The Role of the Home Environment in Perinatal Depression: A Systematic Review and Meta-Analysis of Observational Epidemiological Studies
Bibliographic record
Abstract
Perinatal depression is a leading cause of maternal morbidity worldwide, impacting about one-third of all pregnant individuals. The indoor home environment plays a critical role in the mental health of pregnant individuals, as they spend a substantial amount of their day inside their homes. We conducted a systematic review and meta-analysis to identify, synthesize, and evaluate the available scientific literature on the association between home environment attributes related to stability, quality, and indoor exposures and perinatal depression. Comprehensive electronic searches were conducted in four major bibliographic databases. Dual independent screening, data extraction, and quality assessment were completed. Weighted Z-meta-analysis was conducted to synthesize the available evidence. The review included 27 observational epidemiological studies published between 2003 and 2024, involving 174,914 pregnant and/or postpartum individuals, which investigated the role of at least one home environment attribute in relation to perinatal depression. We found very strong evidence linking indoor air pollutants, particularly household tobacco smoke, to perinatal depression. We found strong evidence for the impact of housing instability on perinatal depression. In contrast, the evidence for associations involving housing quality and residential noise was weak. Our findings underscore the significance of incorporating home environment-focused initiatives in public health efforts to improve perinatal mental health. Further research is needed to identify common household attributes associated with poor perinatal mental health to inform future public intervention and policies.
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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 | 0.017 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".