SHIELDING THE VULNERABLE USING A RISK CALCULATOR – HERE’S WHY IT WON’T BE ENOUGH 
This article by Andrew Kunzmann, Patrick G. Johnston Fellow in the Cancer Epidemiology Research Group at the Centre for Public Health at Queen's University Belfast and Justin Feldman Health & Human Rights Fellow, Harvard T.H. Chan School of Public Health, first appeared in The Conversation on 11 November 2020.
13 November 2020
Writing this week in The Conversation, and with the prospect of repeated lockdowns and extended periods of isolation in the offing, Andrew Kunzmann and Justin Feldman consider how an algorithm-based approach to dealing with Covid-19 would impact on the core principles of public health.
In recent weeks, there have been controversial proposals to ask older, more vulnerable adults to isolate from society, while younger adults build herd immunity to COVID-19. These strategies have been criticised by leading figures as “practically impossible” and “unethical”. Yet calls for shielding from COVID “stratified by risk” persist.
A new high-quality algorithm to predict people’s risk of catching and dying from COVID-19, published in the BMJ, may add credence to these proposals. This algorithm could be useful for enhancing shielding support measures for high-risk individuals through furlough schemes or GP advice. But the predictions won’t be as accurate if lower-risk adults, assuming they are safe, are less cautious and increase their risk of catching COVID. Given how quickly coronavirus can spread, an algorithm-based approach that asks young people to risk getting sick could make the A-level results algorithm look like a success.
To properly inform someone that they are at a “low risk” from COVID, we would need better information on exactly what they are at a low risk of. While the algorithm can predict risk of hospitalisation and death from the disease, we can’t yet adequately predict the risk of long-term health effects, known as “long COVID”.
Long COVID is poorly understood, but reports of it causing debilitating fatigue, brain fog or shortness of breath for months in young, healthy people with milder cases suggest that it is an outcome that shouldn’t be ignored.
Lower risk doesn’t mean low risk. Deciding who is at an acceptably low risk – and how many of us this would amount to – will be complex. While most COVID deaths were concentrated in older adults or those with health conditions, half of the admissions to critical care due to COVID were in adults aged under 60 years. Therefore, we may need to shield a considerable proportion of the working population. Many employees will want to decide for themselves whether the risk is acceptable to them, and they may struggle to say no to a boss who wants them back at work.
With infectious disease, the main issue isn’t necessarily individual risk, it’s group risk. Many young people live in multigenerational households, and their main desire may be not to pass it on to more vulnerable loved ones. While rises in infections often start in the young, they quickly pass on to older groups.
Not workable
Separating households for months isn’t a workable solution, especially for families with informal caring responsibilities – and employers may be hesitant to allow low-risk workers who live with high-risk adults to work from home.
Although shielding advice can be helpful, it may not be enough to protect higher-risk people if we were to encourage or accept a higher level of infections in younger populations. The algorithm’s predictions, trained using data when shielding and precautions were in place, show that groups advised to shield remained at a massively disproportionate risk of death.
A further difficulty for shielding strategies could be providing safe medical care for their other health conditions. People receiving chemotherapy may be classed as high risk from COVID but would need to reduce their shielding in order to continue to receive treatment.
Although every effort is being made to make hospitals COVID-free, increased incidence in younger populations, including doctors, nurses, carers and taxi drivers, would make attendance for medical treatments riskier.
Structural inequalities and racism will affect who is able to work from home, take sick leave, rely on public transport and live in crowded households. These all put working-class and minority ethnic individuals at a greater risk from COVID-19.
The desire to reduce these discrepancies probably led to the inclusion of ethnicity and deprivation indicators into the algorithms. However, using an algorithm to selectively exclude people from society and workplaces based on race, age, deprivation or health conditions, isn’t an equitable solution. Particularly if those who are most likely to be asked to isolate live in cramped households.
With a recession looming, already marginalised workers could risk losing their jobs, training or promotions based on their postcode and ethnicity.
Asking vulnerable adults to shoulder the burden of the pandemic, in fearful isolation for an unknown period, would undermine core principles of public health. Isolating everybody indefinitely or having repeated lockdowns do not sound like appealing solutions either. The UK is already in a second lockdown and if it doesn’t get infections low enough to fit on an Excel spreadsheet, it could be facing a third.
Difficult decisions lie ahead on whether we need to pursue a more aggressive suppression strategy in order to reopen more fully.
Further information on research and analysis carried out at Queen's in response to COVID-19, can be found at: https://www.qub.ac.uk/coronavirus/. If you would like to help the University in its efforts to tackle the pandemic, visit our Rapid Response page. To support health-related research projects at Queen’s, visit the Development and Alumni Relations Office website or contact Teresa Sloan, Head of Health Fundraising.
Media enquiries should be addressed to the Communications Officer at Queen’s University Belfast.
Back to Main News
Top of Page