Evidence map›Paper›PMID 41875112›Full record

ArticlePloS one2026

Factors associated with insomnia, anxiety, and depression among antenatal women in China: A cross-sectional hospital-based study.

Qiaoling Liao, Ruoxin Fan, Dandan Zheng, Zuowei Li, Xianmei Yang, Jun Liu, Yaozhi Hu

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Qiaoling LiaoThe Second Department of Severe Psychiatry, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China.
Ruoxin FanMental Health Prevention and Treatment Division, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China.
Dandan ZhengFaculty of Nursing, Mahidol University, Bangkok, Thailand.
Zuowei LiNursing Department, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China.
Xianmei YangMental Health Prevention and Treatment Division, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China.
Jun LiuMental Health Prevention and Treatment Division, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China.
Yaozhi HuThe First Department of Severe Psychiatry, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China.ORCID https://orcid.org/0000-0001-8934-267X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMental health challenges, including insomnia, anxiety, and depression, are common among antenatal women and can affect both maternal and fetal outcomes. This study explores the determinants of these conditions in antenatal women in China, aiming to inform the design of mental health interventions and preventive strategies for this population.

methodsA cross-sectional survey design was employed in this hospital-based study targeting antenatal women at a tertiary hospital in China, conducted from May 2024 to March 2025 during routine antenatal visits. Validated questionnaires assessed insomnia, anxiety, and depression. Multiple linear and logistic regression analyses identified factors associated with symptom severity and occurrence, while Structural Equation Modeling (SEM) was used to explore the relationships and mediating effects between biological and social factors, insomnia, anxiety, and depression.

resultsThe participants had a mean age of 31.48 ± 6.94 years, with most being married (90.7%), living in urban areas (74.3%), and having undergraduate/college education (45.8%). Significant predictors of insomnia included geographical location, with those in central (OR = 1.818, 95% CI: 1.500-2.204) and southern areas (OR = 1.368, 95% CI: 1.143-1.637) showing higher odds compared to the northern region. Living in rural areas (OR = 0.796, 95% CI: 0.718-0.845) and higher education levels (OR = 1.544, 95% CI: 1.012-2.355) were associated with lower odds. Other significant factors included the number of live births and household composition. For anxiety, older age (OR = 0.955, 95% CI: 0.937-0.973) and rural living (OR = 0.675, 95% CI: 0.539-0.845) decreased odds, while living with others (OR = 3.726, 95% CI: 2.463-5.639) increased the risk. Significant predictors of depression included geographical location (central areas: OR = 1.508, 95% CI: 1.106-2.055), income level, and number of live births. The logistic regression Area Under the Curve (AUC) were 0.579 for insomnia, 0.603 for anxiety, and 0.567 for depression. SEM demonstrated an excellent model fit (CFI = 0.994, TLI = 0.999, RMSEA = 0.014). Insomnia was strongly predicted by geographic location, education, and number of live births. In turn, insomnia significantly predicted anxiety (β = 0.741) and depression (β = 0.138). The model explained 54.9% of the variance in anxiety and 70.6% of the variance in depression, indicating partial mediation.

conclusionThis study identifies the multidimensional factors influencing antenatal women's insomnia, anxiety, and depression in China, particularly highlighting the roles of geographical location, current living situation, and household composition. These factors were consistently associated with all three outcomes. Targeted interventions targeting these specific risk factors are recommended to improve the mental health of antenatal women.

Indexed as

AnxietyDepressionPregnancy ComplicationsSleep Initiation and Maintenance DisordersAdultAge FactorsChinaCross-Sectional StudiesEducational StatusFemaleHumansIncomeParityPregnancyRisk FactorsRural Population

Identifiers

PMID41875112
PMCPMC13012504

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.