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Clin Infect Dis. 2019 Jan 1;68(1):150-156. doi: 10.1093/cid/ciy544.

What if They Don't Have Tuberculosis? The Consequences and Trade-offs Involved in False-positive Diagnoses of Tuberculosis.

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Tuberculosis Modelling Group, Tuberculosis Centre and Centre for Mathematical Modelling of Infectious Diseases.
Infectious Disease Epidemiology Department, Faculty of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, United Kingdom.
Research Centre Borstel, National and Supranational Reference Laboratory, Germany.
Department of Global Health and Population, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
Foundation for Innovative New Diagnostics, Geneva, Switzerland.
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.


To find the millions of missed tuberculosis (TB) cases, national TB programs are under pressure to expand TB disease screening and to target populations with lower disease prevalence. Together with imperfect performance and application of existing diagnostic tools, including empirical diagnosis, broader screening risks placing individuals without TB on prolonged treatment. These false-positive diagnoses have profound consequences for TB patients and prevention efforts, yet are usually overlooked in policy decision making. In this article we describe the pathways to a false-positive TB diagnosis, including trade-offs involved in the development and application of diagnostic algorithms. We then consider the wide range of potential consequences for individuals, households, health systems, and reliability of surveillance data. Finally, we suggest practical steps that the TB community can take to reduce the frequency and potential harms of false-positive TB diagnosis and to more explicitly assess the trade-offs involved in the screening and diagnostic process.

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