In-Utero SSRI and SNRI Exposure and the Risk of Neurodevelopmental Disorders in Children: A Population-Based Retrospective Cohort Study Utilizing Linked Administrative Data
Bibliographic record
Abstract
IntroductionMany studies demonstrating an association between in utero exposure to serotonergic antidepressants and higher risk of neurodevelopmental disorders in children are confounded by history of maternal depression and disease severity. We conducted a population-based analysis of women diagnosed with mood/anxiety disorder, a patient population for whom pharmacotherapy is clearly indicated. Objectives and ApproachUsing linked population-based administrative data, we identified all mother-newborn pairs in Manitoba (born 1996 to 2009, with follow-up through 2014). High dimensional propensity scores and inverse probability treatment weighting were used to address confounding by indication and disease severity. The final trimmed cohort consisted of mothers who were diagnosed with a mood/anxiety disorder from 90 days prior to conception until delivery (n=4995). Cox Proportional Hazard Regression models were used to estimate risk of Autism Spectrum Disorder, epilepsy and attention deficit hyperactivity disorder (ADHD) in offspring. In addition to clinical data, we used novel education data to define outcomes in children. ResultsAmong the cohort of mothers diagnosed with a mood/anxiety disorder during pregnancy or up to 90 days before, 16.8% received at least two dispensations of an SSRI or SNRI during pregnancy. We did not observe an association between use of SSRIs/SNRIs during pregnancy and increased risk of Autism Spectrum Disorder (hazard ratio 0.92; 95% CI 0.42 to 2.03), epilepsy (hazard ratio 1.21; 95% CI 0.48 to 3.05), or ADHD (hazard ratio 1.13, 95% CI 0.78 to 1.64) among offspring. Conclusion/ImplicationsIn the absence of randomized control trials, large observation studies using sophisticated data analysis are the gold standard of evidence to help patients and clinicians making the decision to continue antidepressant use during pregnancy. Results of this study reassure women for whom the medication is clinically indicated.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".