Evidence map›Paper›PMID 40356002›Full record

ArticleActas espanolas de psiquiatria2025

The Impact of Omicron-related Stress on Mental Health in the General Population of China.

WenYan Zhao, YuLiang Zhou, YingYing Hu, Jing Wang, Hong Zhu, YaHong Li, ZhiPeng Xu

Abstract read
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Article in Actas espanolas de psiquiatria, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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7 authors.

WenYan ZhaoDepartment of Neuropsychology, Zhongnan Hospital of Wuhan University, 430000 Wuhan, Hubei, China.
YuLiang ZhouDepartment of Neuropsychology, Zhongnan Hospital of Wuhan University, 430000 Wuhan, Hubei, China.
YingYing HuDepartment of Neuropsychology, Zhongnan Hospital of Wuhan University, 430000 Wuhan, Hubei, China.
Jing WangDepartment of Neuropsychology, Zhongnan Hospital of Wuhan University, 430000 Wuhan, Hubei, China.
Hong ZhuDepartment of Neuropsychology, Zhongnan Hospital of Wuhan University, 430000 Wuhan, Hubei, China.
YaHong LiDepartment of Applied Psychology, South-Central Minzu University, 430000 Wuhan, Hubei, China.
ZhiPeng XuDepartment of Neuropsychology, Zhongnan Hospital of Wuhan University, 430000 Wuhan, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOutbreaks of infectious disease represent unique stressors for the general population. In this study, we investigated the prevalence of mental health symptoms and associated risk factors in the general population of China during the Omicron wave.

methodsWe conducted a cross-sectional and large sample online survey during the surge of Omicron cases between 17 December 2022 and 8 January 2023 among Chinese citizens. Then we assessed the prevalence of symptoms of anxiety, depression, insomnia, acute stress disorder, and resilience levels, in the general population of China during the Omicron pandemic by utilizing the coronavirus disease 2019 (COVID-19) anxiety scale (CAS), the Center for Epidemiologic Studies Depression Scale (CES-D), the Insomnia Severity Index scale (ISI), the Stanford Acute Stress Reaction Questionnaire (SASRQ), and the Connor-Davidson Resilience Scale (CD-RISC). Multivariate logistic regression analyses were used to identify demographic and Omicron-related risk factors.

resultsIn total, 2800 respondents across 32 provinces and autonomous regions on the Chinese mainland participated in this survey; 1133 (40.5%) were male, and 1860 (66.4%) were 40 years-of-age or younger. The prevalence of anxiety, depression, insomnia, and acute stress disorder was 52%, 58.3%, 45.2%, and 34.8%, respectively. After adjustment for covariates, female gender, a younger age, being unmarried, low income, and a non-medical post were all associated with mental health problems. During the course of infection, participants had a higher risk of developing symptoms of anxiety (odds ratio [OR]: 1.27; 95% confidence interval [CI]: 1.03-1.57; p = 0.028), depression (OR: 1.76; 95% CI: 1.44-2.16; p < 0.001), insomnia (OR: 1.95; 95% CI: 1.57-2.42, p < 0.001) and acute stress disorder (OR: 1.56; 95% CI: 1.25-1.93, p = 0.001). In addition, we found that a lower resilience among participants was associated with a higher risk of anxiety, depression, insomnia, and acute stress disorder (p < 0.001).

conclusionOmicron-related stress had a profound effect on the mental health of the general population of China, especially among those infected during the course of coronavirus disease 2019 (COVID-19) and with lower resilience. Our findings suggest that mental health can be improved during a pandemic by increasing resilience.

Indexed as

AnxietyCOVID-19DepressionMental HealthStress, PsychologicalAdolescentAdultAgedChinaCross-Sectional StudiesFemaleHumansMaleMiddle AgedPandemicsPrevalence

Identifiers

PMID40356002
PMCPMC12069919

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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.