Evidence map›Paper›PMID 33347423›Full record

ArticleJournal of medical Internet research2021

Maternal Mental Health Status and Approaches for Accessing Antenatal Care Information During the COVID-19 Epidemic in China: Cross-Sectional Study.

Hong Jiang, Longmei Jin, Xu Qian, Xu Xiong, Xuena La, Weiyi Chen, Xiaoguang Yang, Fengyun Yang, Xinwen Zhang, Nazhakaiti Abudukelimu and 8 more

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 5 pooled it
9.9field-weighted citation impact, top 1% of its field
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

37 citing papers in PubMed, 5 syntheses or guidelines pooled it, 63 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Trial
  7. Article
  8. Observational
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
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

18 authors at 7 institutions in 3 countries.

Hong Jiang *School of Public Health, Fudan University, Shanghai, China.ORCID 0000-0002-2810-8000
Longmei Jin *Minhang Branch, School of Public Health, Fudan University, Shanghai, China.ORCID 0000-0002-8273-634X
Xu QianSchool of Public Health, Fudan University, Shanghai, China.ORCID 0000-0001-6816-1589
Xu XiongSchool of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States.ORCID 0000-0001-9292-5059
Xuena LaSchool of Public Health, Fudan University, Shanghai, China.ORCID 0000-0002-4875-9855
Weiyi ChenSchool of Public Health, Fudan University, Shanghai, China.ORCID 0000-0002-5056-220X
Xiaoguang YangSchool of Public Health, Fudan University, Shanghai, China.ORCID 0000-0003-2101-9598
Fengyun YangJiading Maternal and Child Health Care Hospital, Shanghai, China.ORCID 0000-0002-7077-244X
Xinwen ZhangThe Fourth People's Hospital of Shaanxi Province, Xi'an, China.ORCID 0000-0002-7662-2370
Nazhakaiti AbudukelimuSchool of Public Health, Fudan University, Shanghai, China.ORCID 0000-0002-9751-6024
Xingying LiSchool of Public Health, Fudan University, Shanghai, China.ORCID 0000-0003-0113-2294
Zhenyu XiePudong New District Maternal and Child Health Care Hospital, Shanghai, China.ORCID 0000-0002-4517-2936
Xiaoling ZhuLeping Maternal and Child Health Care Hospital, Leping, China.ORCID 0000-0002-5889-7960
Xiaohua ZhangMinhang Branch, School of Public Health, Fudan University, Shanghai, China.ORCID 0000-0002-0265-3115
Lifeng ZhangJiading Maternal and Child Health Care Hospital, Shanghai, China.ORCID 0000-0001-8060-6608
Li WangChangzhou Maternal and Child Health Care Hospital, Changzhou, China.ORCID 0000-0002-9963-2264
Lingling LiChangzheng Hospital, Second Military Medical University, Shanghai, China.ORCID 0000-0002-5951-8387
Mu LiSchool of Public Health, The University of Sydney, Sydney, Australia.ORCID 0000-0002-2371-8950
Fudan University · CNJiading District Central Hospital · CNJingzhou Maternal and Child Health Hospital · CNPudong Maternal and Child Health Hospital · CNSecond Military Medical University · CNThe University of Sydney · AUTulane University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChina was the first country in the world to experience a large-scale COVID-19 outbreak. The rapid spread of the disease and enforcement of public health measures has caused distress among vulnerable populations such as pregnant women. With a limited understanding of the novel, emerging infectious disease, pregnant women have sought ways to access timely and trusted health care information. The mental health status of pregnant women during this public health emergency, as well as how they responded to the situation and where and how they obtained antenatal care information, remain to be understood.

objectiveThis study aimed to evaluate the mental health status of pregnant women during the COVID-19 epidemic in China by measuring their perceived stress, anxiety, and depression levels; explore the approaches used by them to access antenatal health care information; and determine their associations with maternal mental health status.

methodsWe conducted a web-based, cross-sectional survey to assess the mental health status of Chinese pregnant women by using the validated, Chinese version of Perceived Stress Scale, Self-Rating Anxiety Scale, and Edinburgh Depression Scale. We also collected information on the various approaches these women used to access antenatal care information during the early stage of the COVID-19 epidemic, from February 5 to 28, 2020.

resultsA total of 1873 pregnant women from 22 provinces or regions of China participated in the survey. The prevalence of perceived stress, anxiety, and depression among these participants was 89.1% (1668/1873; 95% CI 87.6%, 90.4%), 18.1% (339/1873; 95% CI 16.4%, 19.9%), and 45.9% (859/1873; 95% CI 43.6%, 48.1%), respectively. Hospitals' official accounts on the Chinese social media platforms WeChat and Weibo were the most popular channels among these pregnant women to obtain antenatal care information during the COVID-19 outbreak. Access to antenatal care information via the hospitals' official social media accounts was found to be associated with a significantly lower risk of perceived stress (adjusted odds ratio [aOR] 0.46, 95% CI 0.30-0.72; P=.001), anxiety (aOR 0.53, 95% CI 0.41-0.68; P<.001), and depression (aOR 0.73, 95% CI 0.59-0.91; P=.005). Access to health care information via hospital hotlines or SMS was found to be significantly associated with a lower risk of anxiety only (OR 0.77, 95% CI 0.60-0.98; P=.04).

conclusionsDuring the COVID-19 outbreak in China, pregnant women experienced high levels of perceived stress, anxiety, and depression. During such public health emergencies, mental health care services should be strengthened to reassure and support pregnant women. Specific information targeted at pregnant women, including information on how to cope in an emergency or major disease outbreak, developed and disseminated by health care institutions via social media platforms could be an effective way to mitigate mental health challenges and ensure epidemic preparedness and response in the future.

Indexed as

Health StatusAdultChinaCOVID-19Cross-Sectional StudiesFemaleHumansInternetMental HealthPregnancyPrenatal CareSocial MediaSurveys and Questionnairesantenatal care informationanxietyCOVID-19depressionmental healthperceived stresspregnancysocial media platformwomen

Identifiers

PMID33347423
PMCPMC7817253
OpenAlexW3112616811

What OpenQuestion 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 OpenQuestion graph.