A Bayesian Method for Exposure Prevalence Comparison During Foodborne Disease Outbreak Investigations.


Journal

Foodborne pathogens and disease
ISSN: 1556-7125
Titre abrégé: Foodborne Pathog Dis
Pays: United States
ID NLM: 101120121

Informations de publication

Date de publication:
09 2023
Historique:
medline: 8 9 2023
pubmed: 14 8 2023
entrez: 14 8 2023
Statut: ppublish

Résumé

CDC and health departments investigate foodborne disease outbreaks to identify a source. To generate and test hypotheses about vehicles, investigators typically compare exposure prevalence among case-patients with the general population using a one-sample binomial test. We propose a Bayesian alternative that also accounts for uncertainty in the estimate of exposure prevalence in the reference population. We compared exposure prevalence in a 2020 outbreak of

Identifiants

pubmed: 37578455
doi: 10.1089/fpd.2023.0059
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

414-418

Auteurs

Mohammed A Khan (MA)

Division of Foodborne, Waterborne, and Environmental Diseases, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Epidemic Intelligence Service, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.

Beau B Bruce (BB)

Division of Foodborne, Waterborne, and Environmental Diseases, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.

Lyndsay Bottichio (L)

Division of Foodborne, Waterborne, and Environmental Diseases, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.

Matthew Wise (M)

Division of Foodborne, Waterborne, and Environmental Diseases, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.

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Classifications MeSH