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Record W3015704840 · doi:10.1016/j.jinf.2020.03.030

Characteristics of deaths amongst health workers in China during the outbreak of COVID-19 infection

2020· letter· en· W3015704840 on OpenAlexaboutno aff
Wei Li, Jie Zhang, Shifu Xiao, Lin Sun

Bibliographic record

VenueJournal of Infection · 2020
Typeletter
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsScopusCoronavirus disease 2019 (COVID-19)ChinaMedicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)OutbreakPneumoniaMental health2019-20 coronavirus outbreakHealth careFamily medicineMEDLINEPsychiatryHistoryVirologyInternal medicinePolitical science

Abstract

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Since December, 2019, an outbreak of a novel coronavirus pneumonia (COVID-19) occurred in Wuhan (Hubei, China).1Chen Q. Liang M. Li Y. et al.Mental health care for medical staff in China during the COVID-19 outbreak.The lancet Psychiatry. 2020; Abstract Full Text Full Text PDF Scopus (1426) Google Scholar Recent papers in this journal also described the clinical and computed tomographic imaging features of novel coronavirus pneumonia caused by COVID-19.2YH X. JH D. WM A. et al.Clinical and computed tomographic imaging features of novel coronavirus pneumonia caused by SARS-CoV-2.The Journal of infection. 2020; Google Scholar During nearly two months of fighting against the epidemic, health workers were under great physiological and psychological pressure in China.3Wu P. Fang Y. Guan Z. et al.The psychological impact of the SARS epidemic on hospital employees in China: exposure, risk perception, and altruistic acceptance of risk.Canadian journal of psychiatry Revue canadienne de psychiatrie. 2009; 54: 302-311Crossref PubMed Scopus (1155) Google Scholar For example, due to wearing protective clothing, many health workers avoided drinking water and wore adult diapers for a long time, so that some of them fainted under hypoxia and hypoglycaemia.4Zeng Y. Zhen Y. Chinese medical staff request international medical assistance in fighting against COVID-19.The Lancet Global health. 2020; Crossref PubMed Scopus (17) Google Scholar Previous studies showed that stress could increase the risk of infection5Calefi A.S. de Queiroz Nunes C.A. da Silva Fonseca J.G. Quinteiro-Filho W.M. Ferreira A.J.P. Palermo-Neto J. Heat stress reduces Eimeria spp. infection and interferes with C. perfringens infection via activation of the hypothalamic-pituitary-adrenal axis.Research in veterinary science. 2019; 123: 273-280Crossref PubMed Scopus (2) Google Scholar as well as induce ventricular arrhythmia, and thus sudden cardiac death.6Scorza F.A. Albuquerque R. Arida R.M. et al.What are the similarities between stress, sudden cardiac death in Gallus gallus and sudden unexpected death in people with epilepsy.Arquivos de neuro-psiquiatria. 2010; 68: 788-790Crossref PubMed Scopus (7) Google Scholar As a result, the medical staff in the front-line fighting against the novel coronary pneumonia were facing high risks of virus infection and sudden death. In 2003, more than 1,000 health workers were attacked by severe acute respiratory syndrome (SARS) and 124 deaths were observed in China. As of Mar 16, 2020, 24 health workers had died during the outbreak of COVID-19 infection in China. We retrieved information on 24 cases of deceased health workers based on official reports from governmental institutes, as well as reports from news sites. Data available to the public included gender, age, cause of death, location city, date of disease onset, date of admission, date of death, and hospital levels they worked. We grouped cases into three groups based on the cause of death, which included COVID-19 infection, sudden death, and traffic accident groups. Mann-Whitney U test was applied to compare continuous variables because the data was non-normal distribution, and Fisher exact test was used for categorical variables because the data number was limited. Thirteen (54.2%) cases died of COVID-19 infection, 8 (33.3%) suffered from sudden death including cardiac arrest, myocardial infarction, and other non-confirmed diseases, and 3 (12.5%) died in traffic accidents during work time or after work (Table 1). The basic information of all the deceased health workers was listed in Fig 1A. The median age was 50.5 years (IQR: 36.25-56.5), ranging from 26 to 69 years. A total of 72,314 patient record showed that 81% of dead cases were aged 60 years or older and 12.7% were aged 50 to 59 years.7Novel Coronavirus Pneumonia Emergency Response Epidemiology T.[The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China].Zhonghua Liu Xing Bing Xue Za Zhi. 2020; 41: 145-151PubMed Google Scholar The median age of deceased medical staff was obviously younger than that of the general population, because medical staff were mostly in employment who were younger than 60 years. Up to 83.3% of deceased medical workers were males and no sex differences existed among COVID-19 infection group, sudden death group, and traffic accident group. In the group of infection, 11 deceased cases (84.6%) were males. Zhang reported that the overall case fatality rate of male patients (rough estimate: 2.8%) was significantly higher than that of female patients (rough estimate: 1.7%).7Novel Coronavirus Pneumonia Emergency Response Epidemiology T.[The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China].Zhonghua Liu Xing Bing Xue Za Zhi. 2020; 41: 145-151PubMed Google Scholar In the group of sudden death, 7 cases (87.5%) were males. Previous study revealed that, at 45 years of age, lifetime risks for sudden cardiac death were 10.9% for men and 2.8% for women,8E M. A U.-E. K R. et al.Sudden cardiac arrest during sports activity in middle age.Circulation. 2015; 131: 1384-1391Crossref PubMed Scopus (159) Google Scholar which was similar to the results in our study. The above data suggested that males had a higher risk of death due to COVID-19 infection and sudden death than females.Table 1Demographics of deceased medical workers in China by Mar 16, 2020CharacteristicTotal(n=24)COVID-19 infection(n=13, 54.2%)Sudden death(n=8, 33.3%)Traffic accident(n=3, 12.5%)Z/χ2(P /Fisher P)Age, Median (IQR) -yrs50.5(36.25-56.5)51(38.0-58.0)50(36.25-56.5)/-0.399(0.690)Male, No. (%)20(83.3)11(84.6)7(87.5)2(66.7)1.180(0.579)Hubei resident, No. (%)11(45.8)11(84.6)0(0.0)0(0.0)17.293(0.000*)Wuhan resident, No. (%)9(37.5)9(69.2)0(0.0)0(0.0)11.684(0.001*)Community hospital, No. (%)11(45.8)3(23.1)5(62.5)3(100.0)6.644(0.022*)Onset to admission, Median (IQR)-days/2(1-5.5) (n=9)///Admission to death, Median (IQR)-days/26(21.25-36.5) (n=12)///Onset to death, Median (IQR)-days/30.5(25-35.25) (n=10)/// Open table in a new tab Transmission of COVID-19 occurred in the hospital setting. In the group of COVID-19 infection, there were more medical staff working in Hubei province (84.6%) and Wuhan city (69.2%), which was consistent with the result of 63% of infected medical staffs in Wuhan in a recent report.9Z W. JM M. Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention.Jama. 2020; Google Scholar Due to the severity of COVID-19 infection in Hubei, more nosocomial infections and deaths occurred in Hubei than other provinces. As of February 11, 2020, 3,019 cases have been observed among health workers, of whom there have been 1,716 confirmed cases. Among health workers infected, 14.8% of confirmed cases were classified as severe or critical, and 5 deaths were observed.9Z W. JM M. Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention.Jama. 2020; Google Scholar Among all the deceased medical staff with COVID-19 infection, the median of period from disease onset to hospital admission was 2 days (IQR: 1-5.5), and the median of period from admission to death was 26 days (IQR: 21.25-36.5) (Table 1). Based on the admission date of staff with COVID-19 infection and the death date of staff with sudden death, the new number of deceased health workers per 5 days was listed in Fig 2. Attacked infection mostly occurred on January and sudden death mainly happened from Jan 23 to Feb 10, 2020. Furthermore, there were more health workers who worked in community hospitals suffering from sudden death or traffic accident. Sudden death due to huge work and lack of rest happened since Jan 23, 2020, when comprehensive measures for epidemic prevention and control were taken nationwide. Large-scale work including temperature measurements, door to door visit, medicine delivery, patients transfer, disinfection, etc., had been completed by community or village medical workers. Some village doctors even lived and ate in the village clinics. On Feb 22,2020, the Chinese government took a series of measures to protect and support health workers in the front line, such as improving the quality of life, strengthening personal protection, arranging rest in turns, and relieving mental stress. Afterwards, the incidence of accidental death decreased significantly. In summary, there were more males in the fatality of health workers, more sudden death happening to community health workers, and more death due to COVID-19 infection occurring in Hubei health workers during the outbreak of COVID-19 in China. None This research was funded by grants from Clinical research center project of Shanghai Mental Health Center (CRC2017ZD02), Western medical guidance project of Shanghai Science and Technology Commission (17411970100), and National Natural Science Foundation of China (81301139).

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.373
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations28
Published2020
Admission routes1
Has abstractyes

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