Modelling counterfactual incidence during the transition towards culture-independent diagnostic testing.

Campylobacter, Salmonella, Shigella Bayesian FoodNet Shiga toxin-producing Escherichia coli, Vibrio counterfactual modelling culture-independent diagnostic test

Journal

International journal of epidemiology
ISSN: 1464-3685
Titre abrégé: Int J Epidemiol
Pays: England
ID NLM: 7802871

Informations de publication

Date de publication:
11 Oct 2023
Historique:
received: 22 11 2022
accepted: 20 09 2023
medline: 11 10 2023
pubmed: 11 10 2023
entrez: 11 10 2023
Statut: aheadofprint

Résumé

Culture-independent diagnostic testing (CIDT) provides rapid results to clinicians and is quickly displacing traditional detection methods. Increased CIDT use and sensitivity likely result in higher case detection but might also obscure infection trends. Severe illness outcomes, such as hospitalization and death, are likely less affected by changes in testing practices and can be used as indicators of the expected case incidence trend had testing methods not changed. Using US Foodborne Diseases Active Surveillance Network data during 1996-2019 and mixed effects quasi-Poisson regression, we estimated the expected yearly incidence for nine enteric pathogens. Removing the effect of CIDT use, CIDT panel testing and culture-confirmation of CIDT testing, the modelled incidence in all but three pathogens (Salmonella, Shigella, STEC O157) was significantly lower than the observed and the upward trend in Campylobacter was reversed from an observed 2.8% yearly increase to a modelled -2.8% yearly decrease (95% credible interval: -4.0, -1.4). Severe outcomes may be useful indicators in evaluating trends in surveillance systems that have undergone a marked change.

Sections du résumé

BACKGROUND BACKGROUND
Culture-independent diagnostic testing (CIDT) provides rapid results to clinicians and is quickly displacing traditional detection methods. Increased CIDT use and sensitivity likely result in higher case detection but might also obscure infection trends. Severe illness outcomes, such as hospitalization and death, are likely less affected by changes in testing practices and can be used as indicators of the expected case incidence trend had testing methods not changed.
METHODS METHODS
Using US Foodborne Diseases Active Surveillance Network data during 1996-2019 and mixed effects quasi-Poisson regression, we estimated the expected yearly incidence for nine enteric pathogens.
RESULTS RESULTS
Removing the effect of CIDT use, CIDT panel testing and culture-confirmation of CIDT testing, the modelled incidence in all but three pathogens (Salmonella, Shigella, STEC O157) was significantly lower than the observed and the upward trend in Campylobacter was reversed from an observed 2.8% yearly increase to a modelled -2.8% yearly decrease (95% credible interval: -4.0, -1.4).
CONCLUSIONS CONCLUSIONS
Severe outcomes may be useful indicators in evaluating trends in surveillance systems that have undergone a marked change.

Identifiants

pubmed: 37820050
pii: 7308380
doi: 10.1093/ije/dyad133
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Published by Oxford University Press on behalf of the International Epidemiological Association 2023.

Auteurs

Jessica M Healy (JM)

Division of Foodborne, Waterborne, and Environmental Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA.

Logan Ray (L)

Division of Foodborne, Waterborne, and Environmental Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA.

Danielle M Tack (DM)

Division of Foodborne, Waterborne, and Environmental Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA.

Dana Eikmeier (D)

Minnesota Department of Health, St Paul, MN, USA.

Melissa Tobin-D'Angelo (M)

Georgia Department of Public Health, Atlanta, GA, USA.

Elisha Wilson (E)

Colorado Department of Public Health and Environment, Denver, CO, USA.

Sharon Hurd (S)

Connecticut Emerging Infections Program, Yale School of Public Health, New Haven, CT, USA.

Sarah Lathrop (S)

University of New Mexico Health Sciences Center, Albuquerque, NM, USA.

Suzanne M McGuire (SM)

New York State Department of Health, Albany, NY, USA.

Beau B Bruce (BB)

Division of Foodborne, Waterborne, and Environmental Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA.

Classifications MeSH