Evidence map›Paper›PMID 39368038›Full record

ArticleMaternal and child health journal2024

Association of Maternity Leave Characteristics and Postpartum Depressive Symptoms among Women in New York.

Hannah K Hecht, Angela-Maithy N Nguyen, Kim G Harley

Abstract read
In one paragraph

Article in Maternal and child health journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Hannah K HechtSchool of Public Health, University of California, Berkeley, 2121 Berkeley Way, Berkeley, CA, 94720-7360, USA. hkh.west@gmail.com.ORCID http://orcid.org/0000-0003-0008-7030
Angela-Maithy N NguyenSchool of Public Health, University of California, Berkeley, 2121 Berkeley Way, Berkeley, CA, 94720-7360, USA.
Kim G HarleyWallace Center for Maternal, Child, and Adolescent Health, School of Public Health, University of California, Berkeley, 2121 Berkeley Way, Berkeley, CA, 94720-7360, USA.

Funding

N/A N/A
6 · The paper itself

Abstract

introductionThe United States is the only high-income country without a comprehensive national maternity leave policy guaranteeing paid, job-projected leave. The current study examined associations between maternity leave characteristics (duration of leave, payment status of leave) and postpartum depressive symptoms.

methodsThis study used a sample of 3,515 postpartum women from the New York City and New York State Pregnancy Risk Assessment Monitoring System (PRAMS) from 2016 to 2019. We used logistic regression to examine the association of leave duration and payment status with self-reported postpartum depressive symptoms between 2 and 6 months postpartum.

resultsCompared to having at least some paid leave, having unpaid leave was associated with an increased odds of postpartum depressive symptoms, adjusting for leave duration and selected covariates (adjusted odds ratio [aOR] = 1.41, 95% confidence interval [CI]: 1.04-1.93). There was no significant difference in postpartum depressive symptoms between those with partially and those with fully paid leave. In contrast to prior literature, leave duration was not significantly associated with postpartum depressive symptoms (aOR = 0.99, 95% CI: 0.97-1.02 for each additional week of leave). DISCUSSION: This study suggests that unpaid leave is associated with increased risk of postpartum depression, which can have long-term health effects for both mothers and children. Future studies can help to identify which communities could most benefit from paid leave and help to inform paid leave policies.

Indexed as

Depression, PostpartumParental LeaveAdultFemaleHumansLogistic ModelsMothersNew YorkNew York CityPostpartum PeriodPregnancyWomen, WorkingMaternal mental healthMaternity leavePaid parental leavePostpartum depression

Identifiers

PMID39368038
PMCPMC11534827

What OpenQuestion holds

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.