Trends in incidence of atopic disorders in children and adolescents - Analysis of German claims data
Bibliographic record
Abstract
Background: This claims-based study aimed to assess recent nationwide trends in pediatric incidence of atopic diseases in Germany. Methods: Incidence of atopic dermatitis, asthma, and hay fever was assessed from 2013 to 2021 in annual cohorts of 0- to 17-year-old children and adolescents with statutory health insurance (N = 11,828,525 in 2021). Results: Incidence of atopic dermatitis remained largely unchanged (15.2 cases per 1000 children in 2021) while hay fever incidence exhibited a fluctuating trend over the study period and amounted to 8.8 cases per 1000 in 2021. Asthma incidence decreased gradually between 2013 (12.4/1000) and 2019 (8.9/1000). This downward trend was followed by a further disproportionate reduction from 2019 to 2020 (6.3/1000) and a re-increase in 2021 (7.2/1000). Conclusion: The findings complement nationwide prevalence surveys of atopic diseases in children and adolescents in Germany. Knowledge about temporal variations in risk of atopic diseases are crucial for future investigations of explanatory factors to enhance the development of preventive measures. While asthma incidence followed a declining trend throughout the study period, an unprecedentedly strong reduction in pediatric asthma risk was observed in 2020, the first year of the COVID-19-pandemic.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".