Burden of allergic rhinitis in the United Kingdom
Bibliographic record
Abstract
Introduction: Allergic rhinitis (AR) is a systemic respiratory condition that is associated with a considerable humanistic burden and is frequently underdiagnosed. Despite the known effects of AR on individual patient well-being, the wider impact of AR on the UK healthcare system remains poorly defined. We aimed to compare healthcare resource use (HCRU) posed by this disease across different age groups between patients who were diagnosed in primary care only vs. those who have a secondary care diagnosis. Methods: In this retrospective, observational study, patients with an AR record (AR diagnosis) and patients with a record of presenting with AR symptoms but no previous AR diagnosis (AR presentation) in the UK between 2009 and 2019 were defined from primary care and secondary care databases. Patients in the AR diagnosis cohort were further categorized based on whether they had a diagnostic code in primary care only, or any relevant diagnostic code(s) in secondary care for allergist or Ear, Nose, and Throat (ENT) services referrals. Key outcomes included specialist referrals, general practitioner (GP) visits, respiratory-related hospitalizations, GP-prescribed AR-related prescriptions, and coincident asthma. Results: A total of 3,344,716 patients were defined as presenting signs of AR and 677,771 patients were defined as having an AR diagnosis between 2009 and 2019. Only 11.7% of the AR presentation group received ≥1 referral to an allergist or ENT, and most patients in the AR diagnosis group received a diagnosis in primary care only (89.3%). Compared to their HCRU before diagnosis, patients diagnosed with AR experienced an increase in mean GP visits [7.5-10.0 per patient per year (PPPY)], respiratory-related hospitalizations (5.5-7.1 PPPY), and AR-related medications (mean 8.8-15.0 PPPY). Patients with at least one diagnostic code in secondary care generally reported higher HCRU post-diagnosis than those in primary care. The incidence rate of asthma was lower after AR diagnosis compared to before, with a shorter interval between the onset of asthma and the diagnosis of AR. Conclusion: Patients with AR impose a greater burden on the UK healthcare system following their diagnosis, especially those who require follow-up from respiratory specialists.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".