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Record W4400140665 · doi:10.2196/57340

Hospitalizations for Food-Induced Anaphylaxis Between 2016 and 2021: Population-Based Epidemiologic Study

2024· article· en· W4400140665 on OpenAlexvenueno aff
Rodrigo Jiménez‐García, Ana López‐de‐Andrés, Valentín Hernández‐Barrera, José J. Zamorano‐León, Natividad Cuadrado‐Corrales, Javier de Miguel‐Díez, Jose Luis del-Barrio, Ana Jiménez-Sierra, David Carabantes-Alarcón

Bibliographic record

VenueJMIR Public Health and Surveillance · 2024
Typearticle
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsnot available
Fundersnot available
KeywordsPoisson regressionMedicineIncidence (geometry)EpidemiologyLogistic regressionPopulationObservational studyPublic healthIntensive care unitEnvironmental healthEmergency departmentOral food challengePediatricsDemographyFood allergyEmergency medicineAllergyInternal medicineImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: Food-induced anaphylaxis (FIA) is a major public health problem resulting in serious clinical complications, emergency department visits, hospitalization, and death. OBJECTIVE: This study aims to assess the epidemiology and the trends in hospitalizations because of FIA in Spain between 2016 and 2021. METHODS: An observational descriptive study was conducted using data from the Spanish National Hospital discharge database. Information was coded based on the International Classification of Diseases, Tenth Revision. The study population was analyzed by gender and age group and according to food triggers, clinical characteristics, admission to the intensive care unit, severity, and in-hospital mortality. The annual incidence of hospitalizations because of FIA per 100,000 person-years was estimated and analyzed using Poisson regression models. Multivariable logistic regression models were constructed to identify which variables were associated with severe FIA. RESULTS: A total of 2161 hospital admissions for FIA were recorded in Spain from 2016 to 2021. The overall incidence rate was 0.77 cases per 100,000 person-years. The highest incidence was found in those aged <15 years (3.68), with lower figures among those aged 15 to 59 years (0.25) and ≥60 years (0.29). Poisson regression showed a significant increase in incidence from 2016 to 2021 only among children (3.78 per 100,000 person-years vs 5.02 per 100,000 person-years; P=.04). The most frequent food triggers were "milk and dairy products" (419/2161, 19.39% of cases) and "peanuts or tree nuts and seeds" (409/2161, 18.93%). Of the 2161 patients, 256 (11.85%) were hospitalized because FIA required admission to the intensive care unit, and 11 (0.51%) patients died in the hospital. Among children, the most severe cases of FIA appeared in patients aged 0 to 4 years (40/99, 40%). Among adults, 69.4% (111/160) of cases occurred in those aged 15 to 59 years. Multivariable logistic regression showed the variables associated with severe FIA to be age 15 to 59 years (odds ratio 5.1, 95% CI 3.11-8.36), age ≥60 years (odds ratio 3.87, 95% CI 1.99-7.53), and asthma (odds ratio 1.71,95% CI 1.12-2.58). CONCLUSIONS: In Spain, the incidence of hospitalization because of FIA increased slightly, although the only significant increase (P=.04) was among children. Even if in-hospital mortality remains low and stable, the proportion of severe cases is high and has not improved from 2016 to 2021, with older age and asthma being risk factors for severity. Surveillance must be improved, and preventive strategies must be implemented to reduce the burden of FIA.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.075
GPT teacher head0.386
Teacher spread0.311 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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