Advancing surveillance of antimicrobial resistance: Summary of the 2015 CIDSC Report
Notice bibliographique
Résumé
Background: Antimicrobials are essential for the treatment and control of infectious diseases and therefore, the development and spread of antimicrobial resistance (AMR) is a global health concern.It is recognized that robust AMR surveillance is necessary; however, current gaps in national surveillance programs need to be addressed to enable better evidence-informed program and policy decisions.Objective: To describe how an AMR Surveillance Task Group prioritized national AMR surveillance data requirements for high priority AMR organisms for human health in Canada and made recommendations on addressing the current data gaps.Methods: The 2015 AMR Surveillance Task Group examined the data requirements for previously identified first priority organisms and assessed whether the current system met, partially met or did not meet these requirements.Information was summarized into synopsis tables and a ranking process was used to prioritize the data requirements and develop specific recommendations to address the gaps.Results: First priority organisms identified for AMR surveillance are: Clostridium difficile, Extended-spectrum β-lactamase-producing organisms, Carbapenem-resistant organisms (Acinetobacter + Enterobacteriaceae species), Enterococcus species, Neisseria gonorrhoeae, Streptococcus pyogenes and S. pneumonaea, Salmonella species, Staphylococcus aureus, Mycobacterium tuberculosis and Campylobacter species.For these organisms, there were 19 high priority data requirements identified: 10 of these requirements were met by the current surveillance systems, seven were partially met and two were unmet.For the two high priority data metrics in the community setting, the Task Group recommended conducting a point-prevalence community-based study (i.e., every five years) to follow infection rates of C. difficile infection, and community level antibiogram data on an annual basis for susceptibility data for Enterobacteriaceae species (E. coli and Klebsiella) causing genito-urinary infections.There were eight medium priority data requirements identified: one requirement was met by the current surveillance system, five were partially met and two were unmet.The medium priority unmet data requirements included susceptibility of infection isolates for C. difficile (diarrheal disease) and infection rates for Enterobacteriaceae species causing genito-urinary tract infections in community settings.It was noted that the feasibility of obtaining this medium priority data in the community setting was low.The Task Group identified bloodstream infections as the top priority site of infection for AMR surveillance in the health care setting given the high morbidity and mortality associated with bloodstream infections.The importance of collecting susceptibility data on N. gonorrhoeae in the community was underscored given the rise in resistance and that the current surveillance system only partially collects this data.The Task Group recommended that a review of the national AMR surveillance data requirement priorities should occur on an ongoing basis and when new issues emerge.Conclusion: While current national surveillance programs either capture or partially capture many of the identified data requirements for first priority organisms, several gaps still remain, especially in community settings.A national review of the recommendations of the Task Group is underway.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,056 | 0,054 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,017 | 0,014 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,007 | 0,002 |
| Science ouverte | 0,005 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,003 |
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 source (Gemma direct ou Codex distillé), 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 ».