How can your gut microbiome affect risk of cancer?

Dr Kaitlin H. Wade1,2,3

1 Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN

2 Medical Research Council (MRC-IEU), University of Bristol, Bristol, BS8 2BN

3 Cancer Research UK (CRUK) Integrative Cancer Epidemiology Programme (ICEP), University of Bristol, Bristol, BS8 2BN

The causes of cancer are often preventable

Cancer, a disease that has a profound impact on the lives of individuals all over the world, also has an ever-increasing burden. And yet, evidence indicates that over 40% of all cancers are likely explained by preventable causes. One of the main challenges is identifying so-called ‘modifiable risk factors’ for cancer – aspects of our environment that we can change to reduce the incidence of disease.

Photo by Chloe Russell for ‘Up Your A-Z’, an encyclopaedia of gut bacteria

The gut microbiome could influence cancer risk

The gut microbiome is a system of microorganisms that helps us digest food, produce essential molecules and protects us against harmful infections. There is growing evidence supporting the relationship between the human gut microbiome and risk of cancer, including lung, breast, bowel and prostate cancers. For example, experiments have shown that changing the gut microbiome (e.g., by using pre- or pro-biotics) may reduce the risk of developing colorectal cancer. Research also suggests that people with colorectal cancer have lower microbiota diversity and different types of bacteria within their gut compared to those without a diagnosis.

As the gut microbiome can have a substantial impact on their host’s metabolism and immune response, there are many biological mechanisms by which the gut microbiome could influence cancer development and progression. However, we don’t yet know how the gut microbiome can do this.

Human studies in this context have used small samples of individuals and measure both the microbiome and disease at the same time. These factors can make it difficult to tease apart correlation from causation – i.e., does variation in the gut microbiome change someone’s risk of cancer or is it the existence of cancer that leads to variation in the gut microbiome? This is an important question because the main aim of such research is to understand the causes of cancer and how we can prevent the disease. We want to fully understand whether altering the gut microbiome can reduce the burden of cancer at a population level or whether it is simply a marker of cancer itself.

I’m inviting feedback on your knowledge and understanding of the gut microbiome and cancer – please take this 5-minute survey (click here for survey) to contribute your thoughts.

People are interested in their gut microbiome

Even though we don’t yet know much about the causal relevance of the gut microbiome, there is still a growing market for commercial initiatives targeting the microbiome as a consumer-driven intervention. This usually involves companies obtaining a small number of faecal samples from consumers and prescribing “personalised” nutritional information for a “healthier microbiome”. However, these initiatives are very controversial given uncertainty in the likely relationships between the gut microbiome, nutrition and various diseases. What these activities do highlight is the demand for such information at a population level. This shows there is an opportunity to improve understanding of the causal role played by the gut microbiome in human health and disease.

Photo by Chloe Russell for ‘Up Your A-Z’, an encyclopaedia of gut bacteria

Microbiome and variation in our genes

Using information about our genetics can help us find out whether the gut microbiome changes the risk of cancer, or whether cancer changes the gut microbiome. Genetic variation cannot be influenced by the gut microbiome nor disease. Therefore, if people who are genetically predisposed to having a higher abundance of certain bacteria within their gut also have a lower risk of, say, prostate cancer, this would strongly suggest a causal role of those bacteria in prostate cancer development. This approach of using human genetic information to discern correlation from causation is called Mendelian randomization.

Studies relating human genetic variation with the gut microbiome have proliferated in recent years. They have provided evidence for genetic contributions to features of the gut microbiome including the abundance or likelihood of presence (vs. absence) of specific bacteria. This knowledge has given the opportunity to apply Mendelian randomization to better understand the causal impact of gut microbiome variation in health outcomes, including cancer. There are, however, many important caveats and complications to this work. Specifically, there is a (currently unmet) requirement for careful examination of how human genetic variation influences the gut microbiome and interpretation of the causal estimates derived from using Mendelian randomization within this field.

This is exactly what I will be looking at in my new research funded by Cancer Research UK. For more details on the nuances of this work, please see my research feature for Cancer Research UK and paper discussing these complexities.

What’s next for this research?

This research has already shown promise in the application of Mendelian randomization to improve our ability to discern correlation from causation between the gut microbiome and cancer. It has importantly highlighted the need for inter-disciplinary collaboration between population health, genetic and basic sciences. Thus, with the support from my team of experts in microbiology, basic sciences and population health sciences, this Fellowship will set the scene for the integration of human genetics and causal inference methods in population health sciences with microbiome research. This will help us understand the causal role played by the gut microbiome in cancer. Such work acts as a new and important step towards evaluating and prioritising potential treatments or protective factors for cancer prevention.

Acknowledgements

The research conducted as part of this Cancer Research UK Population Research Postdoctoral Fellowship will be supported by the following collaborators: Nicholas Timpson, Caroline Relton, Jeroen Raes, Trevor Lawley, Lindsay Hall and Marc Gunter, and my growing team of interdisciplinary PhD students and postdoctoral researchers. I’d also like to thank the following individuals for comments on this feature: Tom Battram, Laura Corbin, David Hughes, Nicholas Timpson, Lindsey Pike and Philippa Gardom. Additional thanks go to Chloe Russell, a brilliant photographer with whom I collaborated to create “Up Your A-Z” as part of Creative Reactions 2019, who provided the photos for this webpage.

About the author

Dr. Wade’s academic career has focused on the integration of human genetics with population health sciences to improve causality within epidemiological studies. Focusing on relationships across the life-course, her work uses comprehensive longitudinal cohorts, randomized controlled trials and causal inference methods (particularly, Mendelian randomization and recall-by-genotype designs). Kaitlin’s research has focused on understanding the relationships between adiposity and dietary behaviours as risk factors for cardiometabolic diseases and mortality. Having been awarded funding from the Elizabeth Blackwell Institute and Cancer Research UK, Kaitlin’s work uses these methods to understand the causal role played by the human gut microbiome on various health outcomes, such as obesity and cancer. Since pursuing a career in this field, Kaitlin has already led and been key in several fundamental studies that with path the way to resolve – or at least quantify – complex relationships between genetic variation, the gut microbiome and human health. In addition to her research, Kaitlin is actively involved in organising and administering teaching and public engagement activities as well as having many mentorship and supervisory roles within and external to the University of Bristol.

Key publications:

Hughes, D.A., Bacigalupe, R., Wang, J. et al. Genome-wide associations of human gut microbiome variation and implications for causal inference analyses. Nat Microbiol 5, 1079–1087 (2020). https://doi.org/10.1038/s41564-020-0743-8.

Kurilshikov, A., Medina-Gomez, C., Bacigalupe, R. et al. Large-scale association analyses identify host factors influencing human gut microbiome composition. Nat Genet 53, 156–165 (2021). https://doi.org/10.1038/s41588-020-00763-1.

Wade KH and Hall LJ. Improving causality in microbiome research: can human genetic epidemiology help? [version 3; peer review: 2 approved]. Wellcome Open Res 4, 199 (2020). https://doi.org/10.12688/wellcomeopenres.15628.3.

 

 

Breastfeeding research improves lives and advances health, but faces conflicts


Research shows the breast milk of women who have recovered from COVID-19 offers a source of COVID-19 antibodies.
(Shutterstock)

Meghan Azad, University of Manitoba; Katie Hinde, Arizona State University; Lars Bode, University of California San Diego; Luisa Zuccolo, University of Bristol; Merilee Brockway, University of Manitoba; Nathan C. Nickel, University of Manitoba, and Rafael Perez-Escamilla, Yale University

Breastfeeding and breast milk provide big opportunities to support maternal, infant and population health. This is especially true during the current pandemic because breastfeeding can help alleviate food insecurity, and research shows the breast milk of women who have recovered from COVID-19 offers a source of COVID-19 antibodies.

Breastfeeding saves lives and prevents illness. It is environmentally friendly and profoundly important to children’s long-term development. After all, breast milk is the only food that has evolved specifically to feed humans.

Breastfeeding matters

Beyond supplying nutrition, breast milk provides personalized immune protection and shapes the developing microbiome. Scientists have discovered enzymes, hormones, antibodies and live cells in breast milk, and these bioactive components could hold the key to developing new therapies — not only for COVID-19, but also autoimmune diseases, diabetes and cancer.

Yet, remarkably, we still don’t fully understand the composition of breast milk, or the biological basis for its many health effects. In fact, more scientific papers have been published on headaches than breastfeeding, and more federal research dollars from Canadian Institutes of Health Research and the Natural Sciences and Engineering Research Council of Canada have been invested to study corn than breast milk.

The act of breastfeeding also supports mother-infant bonding and helps to prevent breast and ovarian cancer in mothers. Unfortunately, most mothers do not even meet their own breastfeeding goals, let alone achieve recommendations of exclusive breastfeeding for six months, followed by 18 months of breastfeeding along with other foods.

This is particularly concerning during this pandemic, when mothers infected with COVID-19 may be separated from their newborns (despite World Health Organization guidance to the contrary) and breastfeeding support is often unavailable because public health visits are being cancelled and lactation services have been suspended in many places.

Tensions abound

Every parent knows that infant feeding is a complex issue, often evoking strong emotions based on personal experience. Difficult or negative breastfeeding experiences can fuel a defensive “breastfeeding denialism” attitude.

A woman with tattoos breastfeeding her infant
Breastfeeding support such as public health visits may not be available during the pandemic.
(Pexels/Anna Shvets)

Conversely, some breastfeeding advocates refuse to acknowledge that for some families, formula is necessary for medical, personal, societal or socioeconomic reasons. These extreme attitudes cause a tense and unproductive environment for researchers working to generate inclusive evidence-based guidance for infant feeding.

Industry partnerships also cause tension in this field because the infant feeding industry frequently violates the World Health Organization code for marketing of breastmilk substitutes, and transgressions have worsened during the pandemic. However, due to lack of funding for breastfeeding research, scientists are often faced with choosing between industry funding or no funding at all.

Unfortunately, these tensions often detract from the energy and resources that breastfeeding advocates, researchers, health professionals and policy-makers could be using to advance their shared goal of supporting maternal and child health.

What can be done

Of course, members of the diverse breastfeeding advocacy and research communities will not always agree — but we should aim to find common ground and work together. There are many stakeholders involved, each with a role to play:

Governments and non-profit funding organizations should acknowledge the importance of breastfeeding and breast milk and invest more resources into this field.

Researchers should build interdisciplinary teams to study breast milk as a biological system and think broadly about “breastfeeding challenges” in the context of complex social systems – including social inequities, parental leave policies, lactation difficulties and donor breast milk.

Companies, researchers and advocacy groups should co-develop a conflict of interest framework for research on breastfeeding and breast milk and reporting of results.

Messaging is key to achieving these goals. All groups need to communicate effectively with each other, and with the health-care, research and public sectors. This means providing or sharing clear resources to convey scientific evidence free of conflict of interest, targeted to each audience, such as fact sheets for policy-makers, engaging videos for the public and infographics for health-care providers.

Stakeholders also need to actively discredit unfounded claims and misinformation, such as unsubstantiated health claims made by infant nutrition companies, or rumours about the transmission of COVID-19 via breastfeeding, when there is no evidence of this occurring.

Looking forward

A woman breastfeeding a baby
COVID-19 has highlighted both the importance and fragility of breastfeeding support systems.
(Shutterstock)

Progress in breastfeeding, breast milk and lactation research is being hampered by tensions among researchers, advocates and industry.

As breast milk scientists, breastfeeding researchers and lactation specialists, we are concerned about these tensions and their potential to impede or delay discoveries in our field. Last year, we held a workshop to discuss these concerns and develop solutions.

Our workshop paper was written before the pandemic, but its recent publication is timely. The pandemic has brought researchers together in ways that seemed impossible before.

Breast milk research that would normally take years has been completed in months with unprecedented efficiency. A global network of human milk banks was established in a matter of days to share information about safe operations during the pandemic. Milk scientists and breastfeeding researchers are meeting monthly with the WHO to speed up the transition from discovery to policy.

We hope these trends will continue beyond the pandemic and become the new standard for doing and sharing research.

COVID-19 has also emphasized both the importance and fragility of breastfeeding support systems, which have suffered considerably due to current restrictions. The pandemic has also highlighted the potential of breast milk to inform new avenues of biomedical research, such as milk antibodies as potential therapeutics.

We hope this added urgency will encourage researchers, advocates, funders and policy-makers to work together to accelerate progress in supporting breastfeeding and breast milk research.The Conversation

Meghan Azad, Associate Professor of Pediatrics and Child Health; Canada Research Chair in the Developmental Origins of Chronic Disease, University of Manitoba; Katie Hinde, Associate Professor, School of Human Evolution and Social Change, Arizona State University; Lars Bode, Professor of Pediatrics and Director of Mother-Milk-Infant Center of Research Excellence, University of California San Diego; Luisa Zuccolo, Senior Research Fellow, Health Sciences, University of Bristol; Merilee Brockway, Post-doctoral Fellow, Department of Pediatrics and Child Health, University of Manitoba; Nathan C. Nickel, Associate Professor of Community Health Sciences; Co-Director MILC; Associate Director, Manitoba Centre for Health Policy, University of Manitoba, and Rafael Perez-Escamilla, Professor of Public Health, Yale University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Underestimation of Drug Use: A Perennial Problem with Implications for Policy

by Olivia Maynard

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Photo by Louie Castro-Garcia on Unsplash

In a paper recently published in the journal Addiction, Hannah Charles and colleagues suggest that the prevalence of illicit drug use among 23-25 year olds in a Bristol-based birth cohort (ALSPAC) is over twice that reported in the Crime Survey for England and Wales (CSEW). The team propose that these figures reflect under-reporting in the CSEW, although they note that they may reflect higher levels of illicit drug use in Bristol. Here I present some preliminary data supporting their view that the CSEW underestimates illicit drug use.

In March 2020, I recruited 683 UK university students to participate in a short survey on drug use via the online survey platform Prolific which has been shown to produce reliable data. I recruited only students aged 18 to 24 years who reported using alcohol in the past 30 days, and participants reported whether they had used any of MDMA/ecstasy, cocaine or cannabis in the past two years.

Table 1. Prevalence of self-reported illicit drug use across three surveys of young people in the UK

University
students
via ProlificAged 18-24
Bristol, ALSPAC

Aged 23-25

CSEW

Aged 23-25

2 years 1 year Lifetime 1 year Lifetime
Any illicit drug usea 52.7 (360) 36.7 62.8 16.4 40.6
Cannabis 50.2 (343) 29.2 60.5 13.8 37.3
MDMA/ecstasy/amphetaminesb 23.3 (159) 17.0 32.9 3.6 11.1
Cocaine 21.1 (144) 19.6 30.8 4.8 13.9

Notes: Values represent percentage of participants (number of participants). Percentages for CSEW and ALSPAC are taken from Charles et al (1) and are weighted percentages.
a ‘Any illicit drug use’ refers only to the illicit drugs assessed in the respective surveys (only cannabis, MDMA and cocaine in our survey), more drugs in ALSPAC and CSEW – see Charles et al (1).
Our Prolific survey asked about ‘MDMA / ecstasy’ use, ALSPAC categorised ecstasy/MDMA use along with other ‘amphetamine’ use and CSEW asked about ‘ecstasy’ use.

Over half of my sample reported using at least one of cannabis, cocaine or MDMA in the past two years (Table 1). This is markedly higher than the CSEW’s estimates of either past year or lifetime use, and more in line with those reported in ALSPAC. Comparing across drugs, past two-year use of the three drugs is higher in my survey than either past year or lifetime use in the CSEW, and higher than past year, but lower than lifetime use in ALSPAC. Perhaps of more interest than ever use of the drugs over the past two years, I also examined the combinations of drugs students in my survey were using (Table 2). I find that the majority of students who report using illicit drugs have only used cannabis in the past two years (25% of all students), although the second largest group (15%) have used all three of cannabis, MDMA and cocaine.

Table 2. Prevalence of self-reported illicit drug among UK university students

Qualtrics survey of university students (past two years)
All
(n=683)
Female
(n=336)
Male
(n=312)
Other
(n=35)
Illicit drug use 
Cannabis 50.2 (343) 48.5 (163) 53.5 (167) 37.1 (13)
MDMA / ecstasy 23.3 (159) 19.3 (65) 29.2 (91) 8.6 (3)
Cocaine 21.1 (144) 17.6 (59) 26 (81) 11.4 (4)
Illicit drug use profiles
Alcohol only (no illicit drug use) 47.3 (323) 48.2 (162) 44.6 (139) 62.9 (22)
Any illicit drug usea 52.7 (360) 51.8 (174) 55.4 (173) 37.1 (13)
Cannabis only 24.5 (167) 27.4 (92) 21.5 (67) 22.9 (8)
Cannabis + Cocaine + MDMA 15.4 (105) 11.3 (38) 20.8 (65) 5.7 (2)
Cannabis + MDMA 6.3 (43) 6 (20) 7.1 (22) 2.9 (1)
Cannabis + Cocaine 4.1 (28) 3.9 (13) 4.2 (13) 5.7 (2)
Cocaine only 0.9 (6) 1.2 (4) 0.6 (2) 0 (0)
MDMA only 0.9 (6) 0.9 (3) 1 (3) 0 (0)
Cocaine + MDMA 0.7 (5) 1.2 (4) 0.3 (1) 0 (0)

Notes: Values represent percentage of participants (number of participants).
‘Illicit drug use’ refers to participants reporting any use of the three drugs in the past two years.
‘Illicit drug use profiles’ refers to the combinations of drugs participants report using in the past two years.
a ‘Any illicit drug use’ refers only to use of cannabis, MDMA and cocaine.

There are some important differences between my sample and both the CSEW and ALSPAC samples. Some differences may mean that my figures are overestimates, including sampling university students who are more affluent than the general population (although drug use is not necessarily higher among students than non-students) and only including those who reported drinking alcohol (although according to the study authors, over 95% of the ALSPAC participants report past year drinking). Other differences may mean my figures are underestimates, including only asking about use of three drugs (thereby underestimating ‘any illicit drug use’), and the younger average age of my sample. I also report on past two-year use, rather than either lifetime or past year use as per CSEW and ALSPAC. Given these differences, I would like to run a larger, more representative sample on the Prolific platform (Prolific allows researchers to recruit a sample which is representative of the general population), to get an estimate of illicit drug use which is more comparable to ALSPAC and CSEW.

Despite these differences, my data support those reported by Charles and colleagues. Indeed, I find it unsurprising that illicit drug use is under-reported in the Home Office’s CSEW. The validity of self-reports for sensitive issues has long been a concern. Over-reporting of illicit drug use is not considered to be a concern and numerous methods have been developed for preventing under-reporting (see a 1997 NIDA report on this issue, as well as more recent techniques for estimating prevalence of use such as the crosswise method). It is important to consider the context in which surveys are administered, including participants’ perception of who is asking the questions and for what reason. It seems that if drug use is asked about in a research context, (e.g., with a clear research objective, informed consent and no possibility of repercussions), the validity of responses may be higher than when questions are asked by organisations that are perceived to be involved in the punishment of people who use drugs (e.g., governments, universities).

While the CSEW recognises that it does not reliably measure problematic drug use, my data and that of Charles and colleagues provide evidence that CSEW’s claim that it is a ‘good measure of recreational drug use’ may be wrong. Although it may be convenient to believe that only a small subset of the population uses illicit drugs, accurate information may galvanise policy makers (both nationally and locally, including at universities) into developing drugs policies that reflect reality and which support, rather than criminalise, the large proportion of the population who choose to use drugs. Indeed, this is what we’re doing at the University of Bristol, where it has been accepted that drug use is relatively common among our students and we’re providing support and education to those students who need it.

 

This blog posted was originally posted on the Tobacco and Alcohol Research Group blog

Using genetics to understand the relationship between young people’s health and educational outcomes

Amanda Hughes, Kaitlin H. Wade, Matt Dickson, Frances Rice, Alisha Davies, Neil M. Davies & Laura D. Howe

Follow Amanda, Kaitlin, Matt, Alisha, Neil and Laura on twitter

Young people with health problems tend to do less well in school than other students, but it has never been clear why. One explanation is that health problems directly damage educational outcomes. In that case, policymakers aiming to raise educational standards might want to focus first on health as a means of improving attainment.

But are there other explanations? What if falling behind in school can affect health, for instance causing depression? Also, many health problems are more common among children from less advantaged backgrounds – for example, from families with fewer financial resources, or whose parents are themselves unwell. These children also tend to do less well in school, for reasons that may have nothing to do with their own health. How do we know if their health, or their circumstances, are affecting attainment?

It is also unclear if health matters equally for education at all points in development, or particularly in certain school years. Establishing how much health does impact learning, when, and through which mechanisms, would better equip policymakers to improve educational outcomes.

Photo by Edvin Johansson on Unsplash

Using genetic data helps us understand causality

Genetic data can help us answer these questions. Crucially, experiences like family financial difficulties, which might influence both a young person’s health and their learning, cannot change their genes. So, if young people genetically inclined to have asthma are more absent from school, or do less well in their GCSEs, that would strongly suggest an impact of asthma itself. Similarly, while falling behind in school might well trigger depression, it cannot change a person’s genetic propensity for depression. So, a connection between genetic propensity for depression and worse educational outcomes supports an impact of depression itself. This approach, of harnessing genetic information to better understand causal processes, is known as Mendelian randomization.

To find out more, we investigated links between

  • health conditions in childhood and adolescence
  • school absence in years 10 & 11
  • and GCSE results.

We used data from 6113 children born in the Bristol area in 1991-1992. All were participants of the Avon Longitudinal Study of Parents and Children (ALSPAC), also known as Children of the 90s. We focused on six different aspects of health: asthma, migraines, body mass index (BMI), and symptoms of depression, of attention-deficit hyperactivity disorder (ADHD), and of autism spectrum disorder (ASD). These conditions, though diverse, have two important things in common: they affect substantial numbers of young people, and they are at least in part influenced by genetics.

Alongside questionnaire data and education records, we also analysed genetic information from participants’ blood samples. From this information, we were able to calculate for each young person a summary score of genetic propensity for experiencing migraines, ADHD, depression, ASD, and for having a higher BMI.

We used these scores to predict the health conditions, rather than relying just on reports from questionnaire data. In this way, we avoided bias due to the impact of the young people’s circumstances, or of their education on their health rather than vice versa.

Even a small increase in school absence predicted worse GCSEs.

We found that, for each extra day per year of school missed in year 10 or 11, a child’s total GCSE points from their best 8 subjects was a bit less than half (0.43) of a grade lower. Higher BMI was related to increased school absence & lower GCSE grades.

Using the genetic approach, we found that young people genetically predisposed towards a higher BMI were more often absent from school, and they did less well in their GCSEs. A standard-deviation increase* in BMI corresponded to 9% more school absence, and GCSEs around 1/3 grade lower in every subject. Together, these results indicate that increased school absence may be one mechanism by which being heavier could negatively impact learning. However, in other analyses, we found a substantial part of the BMI-GCSEs link was not explained by school absence. It’s unclear which other mechanisms are at play here, but work by other researchers has suggested that weight-related bullying, and negative effects of being heavier on young people’s self-esteem, could interfere with learning.

*equivalent to the difference between the median (50th percentile) in population and the 84th percentile of the population

Diagram showing the pathways through which higher BMI could lead to lower GCSEs; either through more schools absence aged 14-16, or other processes such as weight-related bullying.
Our results suggest increased school absence may partly explain impact of higher BMI on educational attainment, but that other processes are also involved.

ADHD was related to lower GCSE grades, but not increased school absence.

In line with previous research, young people genetically predisposed to ADHD did less well in their GCSEs.  Interestingly, they did not have increased school absence, suggesting that ADHD’s impact on learning works mostly through other pathways. This is consistent with previous research highlighting the importance of other factors on the academic attainment of children with ADHD, including expectations of the school environment, teacher views and attitudes, and bullying by peers.

We found little evidence for an impact of asthma, migraines, depression or ASD on school absence or GCSE results

Our genetic analyses found little support for a negative impact of asthma, migraines, depression or ASD on educational attainment. However, we know relatively little about the genetic influences on depression and ASD, especially compared to the genetics of BMI, which we understand much better. This makes genetic associations with depression or ASD difficult to detect. So, our results should not be taken as conclusive evidence that these conditions do not affect learning.

What does this mean for students and teachers?

Our findings provide evidence of a detrimental impact of high BMI and of ADHD symptoms on GCSE attainment, which for BMI was partially mediated by school absence. When students sent home during the pandemic eventually return to school, the impact on their learning will have been enormous.  And while all students will have been affected, our results highlight that young people who are heavier, who have ADHD, or are experiencing other health problems, will likely need extra support.

Further reading

Hughes, A., Wade, K.H., Dickson, M. et al. Common health conditions in childhood and adolescence, school absence, and educational attainment: Mendelian randomization study. npj Sci. Learn. 6, 1 (2021). https://doi.org/10.1038/s41539-020-00080-6

A version of this blog was posted on the journal’s blog site on 21 Jan 2021.

Contact the researchers

Amanda Hughes, Senior Research Associate in Epidemiology: amanda.hughes@bristol.ac.uk

COVID19 – should schools close early for Christmas?

Sarah Lewis, Marcus Munafo and George Davey Smith

 

 

We have previously written about the limited risk posed to pupils, teachers and the community by schools being open during the Covid19 pandemic. Schools have now been open for almost a full academic term (3 months), so it is time to take another look at the evidence.

School re-openings have coincided with an increase in Covid19 infection rates across all UK nations. This rise in infection rates was anticipated, given the annual pattern of rising respiratory infections in the autumn term. There was also a rapid increase in Covid19 testing rates as children returned to school and presented with mild symptoms. Rates of positivity among children were very low at first, but a rise was observed over the autumn. This corresponded with an increase in rates among adults, and there seems to be a strong correlation between Covid19 positivity in schools and rates in the local community.

But has transmission of Covid19 in schools driven the second wave? And should schools be closed again to reduce infection in the community?

This post argues that there is little case for closing schools, as

  • Schools don’t seem to drive transmission in the community
  • The risk of the virus to most school children is very low
  • The harms of school closures are wide ranging.
Photo by CDC on Unsplash

Infection rates among children have been low

Since September children with COVID19 symptoms have been asked to stay at home and have a test before returning to school. Tests equating to 10% of the school pupil population were carried out during first half term in Scotland; only 0.2% of pupils tested positive during this period. Similarly high volumes of testing have been carried out in Wales, but only 0.6% of pupils tested positive between 1st September and 9th December 2020. Pupils made up 3.5% of cases in Wales over that period, despite making up 16% of the population.

However, the weekly Covid19 incidence among 12-16 year olds in Wales was similar to the national average for the week ending 9th December 2020, suggesting a change in the age demographic of cases.

Transmission levels in school have been low

It is unclear what proportion of children who tested positive contracted the infection in school – many children have similar social circles both in and out of school. When infections are found in schools, most schools have only 1 or 2 cases within a 2-week period (unless levels in the local community are high). This suggests low levels of transmission in schools.

Children and adults have different symptoms

Comparisons of rates of infection between children and adults should be treated with caution. Cases are diagnosed using recognised Covid19 symptoms, and are influenced by the volume of testing in the community. Younger children seem to be less likely to have symptoms – around 50% of infected children tend to be completely asymptomatic.  They also may have somewhat different pattern of symptoms to adults – fatigue, gastrointestinal symptoms, and changes in sense of smell or taste, but only rarely a cough. Therefore, studies relying on symptoms in children may be unreliable.

Random testing is the best way to find out level of infection

Surveys show that while young adults had the highest levels of infection in September, secondary school pupils now have the highest rates.

Studies which test individuals at random in the community are more reliable indicators of the levels of infection among children compared to adults. The UK Office of National Statistics (ONS) infection survey has been randomly testing people from the community since early May. It showed that young adults (school year 11 to age 24) had the highest positivity rates in September. This became more pronounced in early October when universities re-opened to students. By the end of October, rates among secondary school pupils were similar to those in young adults, at around 2%. Secondary school pupils now have the highest rates. Covid19 positive rates among primary school children are about half those in secondary school children and have barely changed since the beginning of the academic year.

Infection rates among teachers

There is no evidence that teachers are more at risk of death from COVID19, and infection rates among teachers do not seem higher than other professions.

ONS data from the first wave of the COVID19 epidemic in the UK showed that teachers were not at increased risk of death from the disease compared to other professionals. Based on ONS data, during October those working in the education sector had an antibody positivity rate of 8.1% (95% CI 5.9-10.8) compared with 6.5% (95% CI 5.9-7.3) among those working in other professions. This suggests perhaps slightly higher infection rates, but this is estimated with uncertainty.

Infection positivity rates – also measured by the ONS survey –  from 2nd September to 16th October showed that teachers were no more likely to test positive than other professions, although again there was a lot of uncertainty in these estimates*.  The Swedish Public Health Agency have linked data on Covid19 infection to occupational data and found no increase in infection rates among teachers, although there was some evidence of an increase in infection rates among teaching assistants, school counsellor and  headteachers. However, infection rates may have been inflated relative to other profession if there is  increased testing among asymptomatic people in the education sector.

Photo by Jeswin Thomas on Unsplash

Could infections in schools be driving community infection rates?

The evidence suggests this is unlikely.

Infection rate increases appear to coincide with school openings, but the R-number was increasing in Scotland and England before school openings. Hospital admissions due to Covid19 had also started to rise before this point. In September, positivity rates were initially highest among young adults, not among children of school age, suggesting that perhaps infections among school children were not driving community rates. The ONS data showed infection rates levelling off over October half term, and climbing again among young adults and secondary school children after half term. However, this trend was not as marked in primary school children, and was not observed in adults, even amongst the 35-49 year age group, to which many parents of school aged children belong. Another study of community-based testing – the REACT-1 study – found a greater decrease in infections among younger children compared with older children following the October half term holiday, but again there was a lot of uncertainty in this estimate.

Contact mixing patterns show that people tend to have the most contacts within the same age group, followed by the age group closest to them. Children have more opportunities to pass on the infection to other children and young adults, and are not significantly influencing rates in older adults.

The current R-number in England is currently estimated to be slightly below 1 despite schools being open. This shows that it is possible to drive down infection rates in the community whilst keeping schools open. Furthermore, when everything else but schools are closed – such as in the case of the national lockdown which occurred in England in November, school children will have more contacts than anyone else and schools will contribute to relatively more transmissions in the community even if transmission rates are low overall.

Closing schools is not the answer

Rates have recently fallen among adults in England, despite schools remaining open and secondary school rates increasing. The evidence suggests low levels of virus transmission within schools. First Minister of Wales Mark Drakeford recently said that behavioural evidence suggests closing schools could place some children “in even riskier environments”. Children being looked after by their grandparents rather than being in school would be more dangerous in terms of the virus being transmitted to a higher risk group.

Any public health intervention should consider the costs as well as the benefits. We know that school closures have wide ranging adverse consequences for children and families as outlined by UNESCO, and such costs are particularly pronounced for the poorest and most vulnerable children in society. Children:

  • who do not have access to technology to participate in online learning
  • whose parents who do not have the resources or the educational background to help

have been shown to fall further behind following school closures. Evidence suggests that children’s mental health deteriorated during the first lockdown, and that vulnerable children were at greater risk of violence and exploitation. School closures can also cause economic hardship due to parents being unable to work.  This has prompted Robert Jenkins Global Chief of Education at UNICEF to issue a statement over the last few days saying:

“Evidence shows that schools are not the main drivers of this pandemic. Yet, we are seeing an alarming trend whereby governments are once again closing down schools as a first recourse rather than a last resort. In some cases, this is being done nationwide, rather than community by community, and children are continuing to suffer the devastating impacts on their learning, mental and physical well-being and safety”.

If schools being open are not major drivers of transmission in the community (which they don’t appear to be), given that the risk of the virus to most school children is very low, there is very little case for closing them given the potential harm this could cause.

Footnote: Secondary schools in Wales were closed early for Christmas on the 11th December 2020

*(estimates ranged from 0.2% (95%CI=0.07-0.53) for primary school teachers to 0.5% teachers of unknown type (95% CI=0.36-0.69) compared with 0.4% (95%=0.39-0.49) for all other professions)

Drinking in pregnancy: the right to record, or the right to privacy?

Luisa Zuccolo , MRC Integrative Epidemiology Unit and Department of Population Health Sciences, Bristol Medical School, University of Bristol

Cheryl McQuire, School for Public Health Research/Centre for Public Health and Department of Population Health Sciences, Bristol Medical School, University of Bristol

Follow Luisa and Cheryl on TwitterCheryl McQuireLuisa Zuccolo

What’s the issue?

Drinking alcohol during pregnancy continues to stir passionate and polarised reactions. The issue has once again come into sharp focus. In England, the National Institute for Health and Care Excellence (NICE) is proposing to measure all alcohol use in pregnancy and transfer this information to a child’s health records, without explicit agreement from the mother. The aim of the proposal is to ensure better diagnosis and support for those with lifelong conditions caused by drinking in pregnancy that can include problems with learning, behaviour and physical abnormalities, known as Fetal Alcohol Spectrum Disorders (FASDs). However, the NICE proposal has been met with strong opposition from some organisations, which say that this breaches pregnant women’s right to medical privacy.

A balancing act

Benefits of introducing the proposed FASD NICE Quality Standards

It’s important to remind ourselves why measuring and sharing information on drinking in pregnancy could be worthwhile, and who will benefit.

Information – the key to understanding

Official guidance recommends that it is safest not to drink at all during pregnancy, or when trying for a baby. But a quarter of people in the UK are not aware of this guidance and the UK has the fourth highest rate of drinking in pregnancy in the world. The new NICE quality standards propose to rectify this by making it compulsory for midwives (or other health care professionals) to have conversations about alcohol with pregnant women.  The idea is to help women to make informed choices about drinking in pregnancy.

Information is in everyone’s interest. We still don’t know enough about the effects of different levels of alcohol use in pregnancy. This is a tricky area to study, in large part because we don’t have enough information. If we don’t measure, we can’t fully understand the effects of alcohol in pregnancy (not even to confirm whether small amounts are safe). This has been our area of research for a number of years, and it is important both because many pregnant women drink alcohol, and because they should have the right to be better informed. Current abstinence guidelines are largely (but not solely) based on the precautionary principle. Our research has provided some evidence that even low levels of use (two drinks a week or fewer) can have negative effects, including smaller babies and preterm birth. We need more information to find out about the full extent of these risks – including whether the risks are genuinely there – to ensure that women can make informed decisions based on the best possible evidence.

Diagnosing fetal alcohol spectrum disorders (FASD)

Fetal alcohol spectrum disorders are severely underdiagnosed. They are characterised by lifelong problems with learning, behaviour and, in some cases, physical abnormalities. Contrary to what many think, these are common disorders. Our recent research suggests that between 6 and 17% of children in the UK could have symptoms consistent with FASD. Without good information on exposure to alcohol in pregnancy, many of these children remain ‘invisible’ to services and do not get the support that they need.

Pregnant woman holding a glass of red wine

Concerns about the Quality Standards

Despite the potential benefits of these proposals, there are several unresolved issues that need tackling urgently.

Stigma and trust

If women feel stigmatised, they might lie about their drinking, invalidating any data collection. If they can’t trust their healthcare providers, then we can’t trust the data – so what would be the point of collecting it?

Tradeoffs

What would women be offered, in exchange for volunteering this information? What’s in it for them? It would be unbalanced and probably unethical to request information about drinking in pregnancy, at the risk of stoking maternal anxiety, without explaining the reasons, or offering support if so desired. The treatment of pregnant women who smoke provides an appropriate model – information is recorded on antenatal notes, and support to quit is offered at the same time. We need to guarantee a non-judgmental and supportive approach to listening when it comes to alcohol too.

Confidentiality

Women should be able to opt out. For those opting in, it should be made clear that the same high levels of confidentiality will apply as are already in place for current information from maternity notes and child health records. These new data on alcohol use should be no different and must be covered by existing guarantees.

Finding the right balance

So, where is the balance between the benefits and risks of the proposed changes? We often talk about burdening pregnant women with anxieties, but we neglect to talk about the lifelong consequences of emotional and behavioural problems arising from exposure to alcohol in pregnancy – these pose real everyday challenges for families, for many years to come. If on the one hand, maternal health is child health, then on the other child health is maternal health.

As we hear in these COVID-19 times, health is a marathon not a sprint

We need to continue shifting the focus from ‘healthy pregnancies’ to ‘healthy families’. The former can be met with resistance by those evoking the dangers of the surveillance state and policing women’s ‘baby-making’ bodies. The latter reminds us of the many individuals involved, all equally important, all of whom need support for the long term beyond those initial nine months.  We believe that NICE should listen to the plurality of women and families’ voices. The debate on recording alcohol in pregnancy will lead to constructive health gains that will benefit all.

Visualising Brexit’s Impact on Food Safety in Britain

Written by Marina Vabistsevits and Oliver Lloydresearchers on PhD studentships linked to the  “Data Mining Epidemiological Relationships” programmeat theMRC IEU. 

Follow us on twitter – @marina_vab,  @PlotThiggins 

Leaving the EU presents many unique challenges to Britain, among which is the crucial task of maintaining our high levels of food safetyAs a submission to the Jean Golding Institute’s data visualisation competition, we briefly investigated the impacts that Brexit may have on British food supplies. The dataset used in this analysis was made available by the Food Standards Agency (FSA) as the focus of the competition, and all code used is freely available in our github repository. 

The Need for Information Recompense 

In the first part of the analysis, we explored cases where food imported to Britain led to an alert being raised. The two biggest sources for such alerts were Britain’s internal alert systems (largely the FSA), and the EU’s Rapid Alert System for Food and Feed (RASFF).  

Since Britain is on course to lose access to RASFF-supplied information once Brexit is finalised in early 2021, we created the visualisation below as a comparison of the FSA and the RASFF in terms of both the number of alerts raised and the corresponding food’s origin country for each alert.  

 

Map of the world where lines between the UK and other countries indicate the countries where alerts from the Rapid Alert System for Food and Feed have originated from
Alerts from the EU Alert System

The arcs show the countries of origin of imports that raised alerts, and the yellow-red density map shows the recorded hazard alert frequency from those origins. Interactive versions of the two map instances can be found by following these linksRASFFUK internal alerts. 

Map of the world where lines between the UK and other countries indicate the 8 countries where alerts from the UK Internal alerts have originated from
Alerts from the UK Alert System

If the UK does indeed lose access to the RASFF, the loss of food hazards information about our own imports will be tremendous. The burden then falls on the FSA to develop and extend their alert system (which currently focuses very little on internationally supplied food) to bridge this information gap and ensure food safety for globally imported goods. As of the time of writing we are unsure what steps are being taken by the FSA, or the government at large, to address this issue. 

Post-Brexit Shifts in Food Hazard Threats  

As an extension of this work, we turned our attention to tariffs and the effect they might have on whom Britain chooses to import from. Upon leaving the EU the UK will have to negotiate new trade deals with both EU and non-EU countries. Since the cost for EU-produced food is expected to rise for Britain after Brexitwe may indeed see Britain importing more from outside of the union, which would naturally bring a shift to the makeup of food hazards that our alert systems will need to detect. Anticipating this shift will allow us to better mitigate the accompanying risk if it does begin to materialise.  

To this end, we explored the differences in food hazard threats posed by EU vs non-EU suppliers of Britain’s largest class of imported food: fruits and vegetables. The plot below shows the relative change in frequency for each category of food hazard in the case that Britain switched from 100% EU imports of fruit and vegetables to 100% non-EUThe hazard categories that are likely to increase in non-EU imports are highlighted in red.  Please note that this is the most extreme case possible and is unlikely to unfold to this extent in reality– this plot is therefore presented as a guide to the different food threats posed by EU vs non-EU imports. 

Bar chart showing difference in frequency of various food hazards, such as foreign bodies and allergens, after switching to non EU imports
Hazard alerts for fruits and vegetables: EU vs non-EU imports

Our full submission ‘Too Much Tooty in the Fruity: Keeping Food Safe in a Post-Brexit Britain’ can be found hereand includes a further breakdown of some of the categories of hazards displayed in the chart above. This work was awarded one of two joint runner-up prizes of the competitiontied with Angharad Stell’s Shiny app: ‘From a data space to knowledge discovery’. The winner of the competition was Robert Eyre, who produced this impressive visualization dashboard using D3. The Jean Golding Institute are hosting a showcase event on the 18th November, where all competition entries will be presented.  

We would like to thank the JGI for hosting the competition, and our PhD supervisors, Prof. Tom Gaunt and Dr. Ben Elsworth, for encouraging us to enter. 

 

How has the COVID-19 lockdown affected children born with a cleft lip/palate?

Written by the Cleft Collective Team

Follow the Cleft Collective on twitter.

The COVID-19 pandemic has been difficult for many families and there is widespread concern about how the lockdown might have affected children’s health, wellbeing and education. This concern may be even greater for families of children with pediatric health conditions such as cleft lip and/or cleft palate.

The Cleft Collective cohort study, linked to the IEU, is a UK-wide research study of the causes and consequences of being born with a cleft, which is a gap in the lip or roof of the mouth. In response to the COVID-19 lockdown, the Cleft Collective team sent out a questionnaire asking parents about how the lockdown had affected their children’s surgeries and treatments, access to schooling and wellbeing.

The first results are summarised in this infographic, which highlights that many children suffered delays in their surgeries and other health care appointments due to the lockdown. They also struggled with homeschooling, worries and negative emotions.

Through links to the NHS cleft teams and the Cleft Lip and Palate Association charity (CLAPA), the Cleft Collective team are sharing their findings with healthcare professionals to help ensure that children born with a cleft are given appropriate support to help them through this time and to lead happy, healthy childhoods.

The Cleft Collective cohort study is based in the MRC Integrative Epidemiology Unit and funded by the Scar Free Foundation and the University of Bristol. The video below explains more about the study.

Epigenetics regulate our genes: but how do they change as we grow up?

Rosa Mulder1,2                    Esther Walton3,4 & Charlotte Cecil1,5,6

Follow Esther and Charlotte on Twitter.

Epigenetics can help explain how our genes and environment interact to shape our development. Interest in epigenetics has grown increasingly within the research community, but until now little was known about how epigenetics change over time. We therefore studied changes in our epigenome from birth to late adolescence and created an interactive website inviting other researchers to explore our findings.

What is epigenetics?

The term ‘epigenetics’ refers to the molecular structures around the DNA in our cells, that affect if, when, and how our genes work. Even though nearly every cell in our body contains the exact same copy of DNA, cells can look and function entirely differently. Epigenetics can explain this. For example, every cell in our body has the potential to store fat, but in adipose tissues the cells’ epigenetic structures cause the cells to actually store fat.

Before birth, epigenetics plays a role in the specialization of cells from conception onwards by turning genes ‘on’ and ‘off’. After birth, epigenetics help our body develop even further, and maintain the specialization of our cells. However, the way epigenetics influence how our cells function is not only programmed by our genes, but may also be affected by the environment. Hence, our development and health is shaped by both our genes and our environment. Researchers are therefore trying to measure epigenetic processes to understand the role that epigenetics plays in this process of ‘nurture affecting nature’.

Both nurture and nature influence our health; understanding epigenetics helps us to find out how they might interact.

How can we measure epigenetics?

One of the types of molecular structures that can affect gene functioning is ‘DNA methylation’. Here, a small molecule (a methyl group of one carbon atom bonded to three hydrogen atoms; Figure 1) is attached to the DNA sequence. DNA methylation affects the three-dimensional structure of the DNA and can thereby turn it ‘on’ or ‘off’. DNA methylation can now easily be measured in the lab with the help of micro-chips; very small chips that can detect hundreds of thousands of methylation sites in the genome at a time, from just a small droplet of blood. Such chips are now used in large epidemiological cohorts such as ALSPAC to measure the level of DNA methylation for each of these sites. In epigenome-wide associations studies (EWASs), researchers study the associations between each of these methylation sites and a trait, such as prenatal smoking, BMI, or stress.

Figure 1: DNA sequence with DNA methylation

How does DNA methylation change throughout development?

Until recently, EWASs have mainly been cross-sectional, studying DNA methylation only at one time-point. So, even though research indicates that epigenetics is important in postnatal development, we do not know how true this is for DNA methylation sites measured with these epigenome-wide arrays. Studying a mechanism that supposedly changes over time without  knowing how it changes can be problematic: say that we find an association between smoking during pregnancy and DNA methylation at birth, can we still expect this association to be there at a later age? To fully interpret EWAS findings, and to compare research findings between different studies, we need a full understanding of how DNA methylation changes throughout development.

We therefore set out to study DNA methylation from birth to late adolescence, using DNA methylation measured in blood from the participants of ALSPAC in the UK, as well as from participants from another large cohort, the Generation R Study in the Netherlands.

We studied the change in levels of DNA methylation over time as well as variation in this change between individuals. If DNA methylation is indeed mainly linked to the basic developmental stages we go through as we grow up, we would expect methylation changes to be largely consistent between individuals. However, if DNA methylation is affected more by the different environments we live in, and individual health profiles, we would expect a proportion of sites to change differently for different individuals.

Between ALSPAC and Generation R, we created a unique dataset containing over 5,000 samples from about 2,500 participants with DNA methylation measurements at almost half a million methylation sites measured repeatedly at birth, 6 years, 10 years, and at 17 years. With various statistical models we studied different trajectories of change in DNA methylation.

We found change in DNA methylation at just over half of the sites (see for an example Figure 2a). At about a quarter of sites, DNA methylation changed at a different rate for different individuals (Figure 2b). We further saw that sometimes change only happened in a specific time period; for example, only in between birth and the age of 6 years after which DNA methylation remained stable (Figure 2c), and that sometimes differences in the rate of change only started from the age of 9 years (Figure 2d). Last, for less than 1% of the sites on the chromosomes tested (we did exclude the sex chromosomes), we saw that DNA methylation changed differently for boys and girls (Figure 2e).

Figure 2. Different examples of methylation sites, with every graph representing one methylation site with age on the x-axis and level of DNA methylation on the y-axis. Every line represents change in DNA methylation over time for one individual, showing (a) change in DNA methylation, (b) different rates of change for different individuals, (c) change during the first six years of life, (d) different rates of change starting from 9 years of age, (e) different change for boys and girls, and (f) change, but no differences in rate of change in a site associated to prenatal smoking.

How can we use these findings in future research?

These results show that there are sites in the genome for that show change in DNA methylation that is consistent between individuals, as well as sites that change at a different rate for different individuals. We have published the trajectories of change for each methylation site on a publicly available website. This makes it easier for other researchers to find sites that are developmentally important and may be of relevance for health and disease. For example, a methylation site previously associated with prenatal smoking, remained stable over time (Figure 1f), indicating that prenatal influences of smoking may be long-lasting, at least up to adolescence. In the future, we hope to associate traits, such as stress and BMI, to these longitudinal changes, to further our understanding of the developmental nature of DNA methylation and the associated biological pathways leading to health and disease.

 

1Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands

2 Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands

3 MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK

4 Department of Psychology, University of Bath, Bath, UK

5 Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands

6 Department of Psychology, Institute of Psychology, Psychiatry & Neuroscience, King’s College London, London, UK

 

Further reading

Mulder, R. H., Neumann, A. H., Cecil, C. A., Walton, E., Houtepen, L. C., Simpkin, A. J., … & Jaddoe, V. W. (2020). Epigenome-wide change and variation in DNA methylation from birth to late adolescence. bioRxiv. (preprint)

Epidelta project website: http://epidelta.mrcieu.ac.uk/

Can we ever achieve “zero COVID”?

Marcus Munafo and George Davey Smith

Follow Marcus and George on Twitter

An important ongoing debate is whether the UK’s COVID strategy should focus on suppression (maintaining various restrictions to ensure the reproduction rate of the SARS-CoV-2 virus remains at or below 1), or elimination (reducing the number of infections to a sufficiently low level that restrictions could be removed). Independent SAGE has explicitly called for a “zero COVID UK”.

The latter is attractive, in that it brings the promise of a return to normality, rather than the ongoing maintenance of distancing measures, use of face coverings, etc. Independent SAGE has suggested that “a seven day rolling average of one new case per million per day could represent ‘control’” under a “zero COVID” regime. In other words, around 60 to 70 new cases per day across the UK.

But is “zero COVID”, in the context of ongoing large-scale testing, ever likely to be possible?

It’s unclear how accurate COVID19 tests are – which presents a challenge for the aim of reaching ‘zero COVID’.

Knowing how many cases there are in a population requires testing. But even the best tests are not perfect. Unfortunately, it might be difficult to know exactly how accurate COVID tests are – the RT-PCR (antigen) tests for SARS-CoV-2 are likely to be highly specific, but in the absence of an alternative gold standard to compare these against, calculating the precise specificity is challenging

If we assume excellent specificity (let’s say 99.9%), at current levels of daily testing in the UK (74,783 tests per day processed across pillars 1 and 2, as of the 28th July update), that would mean around 75 false positive results per day even if there were no true cases of COVID in the UK. A sensitivity of 98% would mean over 1400 false positives *.

Any call for “zero COVID” needs to consider the impact of false positives on the achievability of the criterion that would constitute this, against a background of high levels of testing. Whilst testing is only one source of information that needs to be interpreted in the light of other clinical and epidemiological data, on their own they will be important drivers of any response.

As cases fall to a low level, perhaps we could reduce levels of testing (and therefore the number of false positives). But, given the high potential for substantial undocumented infection and transmission, it is likely that large-scale testing will remain essential for some time, if only to monitor the rise and fall in infections, the causes of which we still don’t fully understand.

The generic Situationist slogan “be realistic, demand the impossible” is one that many political campaigns for equality and freedom can understand.

But in many concrete situations well-meaning phrases can prove to be meaningless when scrutinised. If attempts to achieve zero COVID before relaxing restrictions leads to a delay in the reopening of schools, for example, that will result in vast increases in future levels of inequality in educational outcomes, and the future social trajectories dependent on these.

As with other endemic human coronaviruses, SARS-CoV-2 will likely show high variability and fall to very low levels within any particular population for sustained periods; it will not be permanently eliminated on a continental scale, however. Perhaps a better alternative to the setting of laudable but effectively unachievable targets is to recognise this and plan accordingly.

Marcus Munafò and George Davey Smith

* The importance of the sensitivity (and specificity) of tests for COVID antibodies has been discussed here, and the same logic broadly applies to antigen tests.