Super-factors associated with transmission of occupational COVID-19 infection among healthcare staff in Wuhan, China

J Hosp Infect. 2020 Sep;106(1):25-34. doi: 10.1016/j.jhin.2020.06.023. Epub 2020 Jun 20.


Background: Globally, there have been many cases of coronavirus disease 2019 (COVID-19) among medical staff; however, the main factors associated with the infection are not well understood.

Aim: To identify the super-factors causing COVID-19 infection in medical staff in China.

Methods: A cross-sectional study was conducted between January 1st and February 30th, 2020, in which front-line members of medical staff who took part in the care and treatment of patients with COVID-19 were enrolled. Epidemiological and demographic data between infected and uninfected groups were collected and compared. Social network analysis (SNA) was used to establish socio-metric social links between influencing factors.

Findings: A total of 92 medical staff were enrolled. In all participant groups, the super-factor identified by the network was wearing a medical protective mask or surgical mask correctly (degree: 572; closeness: 25; betweenness centrality: 3.23). Touching the cheek, nose, and mouth while working was the super-factor in the infected group. This was the biggest node in the network and had the strongest influence (degree: 370; closeness: 29; betweenness centrality: 0.37). Self-protection score was the super-factor in the uninfected group but was the isolated factor in the infected group (degree: 201; closeness: 28; betweenness centrality: 5.64). For family members, the exposure history to Huanan Seafood Wholesale Market and the contact history to wild animals were two isolated nodes.

Conclusion: High self-protection score was the main factor that prevented medical staff from contracting COVID-19 infection. The main factor contributing to COVID-19 infections among medical staff was touching the cheek, nose, and mouth while working.

Keywords: Infection; Medical staff; SARS-CoV-2; Social network analysis; Super-factors.

MeSH terms

  • Adult
  • Betacoronavirus
  • COVID-19
  • China / epidemiology
  • Coronavirus Infections / epidemiology*
  • Coronavirus Infections / transmission*
  • Cross-Sectional Studies
  • Female
  • Health Personnel / statistics & numerical data*
  • Humans
  • Male
  • Middle Aged
  • Occupational Diseases / epidemiology*
  • Pandemics
  • Pneumonia, Viral / epidemiology*
  • Pneumonia, Viral / transmission*
  • Risk Factors
  • SARS-CoV-2
  • Surveys and Questionnaires