Quantifying progression and regression across the spectrum of pulmonary tuberculosis: a data synthesis study

Abstract

Background: Prevalence surveys show a substantial burden of subclinical (asymptomatic but infectious) tuberculosis, from which individuals can progress, regress, or even persist in a chronic disease state. We aimed to quantify these pathways across the spectrum of tuberculosis disease.

Methods: We created a deterministic framework of untreated tuberculosis disease with progression and regression between three states of pulmonary tuberculosis disease: minimal (non-infectious), subclinical (asymptomatic but infectious), and clinical (symptomatic and infectious). We obtained data from a previous systematic review of prospective and retrospective studies that followed and recorded the disease state of individuals with tuberculosis in a cohort without treatment. These data were considered in a Bayesian framework, enabling quantitative estimation of tuberculosis disease pathways with rates of transition between states and 95% uncertainty intervals (UIs).

Findings: We included 22 studies with data from 5942 individuals in our analysis. Our model showed that after 5 years, 40% (95% UI 31·3-48·0) of individuals with prevalent subclinical disease at baseline recover and 18% (13·3-24·0) die from tuberculosis, with 14% (9·9-19·2) still having infectious disease, and the remainder with minimal disease at risk of re-progression. Over 5 years, 50% (40·0-59·1) of individuals with subclinical disease at baseline never develop symptoms. For those with clinical disease at baseline, 46% (38·3-52·2) die and 20% (15·2-25·8) recover from tuberculosis, with the remainder being in or transitioning between the three disease states after 5 years. We estimated the 10-year mortality of people with untreated prevalent infectious tuberculosis to be 37% (30·5-45·4).

Interpretation: For people with subclinical tuberculosis, classic clinical disease is neither an inevitable nor an irreversible outcome. As such, reliance on symptom-based screening means a large proportion of people with infectious disease might never be detected.

Funding: TB Modelling and Analysis Consortium and European Research Council.

Figure 1

Model structure and results of the fitting process compared with the data (A) Model structure with solid lines representing parameters fitted to the data collated by the systematic review and dotted lines representing parameters fitted during the calibration process without data from the review. (B–E) The middle column is a visual description of the transition being fitted on each row, where M=minimal, S=subclinical, and C=clinical. The graphs in the two left hand columns are the fits for the cumulative data, and those in the two right hand columns are the fits for the single follow-up data. Rows show progression and regression between minimal and subclinical disease (B), subclinical and clinical disease (C), minimal and clinical disease (D), and minimal and infectious disease (E). The dots in each graph are the point values provided by each study, with error bars representing the weighting of that point value as provided in the fit (appendix pp 18–19). The solid line represents the median trajectory of that transition, with the shaded area representing the 95% uncertainty interval.

Figure 2

Trajectories of disease over time in individuals starting in the subclinical disease state cohort (A) and the clinical disease state cohort (B)

Figure 3

Final state after 5 years in people starting with subclinical or clinical disease