MétaCan
Menu
Retour à la cohorte
Enregistrement W4383873008 · doi:10.1111/all.15807

Global, regional, and national burden of allergic disorders and their risk factors in 204 countries and territories, from 1990 to 2019: A systematic analysis for the Global Burden of Disease Study 2019

2023· article· en· W4383873008 sur OpenAlexfundno aff
Youn Ho Shin, Rosie Kwon, Seung Won Lee, Min Seo Kim, Jae Il Shin, Dong Keon Yon

Notice bibliographique

RevueAllergy · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueAsthma and respiratory diseases
Établissements canadiensnon disponible
Organismes subventionnairesDivision of Human Resource DevelopmentNational Institute on AgingEuropean Regional Development FundMedical Research CouncilManchester Biomedical Research CentreDilla UniversityHellenic Foundation for Research and InnovationNational Science and Technology CouncilFakultet Medicinskih Nauka, Univerziteta U KragujevcuRajshahi UniversityJawaharlal Institute Of Postgraduate Medical Education and ResearchKhulna UniversityZagazig UniversityUniversity of TabrizUniversidade do PortoShahid Beheshti University of Medical SciencesUniversitair Medisch Centrum GroningenJimma UniversityEuropean Academy of Allergy and Clinical ImmunologyTaipei Medical UniversityTabriz University of Medical SciencesDirectorate for Biological SciencesKing Abdulaziz UniversityMinistero della SaluteCase Western Reserve UniversityBanaras Hindu UniversityNational Health and Medical Research CouncilPohang University of Science and TechnologyFondazione CariploNational Research Foundation of KoreaNational Natural Science Foundation of ChinaVictoria University of WellingtonTehran University of Medical Sciences and Health ServicesVictoria UniversityMacquarie UniversityAstraZenecaEuropean CommissionUniversity of LeedsUniversity of WarwickNational Research FoundationNational Institute for Health and Care ResearchTribhuvan UniversityAgency for Science, Technology and ResearchUniversity of Engineering and Technology, LahoreIran University of Medical SciencesMashhad University of Medical SciencesPublic Health EnglandRafsanjan University of Medical SciencesAmgenPublic Health Agency of CanadaAcademy of Scientific Research and TechnologyFederation University AustraliaU.S. Department of Veterans AffairsJazan UniversityFlinders UniversityBGI GroupUniversity of WollongongHarvard UniversityEmory UniversitySungkyunkwan UniversityUniversidad de AntioquiaSanofiTeva Pharmaceutical IndustriesLEO PharmaUniversitas UdayanaEli Lilly and CompanyJohns Hopkins UniversityFundação para a Ciência e a TecnologiaPfizerBill and Melinda Gates FoundationKyung Hee UniversityInstitute for Health Metrics and EvaluationSamsungUniversity of Southern CaliforniaYonsei UniversityCilagAin Shams UniversityRijksuniversiteit GroningenUniversitetet i BergenInstitut für Arbeitsmarkt- und BerufsforschungNational Medical Research CouncilSchool of Medicine, University of Alabama at BirminghamUniversity of GujratMazandaran University of Medical SciencesCleveland ClinicEuropean Cooperation in Science and Technology
Mots-clésMedicineAsthmaAtopic dermatitisPopulationDisease burdenDemographyEnvironmental healthIncidence (geometry)Body mass indexConfidence intervalPediatricsImmunologyInternal medicine

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Asthma and atopic dermatitis (AD) are chronic allergic conditions, along with allergic rhinitis and food allergy and cause high morbidity and mortality both in children and adults. This study aims to evaluate the global, regional, national, and temporal trends of the burden of asthma and AD from 1990 to 2019 and analyze their associations with geographic, demographic, social, and clinical factors. METHODS: Using data from the Global Burden of Diseases (GBD), Injuries, and Risk Factors Study 2019, we assessed the age-standardized prevalence, incidence, mortality, and disability-adjusted life years (DALYs) of both asthma and AD from 1990 to 2019, stratified by geographic region, age, sex, and socio-demographic index (SDI). DALYs were calculated as the sum of years lived with disability and years of life lost to premature mortality. Additionally, the disease burden of asthma attributable to high body mass index, occupational asthmagens, and smoking was described. RESULTS: In 2019, there were a total of 262 million [95% uncertainty interval (UI): 224-309 million] cases of asthma and 171 million [95% UI: 165-178 million] total cases of AD globally; age-standardized prevalence rates were 3416 [95% UI: 2899-4066] and 2277 [95% UI: 2192-2369] per 100,000 population for asthma and AD, respectively, a 24.1% [95% UI: -27.2 to -20.8] decrease for asthma and a 4.3% [95% UI: 3.8-4.8] decrease for AD compared to baseline in 1990. Both asthma and AD had similar trends according to age, with age-specific prevalence rates peaking at age 5-9 years and rising again in adulthood. The prevalence and incidence of asthma and AD were both higher for individuals with higher SDI; however, mortality and DALYs rates of individuals with asthma had a reverse trend, with higher mortality and DALYs rates in those in the lower SDI quintiles. Of the three risk factors, high body mass index contributed to the highest DALYs and deaths due to asthma, accounting for a total of 3.65 million [95% UI: 2.14-5.60 million] asthma DALYs and 75,377 [95% UI: 40,615-122,841] asthma deaths. CONCLUSIONS: Asthma and AD continue to cause significant morbidity worldwide, having increased in total prevalence and incidence cases worldwide, but having decreased in age-standardized prevalence rates from 1990 to 2019. Although both are more frequent at younger ages and more prevalent in high-SDI countries, each condition has distinct temporal and regional characteristics. Understanding the temporospatial trends in the disease burden of asthma and AD could guide future policies and interventions to better manage these diseases worldwide and achieve equity in prevention, diagnosis, and treatment.

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: Méta-analyse · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,082
Score d'incertitude au seuil0,163

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,005
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,008
Bibliométrie0,0050,010
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,014
Tête enseignante GPT0,276
Écart entre enseignants0,262 · 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'étudeMéta-analyse
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

Citations391
Publié2023
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueAllergyMême sujetAsthma and respiratory diseasesTravaux en français237 207