Assessing spatiotemporal variability in SARS-CoV-2 infection risk for hospital workers using routinely-collected data.
Jared K Wilson-AggarwalNick GottsKellyn ArnoldMoira J SpyerCatherine F HoulihanEleni NastouliEd ManleyPublished in: PloS one (2023)
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.
Keyphrases
- sars cov
- coronavirus disease
- human health
- respiratory syndrome coronavirus
- electronic health record
- healthcare
- adverse drug
- newly diagnosed
- public health
- end stage renal disease
- chronic kidney disease
- risk assessment
- climate change
- long term care
- depressive symptoms
- artificial intelligence
- data analysis
- high resolution
- machine learning
- single molecule
- mass spectrometry