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Enregistrement W2990627574 · doi:10.4103/jgid.jgid_178_18

State of the globe: Antimicrobial resistance: Need for de-compartmentalization of action

2019· editorial· en· W2990627574 sur OpenAlexaboutno aff
SunilKumar Raina

Notice bibliographique

RevueJournal of Global Infectious Diseases · 2019
Typeeditorial
Langueen
DomaineImmunology and Microbiology
ThématiqueAntibiotic Use and Resistance
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCompartmentalization (fire protection)GlobeAction (physics)State (computer science)AntimicrobialBiologyComputer scienceMicrobiologyNeurosciencePhysicsAlgorithm

Résumé

récupéré en direct d'OpenAlex

“If we fail to act, we are looking at an almost unthinkable scenario where antibiotics no longer work and we are cast back into the dark ages of medicine.” These remarks by David Cameron, former United Kingdom Prime Minister in response to the Review on Antimicrobial Resistance (AMR), commissioned in July 2014 by the UK Government to analyze the global problem of rising drug resistance and propose concrete actions to tackle it internationally, perhaps best define the public health importance of AMR.[1] Resistant microorganisms, sometimes referred to as “superbugs,” are cause for about 558,000 people (range, 483,000–639,000) developing resistance to rifampicin-tuberculosis (RR-TB), the most effective first-line drug, 82% of these developing multidrug-resistant TB (MDR-TB) across the world in the year 2017.[2]Drug resistance is starting to complicate the fight against HIV and malaria, in addition to other public health problems. As per the estimates, among cases of MDR-TB in 2017, 8.5% (95% confidence interval, 6.2%–11%) were estimated to have extensively drug-resistant TB (XDR-TB).[2] AMR is a serious threat to prevention and effective treatment of an ever-increasing range of infections caused by bacteria, parasites, viruses, and fungi. Without effective antibiotics, the success of major medical and surgical interventions will stand compromised thereby leading to an increase in morbidity, mortality, and disability due to procedures such as organ transplantation and cancer chemotherapy.[3]This will also complicate the management of noncommunicable disease such as diabetes management. From health economics, point-of-view AMR will increase the cost of health care with lengthier stays in hospitals and more intensive care requirements. AMR is threatening to offset the gains made by the Millennium Development Goals and raising doubts about achievement of the Sustainable Development Goals.[3] Although AMR is known to occur naturally over time, usually through genetic changes, the overuse and misuse of antimicrobials is accelerating this process. The worst part of this story is that this overuse and misuse is not limited to humans only. The extent of AMR in animals is also on the increase. Some of the common examples include the use of antibiotics in viral infections such as colds and flu in humans and use as growth promoters in animals.[4]As a result, antimicrobial-resistant microbes are found in people, animals, food, and the environment (in water, soil, and air). These microbes can spread from humans to animals and vice versa, to humans from food of animal origin, and from humans to other human beings. AMR is a global phenomenon. Key examples are (1) Klebsiella pneumoniae – even though the last resort treatment for K. pneumoniae across the world now is carbapenem antibiotics in some countries, because of resistance, carbapenem antibiotics do not work in more than half of people treated for K. pneumoniae infections; (2) treatment with fluoroquinolone antibiotics for Escherichia coli in many parts of the world is now ineffective in more than half of patients; (3) resistance for gonorrhea has been confirmed in at least 10 countries (Australia, Austria, Canada, France, Japan, Norway, Slovenia, South Africa, Sweden, and the United Kingdom of Great Britain and Northern Ireland); and (4) methicillin-resistant Staphylococcus aureus. However, probably, one of the most worrisome will continue to resistance in TB. Emergence of XDR-TB is making the matters worse as an estimated 9.7% of people with MDR-TB have XDR-TB. AMR is a complex problem across the globe, in humans, animals, and the environment. Therefore, compartmentalized and isolated interventions will have a limited impact. Compartmentalized approaches will prevent governments from delivering the coordinated care that is necessary to tackle AMR. Probably sensing this, the WHO is providing technical assistance to help countries develop their national action plans. The idea is to help governments strengthen their health and surveillance systems to prevent and manage AMR. In addition, the WHO is also working closely with the Food and Agriculture Organization of the United Nations and the World Organization for Animal Health. This initiative, a part of its “One Health” approach, is to promote best practices to avoid the emergence and spread of antibiotic resistance and includes promoting optimal use of antibiotics in both humans and animals. Article by Iyer V, et al on antimicrobial resistance surveillance in typhoidal Salmonella in Ahmedabad is in line with the broader ojective of reducing global antimicrobial resistance.[5] On the positive side, the endorsement by the Heads of State at the United Nations General Assembly in New York in September 2016 signaled the world's commitment to initiate a broad, coordinated approach to address AMR across multiple sectors, especially human health, animal health, and agriculture. What however will need to be seen is the extent of political will on the part of individual countries to follow up on their endorsements.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,664
Score d'incertitude au seuil0,591

Scores Codex et Gemma par catégorie

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

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 Opus0,006
Tête enseignante GPT0,268
Écart entre enseignants0,263 · 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 tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
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

Citations1
Publié2019
Routes d'admission1
Résumé présentoui

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