| Literature DB >> 32315963 |
Md Zahir Ahmed1, Oli Ahmed2, Zhou Aibao3, Sang Hanbin3, Liu Siyu4, Akbaruddin Ahmad5.
Abstract
The world is experiencing pandemic of the COVID-19 now, a RNA virus that spread out from Wuhan, China. Two countries, China first and later Italy, have gone to full lock down due to rapid spread of this virus. Till to date, no epidemiological data on mental health problems due to outbreak of the COVID-19 and mass isolation were not available. To meet this need, the present study was undertaken to assess the mental health status of Chinese people. An online survey was conducted on a sample of 1074 Chinese people, majority of whom from Hubei province. Lack of adequate opportunities to conduct face to face interview, anxiety, depression, mental well-being and alcohol consumption behavior were assessed via self-reported measures. Results showed higher rate of anxiety, depression, hazardous and harmful alcohol use, and lower mental wellbeing than usual ratio. Results also revealed that young people aged 21-40 years are in more vulnerable position in terms of their mental health conditions and alcohol use. To address mental health crisis during this epidemic, it is high time to implement multi-faceted approach (i.e. forming multidisciplinary mental health team, providing psychiatric treatments and other mental health services, utilizing online counseling platforms, rehabilitation program, ensuring certain care for vulnerable groups, etc.).Entities:
Keywords: Alcohol use disorder; Anxiety; COVID-19; China; Depression; Epidemic
Mesh:
Year: 2020 PMID: 32315963 PMCID: PMC7194662 DOI: 10.1016/j.ajp.2020.102092
Source DB: PubMed Journal: Asian J Psychiatr ISSN: 1876-2018
Participants’ distribution in terms of their socio-demographic characteristics.
| Variables | Groups | Frequency (%) |
|---|---|---|
| Female | 503 (46.8 %) | |
| Male | 571 (53.2 %) | |
| Junior school and below | 197 (18.3 %) | |
| High school/technical secondary school/ technical school | 297 (27.7 %) | |
| University degree (specialized) | 290 (27 %) | |
| Bachelor | 249 (23.2 %) | |
| Masters | 41 (3.8 %) | |
| Hubei | 678 (63.1 %) | |
| Other | 396 (36.9 %) | |
| Student | 184 (17.1 %) | |
| Government official | 91 (8.5 %) | |
| Enterprise manager | 98 (9.1 %) | |
| General Staff | 185 (11.2 %) | |
| Professional | 216 (20.1 %) | |
| Ordinary worker | 148 (13.8 %) | |
| Agriculture/forestry/animal husbandry/ fishery worker | 27 (2.5 %) | |
| Retired | 38 (3.5 %) | |
| Unemployed | 36 (3.4 %) | |
| Other | 51 (4.7 %) |
Prevalence statistics of anxiety, depression and alcohol abuse and dependence, and overall mental well-beingstatus after the COVID-19 epidemic.
| Levels | percentages | |
|---|---|---|
| Mild | 10.1 % | |
| Moderate | 6.0 % | |
| Severe | 12.9 % | |
| Mild | 10.2 % | |
| Moderate | 17.8 % | |
| Severe | 9.1 % | |
| Hazardous drinking | 29.1 % | |
| Harmful drinking | 9.5 % | |
| Alcohol dependent | 1.6 % | |
| Lower | 32.1 % | |
| Average | 49.4 % | |
| Higher | 18.4 % |
Differences in anxiety, depression, alcohol use, and mental well-being among people from Hubei and other province after the COVID-19 epidemic.
| Province | χ2 ( | Effect size | ||
|---|---|---|---|---|
| Hubei | Others | |||
| Mild | 67 (9.9 %) | 42 (10.6 %) | 4.340 (.227) | .064 |
| Moderate | 45 (6.6 %) | 19 (4.8 %) | ||
| Severe | 96 (14.2 %) | 43 (10.9 %) | ||
| Mild | 68 (10.0 %) | 42 (10.6 %) | 11.908 (.008) | .105 |
| Moderate | 123 (18.1 %) | 68 (17.2 %) | ||
| Severe | 77 (11.4 %) | 21 (5.3 %) | ||
| Hazardous users | 227 (33.5 %) | 85 (21.5 %) | 30.772 (<.001) | .169 |
| Harmful users | 75 (11.1 %) | 13 (1.9 %) | ||
| Dependent users | 27 (6.8 %) | 4 (1%) | ||
| Lower | 221 (32.6 %) | 124 (31.3 %) | 3.874 (.144) | .060 |
| Average | 344 (50.7 %) | 187 (47.2 %) | ||
| Upper | 113 (16.7 %) | 85 (21.5 %) | ||
Gender differences in anxiety, depression, alcohol use, and mental well-beingafter to the COVID-19 epidemic.
| Gender | χ2 ( | Effect size | ||
|---|---|---|---|---|
| Male | Female | |||
| Mild | 52 (9.1 %) | 57 (11.3 %) | 6.530 (.088) | .078 |
| Moderate | 34 (6.0 %) | 30 (6.1 %) | ||
| Severe | 87 (15.2 %) | 52 (10.3 %) | ||
| Mild | 55 (9.6 %) | 55 (10.9 %) | 4.008 (.261) | .061 |
| Moderate | 111 (19.4 %) | 80 (15.9 %) | ||
| Severe | 57 (10.0 %) | 41 (8.2 %) | ||
| Hazardous users | 187 (32.7 %) | 125 (24.9 %) | 19.696 (<.001) | .135 |
| Harmful users | 66 (11.6 %) | 11 (1.9 %) | ||
| Dependent users | 36 (7.2 %) | 6 (1.2 %) | ||
| Lower | 170 (29.8 %) | 175 (34.8 %) | 4.121 (.127) | .060 |
| Average | 286 (50.1 %) | 245 (48.7 %) | ||
| Upper | 115 (20.1 %) | 83 (16.5 %) | ||
Differences in anxiety, depression, and alcohol abuse among different age groups related to the COVID-19 epidemic.
| Age groups | χ2 ( | Effect size | |||||
|---|---|---|---|---|---|---|---|
| ≥ 20 years | 21−30 years | 31−40 years | 41−50 years | ≤50 years | |||
| Anxiety | |||||||
| Mild | 2 (3.2 %) | 57 (12.0 %) | 24 (9.5 %) | 18 (9.4 %) | 8 (8.5 %) | 39.484(<.001) | .192 |
| Moderate | 3 (4.8 %) | 31 (6.5 %) | 16 (6.3 %) | 11 (5.8 %) | 3 (3.2 %) | ||
| Severe | 3 (4.8 %) | 89 (18.8 %) | 23 (9.1 %) | 14 (7.3 %) | 10 (10.6 %) | ||
| Depression | |||||||
| Mild | 6 (9.7 %) | 59 (12.4 %) | 23 (9.1 %) | 14 (7.3 %) | 8 (8.5 %) | 38.830 (<.001) | .190 |
| Moderate | 8 (12.9 %) | 108 (22.8 %) | 44 (17.4 %) | 20 (10.5 %) | 11 (11.7 %) | ||
| Severe | 3 (4.8 %) | 51 (10.8 %) | 26 (10.3 %) | 12 (6.3 %) | 6 (6.4 %) | ||
| Hazardous users | 16 (25.8 %) | 138 (29.1 %) | 83 (32.8 %) | 53 (27.7 %) | 22 (29.1 %) | 18.764 (.094) | .132 |
| Harmful users | 3 (4.8 %) | 59 (12.4 %) | 23 (9.1 %) | 13 (6.8 %) | 4 (4.3 %) | ||
| Dependent users | – | 6 (1.3 %) | 5 (2%) | 4 (2.1 %) | 2 (2.1 %) | ||
| Mental wellbeing | |||||||
| Lower | 18 (29 %) | 178 (37.6 %) | 76 (30 %) | 50 (26.2 %) | 23 (24.5 %) | 21.259 (.006) | .141 |
| Average | 33 (53.2 %) | 225 (47.5 %) | 133 (52.6 %) | 96 (50.3 %) | 44 (46.8 %) | ||
| Upper | 11 (17.7 %) | 71 (15 %) | 44 (17.4 %) | 45 (23.6 %) | 27 (28.7 %) | ||