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Enregistrement W3125314639

Towards effective emerging infectious disease surveillance: H1N1 in the United States 1976 and Mexico 2009

2011· article· en· W3125314639 sur OpenAlexaboutno aff
Sophal Ear

Notice bibliographique

RevueMunich Personal RePEc Archive (Ludwig Maximilian University of Munich) · 2011
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueVaccine Coverage and Hesitancy
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTransparency (behavior)PreparednessOutbreakPoliticsPolitical sciencePublic healthEconomic growthStrengths and weaknessesDevelopment economicsPublic administrationBusinessMedicineEconomicsVirologyLaw
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The comparison of Mexico’s 2009 A/H1N1 outbreak with the U.S. H1N1 outbreak of 1976 provides notable observations—based on the strengths and weaknesses of each country’s response—that can be used as a starting point of discussion for the design of effective Emerging Infectious Diseases (EIDs) surveillance programs in developing and middle-income countries.\n\nStrengths\nMexico’s strongest characteristics were its transparency, as well as the cooperation the country exhibited with other nations, particularly the U.S. and Canada. These were the result of Mexico’s existing professional relationships with other scientific communities—informal networks, existing without institutional ties, which proved highly beneficial.\n\nMexico also showed savvy in its effective management of public and media relations. By maintaining transparency and a united political front as it disseminated public health information, Mexico was able to mobilize in this area—something the U.S. handled less effectively in 1976. Uneven economic development was a barrier that prevented full dissemination across more rural regions of Mexico, but on a larger scale, public relations were handled relatively well.\n\nIn the U.S., the speed and efficiency of the 1976 U.S. mobilization against H1N1 was laudable. Although the U.S. response to the outbreak is seldom praised, the unity of the scientific and political communities demonstrated the national ability to respond to the situation. In parallel, Mexico also effectively responded to the situation, but in addition it had a preparedness plan for such a pandemic or bio-safety threat, which highlights the necessity of working out such strategies ahead of time.\n\nMexico’s effective pandemic-preparedness plan was comprehensive, but it was also based on simple issues: logistics, administrative structure, and information. The questions it answered included: Is there a national database on the cases of the virus at hand? Is there a network or panel of specialists that the government can pull to their aid? Who is maintaining this network? Are there designated transportation routes and potential central facilities to hold vaccines? Is there a designated individual who reviews the plan? \n\nWeaknesses\nIn the U.S., the major weakness was turning the response to the outbreak into a single go-or-no-go decision instead of splitting the decision into smaller action tasks or phases of implementation from which decisions could then be made. What made this situation more difficult was the unquestioning support of the Center for Disease Control’s (CDC) decision to execute a massive immunization campaign. While then President Ford and CDC Director David Sencer may have acted reasonably considering the circumstances, the move to immunize has since been much criticized, especially owing to the following rise in cases of Guillain-Barré Syndrome and the fact that H1N1 was never identified outside the Fort Dix, New Jersey, army base where it was first detected. \n\nIn Mexico, despite the country’s overall success in handling A/H1N1, there were myriad political weaknesses that hampered efforts, and these problems persist. Loyalty to political groups is prized above competence. In addition, individuals who are qualified for their position are perennially moved or must leave when there is a change in government, causing the loss of valuable institutional knowledge and relationships. These issues are hardly unique to Mexico, and will be especially important for countries developing EID surveillance tools to address in the coming years. \n\nAn even greater challenge for Mexico was an inflexible workplace culture that did not encourage workers to report abnormalities in patients and therefore delayed the identification of A/H1N1. Inefficiencies can be eliminated if laboratory employees are given the freedom to question situations and are provided with the hardware and tools for executing their duties. Worker compensation, relatively low for an Organization for Economic Cooperation and Development member country, could be an important factor as well.

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)0,003
score de la tête « metaresearch » (Gemma)0,005
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 enseignants0,527
Score d'incertitude au seuil0,951

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,001
Études des sciences et des technologies0,0020,001
Communication savante0,0030,002
Science ouverte0,0010,003
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,018
Tête enseignante GPT0,241
Écart entre enseignants0,224 · 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
Publié2011
Routes d'admission1
Résumé présentoui

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