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Assessing spatiotemporal variability in SARS-CoV-2 infection risk for hospital workers using routinely-collected data
被引:1
作者:

Wilson-Aggarwal, Jared K. J.
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机构:
Univ Leeds, Sch Geog, Woodhouse, Leeds, England Univ Leeds, Sch Geog, Woodhouse, Leeds, England

Gotts, Nick F.
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h-index: 0
机构:
Univ Leeds, Sch Geog, Woodhouse, Leeds, England Univ Leeds, Sch Geog, Woodhouse, Leeds, England

Arnold, Kellyn
论文数: 0 引用数: 0
h-index: 0
机构:
Univ Leeds, Sch Geog, Woodhouse, Leeds, England Univ Leeds, Sch Geog, Woodhouse, Leeds, England

Spyer, Moira J.
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h-index: 0
机构:
Univ Coll London Hosp NHS Fdn Trust, Dept Clin Virol, London, England
UCL, UCL GOS Inst Child Hlth, Dept Infect Immun & Inflammat, London, England Univ Leeds, Sch Geog, Woodhouse, Leeds, England

Houlihan, Catherine F.
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h-index: 0
机构:
Univ Coll London Hosp NHS Fdn Trust, Dept Clin Virol, London, England
UCL, Dept Infect & Immun, London, England Univ Leeds, Sch Geog, Woodhouse, Leeds, England

Nastouli, Eleni
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h-index: 0
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Univ Coll London Hosp NHS Fdn Trust, Dept Clin Virol, London, England
UCL, UCL GOS Inst Child Hlth, Dept Infect Immun & Inflammat, London, England Univ Leeds, Sch Geog, Woodhouse, Leeds, England

Manley, Ed
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h-index: 0
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Univ Leeds, Sch Geog, Woodhouse, Leeds, England Univ Leeds, Sch Geog, Woodhouse, Leeds, England
机构:
[1] Univ Leeds, Sch Geog, Woodhouse, Leeds, England
[2] Univ Coll London Hosp NHS Fdn Trust, Dept Clin Virol, London, England
[3] UCL, UCL GOS Inst Child Hlth, Dept Infect Immun & Inflammat, London, England
[4] UCL, Dept Infect & Immun, London, England
来源:
PLOS ONE
|
2023年
/
18卷
/
04期
关键词:
HEALTH-CARE WORKERS;
COVID-19;
SEROPREVALENCE;
CONTACTS;
D O I:
10.1371/journal.pone.0284512
中图分类号:
O [数理科学和化学];
P [天文学、地球科学];
Q [生物科学];
N [自然科学总论];
学科分类号:
07 ;
0710 ;
09 ;
摘要:
The COVID-19 pandemic has emphasised the need to rapidly assess infection risks for healthcare workers within the hospital environment. Using data from the first year of the pandemic, we investigated whether an individual's COVID-19 test result was associated with behavioural markers derived from routinely collected hospital data two weeks prior to a test. The temporal and spatial context of behaviours were important, with the highest risks of infection during the first wave, for staff in contact with a greater number of patients and those with greater levels of activity on floors handling the majority of COVID-19 patients. Infection risks were higher for BAME staff and individuals working more shifts. Night shifts presented higher risks of infection between waves of COVID-19 patients. Our results demonstrate the epidemiological relevance of deriving markers of staff behaviour from electronic records, which extend beyond COVID-19 with applications for other communicable diseases and in supporting pandemic preparedness.
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