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

Posted on January 22, 2021January 21, 2021 by lindsey.pike

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

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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.

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 (50^th percentile) in population and the 84^th 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.

Contact the researchers

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

COVID19 – should schools close early for Christmas?

Posted on December 15, 2020July 1, 2024 by lindsey.pike

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.

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 1^st September and 9^th 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 9^th 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 2^nd 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.

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 11^th 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)

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

Posted on September 21, 2020 by lindsey.pike

Written by the Cleft Collective Team

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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.

Are schools in the COVID-19 era safe?

Posted on July 1, 2020July 1, 2024 by lindsey.pike

Sarah Lewis, Marcus Munafo and George Davey Smith

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The COVID-19 pandemic caused by the SARS-COV2 virus in 2020 has so far resulted in a heavy death toll and caused unprecedented disruption worldwide. Many countries have opted for drastic measures and even full lockdowns of all but essential services to slow the spread of disease and to stop health care systems becoming overwhelmed. However, whilst lockdowns happened fast and were well adhered to in most countries, coming out of lockdown is proving to be more challenging. Policymakers have been trying to balance relaxing restriction measures with keeping virus transmission low. One of the most controversial aspects has been when and how to reopen schools.

Many parents and teachers are asking: Are schools safe?

The answer to this question depends on how much risk an individual is prepared to accept – schools have never been completely “safe”. Also, in the context of this particular pandemic, the risk from COVID-19 to an individual varies substantially by age, sex and underlying health status. However, from a historical context, the risk of death from contracting an infectious disease in UK schools (even in the era of COVD-19) is very low compared to just 40 years ago, when measles, mumps, rubella and whooping cough were endemic in schools. Similarly, from a global perspective UK schools are very safe – in Malawi, for example, the mortality rate for teachers is around five times higher than in the UK, with tuberculosis causing more than 25% of deaths among teachers.

In this blog post we use data on death rates to discuss safety, because there is currently better evidence on death rates by occupational status than, for example, infection rates. This is because death rates related to COVID-19 have been consistently reported by teh Office for National Statistics, whereas data on infection rates depends very much on the level of testing in the community (which has changed over time and differs by region).

Risks to children

Thankfully the risk of serious disease and death to children throughout the pandemic, across the UK and globally, has been low. Children (under 18 years) make up around 20% of the UK population, but account for only around 1.5% of those hospitalised with COVID-19. This age group have had better outcomes according to all measures compared to adults. As of the 12^th June 2020, there have been 6 deaths in those with COVID-19 among those aged under 15 years across England and Wales. Whilst extremely sad, these deaths represent a risk of around 1 death per 2 million children. To place this in some kind of context, the number of deaths expected due to lower respiratory tract infections among this age group in England and Wales over a 3 month period is around 50 and 12 children would normally die due to road traffic accidents in Great Britain over a 3-month period.

Risks to teachers

Our previous blog post concluded that based on available evidence the risk to teachers and childcare workers within the UK from Covid-19 did not appear to be any greater than for any other group of working age individuals. It considered mortality from COVID-19 among teachers and other educational professionals who were exposed to the virus prior to the lockdown period (23^rd March 2020) and had died by the 20^th April 2020 in the UK. This represents the period when infection rates were highest, and when children were attending school in large numbers. There were 2,494 deaths among working-age individuals up to this date, and we found that the 47 deaths among teachers over this period represented a similar risk to all professional occupations – 6.7 (95% CI 4.1 to 10.3) per 100,000 among males and 3.3 (95% CI 2.0 to 4.9) per 100,000 among females.

The Office for National Statistics (ONS) has since updated the information on deaths according to occupation to include all deaths up to the 25^th May 2020. The new dataset includes a further 2,267 deaths among individuals with COVID-19. As the number of deaths had almost doubled during this extended period, so too had the risk. A further 43 deaths had occurred among teaching and education professionals, bringing the total number of deaths involving COVID-19 among this occupational group to 90. It therefore appears that lockdown (during which time many teachers have not been in school) has not had an impact on the rate at which teachers have been dying from COVID-19.

As before, COVID-19 risk does not appear greater for teachers than other working age individuals

The revised risk to teachers of dying from COVID-19 remains very similar to the overall risk for all professionals at 12.9 (95% CI 9.3 to 17.4) per 100,000 among all male teaching and educational professionals and 6.0 (95% CI 4.2 to 8.1) per 100,000 among all females, compared with 11.6 (95%CI 10.2 to 13.0) per 100,000 and 8.0 (95%CI 6.8 to 9.3) per 100,000 among all male and female professionals respectively. It is useful to look at the rate at which we would normally expect teaching and educational professionals to die during this period, as this tells us by how much COVID-19 has increased mortality in this group. The ONS provide this in the form of average mortality rates for each occupational group for same 11 week period over the last 5 years. The mortality due to COVID-19 during this period represents 33% for males and 19% for females of their average mortality over the last 5 years for the same period. For male teaching and educational professionals, the proportion of average mortality due to COVID-19 is very close to the value for all working-aged males (31%) and all male professionals (34%). For females the proportion of average mortality due to COVID-19 is lower than for all working-aged females (25%) and for female professionals (25%). During the pandemic period covered by the ONS, there was little evidence that deaths from all causes among the group of teaching and educational professionals were elevated above the 5-year average for this group.

Teaching is a comparatively safe profession

It is important to note that according to ONS data on adults of working age (20-59 years) between 2001-2011, teachers and other educational professionals have low overall mortality rates compared with other occupations (ranking 3^rd safest occupation for women and 6^th for men). The same study found a 3-fold difference between annual mortality among teachers and among the occupational groups with the highest mortality rates (plant and machine operatives for women and elementary construction occupations among men). These disparities in mortality from all causes also exist in the ONS data covering the COVID-19 pandemic period, but were even more pronounced with a 7-fold difference between males teaching and educational professionals and male elementary construction occupations, and a 16-fold difference between female teachers and female plant and machine operatives.

There is therefore currently no indication that teachers have an elevated risk of dying from COVID-19 relative to other occupations, and despite some teachers having died with COVID19, the mortality rate from all causes (including COVID19) for this occupational group over this pandemic period is not substantially higher than the 5 year average.

Will reopening schools increase risks to teachers?

One could argue that the risk to children and teachers has been low because schools were closed for much of the pandemic, and children have largely been confined to mixing with their own households, so that when schools open fully risk will increase. However, infection rates in the community are now much lower than they were at their peak, when schools were fully open to all pupils without social distancing. Studies which have used contract tracing to determine whether infected children have transmitted the disease to others have consistently shown that they have not, although the number of cases included has been small, and asymptomatic children are often not tested. Modelling studies estimate that even if schools fully reopen without social distancing, this is likely to have only modest effects on virus transmission in the community. If infection levels can be controlled – for example by testing and contact tracing efforts – and cases can be quickly isolated, then we believe that schools pose a minimal risk in terms of the transmission of COVID, and to the health of teachers and children. Furthermore, the risk is likely to be more than offset by the harms caused by ongoing disruption to children’s educational opportunities.

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.

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.

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

Are teachers at high risk of death from Covid19?

Posted on May 26, 2020July 1, 2024 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 20^th 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 1^st 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 13^th 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 8^th 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 20^th 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.

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

Why are people who stay in school longer less likely to get heart disease?

Posted on May 23, 2019May 23, 2019 by lindsey.pike

Alice Carter, PhD researcher at the IEU, outlines the key findings from a paper published in BMJ today.

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Heart disease remains the leading cause of death globally, causing over 17.5 million deaths annually. Whilst death rates from heart disease are decreasing in high income countries, the most socioeconomically deprived individuals remain at the greatest risk of developing heart disease. Socioeconomic causes and the wider determinants of health (including living and working conditions, health care services, housing and a number of other wider factors) and are suggested to be the most important driver of health. Behavioural and lifestyle factors, such as smoking, alcohol consumption, diet and exercise, are the second most important contributor to health and disease.

Why does education matter?

Staying in school for longer has been shown to lead to better lifelong health, including reducing the risk of heart disease (cardiovascular disease) and dementia. We also know that those who stay in school are more likely to adopt healthy behaviours. For example, they are less likely to smoke, but more likely to eat a healthy diet and take part in physical activity. These factors, can in turn, reduce the risk of heart disease, such as by reducing body mass index (BMI) or blood pressure. We wanted to understand if these risk factors (BMI, systolic blood pressure and lifetime smoking behaviour) could explain why those who stay in school for longer are less likely to get heart disease, and how much of this effect they explained.

What did we find?

We found that individually, BMI, systolic blood pressure and smoking behaviour explained up to 18%, 27% and 34% of the effect of education on heart disease respectively. When we looked at all three risk factors together, they explain around 40% of the effect. This means that up to 40% of the effect of staying in school reducing the risk of heart disease can be explained by the fact that those who stay in school tend to lead healthier lives. In this work we looked at four outcomes, coronary heart disease (gradual build-up of fatty deposits in arteries), stroke, myocardial infarction (heart attack) and all subtypes of heart disease combined. For all the outcomes we looked at, we found similar results. Notably, the 40% combined effect is smaller than the amount estimated simply from summing the individual effects together. This suggests there is overlap between the three risk factors in how they cause heart disease.

How did we do this?

In our work, we used a few different methods and data sources to answer our questions.

We started by looking at observational data (that is the data self-reported by the participants of the study) in UK Biobank – a large population cohort study of around 500 000 individuals. Of these, almost 220 000 people were eligible to be in our analysis.
We looked at how their education affected their risk of four types of heart disease. We then looked at how the intermediate factors, BMI, blood pressure and smoking, could help explain these results.
Secondly, we replicated these analyses using two types of Mendelian randomisation analyses (a form of genetic instrumental variable analysis, see below), firstly in the UK Biobank group and secondly by using summary data from other studies in the area.

Why use genetic data?

Typically, epidemiologists collect data by asking people to report their behaviours, lifestyle characteristics and any diagnoses from a doctor. Alternatively, people in a study may have been to a clinic where their BMI or blood pressure is measured. However, this type of data can lead to inaccuracies in analyses. This could be because:

measures are not reported (or measured) accurately. For example, it can be difficult to get an accurate measure of blood pressure, where it changes throughout the day, and even just going to a clinic can result in higher blood pressure.
there may be other variables associated with both the exposure and outcome (confounding). One example of this is suggesting that grey hair causes cancer. Really, age is responsible for i) leading to grey hair and ii) leading to cancer. Without accounting for age, we might suggest a false association exists (see figure 1). In our study using education, this could be ethnicity for example, where it influences both staying in school and risk of heart disease.
or an individual with ill health may have been advised to change their lifestyle (reverse causality). For example, an individual with a high BMI may have had a heart attack and have been advised by their doctor to lose weight to avoid having a second heart attack.

Diagram showing a picture of grey hair with an arrow linking to cancer, and a third variable - age - above, which explains both. — Figure 1: Does grey hair really cause cancer?

One way to overcome these limitations is to use Mendelian randomisation. This method uses the genetic variation in an individual’s DNA to help understand causal relationships. Every individual has their own unique genetic make-up, which is determined, and fixed, at the point of conception.

We are interested in single changes to the DNA sequence, called single nucleotide polymorphisms (or SNPs). For all of our risk factors of interest (education, BMI, blood pressure and smoking) there are a number of SNPs that contribute towards the observed measures, that are not influenced by factors later in life. This means, Mendelian randomisation estimates are unlikely to be affected by bias such as confounding, reverse causality or measurement error, as we might expect when we rely on observational data. By using these genetic variants, we can improve our understanding of if, or how, a risk factor truly causes an outcome, or whether it might be spurious.

What else might be important?

Although we find BMI, blood pressure and smoking behaviour explain a very large amount of the effect, over 50% of the effect of education on heart disease is still unknown. In some small sensitivity analyses we looked at the role of diet and exercise as intermediate risk factors; however, these risk factors did not contribute anything beyond the three main risk factors we looked at. Other social factors may be involved. For example, education is linked to higher income and lower levels of workplace stress, but these factors may also be related to those we’ve looked at in this analysis.

One further suggestion for what may be responsible is medication prescribing and subsequent adherence (or compliance). For example, individuals with higher education may be more likely to be prescribed statins (cholesterol lowering drugs) compared to someone who left school earlier, but with the same requirement for medication. Subsequently, of those who are prescribed statins for example, perhaps those with higher education are more likely to take them every day, or as prescribed. We have work ongoing to see whether these factors play a role.

What does this mean for policy?

Past policies that increase the duration of compulsory education have improved health and such endeavours must continue. However, intervening directly in education is difficult to achieve without social and political reforms.

Although we did not directly look at the impact of interventions in this area, our work suggests that by intervening on these three risk factors, we could reduce the number of cases of heart disease attributable to lower levels of education. Public health policy typically aims to improve health by preventing disease across the population. However, perhaps a targeted approach is required to reduce the greatest burden of disease.

In order to achieve maximum reductions in heart disease we now need to i) identify what other intermediate factors may be involved and ii) work to understand how effective interventions could be designed to reduce levels of BMI, blood pressure and smoking in those who leave school earlier. Additionally, our work looked at predominantly European populations, therefore replicating analyses on diverse populations will be important to fully understand the population impact.

We hope this work provides a starting point for considering how we could reduce the burden of heart disease in those most at risk, and work to reduce health inequalities.

Read the full paper in the BMJ

Tag: Education