Epidemiology of Influenza Viruses in Canada over the 2011–2012 to 2013–2014 Seasons: A Study from the Serious Outcomes Surveillance (SOS) Network of the Canadian Immunization Research Network (CIRN)
Notice bibliographique
Résumé
Abstract Background Influenza virus activity varies seasonally and within season. Epidemiology of serious influenza outcomes is contingent on the prevalent circulating strain/s and susceptible age group/s. Given the strain variability over the 2011–2012 through 2013–2014 seasons in Canada, this study examined the clinical and epidemiological profiles of different influenza strains causing adult hospitalizations. Methods During these three influenza seasons, the Serious Outcomes Surveillance (SOS) Network of the Canadian Immunization Research Network (CIRN) enrolled adults hospitalized with acute respiratory illness across Canada. Nasopharyngeal swabs (NPs) from influenza cases were tested for strain characterization using real-time reverse transcriptase polymerase chain reaction (rtRT-PCR). A primary assay differentiated A and B influenza viruses. Subsequently, influenza A viruses were subtyped as H1N1 or H3N2, and influenza B lineages were differentiated as Victoria or Yamagata. Laboratory results were compared with patient demographic data and clinical outcomes. Results Over three consecutive influenza seasons, 3394 cases of hospitalized acute respiratory illness were laboratory-confirmed as influenza. At 72.4%, influenza A was predominant across all seasons, while influenza B caused 27.6%. Most of the influenza A cases were due to H3N2 (58.7%), while H1N1 accounted for 41.3%. For influenza B, the Yamagata lineage was predominant at 88.4% whereas the Victoria lineage accounted for 11.6%. Outcome analyses are presented for each influenza A subtype and influenza B lineage, overall and per season. Considering serious outcomes in patients ≥65, higher proportions of patients hospitalized with the H1N1 strain experienced intensive care unit (ICU) admission and need for mechanical ventilation, while higher proportions of patients hospitalized with B/Yamagata and H3N2 died within 30 days of admission. Conclusion Comprehensive collection of surveillance data paired with NP specimens by the CIRN SOS Network was conducive to broader understanding of influenza strain activity and associated outcomes at the subtype and lineage level. This data is important to make informed recommendations for the use of multicomponent influenza vaccines. Disclosures M. Elsherif, Canadian Institutes of Health Research: Investigator, Research grant. Public Health Agency of Canada: Investigator, Research grant. GSK: Investigator, Research grant. T. Hatchette, GSK: Grant Investigator, Grant recipient; Pfizer: Grant Investigator, Grant recipient. Abbvie: Speaker for a talk on biologics and risk of TB reactivation, Speaker honorarium.M. K. Andrew, GSK: Grant Investigator, Research grant. Pfizer: Grant Investigator, Research grant. Sanofi-Pasteur: Grant Investigator, Research grant. J. McElhaney, GSK Vaccines: Scientific Advisor, Speaker honorarium. A. Mcgeer, Hoffman La Roche: Investigator, Research grant. GSK: Investigator, Research grant. sanofi pasteur: Investigator, Research grant. J. Powis, Merck: Grant Investigator, Research grant. GSK: Grant Investigator, Research grant. Roche: Grant Investigator, Research grant. Synthetic Biologicals: Investigator, Research grant. M. Semret, GSK: Investigator, Research grant. Pfizer: Investigator, Research grant. S. Trottier, Canadian Institutes of Health Research: Investigator, Research grant. L. Valiquette, GSK: Investigator, Research grant. S. McNeil, GSK: Contract Clinical Trials and Grant Investigator, Research grant. Merck: Contract Clinical Trials and Speaker’s Bureau, Speaker honorarium. Novartis: Contract Clinical Trials, No personal renumeration. sanofi pasteur: Contract Clinical Trials, No personal renumeration
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».