Evidence map›Paper›PMID 29420535›Full record

ArticlePloS one2018

Sociodemographic, obstetric characteristics, antenatal morbidities, and perinatal depressive symptoms: A three-wave prospective study.

Ying Lau, Tha Pyai Htun, Ho Keung Dennis Kwong

Abstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

12 citing papers in PubMed.

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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.

Ying LauDepartment of Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.ORCID 0000-0002-8289-3441
Tha Pyai HtunDepartment of Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
Ho Keung Dennis KwongSchool of Health Sciences, Macao Polytechnic Institutes, Macao Special Administrative Region of the People's Republic of China, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study aimed (1) to investigate the pattern of perinatal depressive symptoms, and (2) to determine the relationships between sociodemographic characteristics, obstetric factors, antenatal morbidities, postnatal conditions, and perinatal depressive symptoms using a structural equation model (SEM).

methodA three-wave prospective longitudinal design was used for 361 women in their second trimester, third trimester, and at six weeks postpartum. The Edinburgh Postnatal Depression Scale (EPDS) was used to assess the depressive symptoms.

resultsThe intensity of depressive symptoms was the highest in the second trimester among the three waves. The SEM showed that unmarried status, unplanned pregnancy, gestational diabetes, and headache were significantly associated with EPDS in the first and second waves. The EPDS in the first wave was able to predict the EPDS in the second and third waves. The SEM has satisfactorily fit with the data (chi-square/degree of freedom = 1.42, incremental fit index = 0.91, Tucker-Lewis index = 0.90, comparative fit index = 0.91, and root mean square error of approximation = 0.03).

conclusionThe findings highlight the significance of monitoring depressive symptoms in the second trimester. Findings from this study could be useful in the design of effective intervention among women with unmarried status, unplanned pregnancy, gestational diabetes, and headache in order to reduce risk of perinatal depressive symptoms.

Indexed as

DemographySocioeconomic FactorsAdolescentAdultDepressionFemaleHumansMacauPregnancyPregnancy ComplicationsProspective StudiesRisk FactorsYoung Adult

Identifiers

PMID29420535
PMCPMC5805167

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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.