The predicted impact of tuberculosis preventive therapy: the importance of disease progression assumptions

Abstract

Background: Following infection with Mycobacterium tuberculosis (M.tb), individuals may rapidly develop tuberculosis (TB) disease or enter a "latent" infection state with a low risk of progression to disease. Mathematical models use a variety of structures and parameterisations to represent this process. The effect of these different assumptions on the predicted impact of TB interventions has not been assessed.

Methods: We explored how the assumptions made about progression from infection to disease affect the predicted impact of TB preventive therapy. We compared the predictions using three commonly used model structures, and parameters derived from two different data sources.

Results: The predicted impact of preventive therapy depended on both the model structure and parameterisation. At a baseline annual TB incidence of 500/100,000, there was a greater than 2.5-fold difference in the predicted reduction in incidence due to preventive therapy (ranging from 6 to 16%), and the number needed to treat to avert one TB case varied between 67 and 157. The relative importance of structure and parameters depended on baseline TB incidence and assumptions about the efficacy of preventive therapy, with the choice of structure becoming more important at higher incidence.

Conclusions: The assumptions use to represent progression to disease in models are likely to influence the predicted impact of preventive therapy and other TB interventions. Modelling estimates of TB preventive therapy should consider routinely incorporating structural uncertainty, particularly in higher burden settings. Not doing so may lead to inaccurate and over confident conclusions, and sub-optimal evidence for decision making.

Keywords: Modelling; Structure; Tuberculosis; Uncertainty.

Fig. 1

Schematic of model structures. S = susceptible; L_F = “fast” latent state; L_S = “slow” latent state; I = TB disease; P_F = post preventive therapy (from “fast” latent state); P_S = post preventive therapy (from “slow” latent state). Red lines and boxes show the preventive therapy components of the model. Definitions of model parameters are given in Table 1

Fig. 2

Results of simulating 10 years of preventive therapy as a function of steady state TB incidence. Left: Percentage reduction in TB incidence from steady state equilibrium. Right: average number needed to treat with preventive therapy to avert one case of TB. Colours indicate the different models. Line types indicate the different sources of parameter estimates. Shaded areas illustrate the range of predictions for each model across parameter sets

Fig. 3

Results of simulating 10 years of preventive therapy as a function of steady state TB incidence for different efficacy of preventive therapy. Colours indicate the different models. Line types indicate the different sources of parameter estimates. Shaded areas illustrate the range of predictions for each model across parameter sets