Interactions among 17 respiratory pathogens: a cross-sectional study using clinical and community surveillance data.
Journal
medRxiv : the preprint server for health sciences
Titre abrégé: medRxiv
Pays: United States
ID NLM: 101767986
Informations de publication
Date de publication:
06 Feb 2022
06 Feb 2022
Historique:
pubmed:
17
2
2022
medline:
17
2
2022
entrez:
16
2
2022
Statut:
epublish
Résumé
Co-circulating respiratory pathogens can interfere with or promote each other, leading to important effects on disease epidemiology. Estimating the magnitude of pathogen-pathogen interactions from clinical specimens is challenging because sampling from symptomatic individuals can create biased estimates. We conducted an observational, cross-sectional study using samples collected by the Seattle Flu Study between 11 November 2018 and 20 August 2021. Samples that tested positive via RT-qPCR for at least one of 17 potential respiratory pathogens were included in this study. Semi-quantitative cycle threshold (Ct) values were used to measure pathogen load. Differences in pathogen load between monoinfected and coinfected samples were assessed using linear regression adjusting for age, season, and recruitment channel. 21,686 samples were positive for at least one potential pathogen. Most prevalent were rhinovirus (33·5%), Viral load data can be used to overcome sampling bias in studies of pathogen-pathogen interactions. When applied to respiratory pathogens, we found evidence of viral-
Sections du résumé
Background
UNASSIGNED
Co-circulating respiratory pathogens can interfere with or promote each other, leading to important effects on disease epidemiology. Estimating the magnitude of pathogen-pathogen interactions from clinical specimens is challenging because sampling from symptomatic individuals can create biased estimates.
Methods
UNASSIGNED
We conducted an observational, cross-sectional study using samples collected by the Seattle Flu Study between 11 November 2018 and 20 August 2021. Samples that tested positive via RT-qPCR for at least one of 17 potential respiratory pathogens were included in this study. Semi-quantitative cycle threshold (Ct) values were used to measure pathogen load. Differences in pathogen load between monoinfected and coinfected samples were assessed using linear regression adjusting for age, season, and recruitment channel.
Results
UNASSIGNED
21,686 samples were positive for at least one potential pathogen. Most prevalent were rhinovirus (33·5%),
Conclusions
UNASSIGNED
Viral load data can be used to overcome sampling bias in studies of pathogen-pathogen interactions. When applied to respiratory pathogens, we found evidence of viral-
Identifiants
pubmed: 35169816
doi: 10.1101/2022.02.04.22270474
pmc: PMC8845514
pii:
doi:
Types de publication
Preprint
Langues
eng
Subventions
Organisme : NIGMS NIH HHS
ID : R35 GM119774
Pays : United States