Association of premature birth and maternal education level on attention deficit hyperactivity disorder in children: A meta-analysis
Bibliographic record
Abstract
BACKGROUND Attention deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in childhood. There is growing evidence that both preterm birth and maternal education levels substantially affect the likelihood of ADHD in children. However, there are limited systematic reviews and meta-analyses examining these associations. AIM To systematically review and conduct a meta-analysis on the association of preterm birth and maternal education level on the risk of ADHD in children. METHODS We conducted a comprehensive literature search across MEDLINE (PubMed), Web of Science, Embase, and the Cochrane Library, including studies published up to June 17, 2024. Data synthesis was performed using random-effect models, and the quality of studies was assessed using the Newcastle-Ottawa Scale. RESULTS This study included twelve studies, which revealed a significant association between premature delivery and an increased risk of ADHD in children [odds ratio (OR) = 2.76, 95% confidence interval (CI): 2.52-3.04, P < 0.001, I² = 1.9%). Conversely, higher maternal education levels were significantly associated with a reduced risk of ADHD in children (OR = 0.59, 95%CI: 0.48-0.73, P < 0.001, I² = 47.1%). Subgroup analysis further indicated that maternal education levels significantly influenced ADHD risk, particularly in studies conducted in China (OR = 0.59, 95%CI: 0.46-0.75, P < 0.001, I² = 81.2%), while no significant association was observed in studies from other regions (OR = 1.25, 95%CI: 0.66-2.40, P = 0.495, I² = 92.3%). The sensitivity analysis confirmed the robustness of our findings, showing no significant publication bias. CONCLUSION This study found that preterm birth significantly increases the risk of ADHD in children, while a higher maternal education level serves as a protective factor against ADHD. To reduce the incidence of ADHD in children, public health policies should focus on early intervention for preterm infants and improving maternal education levels.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.062 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".