Prevalence trends and risk factors for allergic rhinoconjunctivitis, asthma and eczema in the UK
Bibliographic record
Abstract
BACKGROUND: Allergic rhinoconjunctivitis (ARC), asthma and eczema carry a substantial morbidity. These conditions often co-exist within the same individual and their prevalence can differ based on age, ethnicity and gender. OBJECTIVES: Using a UK primary care database, we estimated the trends in prevalence over the last decade for ARC, asthma and eczema and associated risk factors. METHODS: Longitudinal cohort analysis of the health improvement (THIN) database between 1st Jan 2010 and 1st Jan 2019. Logistic regression analysis was used to explore risk factors for diagnosis of these conditions. RESULTS: An average of 4.17 million records per year were analysed, 19.4% were children and 49.75% were male. There was an increase in prevalence of ARC, asthma and eczema amongst adults during the study period, whereas ARC and asthma prevalence amongst children has fallen. By 2018, 1:8 adults and 1:14 children had ARC; asthma was diagnosed in 1:7 adults and 1:10 children whereas eczema was diagnosed in 1:6 adults and 1:4 children respectively. There were regional discrepancies in allergy prevalence across the UK. Caucasians generally had the highest rates of asthma and lower rates of ARC compared with other ethnic groups. Having other allergies substantially increases the odds of having asthma, eczema and ARC. CONCLUSION: The population burden of ARC, asthma and eczema in the UK is substantial. These conditions are often associated with other allergies and can, therefore, be complex to manage. These data support calls for improvement of pathways of care for allergy patients in the UK.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".