Adverse childhood experiences and maternal anxiety and depression: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: It has been proposed that adverse childhood experiences (ACEs) can put women at risk for mental illness in the pregnancy and postpartum periods. While some studies have found strong support for this proposition, others have found weak or no support. This study is a meta-analysis of the association between ACEs and maternal mental health to resolve between-study discrepancies, and to examine potential moderators of associations. METHODS: Three electronic databases (i.e., MEDLINE, Embase, and PsycINFO) were searched up to November 2018 by a health sciences librarian. A hand search was conducted in January 2020 and relevant studies were added. Included studies reported on associations between ACEs and maternal depression and/or anxiety in the perinatal period (pregnancy to 1-year postpartum). Pregnancy and postpartum outcomes were examined separately for both depression and anxiety. Random-effect meta-analyses were conducted. Moderator analyses were conducted using meta-regression. Study quality was evaluated using a 15-point scale. RESULTS: The initial search yielded 4646 non-duplicate records and full text review occurred for 196 articles. A total of 15 studies (N = 7788) were included in the meta-analyses, of which 2 were also described narratively. Publication year ranged from 1998 to 2019. Mothers were approximately 28.93 years of age when they retrospectively reported on their ACEs. All studies had maternal self-report questionnaires for the mental health outcomes. Study quality ranged from 7 to 12. The pooled effect sizes between ACEs and prenatal (N = 12; r = .19; 95% CI= .13, .24) and postpartum (N = 7; r = .23; 95% CI = .06 to .39) depressive symptoms were significant. The pooled effect size between ACEs and prenatal anxiety was also significant (N = 5; r = .14; 95% CI= .07, .21). Moderator analyses indicated that timing of depressive and anxiety symptoms may be important for understanding associations. CONCLUSIONS: ACEs confer risk to maternal mental health, albeit effect sizes are small to moderate in magnitude. Trauma-informed approaches, as well as increased mental health support during and after pregnancy, may help to offset the relative risk of ACEs on maternal mental health.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.062 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".