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Record 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 on OpenAlexfundno aff
Youn Ho Shin, Rosie Kwon, Seung Won Lee, Min Seo Kim, Jae Il Shin, Dong Keon Yon

Bibliographic record

VenueAllergy · 2023
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsnot available
FundersDivision 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
KeywordsMedicineAsthmaAtopic dermatitisPopulationDisease burdenDemographyEnvironmental healthIncidence (geometry)Body mass indexConfidence intervalPediatricsImmunologyInternal medicine

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.082
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.008
Bibliometrics0.0050.010
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.276
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations391
Published2023
Admission routes1
Has abstractyes

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