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


Journal

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
ISSN: 1537-6591
Titre abrégé: Clin Infect Dis
Pays: United States
ID NLM: 9203213

Informations de publication

Date de publication:
01 01 2019
Historique:
received: 24 04 2018
accepted: 28 06 2018
pubmed: 10 7 2018
medline: 21 1 2020
entrez: 9 7 2018
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 29982375
pii: 5049169
doi: 10.1093/cid/ciy544
pmc: PMC6293007
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

150-156

Subventions

Organisme : NIAID NIH HHS
ID : R01 AI112438
Pays : United States

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Auteurs

Rein M G J Houben (RMGJ)

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.

Marek Lalli (M)

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.

Katharina Kranzer (K)

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.

Nick A Menzies (NA)

Department of Global Health and Population, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.

Samuel G Schumacher (SG)

Foundation for Innovative New Diagnostics, Geneva, Switzerland.

David W Dowdy (DW)

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.

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Classifications MeSH