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

Alexandra S Richards; Bianca Sossen; Jon C Emery; Katherine C Horton; Torben Heinsohn; Beatrice Frascella; Federica Balzarini; Aurea Oradini-Alacreu; Brit Häcker; Anna Odone; Nicky McCreesh; Alison D Grant; Katharina Kranzer; Frank Cobelens; Hanif Esmail; Rein M G J Houben

doi:10.1016/S2214-109X(23)00082-7

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

Lancet Glob Health. 2023 May;11(5):e684-e692. doi: 10.1016/S2214-109X(23)00082-7. Epub 2023 Mar 23.

Authors

Alexandra S Richards¹, Bianca Sossen², Jon C Emery³, Katherine C Horton³, Torben Heinsohn⁴, Beatrice Frascella⁵, Federica Balzarini⁶, Aurea Oradini-Alacreu⁵, Brit Häcker⁷, Anna Odone⁸, Nicky McCreesh³, Alison D Grant⁹, Katharina Kranzer¹⁰, Frank Cobelens¹¹, Hanif Esmail¹², Rein M G J Houben³

Affiliations

¹ TB Modelling Group, TB Centre, London School of Hygiene & Tropical Medicine, London, UK; Infectious Disease Epidemiology Department, London School of Hygiene & Tropical Medicine, London, UK. Electronic address: alexandra.richards@lshtm.ac.uk.
² Department of Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.
³ TB Modelling Group, TB Centre, London School of Hygiene & Tropical Medicine, London, UK; Infectious Disease Epidemiology Department, London School of Hygiene & Tropical Medicine, London, UK.
⁴ Institute for Global Health, University College London, London, UK; Helmholtz Centre for Infection Research, Braunschweig, Germany; German Centre for Infection Research, Braunschweig, Germany.
⁵ School of Public Health, Vita-Salute San Raffaele University, Milan, Italy.
⁶ School of Public Health, Vita-Salute San Raffaele University, Milan, Italy; Local Health Authority of Bergamo, Bergamo, Italy.
⁷ Department of Public Health, Experimental and Forensic Medicine, University of Pavia, Pavia, Italy.
⁸ German Central Committee Against Tuberculosis, Berlin, Germany.
⁹ TB Centre, London School of Hygiene & Tropical Medicine, London, UK; Africa Health Research Institute, School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa; School of Public Health, University of the Witwatersrand, Johannesburg, South Africa.
¹⁰ Clinical Research Department, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK; Biomedical Research and Training Institute, Harare, Zimbabwe; Division of Infectious Diseases and Tropical Medicine, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany.
¹¹ Department of Global Health and Amsterdam Institute for Global Health and Development, Amsterdam University Medical Centers, Amsterdam, Netherlands.
¹² Wellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, Cape Town, South Africa; Institute for Global Health, University College London, London, UK; MRC Clinical Trials Unit, University College London, London, UK.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Bayes Theorem
Communicable Diseases*
Humans
Retrospective Studies
Tuberculosis* / epidemiology
Tuberculosis, Pulmonary* / diagnosis
Tuberculosis, Pulmonary* / epidemiology

Abstract

Publication types

MeSH terms

Grants and funding