Emma Pritchard is a Medical Statistician in the Modernising Medical Microbiology team at the John Radcliffe Hospital in Oxford. She works in the Oxford BRC’s Antimicrobial Resistance and Modernising Microbiology Theme. In this blog, Emma explains how she used the ongoing Office for National Statistics COVID-19 Infection Survey to understand the spread of coronavirus in communities across the country.
You may have heard the saying “canary in the coal mine”. It is often used to express that something is an early sign of danger. The saying originated when caged canaries were sent into coal mines. If there was methane gas present, the birds would keel over and this would act as an early signal for miners that there was danger….and they should get out quickly!
We wanted to see if we could design our own “canary” which we could use to tell us where, and in whom, coronavirus was the rising fastest. So, we set out to develop a process we could use to identify any characteristics that could act as an early warning signal for rising coronavirus cases in the community. We could then use these warning signals to target testing in certain groups of people or to inform public health policy, such as offer advice on current coronavirus restrictions (getting the miners out of the mine!).
The miners knew that the threat of methane gas may change over time. Similarly, we wanted our process to be easy to use on a regular basis as the risk of catching coronavirus can change over time in different groups of people. This can happen for lots of reasons, but could include new variants appearing or lockdowns being enforced. We therefore set out to design a process that we could use each week to alert us of groups of people in the community who were more likely to test positive for coronavirus.
How we did it
We used data from the Office for National Statistics COVID-19 Infection Survey, a large representative survey of individuals living in the community. Participants get visited regularly by study workers who supervise a swab test to check for coronavirus particles in the nose and throat. When the swab is taken, participants are also asked about:
- Demographics (for example, age, sex, ethnicity)
- Work and employment (where do you work? how do you travel to work?)
- Health (have you been vaccinated against coronavirus? Do you have any long-term health conditions?)
- Social behaviours (how often do you socialise outside your home?)
Altogether, there were 60 characteristics across these four groups. We designed our process to check every fortnight which groups of people, based on these characteristics, were more likely to have coronavirus.
To assess how well our process worked, we took one year of data from the survey (July 2020- July 2021) and chopped it up into fortnightly chunks. In each of these chunks we had information from all participants who have a visit from a study worker within that fortnight, including the result from the swab test and answers to all questions.
We implemented our process in each of these fortnights and summarised which groups of people were more likely to test positive at that time. Find out more about this process.
What we found
We found that our process picked out groups of people who were more likely to test positive for coronavirus in each fortnight. Some of these characteristics were persistent across the whole pandemic, whereas some came and went as the pandemic changed. Others rarely appeared important at all.
Find out which of the 60 characteristics were most important in each fortnight of the period studied. The five most interesting ones are explained here:
Five important characteristics driving coronavirus positivity
- Geographical region
Where people lived in the UK had a big effect on whether they had coronavirus. During September and November 2020, we saw more cases in northern areas of England. When the Alpha variant began to circulate in December 2020, we instead saw an increased chance of having coronavirus if you lived in southern areas of England, and especially London.
The vaccination roll-out programme began in December 2020, and we quickly saw that people who had been vaccinated were less likely to test positive for coronavirus. We have seen this in every fortnight since!
- Social distancing at work
Lots of jobs require staff to work away from home, and some jobs require you to be closer to colleagues/patients/clients than others. When coronavirus cases were high across England (mid-September 2020 to February 2021), we saw that those working outside of home were more likely to test positive for coronavirus, compared with those working from home. Additionally, those in jobs where social distancing was hardest were even more likely to test positive for coronavirus.
- Travel Abroad
Those who were lucky enough to jet off for a holiday abroad between July and October 2020 were also more likely to test positive for coronavirus soon after they returned, compared with people who stayed in the UK.
We didn’t see any difference in how likely you were to have coronavirus between men and women during most of the pandemic until June 2021, when we saw that men were more likely to test positive than women. If you remember back to June 2021 (or if you’re an England football fan, maybe you’d rather forget…), this coincided with the European Football Championship where men may have been engaging more in social mixing compared with women.
We managed to develop a process which we could use every two weeks to alert us of rising coronavirus cases in different groups of people. This process is now being used regularly to check which characteristics are driving rising cases of coronavirus in the community.