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Record W3211083157 · doi:10.1016/j.jaip.2021.10.049

Impact of Allergic Rhinitis and Asthma on COVID-19 Infection, Hospitalization, and Mortality

2021· article· en· W3211083157 on OpenAlexaff
Jianjun Ren, Wendu Pang, Yaxin Luo, Danni Cheng, Ke Qiu, Yufang Rao, Yongbo Zheng, Yijun Dong, Jiajia Peng, Yao Hu, Zhiye Ying, Haopeng Yu, Xiaoxi Zeng, Zhiyong Zong, Geoffrey Liu, De Yun Wang, Gang Wang, Wei Zhang, Wei Xu, Yu Zhao

Bibliographic record

VenueThe Journal of Allergy and Clinical Immunology In Practice · 2021
Typearticle
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsUniversity of TorontoUniversity Health NetworkPrincess Margaret Cancer Centre
FundersChengdu Science and Technology BureauFundamental Research Funds for the Central UniversitiesSichuan UniversityWest China Hospital, Sichuan UniversityHealth Commission of Sichuan ProvinceNational University's Basic Research Foundation of ChinaNational Natural Science Foundation of ChinaDepartment of Science and Technology of Sichuan ProvinceMedical Research CouncilChina Postdoctoral Science FoundationHealth Department of Sichuan Province
KeywordsMedicineAsthmaConfidence intervalRelative riskInternal medicineCoronavirus disease 2019 (COVID-19)PediatricsDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

BACKGROUND: It remains unclear if patients with allergic rhinitis (AR) and/or asthma are susceptible to corona virus disease 2019 (COVID-19) infection, severity, and mortality. OBJECTIVE: To investigate the role of AR and/or asthma in COVID-19 infection, severity, and mortality, and assess whether long-term AR and/or asthma medications affected the outcomes of COVID-19. METHODS: Demographic and clinical data of 70,557 adult participants completed SARS-CoV-2 testing between March 16 and December 31, 2020, in the UK Biobank were analyzed. The rates of COVID-19 infection, hospitalization, and mortality in relation to pre-existing AR and/or asthma were assessed based on adjusted generalized linear models. We further analyzed the impact of long-term AR and/or asthma medications on the risk of COVID-19 hospitalization and mortality. RESULTS: Patients with AR of all ages had lower positive rates of SARS-CoV-2 tests (relative risk [RR]: 0.75, 95% confidence interval [CI]: 0.69-0.81, P < .001), with lower susceptibility in males (RR: 0.74, 95% CI: 0.65-0.85, P < .001) than females (RR: 0.8, 95% CI: 0.72-0.9, P < .001). However, similar effects of asthma against COVID-19 hospitalization were only major in participants aged <65 (RR: 0.93, 95% CI: 0.86-1, P = .044) instead of elderlies. In contrast, patients with asthma tested positively had higher risk of hospitalization (RR: 1.42, 95% CI: 1.32-1.54, P < .001). Neither AR nor asthma had an impact on COVID-19 mortality. None of conventional medications for AR or asthma, for example, antihistamines, corticosteroids, or β2 adrenoceptor agonists, showed association with COVID-19 infection or severity. CONCLUSION: AR (all ages) and asthma (aged <65) act as protective factors against COVID-19 infection, whereas asthma increases risk for COVID-19 hospitalization. None of the long-term medications had a significant association with infection, severity, and mortality of COVID-19 among patients with AR and/or asthma.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.036
GPT teacher head0.395
Teacher spread0.359 · 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 designObservational
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

Citations135
Published2021
Admission routes1
Has abstractyes

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