Oral Health, Anxiety, Depression, and Stress in Pregnancy: A Rapid Review of Associations and Implications for Perinatal Care
Bibliographic record
Abstract
Research demonstrates associations between oral health and specific mental health conditions in the general population, yet these relationships remain understudied during pregnancy, despite pregnancy's profound effects on both oral and psychological well-being. Our rapid review examines current evidence on associations between oral health conditions and psychological states (anxiety, depression, and stress) during pregnancy, aiming to inform and strengthen integrated prenatal care strategies. Following PRISMA-RR guidelines, we conducted a systematic search on OVID Medline, CINAHL, and PsycINFO (January 2000-November 2024) for studies examining relationships between oral health conditions (periodontal disease, dental caries) and psychological status during pregnancy and up to one year postpartum. Systematic screening of 1201 records yielded 22 eligible studies (13 cross-sectional studies, 3 longitudinal cohort studies, 3 comparative studies, 2 prospective studies, and 1 case-control study). Analysis confirmed significant associations between oral health and psychological well-being during pregnancy through three pathways: psychological (dental anxiety directly limits oral healthcare utilization), behavioral (maternal depression reduces oral health self-efficacy), and physiological (elevated stress biomarkers correlate with periodontal disease, and periodontal therapy is associated with reduced salivary cortisol). These interactions extend intergenerationally, with maternal psychological distress showing significant associations with children's caries risk. Evidence suggests interactions between oral health conditions and psychological states during pregnancy, warranting integrated care approaches. We recommend: (1) implementing combined oral-mental health screening in prenatal care, (2) developing interventions targeting both domains, and (3) establishing care pathways that address these interconnections. This integrated approach could improve both maternal and child health outcomes.
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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".