Le Centre de collaboration nationale des méthodes et outils (CCNMO) : soutenir la prise de décisions fondée sur des données probantes en santé publique au Canada

Le Centre de collaboration nationale des méthodes et outils (CCNMO) fait partie d’un réseau de six centres de collaboration nationale en santé publique (CCN) créé en 2005 par le gouvernement fédéral à la suite de l’épidémie de syndrome respiratoire aigu sévère (SRAS) afin de renforcer l’infrastructure de la santé publique au Canada. Le travail du CCNMO, qui vise à soutenir la prise de décisions fondée sur des données probantes dans le domaine de la santé publique au Canada, est accompli par l’organisation des données probantes dignes de confiance, le renforcement des compétences dans l’utilisation des données probantes, et l’accélération du changement dans la prise de décisions fondée sur des données probantes est décrit. La consultation permanente auprès de ses publics cibles garantit la pertinence du CCNMO et sa capacité à répondre à l’évolution des besoins en matière de santé publique. Cela s’est avéré particulièrement crucial lors de la pandémie de maladie à coronavirus 2019 (COVID-19). Le CCNMO a alors modifié la direction de ses activités pour soutenir l’intervention de la santé publique en effectuant des revues rapides sur les questions prioritaires identifiées par les décideurs, à l’échelle fédérale ou à l’échelle locale, ainsi qu’en créant et en maintenant un répertoire national de synthèses en cours ou terminées. Ces efforts, ainsi que le partenariat avec le COVID-19 Evidence Network to support Decision-Making (COVID-END), visaient à réduire les doublons, à accroître la coordination des efforts de synthèse et à aider les décideurs à utiliser les meilleures données probantes dont on dispose dans la prise de décisions. Les données tirées des statistiques du site Web illustrent le succès de ces initiatives au Canada et à l’étranger.

Cropping up Crisis at the Nexus Between COVID-19 and AMR in Africa: A Scoping Review and Synthesis of Early Evidence

Objective In this study, we aim to synthesize some evidence on the impacts that COVID-19 is having on the epidemiology of Antimicrobial Resistance (AMR) in Africa since it was declared global pandemic by WHO in March 2020. Methodology A scoping review was undertaken by collecting and curating relevant resources from peer-reviewed articles and also from the gray literature. Mixed approaches of extracting data (qualitative and quantitative) were employed in synthesizing evidence, as suggested by Health Evidence Network (HEN). Findings A model constructed based on the synthesis of early evidences available on the effects of factors linked to COVID-19 in impacting the evolution of AMR in Africa predicted that, in cumulative terms, those factors favoring the evolution of AMR outpace those disfavoring it by no less than three folds. Conclusion COVID-19 is fueling the evolution of AMR almost unhindered in Africa. Due recognition of this crisis, concerted efforts for resource mobilization and global cooperation are needed to tackle it.

Optimal fault diagnosis strategy for complex systems considering common cause failure under epistemic uncertainty

Purpose This paper aims to deal with the problems such as epistemic uncertainty, common cause failure (CCF) and dynamic fault behaviours that arise in complex systems and develop an effective fault diagnosis method to rapidly locate the fault when these systems fail. Design/methodology/approach First, a dynamic fault tree model is established to capture the dynamic failure behaviours and linguistic term sets are used to obtain the failure rate of components in complex systems to deal with the epistemic uncertainty. Second, a β factor model is used to construct a dynamic evidence network model to handle CCF and some parameters obtained by reliability analysis are used to build the fault diagnosis decision table. Finally, an improved Vlsekriterijumska Optimizacija I Kompromisno Resenje algorithm is developed to obtain the optimal diagnosis sequence, which can locate the fault quickly, reduce the maintenance cost and improve the diagnosis efficiency. Findings In this paper, a new optimal fault diagnosis strategy of complex systems considering CCF under epistemic uncertainty is presented based on reliability analysis. Dynamic evidence network is easy to carry out the quantitative analysis of dynamic fault tree. The proposed diagnosis algorithm can determine the optimal fault diagnosis sequence of complex systems and prove that CCF should not be ignored in fault diagnosis. Originality/value The proposed method combines the reliability theory with multiple attribute decision-making methods to improve the diagnosis efficiency.

Corrigendum

Patel T, Slonim K, Lee L. Use of potentially inappropriate medications among ambulatory home-dwelling elderly patients with dementia: a review of the literature. Can Pharm J (Ott) 2017;150:169-83. (Original doi: 10.1177/1715163517701770) In the Funding section on page 182 of this article in the May/June 2017 issue of Canadian Pharmacists Journal, a funder for the work was omitted. The Ontario Pharmacy Evidence Network (OPEN) should have been included. The corrected statement is provided below: Funding: This review was conducted in response to an applied health research question submitted by Alzheimer Society Ontario to the Innovations Strengthening Primary Healthcare through Research (INSPIRE–PHC) Program and the Ontario Pharmacy Evidence Network (OPEN) supported by grants from the Government of Ontario (INSPIRE-PHC - Ministry grant 06547 and OPEN- Ministry grant 06674). The views expressed in this article are those of the authors and do not necessarily reflect those of the Government of Ontario.