Assessing spatiotemporal variability in SARS-CoV-2 infection risk for hospital workers using routinely-collected data.

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.

Investigating healthcare worker mobility and patient contacts within a UK hospital during the COVID-19 pandemic.

BACKGROUND: Insights into behaviours relevant to the transmission of infections are extremely valuable for epidemiological investigations. Healthcare worker (HCW) mobility and patient contacts within the hospital can contribute to nosocomial outbreaks, yet data on these behaviours are often limited. METHODS: Using electronic medical records and door access logs from a London teaching hospital during the COVID-19 pandemic, we derive indicators for HCW mobility and patient contacts at an aggregate level. We assess the spatial-temporal variations in HCW behaviour and, to demonstrate the utility of these behavioural markers, investigate changes in the indirect connectivity of patients (resulting from shared contacts with HCWs) and spatial connectivity of floors (owing to the movements of HCWs). RESULTS: Fluctuations in HCW mobility and patient contacts were identified during the pandemic, with the most prominent changes in behaviour on floors handling the majority of COVID-19 patients. The connectivity between floors was disrupted by the pandemic and, while this stabilised after the first wave, the interconnectivity of COVID-19 and non-COVID-19 wards always featured. Daily rates of indirect contact between patients provided evidence for reactive staff cohorting in response to the number of COVID-19 patients in the hospital. CONCLUSIONS: Routinely collected electronic records in the healthcare environment provide a means to rapidly assess and investigate behaviour change in the HCW population, and can support evidence based infection prevention and control activities. Integrating frameworks like ours into routine practice will empower decision makers and improve pandemic preparedness by providing tools to help curtail nosocomial outbreaks of communicable diseases.

Movement of healthcare workers and their patient contacts can contribute to outbreaks of infection in the healthcare environment. We use electronic medical records and door access logs from a London hospital to derive indicators for staff behaviour during the COVID-19 pandemic. Changes in staff behaviour were most prominent on floors handling the majority of COVID-19 patients. We also show how the flow of staff between COVID-19 and non-COVID-19 wards continued throughout the pandemic, but find evidence that indirect contact between COVID-19 positive and negative patients reduced as COVID-19 prevalence increased. We suggest these routinely collected data on HCW behaviour should be used to support decision makers in activities to help curtail disease outbreaks in healthcare settings.

Triggering change: towards a conceptualisation of major change processes in farm decision-making.

In this paper, we present a broad conceptualisation of major change in farm level trajectories. We argue that as a result of path dependency, major changes in farming practice primarily occur in response to 'trigger events', after which farm managers intensify their consideration of the options open to them, and may set a new course of action. In undertaking new actions, the farm system enters a period of instability, while new practices become established. Over time these new practices, if successfully achieving anticipated aims, lead to a further period of path dependency. Recognising and capitalising upon this pattern of events is important for the development of policies oriented towards incentivising major change in farming practices, and may explain why similar projects and/or policies influence some 'types' of farmers differently, and at different times. To illustrate our arguments, examples of this process are described in relation to empirical examples of major on-farm change processes, drawn from qualitative interviews with organic and conventional farmers in two English case study areas.