Prevalence and Risk Factors of Asthma in Children: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Objective The present study aims to evaluate the prevalence and risk factors of asthma in children through a meta-analysis.Data sources The PubMed, Embase, Cochrane, and Web of Science databases were comprehensively retrieved for studies on the prevalence and risk factors of childhood asthma (CA) published between January 1, 2015, and July 8, 2024. Studies were screened and selected based on predefined eligibility criteria, and pertinent data were extracted. The quality of eligible studies was evaluated through the Newcastle-Ottawa Scale (NOS). Statistical analyses were undertaken via Stata 16 and R 4.4.1.Study selection 45 studies comprising 647,414 participants were included.Results The pooled prevalence of CA was 11.9% (95% CI: 8.8–15.8%). The meta-analysis identified several risk factors for CA, including prenatal exposure to per- and polyfluoroalkyl substances (PFAS) (OR = 0.89, 95% CI: 0.80–0.98, P = 0.021), prenatal exposure to acid-suppressive medications (OR = 1.11, 95% CI: 1.04–1.19, P = 0.002), maternal folic acid supplementation during pregnancy (OR = 1.18, 95% CI: 1.10–1.27, p < 0.001), as well as Helicobacter pylori infection in childhood (OR = 2.07, 95% CI: 1.35–3.15, p = 0.001).Conclusions The prevalence rate of asthma among children was approximately 11.9%. Prenatal exposure to PFAS and acid-suppressive medications, Helicobacter pylori infection in childhood were proved to be risk factors for asthma. Folic acid supplementation during pregnancy is positively associated with a reduced risk of asthma in children. Further large-scale prospective research is warranted to unveil the roles and significance of these factors.
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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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.055 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".