Maternal Mental Disorders before Delivery and the Risk of Dental Caries in Children
Bibliographic record
Abstract
Prevention of childhood caries is an ongoing public health challenge, but the possibility of an association with maternal mental disorders has received limited attention. We estimated the extent to which maternal mental disorders are associated with an increased risk of hospitalization due to dental caries. We conducted a longitudinal cohort study of 790,758 infants born in Quebec, Canada between 2006 and 2016, with follow-up extending to 2018. We identified women with mental disorders before or during pregnancy and computed the incidence of dental caries in their children. We estimated HR and 95% CI for the association of maternal mental disorders with the risk of dental caries, adjusted for personal characteristics. Infants of women with mental disorders before or during pregnancy had a higher incidence of dental caries compared to children of women with no mental disorder (56.1 vs. 27.2 per 10,000 person-years). Maternal stress and anxiety disorders (HR = 1.73; 95% CI 1.60-1.86), depression (HR = 1.81; 95% CI 1.60-2.03), schizophrenia and delusional disorders (HR = 1.69; 95% CI 1.29-2.22), and personality disorders (HR = 1.89; 95% CI 1.70-2.11) were associated with the risk of dental caries. The associations were present throughout childhood, including after 7 years (HR = 1.65; 95% CI 1.38-1.96). Maternal mental disorders were associated with caries of the enamel, dentin, and cementum and caries that reached the dental pulp. Maternal mental disorders before or during pregnancy, especially stress and anxiety, depression, schizophrenia, and personality disorders, are associated with the risk of childhood caries. Women with a history of mental disorders may benefit from enhanced strategies for prevention of dental caries in their children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".