Lesson Learned from the Management of the COVID-19 Pandemic: The Influenza Morbidity and Mortality during the Pre-COVID-19 Era could Be Reduced
Notice bibliographique
Résumé
Dear Editor, We have read with interest the paper by Sabeena et al.[1] which concluded in a systematic review and meta-analysis that globally there was a decline in influenza surveillance during the COVID-19 pandemic except in Canada. As a tremendous decline in influenza cases was observed even though influenza surveillance was maintained overall in Canada, we made the following hypothesis: we were not doing enough against influenza in the pre-COVID-19 era. Indeed, from March 28 to mid-September 2020, influenza surveillance in Quebec suffered from a significant decrease in the number of tests carried out by Sentinel Laboratories due to the massive efforts deployed to fight COVID-19. Fortunately, these changed quickly the following year. Despite the increasing number of samples tested in 2021–2022 for seasonal influenza from week 44 to week 13 in Sentinel laboratories, <1% were positives, whereas we reached 36% in the pre-COVID-19 years 2019–2020. A similar trend was found from the Canadian national surveillance data: FLuWatch surveillance (Flu [influenza]: FluWatch surveillance-Canada.ca). The epidemiological situation in Quebec, Canada, the United States, and Europe showed that influenza was almost absent in all areas in 2020–2021. These were attributed to both artifactual changes related to declines in routine health-seeking for respiratory illness as well as real changes in influenza virus circulation due to the widespread implementation of measures to mitigate the transmission of SARS-CoV-2.[2] The sustained use of infection prevention and control (IPAC) measures at all levels may explain the virtual disappearance of influenza during the COVID-19 pandemic. Unfortunately, maintaining preventive measures over time, and doing so consistently, is not feasible in the long-term perspectives. Thus, it is uncertain, unlikely, and unacceptable to apply these preventive measures with the same intensity after the pandemic. Besides flu vaccine and antiviral drugs, there is surely a threshold for raising and applying preventive actions that would significantly reduce the morbidity and mortality of both influenza and COVID-19 without necessarily harming the physical and mental health of anyone affected as it had been with more restrictive measures during the pandemic era.[3,4]Figure 1 shows the reduction in influenza mortality-morbidity as more restrictives measures are applied, but with a raised in mortality-morbidity due social isolation. Theoretically, from A to B (Arrow in the figure), mortality and morbidity could be reduced by rigorous and consistent application of preventive measures without increasing adverse effects due to a lack of social interactions.Figure 1: Preventive measures, influenza morbidity-mortality, and health harm trend.While writing this paper, we have to consider the following. First, new variants spread more quickly. Second, vaccines and infection are transforming SARS-COV-2 into a manageable “endemic” respiratory virus. Thus, it is about preventing severe disease, protecting vulnerable people, and protecting the health system. Thereby, we hope that the acquired living way, processes, and protocols put in place when fighting COVID-19 will have a downward impact on the burden of influenza on the health-care system. Rational plans are needed to lower both influenza and COVID-19 burdens using IPAC measures without necessarily confining and restricting people’s mobility. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
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 | 0,015 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,008 |
| 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 ».