The COVID-19 pandemic has had a dramatic effect on people’s lives globally. For academics working in fields such as primary care and public health, the pandemic led to major changes in professional roles as I discuss in an article published in the JRSM. Universities across the United Kingdom closed their campuses in March 2020 and switched to remote working. Staff began to work from home and teaching of students moved online. University staff rapidly had to put in place systems for teaching, monitoring and assessing students remotely. For many universities, these changes will be in place until the end of 2020, with no return to a more normal mode of working until January 2021 at the earliest.
The ‘lockdown’ of the United Kingdom on 23 March had pronounced impacts on travel patterns as we discussed in our recent JRSM paper. As many millions of people moved to either working at home or were furloughed from their jobs, there were large decreases in trips to workplaces alongside even steeper decreases in recreational journeys. Transport is an often overlooked influence on the health of populations and health inequalities, affecting physical activity, road traffic incidents and air pollution, in addition to being a major contributor to climate change. There is ongoing uncertainty around the longer-term trajectory of COVID-19, including risks of a second wave, meaning that the medium-term changes to transport and society are hard to predict. Nevertheless, the current easing of the lockdown in England presents both opportunities and threats to the health impacts of transport.
Primary Care Networks (PCNs) are a new organisational hierarchy with wide-ranging responsibilities introduced in the National Health Service (NHS) Long Term Plan. The vision is that PCNs should represent ‘natural’ communities of general practices (GP practices) collaborating at scale and covering a geography that fits well with practices, other healthcare providers and local communities. Our study published in BMJ Open aims to identify natural communities of GP practices based on patient registration patterns using Markov Multiscale Community Detection, an unsupervised network-based clustering technique to create catchments for these communities. With PCNs expected to take a role in population health management and with community providers expected to reconfigure around them, it is vital to recognise how PCNs represent their communities. Our method may be used by policymakers to understand the populations and geography shared between networks.