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Enregistrement W4311815793 · doi:10.1101/2022.12.16.22283251

Sustained reductions in life-threatening invasive bacterial diseases during the first two years of the COVID-19 pandemic: analyses of prospective surveillance data from 30 countries participating in the IRIS Consortium

2022· preprint· en· W4311815793 sur OpenAlexaff
David Shaw, Raquel Abad, Zahin Amin‐Chowdhury, Désirée E. Bennett, Karen Broughton, Bin Cao, Carlo Casanova, Eun Hwa Choi, Yiu-Wai Chu, Heike Claus, Juliana Coelho, Mary Corcoran, Simon Cottrell, Robert Cunney, Lize Cuypers, Tine Dalby, Heather Davies, Linda de Gouveia, Ala‐Eddine Deghmane, Walter Demczuk, Stefanie Desmet, Mirian Domenech, Richard J. Drew, Mignon du Plessis, Carolina Duarte, Helga Erlendsdóttir, Norman K. Fry, Kurt Fuursted, Thomas Hale, Desirée Henares, Birgitta Henriques‐Normark, Markus Hilty, Steen Hoffmann, H. Humphreys, Margaret Ip, Susanne Jacobsson, Christopher R. Johnson, Jillian Johnston, Keith A. Jolley, Aníbal Kawabata, Jana Kozáková, Karl G. Kristinsson, Pavla Křížová, Alicja Kuch, Shamez Ladhani, Thiên‐Trí Lâm, León María Eugenia, Laura Lindholm, David Litt, Martin Maiden, Irene Martín, Delphine Martiny, Wesley Mattheus, Noel McCarthy, Martha McElligott, Mary Meehan, Susan Meiring, Paula Mölling, Eva Morfeldt, Julie Morgan, Robert Mulhall, Carmen Muñoz‐Almagro, David R. Murdoch, Joy Murphy, Martin Musílek, A. Mzabi, Ludmila Nováková, Shahin Oftadeh, Amaresh Pérez-Argüello, Marı́a Pérez-Vázquez, Monique Perrin, Malorie Perry, Benoît Prévost, Maria Roberts, Assaf Rokney, M. Ron, Olga Sanabria, Kevin J Scott, Carmen Sheppard, Lotta Siira, Vitali Sintchenko, Anna Skoczyńska, Monica Sloan, Hans‐Christian Slotved, Andrew Smith, Anneke Steens, Muhamed‐Kheir Taha, Maija Toropainen, Georgina Tzanakaki, Anni Vainio, Mark PG van der Linden, Nina M. van Sorge, Emmanuelle Varon, Sandra Vohrnova, Anne von Gottberg, José Yuste, Rosemeire Cobo Zanella, Fei Zhou, Angela B. Brueggemann

Notice bibliographique

RevuemedRxiv · 2022
Typepreprint
Langueen
DomaineMedicine
ThématiquePneumonia and Respiratory Infections
Établissements canadiensPublic Health Agency of Canada
Organismes subventionnairesWellcome Trust
Mots-clésPandemicStreptococcus pneumoniaeHaemophilus influenzaeNeisseria meningitidisMedicineIncidence (geometry)SerotypeDisease surveillanceConfidence intervalDiseaseCoronavirus disease 2019 (COVID-19)VirologyBiologyInternal medicineInfectious disease (medical specialty)Microbiology

Résumé

récupéré en direct d'OpenAlex

Summary Background The Invasive Respiratory Infection Surveillance (IRIS) Consortium was established to assess the impact of the COVID-19 pandemic on invasive diseases caused by Streptococcus pneumoniae, Haemophilus influenzae, Neisseria meningitidis and Streptococcus agalactiae . Here we analyse the incidence and distribution of disease during the first two years of the pandemic. Methods Laboratories in 30 countries/territories representing five continents submitted case data from 2018-2021 to private projects within databases in PubMLST. The impact of COVID-19 containment measures on the overall number of cases was analysed, and changes in disease distributions by patient age and serotype/group were examined. Interrupted time series analyses quantified the impact of pandemic response measures and their relaxation on disease rates, and autoregressive integrated moving average models estimated effect sizes and forecasted counterfactual trends by hemisphere. Findings Overall, 116,841 cases were analysed: 76,481 (2018-2019, pre-pandemic) plus 40,360 (2020-2021, pandemic). During the pandemic there was a significant reduction in the risk of disease caused by S pneumoniae (risk ratio: 0.47; 95% confidence interval: 0.40-0.55), H influenzae (0.51; 0.40-0.66) and N meningitidis (0.26; 0.21-0.31), whereas no significant changes were observed for the non-respiratory-transmitted pathogen S agalactiae (1.02; 0.75-1.40). No major changes in the distribution of cases were observed when stratified by patient age or serotype/group. An estimated 36,289 (17,145-55,434) cases of invasive bacterial disease were averted during the first two years of the pandemic among IRIS participating countries/territories. Interpretation COVID-19 containment measures were associated with a sustained decrease in the incidence of invasive disease caused by S pneumoniae, H influenzae and N meningitidis during the first two years of the pandemic, but cases began to increase in some countries as pandemic restrictions were lifted. Research in context Evidence before this study Early in the COVID-19 pandemic the IRIS Consortium reported a significant reduction in invasive disease due to respiratory-transmitted bacterial pathogens, which was associated with the implementation of COVID-19 stringency measures and changes in human social behaviour. All 26 countries/territories participating in IRIS at the time experienced a significant reduction in infections between January and May 2020, compared with the previous two years. In particular, S pneumoniae infections decreased by 68% at four weeks after COVID-19 containment measures were imposed, and by 82% at eight weeks. Added value of this study These new data from the expanded IRIS Consortium across 30 countries/territories demonstrated a sustained reduction in invasive disease throughout the first two years of the COVID-19 pandemic. Using time series modelling, we estimated that over 36,000 cases of invasive bacterial disease were averted in 2020-2021 among the countries participating in IRIS; however, minor increases in disease in the latter half of 2021 require close monitoring to understand the nature of re-emerging cases. Implications of all the available evidence Future epidemics and pandemics will occur, and we need to understand not only the pathogen that is directly responsible for the pandemic, but also that population-level responses to an epidemic or pandemic more broadly affect overall human health and other microbes. IRIS provides evidence for the effects of such public health responses on severe invasive bacterial infections across many countries. Moreover, these IRIS data provide a better understanding of microbial transmission, will inform vaccine development and implementation, and can contribute to healthcare service planning and provision of policies.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,013
Score d'incertitude au seuil0,039

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,008
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,109
Tête enseignante GPT0,382
Écart entre enseignants0,273 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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