Behavioural patterns in allergic rhinitis medication in Europe: A study using MASK‐air<sup>®</sup> real‐world data
Bibliographic record
Abstract
Abstract Background Co‐medication is common among patients with allergic rhinitis (AR), but its dimension and patterns are unknown. This is particularly relevant since AR is understood differently across European countries, as reflected by rhinitis‐related search patterns in Google Trends. This study aims to assess AR co‐medication and its regional patterns in Europe, using real‐world data. Methods We analysed 2015–2020 MASK‐air ® European data. We compared days under no medication, monotherapy and co‐medication using the visual analogue scale (VAS) levels for overall allergic symptoms (‘VAS Global Symptoms’) and impact of AR on work. We assessed the monthly use of different medication schemes, performing separate analyses by region (defined geographically or by Google Trends patterns). We estimated the average number of different drugs reported per patient within 1 year. Results We analysed 222,024 days (13,122 users), including 63,887 days (28.8%) under monotherapy and 38,315 (17.3%) under co‐medication. The median ‘VAS Global Symptoms’ was 7 for no medication days, 14 for monotherapy and 21 for co‐medication ( p < .001). Medication use peaked during the spring, with similar patterns across different European regions (defined geographically or by Google Trends). Oral H 1 ‐antihistamines were the most common medication in single and co‐medication. Each patient reported using an annual average of 2.7 drugs, with 80% reporting two or more. Conclusions Allergic rhinitis medication patterns are similar across European regions. One third of treatment days involved co‐medication. These findings suggest that patients treat themselves according to their symptoms (irrespective of how they understand AR) and that co‐medication use is driven by symptom severity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".