Thirty year trends in childhood asthma and allergic conditions in Poland
Bibliographic record
Abstract
BACKGROUND: Asthma and allergic conditions are among the most common chronic diseases in children. Their prevalence has been rising globally, with regional variability. We aimed to evaluate long-term trends in asthma and other allergic conditions among children in Chorzów, Poland, over a 30-year period. METHODS: We conducted five repeated cross-sectional surveys (1993, 2002, 2007, 2014, and 2023) among children aged 7-10 years. Parents completed standardized questionnaires on respiratory symptoms, physician-diagnosed conditions, and treatment. Trends were assessed using chi-square tests for trends and logistic regression adjusted for age and sex. Analyses were also completed after stratification by sex. RESULTS: Asthma prevalence increased from 3.4 % in 1993 to 12.6 % in 2014, before slightly declining to 10.4 % in 2023. Allergic rhinitis, conjunctivitis, and dermatitis showed consistent increases, with dermatitis rising from 2.4 % to 18.1 %. Food allergy prevalence stabilized at ∼17 % after 2014. Sex-specific analyses revealed diverging patterns: asthma prevalence declined in girls but rose in boys between 2014 and 2023. Despite reductions in treatment use in 2023, some asthma-related symptoms decreased, suggesting improved management. CONCLUSION: Asthma prevalence among Polish children peaked in 2014 and has since stabilized, while allergic diseases continue to rise. These findings highlight evolving environmental and clinical influences and underscore the need for continued monitoring and targeted public health interventions.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".