using these 2 different parameter sources on the model predictions of the impact of preventive therapy.…”

“…Two recent papers [5,6] have compared different model structures to describe the progression from infection to disease and assessed their ability to reproduce observations of the time from M.tb infection to TB disease onset. These analyses suggested that certain model structures capture these dynamics better than others, and that the parameters and structures used in many published modelling studies do not accurately reproduce the temporal pattern of disease following exposure to M.tb observed in many settings.…”