Month: May 2020

Are teachers at high risk of death from Covid19?

Posted on by lindsey.pike

Sarah Lewis, George Davey Smith and Marcus Munafo

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Due to the SARS-CoV-2 pandemic schools across the United Kingdom were closed to all but a small minority of pupils (children of keyworkers and vulnerable children) on the 20th March 2020, with some schools reporting as few as 5 pupils currently attending. The UK government have now issued guidance that primary schools in England should start to accept pupils back from the 1st June 2020 with a staggered return, starting with reception, year 1 and year 6.

Concern from teachers’ unions

This has prompted understandable concern from the  teachers’ unions, and on the 13th May, nine unions which represent teachers and education professionals signed a joint statement calling on the government to postpone reopening school on the 1st June, “We all want schools to re-open, but that should only happen when it is safe to do so. The government is showing a lack of understanding about the dangers of the spread of coronavirus within schools, and outwards from schools to parents, sibling and relatives, and to the wider community.” At the same time, others have suggested that the harms to many children due to neglect, abuse and missed educational opportunity arising from school closures outweigh the small increased risk to children, teachers and other adults of catching the virus.

What risk does Covid19 pose to children?

Weighing up the risks to children and teachers

So what do we know about the risk to children and to teachers? We know that children are about half as likely to catch the virus from an infected person as adults, and  if they do catch the virus they  are likely to have only mild symptoms.  The current evidence, although inconclusive, also suggests that they may be less likely to transmit the virus than adults.  However, teachers have rightly pointed out that there is a risk of transmission between the teachers themselves and between parents and teachers.

The first death from COVID-19 in England was recorded at the beginning of March 2020 and by the 8th May 2020 39,071 deaths involving COVID-19 had been reported in England and Wales. Just three of these deaths were among children aged under 15 years and  only a small proportion of the deaths (4416 individuals, 11.3%) were among working aged people.  Even among this age group risk is not uniform; it increases sharply with age from 2.6 in 100,000 for 25-44 years olds with a ten fold increase to 26 in  100,000 individuals for those aged 45-64.

Risks to teachers compared to other occupations

In addition, each underlying health condition increases the risk of dying from COVID-19, with those having at least 1 underlying health problem making up most cases.   The Office for National Statistics in the UK have published age standardised deaths by occupation for all deaths involving COVID-19 up to the 20th April 2020. Most of the people dying by this date would have been infected at the peak of the pandemic in the UK  prior to the lockdown period. They found that during this period there were 2494 deaths involving Covid-19 in the working age population. The mortality rate for Covid-19 during this period was 9.9 (95% confidence intervals 9.4-10.4) per 100,000 males and 5.2 (95%CI 4.9-5.6) per 100,000 females, with Covid-19 involved in around 1 in 4 and 1 in 5 of all deaths among males and females respectively.

Amongst teaching and education professionals (which includes school teachers, university lecturers and other education professionals) a total of 47 deaths (involving Covid-19) were recorded, equating to mortality rates of 6.7 (95%CI 4.1-10.3) per 100,000 among males and 3.3 (95%CI 2.0-4.9) per 100,000 among females, which was very similar to the rates of 5.6 (95%CI 4.6-6.6) per 100,000 among males and 4.2(95%CI 3.3-5.2) per 100,000 females for all professionals. The mortality figures for all education professionals includes 7 out of 437000 (or 1.6 per 100,000 teachers) primary and nursery school teachers and 17 out of 395000 (or 4.3 per 100,000 teachers) secondary school teachers.  A further 20 deaths occurred amongst childcare workers giving a mortality rate amongst this group of 3.4 (95%CI=2.0-5.5) per 100,000 females (males were highly underrepresented in this group), this is in contrast to rates of 6.5 (95%CI=4.9-9.1) for female sales assistants and 12.7(95%CI= 9.8-16.2) for female care home workers.

Covid-19 risk does not appear greater for teachers than other working age individuals

In summary, based on current evidence the risk to teachers and childcare workers within the UK from Covid-19 does not appear to be any greater than for any other group of working age individuals. However, perceptions of elevated risk may have occurred, prompting some to ask “Why are so many teachers dying?” due to the way this issue is portrayed in the media with headlines such as “Revealed: At least 26 teachers have died from Covid-19” currently on the https://www.tes.com website. This kind of reporting, along with the inability of the government to communicate the substantial differences in risk between different population groups – in particular according to age – has caused understandable anxiety among teachers. Whilst, some teachers may not be prepared to accept any level of risk of becoming infected with the virus whilst at work, others may be reassured that the risk to them is small, particularly given that we all accept some level of risk in our lives, a value that can never be zero.

Likely impact on transmission in the community is unclear

As the majority of parents or guardians of school aged children will be in the 25-45 age range, the risk to them  is also likely to be small. Questions remain however around the effect of school openings on transmission in the community and the associated risk. This will be affected by many factors including the existing infection levels in the community, the extent to which pupils, parents and teachers are mixing outside of school (and at the school gate) and mixing between individuals of different age groups. This is the primary consideration of the government Scientific Advisory Group for Emergencies (SAGE) who are using modelling based on a series of assumptions to determine the effect of school openings on R0.

 

Sarah Lewis is a Senior Lecturer in Genetic Epidemiology in the department of Population Health Sciences, and is an affiliated member of the MRC Integrative Epidemiology Unit (IEU), University of Bristol

George Davey Smith is a Professor of Clinical Epidemiology, and director of the MRC IEU, University of Bristol

Marcus Munafo is a Professor of Biological Psychology, in the School of Psychology Science and leads the Causes, Consequences and Modification of Health Behaviours programme of research in the IEU, University of Bristol.

 

Collider bias: why it’s difficult to find risk factors or effective medications for COVID-19 infection and severity

Posted on by lindsey.pike

Dr Gemma Sharp and Dr Tim Morris

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The COVID-19 pandemic is proving to be a period of great uncertainty. Will we get it? If we get it, will we show symptoms? Will we have to go to hospital? Will we be ok? Have we already had it?

These questions are difficult to answer because, currently, not much is known about who is more at risk of being infected by coronavirus, and who is more at risk of being seriously ill once infected.

Researchers, private companies and government health organisations are all generating data to help shed light on the factors linked to COVID-19 infection and severity. You might have seen or heard about some of these attempts, like the COVID-19 Symptom Tracker app developed by scientists at King’s College London, and the additional questions being sent to people participating in some of the UK’s biggest and most famous health studies, like UK Biobank and the Avon Longitudinal Study of Parents and Children (ALSPAC).

These valuable efforts to gather more data will be vital in providing scientific evidence to support new public health policies, including changes to the lockdown strategy. However, it’s important to realise that data gathered in this way is ‘observational’, meaning that study participants provide their data through medical records or questionnaires but no experiment (such as comparing different treatments) is performed on them. The huge potential impact of COVID-19 data collection efforts makes it even more important to be aware of the difficulties of using observational data.

Image by Engin Akyurt from Pixabay

Correlation does not equal causation (the reason observational epidemiology is hard)

These issues boil down to one main problem with observational data: that it is difficult to tease apart correlation from causation.

There are lots of factors that correlate but clearly do not actually have any causal effect on each other. Just because, on average, people who engage in a particular behaviour (like taking certain medications) might have a higher rate of infection or severe COVID-19 illness, it doesn’t necessarily mean that this behaviour causes the disease. If the link is not causal, then changing the behaviour (for example, changing medications) would not change a person’s risk of disease. This means that a change in behaviour would provide no benefit, and possibly even harm, to their health.

This illustrates why it’s so important to be sure that we’re drawing the right conclusions from observational data on COVID-19; because if we don’t, public health policy decisions made with the best intentions could negatively impact population health.

Why COVID-19 research participants are not like everyone else

One particular issue with most of the COVID-19 data collected so far is that the people who have contributed data are not a randomly drawn or representative sample of the broader general population.

Only a small percentage of the population are being tested for COVID-19, so if research aims to find factors associated with having a positive or negative test, the sample is very small and not likely to be representative of everyone else. In the UK, people getting the test are likely to be hospital patients who are showing severe symptoms, or healthcare or other key workers who are at high risk of infection and severe illness due to being exposed to large amounts of the virus. These groups will be heavily over-represented in COVID-19 research, and many infected people with no or mild symptoms (who aren’t being tested) will be missed.

Aside from using swab tests, researchers can also identify people who are very likely to have been infected by asking about classic symptoms like a persistent dry cough and a fever. However, we have to consider that people who take part in these sorts of studies are also not necessarily representative of everyone else. For example, they are well enough to fill in a symptom questionnaire. They also probably use social media, where they likely found out about the study. They almost certainly own a smartphone as they were able to download the COVID-19 Symptom Tracker app, and they are probably at least somewhat interested in their health and/or in scientific research.

Why should we care about representativeness?

The fact that people participating in COVID-19 research are not representative of the whole population leads to two problems, one well-known and one less well-known.

Firstly, as often acknowledged by researchers, research findings might not be generalisable to everyone in the population. Correlations or causal associations between COVID-19 and the characteristics or behaviours of research participants might not exist amongst the (many more) people who didn’t take part in the research, but only in the sub-group who participated. So the findings might not translate to the general population: telling everyone to switch up their medications to avoid infection may only work for some people who are like those studied.

But there is a second problem, called ‘collider bias’ (sometimes also referred to using other names such as selection bias or sampling bias), that is less well understood and more difficult to grasp. Collider bias can distort findings so that certain factors appear related even when there is no relationship in the wider population. In the case of COVID-19 research, relationships between risk factors and infection (or severity of infection) can appear related when no causal effect exists, even within the sample of research participants.

As an abstract example, consider a private school where pupils are admitted only if they have either a sports scholarship or an academic scholarship. If a pupil at this school is not good at sports, we can deduce that they must be good at academic work. This correlation between being poor at sports but being good academically doesn’t exist in the real world outside of this school, but in the sample of school pupils, it appears. And so, with COVID-19 research, in the sample of people included in a COVID-19 dataset (e.g. people who have had a COVID-19 test), two factors that influence inclusion (e.g. having COVID-19 symptoms that were severe enough to warrant hospitalisation, and taking medications for a health condition that puts you at high risk of dying from COVID-19) would appear to be associated, even when they are not. That is, to be in the COVID-19 dataset (to be tested), people are likely to have had either more severe symptoms or to be on medication. The erroneous conclusion would follow that changing one factor (e.g. changing or stopping medications) would affect the other (i.e. lower the severity of COVID-19). Because symptom severity is related to risk of death, stopping medication would appear to reduce the chance of death. As such, any resulting changes to clinical practice would be ineffective or even harmful.

Policymaking is a complex process at the best of times, involving balancing evidence from research, practice, and personal experience with other constraints and drivers, such as resource pressures, politics, and values. Add into that the challenge of making critical decisions with incomplete information under intense time pressure, and the need for good quality evidence becomes even more acute. The expertise of statisticians, who can double check analyses and ensure that conclusions are as robust as possible, should be a central part of the decision making process at this time – and especially to make sure that erroneous conclusions arrived at as a result of collider bias do not translate into harmful practice for people with COVID-19.

 

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The main aim of this blog post was to highlight the issue of collider bias, which is notoriously tricky to grasp. We hope we’ve done this but would be interested in your comments.

For those looking for more information, read on to discover some of the statistical methods that can be used to address collider bias….

Now we know collider bias is a problem: how do we fix it?

It is important to consider the intricacies of observational data and highlight the very real problems that can arise from opportunistically collected data.  However, this needs to be balanced against the fact that we are in the middle of a pandemic, that important decisions need to be made quickly, and this data is all we have to guide decisions. So what can we do?

There are a few strategies, developed by statisticians and other researchers in multiple fields, that should be considered when conducting COVID-19 research:

  •       Estimate the extent of the collider bias:

o  Think about the profile of people in COVID-19 samples – are they older/younger or more/less healthy than individuals in the general population?

o Are there any unexpected correlations in the sample that ring alarm bells?

  • Try to balance out the analysis by ‘weighting’ individuals, so that people from under-represented groups count more than people from over-represented groups.
  • Carry out additional analysis, known as ‘sensitivity analysis’, to assess the extent to which plausible patterns of sample selection could alter measured associations.

For those who would like to read even more, here’s a pre print on collider bias published by our team:

Collider bias undermines our understanding of COVID-19 disease risk and severity
Gareth Griffith, Tim T Morris, Matt Tudball, Annie Herbert, Giulia Mancano, Lindsey Pike, Gemma C Sharp, Tom M Palmer, George Davey Smith, Kate Tilling, Luisa Zuccolo, Neil M Davies, Gibran Hemani
medRxiv 2020.05.04.20090506; doi: https://doi.org/10.1101/2020.05.04.20090506 

 

The MRC Integrative Epidemiology Unit at the University of Bristol (IEU) conducts some of the UK's most advanced population health science research. It uses genetics, population data and experimental interventions to look for the underlying causes of chronic disease.