Prevalence of mental disorders in China: a cross-sectional epidemiological study

Huang, Yueqin; Wang, Yu; Wang, Hong; Liu, Zhaorui; Yu, Xinguang; Yan, Jie; Yu, Yaqin; Kou, Changgui; Xu, Xiufeng; Lü, Jin; Wang, Zhizhong; He, Shulan; Xu, Yifeng; He, Yulong; Li, Tao; Guo, Wanjun; Tian, Hongjun; Xu, Guangming; Xu, Xiaoli; Ma, Yanjuan; Wang, Lınhong; Wang, Limin; Yan, Yongping; Wang, Bo; Xiao, Shuiyuan; Zhou, Liang; Li, Lingjiang; Tan, Liwen; Zhang, Tingting; Ma, Chao; Li, Qiang; Ding, Hua; Geng, Hongchun; Jia, Fu‐Jun; Shi, Jianfei; Wang, Shiliang; Zhang, Ning; Du, Xinbai; Du, Xiangdong; Wu, Yue

doi:10.1016/s2215-0366(18)30511-x

Cited by 1,596 publications

(1,106 citation statements)

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“…Mental health problems. According to a previous study two major mental disordersdepression and anxiety [13] were assessed in the current study. Depression was assessed by The Chinese version of WHO-Five Well-Being Index (WHO-5) [14], which consists of five positively worded items that reflect the presence or absence of well-being rather than depressive symptomatology.…”

Section: Measurementsmentioning

confidence: 99%

“…But there was no study to examine the association between social media exposure and mental health problems. So, the current study aims to describes the prevalence and distribution of two major mental disorders-anxiety and depression among Chinese population [13], and examine their associations with social media exposure by rapid assessment during COVID-19 outbreak.…”

Section: Introductionmentioning

confidence: 99%

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Mental health problems and social media exposure during COVID-19 outbreak

et al. 2020

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Huge citizens expose to social media during a novel coronavirus disease (COVID-19) outbroke in Wuhan, China. We assess the prevalence of mental health problems and examine their association with social media exposure. A cross-sectional study among Chinese citizens aged�18 years old was conducted during Jan 31 to Feb 2, 2020. Online survey was used to do rapid assessment. Total of 4872 participants from 31 provinces and autonomous regions were involved in the current study. Besides demographics and social media exposure (SME), depression was assessed by The Chinese version of WHO-Five Well-Being Index (WHO-5) and anxiety was assessed by Chinese version of generalized anxiety disorder scale (GAD-7). multivariable logistic regressions were used to identify associations between social media exposure with mental health problems after controlling for covariates. The prevalence of depression, anxiety and combination of depression and anxiety (CDA) ) during COVID-19 outbroke in Wuhan, China. More than 80% (95%CI:80.9%-83.1%) of participants reported frequently exposed to social media. After controlling for covariates, frequently SME was positively associated with high odds of anxiety (OR = 1.72, 95%CI: 1.31-2.26) and CDA (OR = 1.91, 95%CI: 1.52-2.41) compared with less SME. Our findings show there are high prevalence of mental health problems, which positively associated with frequently SME during the COVID-19 outbreak. These findings implicated the government need pay more attention to mental health problems, especially depression and anxiety among general population and combating with "infodemic" while combating during public health emergency.

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Section: Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mental health problems and social media exposure during COVID-19 outbreak

et al. 2020

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“…A cross-sectional epidemiological study in China reported that the weighted lifetime prevalence of psychiatric disorders excluding dementia was 16.6% (95% CI: 13.0-20.2) (Huang et al, 2019). It is estimated that tens of millions of people with psychiatric disorders in China are at risk of infection with the COVID-19 virus.…”

Section: Introductionmentioning

confidence: 99%

Knowledge and attitudes of medical staff in Chinese psychiatric hospitals regarding COVID-19

Shi

Wang

Yang

et al. 2020

Brain, Behavior, & Immunity - Health

293

298

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“…Schizophrenia (SCZ) is a severe mental disorder with a lifetime prevalence of 1% and an estimated heritability of 64–80% (Huang et al, ; Lichtenstein et al, ; Thaker & Carpenter Jr., ). Although unaffected first‐degree relatives (FDRs) of SCZ patients have a 10‐fold increased risk of developing SCZ (Byrne et al, ; Dodell‐Feder, DeLisi, & Hooker, ), only 11–15% of the children of patients may develop adult‐onset SCZ (Erlenmeyerkimling et al, ).…”

Section: Introductionmentioning

confidence: 99%

Machine learning identifies unaffected first‐degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients

Jing

Ding

Lin

et al. 2019

Human Brain Mapping

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Schizophrenia (SCZ) patients and their unaffected first‐degree relatives (FDRs) share similar functional neuroanatomy. However, it remains largely unknown to what extent unaffected FDRs with functional neuroanatomy patterns similar to patients can be identified at an individual level. In this study, we used a multivariate pattern classification method to learn informative large‐scale functional networks (FNs) and build classifiers to distinguish 32 patients from 30 healthy controls and to classify 34 FDRs as with or without FNs similar to patients. Four informative FNs—the cerebellum, default mode network (DMN), ventral frontotemporal network, and posterior DMN with parahippocampal gyrus—were identified based on a training cohort and pattern classifiers built upon these FNs achieved a correct classification rate of 83.9% (sensitivity 87.5%, specificity 80.0%, and area under the receiver operating characteristic curve [AUC] 0.914) estimated based on leave‐one‐out cross‐validation for the training cohort and a correct classification rate of 77.5% (sensitivity 72.5%, specificity 82.5%, and AUC 0.811) for an independent validation cohort. The classification scores of the FDRs and patients were negatively correlated with their measures of cognitive function. FDRs identified by the classifiers as having SCZ patterns were similar to the patients, but significantly different from the controls and FDRs with normal patterns in terms of their cognitive measures. These results demonstrate that the pattern classifiers built upon the informative FNs can serve as biomarkers for quantifying brain alterations in SCZ and help to identify FDRs with FN patterns and cognitive impairment similar to those of SCZ patients.

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Prevalence of mental disorders in China: a cross-sectional epidemiological study

Cited by 1,596 publications

References 7 publications

Mental health problems and social media exposure during COVID-19 outbreak

Mental health problems and social media exposure during COVID-19 outbreak

Knowledge and attitudes of medical staff in Chinese psychiatric hospitals regarding COVID-19

Machine learning identifies unaffected first‐degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients

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