Evidence map›Paper›PMID 39210273›Full record

Observational studyBMC infectious diseases2024

Co-detection of respiratory pathogens among ILI patients: characterization of samples collected during the 2018/19 and 2019/20 pre-pandemic seasons.

Allegra Ferrari, Irene Schiavetti, Matilde Ogliastro, Carola Minet, Raffaella Sibilio, Irene Giberti, Elisabetta Costa, Elvira Massaro, Piero Luigi Lai, Stefano Mosca and 4 more

Abstract readObservational Study
In one paragraph

Observational study in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Allegra FerrariDepartment of Health Sciences (DISSAL), University of Genoa, Genoa, Italy. allegraferrari@virgilio.it.
Irene SchiavettiDepartment of Health Sciences (DISSAL), University of Genoa, Genoa, Italy.
Matilde OgliastroDepartment of Health Sciences (DISSAL), University of Genoa, Genoa, Italy.
Carola MinetDepartment of Health Sciences (DISSAL), University of Genoa, Genoa, Italy.
Raffaella SibilioDepartment of Health Sciences (DISSAL), University of Genoa, Genoa, Italy.
Irene GibertiDepartment of Health Sciences (DISSAL), University of Genoa, Genoa, Italy.
Elisabetta CostaDepartment of Health Sciences (DISSAL), University of Genoa, Genoa, Italy.
Elvira MassaroDepartment of Health Sciences (DISSAL), University of Genoa, Genoa, Italy.
Piero Luigi LaiDepartment of Health Sciences (DISSAL), University of Genoa, Genoa, Italy.
Stefano MoscaInteruniversity Research Center On Influenza and Other Transmissible Infections (CIRI-IT), Genoa, Italy.
Bianca BruzzoneIRCCS Ospedale Policlinico San Martino, Genoa, Italy.
Andrea OrsiDepartment of Health Sciences (DISSAL), University of Genoa, Genoa, Italy.
Donatella PanattoDepartment of Health Sciences (DISSAL), University of Genoa, Genoa, Italy.
Giancarlo IcardiDepartment of Health Sciences (DISSAL), University of Genoa, Genoa, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Influenza-like illness (ILI) patients co-detected with respiratory pathogens exhibit poorer health outcomes than those with single infections. To address the paucity of knowledge concerning the incidence of concurrent respiratory pathogens, their relationships, and the clinical differences between patients detected with single and multiple pathogens, we performed an in-depth characterization of the oropharyngeal samples of primary care patients collected in Genoa (Northwest Italy), during winter seasons 2018/19-2019/20.The apriori algorithm was employed to evaluate the incidence of viral, bacterial, and viral-bacterial pairs during the study period. The grade of correlation between pathogens was investigated using the Phi coefficient. Factors associated with viral, bacterial or viral-bacterial co-detection were assessed using logistic regression.The most frequently identified pathogens included influenza A, rhinovirus, Haemophilus influenzae and Streptococcus pneumoniae. The highest correlations were found between bacterial-bacterial and viral-bacterial pairs, such as Haemophilus influenzae-Streptococcus pneumoniae, adenovirus-Haemophilus influenzae, adenovirus-Streptococcus pneumoniae, RSV-A-Bordetella pertussis, and influenza B Victoria-Bordetella parapertussis. Viruses were detected together at significantly lower rates. Notably, rhinovirus, influenza, and RSV exhibited significant negative correlations with each other. Co-detection was more prevalent in children aged < 4, and cough was shown to be a reliable indicator of viral co-detection.Given the evolving epidemiological landscape following the COVID-19 pandemic, future research utilizing the methodology described here, while considering the circulation of SARS-CoV-2, could further enrich the understanding of concurrent respiratory pathogens.

Indexed as

CoinfectionRespiratory Tract InfectionsAdenoviridaeAdolescentAdultAgedAged, 80 and overAlgorithmsBordetella parapertussisBordetella pertussisCase-Control StudiesChildChild, PreschoolEuropeFemaleHaemophilus influenzaeInfluenza VaccinesCo-infectionLRIRespiratory pathogensRespiratory tract infectionSyndromic surveillanceURI

Identifiers

PMID39210273
PMCPMC11361097

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.