Risk of attention‐deficit hyperactivity disorder in offspring of mothers with infections during pregnancy
Bibliographic record
Abstract
Background: Maternal infections during pregnancy are common events that have been suggested to be risk factors for Attention-deficit hyperactivity disorder (ADHD) in offspring. Only a few studies have been conducted to date and results are conflicting. The current study investigates the associations between specific groups of prenatal maternal infections and offspring ADHD, considering timing of exposure and the role of fever. Methods: We used data from the prospective Norwegian Mother, Father and Child Cohort Study (MoBa), including more than 112,000 pregnancies, linked with data from the Medical Birth Registry of Norway and the Norwegian Patient Registry to estimate odds ratios for the likelihood that children develop ADHD after being exposed to maternal infections during gestation. Results: Children exposed to any maternal infection during pregnancy showed increased risk of receiving an ADHD diagnosis (OR = 1.15, CI = 1.03-1.27). Specifically, increased ADHD risk was observed after exposure to genitourinary infections in second (OR = 1.42, CI = 1.06-1.90) or third trimester (OR = 2.04, CI = 1.19-3.49), and to respiratory infections in second trimester (OR = 1.31, CI = 1.12-1.54), provided these infections were accompanied by episodes of fever. Increased ADHD risk was also observed after exposure to diarrhea without fever in the third trimester (OR = 1.25, CI = 1.07-1.46). Conclusions: Overall, our results suggest that prenatal exposure to maternal infections, particularly with co-occurring episodes of fever, are risk factors for ADHD. Fever (or severity of the infection) appears to be more important in mid-pregnancy associations. Our results indicate that type of infection and timing of exposure might influence the associations, but small effect sizes require careful interpretations. The association between infection and ADHD should be estimated using discordant siblings or other negative control designs that give better adjustment for unmeasured familial confounding.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".