Analyse Morphologique Générale Dans La Gestion Des Urgences De Santé Publique : Un Examen Environnemental
DOI :
https://doi.org/10.25071/gz1kfx32Mots-clés :
AMG, santé publique, gestion des urgences, aléas naturelsRésumé
Contexte : La gestion des urgences de santé publique (GUSP est caractérisée par une incertitude inhérente, découlant de la nature imprévisible de certaines urgences et de l’interaction entre les menaces pour la santé publique et leurs facteurs déterminants. Les praticiens de la GUSP doivent continuellement développer et adapter des méthodes pour gérer cette incertitude. L’analyse morphologique générale (AMG) est une méthode de modélisation de scénarios assistée par ordinateur qui peut traiter des problèmes complexes et incertains. L’AMG (Analyse Morphologique Générale – General Morphological Analysis) examine les composantes d’un problème complexe et permet aux praticiens d’envisager les liens et résultats potentiels. Grâce à des étapes itératives, l’AMG peut générer de nouvelles connaissances et perspectives dans l’élaboration de scénarios afin de faciliter la prise de décision et la planification dans le cadre de la GUSP.
Méthode : Une analyse environnementale a été conçue afin d’identifier les articles ayant utilisé l’AMG comme l’une des principales méthodologies pour soutenir la gestion des aléas naturels, avec une extrapolation potentielle à la GUSP. Les bases de données académiques comprenaient PubMed et ResearchGate. Une vaste stratégie de recherche a été appliquée pour examiner la littérature grise, y compris Google Scholar.
Résultats : Cette analyse environnementale a permis d’identifier dix exemples d’utilisation de l’AMG présentant une pertinence pour la GUSP, dans plusieurs pays et organisations. Les exemples cités dans la littérature ciblaient soit un risque naturel spécifique, soit, de manière plus générale, l’ensemble des aléas naturels connus. Les résultats peuvent être regroupés en trois catégories interconnectées : (a) la modélisation de scénarios pour la gestion des catastrophes naturelles, (b) les outils de développement de stratégies et de hiérarchisation des priorités, et (c) les outils d’aide à la décision pour les équipes de gestion des urgences.
Conclusion : L’AMG peut faciliter l’élaboration de scénarios et de stratégies afin d’éclairer la prise de décision quant aux mesures à prendre pour faire face aux incertitudes. Cette méthode de modélisation fait appel à des experts en la matière pour mettre en évidence des liens et des résultats imprévus lors de la gestion de problèmes complexes, comme ceux observés dans le cadre de la GUSP. Les recherches futures pourraient porter sur l’application de l’AMG à la GUSP dans un contexte canadien. Actuellement, l’Agence de la santé publique du Canada applique l’AMG à des événements cycliques (p. ex., feux de forêt, inondations, vagues de chaleur extrême et autres événements météorologiques extrêmes) afin de créer des scénarios selon une perspective GUSP. Les pratiques futures devraient intégrer l’AMG avec d’autres méthodologies de la GUSP afin d’améliorer les stratégies de prévention, de préparation, d’intervention et de rétablissement face
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