Prevalence of antenatal and postnatal anxiety: Systematic review and meta-analysis
Bibliographic record
Abstract
Background Maternal anxiety negatively influences child outcomes. Reliable estimates have not been established because of varying published prevalence rates. Aims To establish summary estimates for the prevalence of maternal anxiety in the antenatal and postnatal periods. Method We searched multiple databases including MEDLINE, Embase, and PsycINFO to identify studies published up to January 2016 with data on the prevalence of antenatal or postnatal anxiety. Data were extracted from published reports and any missing information was requested from investigators. Estimates were pooled using random-effects meta-analyses. Results We reviewed 23 468 abstracts, retrieved 783 articles and included 102 studies incorporating 221 974 women from 34 countries. The prevalence for self-reported anxiety symptoms was 18.2% (95% CI 13.6–22.8) in the first trimester, 19.1% (95% CI 15.9–22.4) in the second trimester and 24.6% (95% CI 21.2–28.0) in the third trimester. The overall prevalence for a clinical diagnosis of any anxiety disorder was 15.2% (95% CI 9.0–21.4) and 4.1% (95% CI 1.9–6.2) for a generalised anxiety disorder. Postnatally, the prevalence for anxiety symptoms overall at 1–24 weeks was 15.0% (95% CI 13.7–16.4). The prevalence for any anxiety disorder over the same period was 9.9% (95% CI 6.1–13.8), and 5.7% (95% CI 2.3–9.2) for a generalised anxiety disorder. Rates were higher in low- to middle-income countries. Conclusions Results suggest perinatal anxiety is highly prevalent and merits clinical attention. Research is warranted to develop evidence-based interventions.
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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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.009 | 0.008 |
| 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) | 0.005 | 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".