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Enregistrement W3167091319 · doi:10.31579/2690-4861/109

Trends, patterns, and incidence rate of seasonal influenza among Dubai population and some associated factors, 2017-2-19

2021· article· en· W3167091319 sur OpenAlexaboutno aff
Hamid Hussain

Notice bibliographique

RevueInternational Journal of Clinical Case Reports and Reviews · 2021
Typearticle
Langueen
DomaineMedicine
ThématiqueViral Infections and Vectors
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésIncidence (geometry)DemographySeasonal influenzaPopulationAge groupsMedicineQuarter (Canadian coin)SeasonalityDiseaseGeographyEnvironmental healthCoronavirus disease 2019 (COVID-19)Internal medicineBiologyInfectious disease (medical specialty)

Résumé

récupéré en direct d'OpenAlex

Background: WHO estimates that seasonal influenza may result in 290 000-650 000 deaths each year due to respiratory diseases alone. In addition, affected more than 10% of total population annually worldwide, Seasonal influenza is highly contagious disease; spreads easily, with rapid transmission in crowded areas including schools and nursing homes. Objectives: To Study incidence rate, trends and patterns of seasonal influenza among Dubai population for the period 2017-2019, to Study some of the associated factors. Materials & Subjects: A retrospective records review study was carried out of convenience sample of 29158 confirmed seasonal influenza case reported in Emirate of Dubai for the period 2017-2019. All age groups, genders, nationalities, occupations, education and seasons were considered. Findings: The study showed that 53.42% of total seasonal influenza cases were among male groups in Dubai, almost 50% % of the cases were among age group less than 15 years old, and almost one quarter of cases were among the age group between 30-40 years old, the present study showed that 54.37% were among Asian groups, 14.59% of the seasonal influenza incidence in Dubai during 2017-2019 were among Emirati population and 18.79% were among Arabs groups .As per occupation, the study showed that 30.74% of total seasonal influenza cases were among students in Dubai, on the other hand the study revealed that 84.53% of the total seasonal influenza cases during 2017-2019 were handled at outpatient level, yet 15.47% were sever enough cases to be admitted and treated at inpatient facilities. Incidence rate per 100000 population were increased respectively from 2017 through out 2019 (168, 297,466). The study revealed as well that the rate as per nationality the seasonal influenza incidence rate in Dubai from 2017=2019 650/100000 among Jordanian living in Dubai,, almost 50% % of the cases were among age group less than 15 years old, and almost one quarter of cases were among the age group between 30-40 years old, the present study showed that 54.37% were among Asian groups, 14.59% of the seasonal influenza incidence in Dubai during 2017-2019 were among Emirati population and 19.71% were among Arabs groups . The study showed that 30.74% of total seasonal influenza were students in Dubai, 84.53% of the total seasonal influenza cases during 2017-2019 were managed at outpatient. yet 15.47% were sever enough cases to be admitted and treated at inpatient level of different health care facilities in Dubai. Incidence among Egyptian was 557/100000, while among Emirates, 325 /100000, Incidence rate of seasonal influenza 2017-2019 according to age distributions showed that 30.7%among students, and 7.8%among children preschool age, and 5.22%among housewives. The present study showed that the incidence rate of seasonal influenza in Dubai in 2017-2019 as per moth distributions was the highest, 21.4%in November followed by 18.2%in December, and the least was 2%in July. Conclusions: incidence rate of seasonal influenza in Dubai keep increasing during the last three years, the highest rates significantly come from children segment of population specially students and elderly group as well, the period from October to end of February of each years.

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)0,000
score de la tête « metaresearch » (Gemma)0,001
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 enseignants0,022
Score d'incertitude au seuil0,043

Scores du classifieur distillé par catégorie (deux têtes)

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

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 Opus0,116
Tête enseignante GPT0,454
Écart entre enseignants0,338 · 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
Publié2021
Routes d'admission1
Résumé présentoui

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Même revueInternational Journal of Clinical Case Reports and ReviewsMême sujetViral Infections and VectorsTravaux en français237 207