Evidence map›Paper›PMID 35245344›Full record

SynthesisPloS one2022

Risk factors for depression and anxiety in pregnant women during the COVID-19 pandemic: Evidence from meta-analysis.

Yupeng Luo, Kui Zhang, Mengxue Huang, Changjian Qiu

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 2 pooled it
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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

18 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

4 authors.

Yupeng LuoWest China hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.
Kui ZhangDepartment of Forensic Pathology, West China School of BasicMedical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan, People's Republic of China.
Mengxue HuangHua Da appraisal center, Chengdu, Sichuan, People's Republic of China.
Changjian QiuWest China hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.ORCID 0000-0003-2769-1940

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe prevalence of anxiety and depression in pregnant women has significantly increased after the spread of COVID-19 throughout the world. We carried out this meta-analysis to reveal the information about risk factors for depression and anxiety in pregnant women during the COVID-19 pandemic.

methodsWe searched the PubMed, Embase and CNKI (China National Knowledge Infrastructure) databases for all articles. The odds ratio (OR) corresponding to the 95% confidence interval (95% CI) was used to assess the risk factors for mental health. The statistical heterogeneity among studies was assessed with the Q-test and I2 statistics.

resultsWe collected 17 studies including 15,050 pregnant women during the COVID-19 pandemic. Our results found that factors including decrease in the perception of general support and difficulties in household finances have damage effects on anxiety, and factors including undereducated, unemployed during pregnancy, with a chronic physical illness before pregnancy, decrease in the perception of general support, difficulties in household finances, disobey the isolation rules, and smoking during pregnancy have increased risk of depression.

conclusionOur meta-analysis revealed some risk factors for mental health in pregnant women during COVID-19 pandemic. Mental health interventions in pregnant women may involve targeted methods individually.

Indexed as

SARS-CoV-2AdultAnxietyCOVID-19DepressionFemaleHumansPregnancyPregnancy ComplicationsPregnant PeopleRisk Factors

Identifiers

PMID35245344
PMCPMC8896698

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Registered trials

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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.