Study collecting the views of young people, parents of children with long COVID, and doctors, finds that long COVID in children is poorly understood by doctors

Dr Katharine Looker

‘Enhancing the utilization of COVID-19 testing in schools’, is a study which will look at the characteristics of long COVID and COVID-19 infection in children. ‘Long COVID’ is commonly used to describe signs and symptoms that continue or develop after acute COVID‑19. The study is being funded as a result of a rapid funding call by Health Data Research UK (HDR UK), the Office for National Statistics (ONS) and UK Research and Innovation (UKRI). The study forms part of the larger Data and Connectivity National Core Study, which is led by HDR UK in partnership with ONS.

The COVID-19 testing in schools study is related to the CoMMinS (COVID-19 Mapping and Mitigation in Schools) study being undertaken by the University of Bristol in partnership with Bristol City Council, Public Health England [PHE] and Bristol schools. CoMMinS aims to give us an understanding of COVID-19 infection dynamics centred around school pupils and staff and onward transmission to family contacts, using regular testing. Our study will jointly analyse data from CoMMinS, along with information from Electronic Patient Records, and data from the COVID-19 Schools Infection Survey (SIS; jointly led by the London School of Hygiene & Tropical Medicine [LSHTM], PHE, and ONS). The SIS is a study similar to CoMMinS but carried out nationally.

To help inform research questions and methods for the study, members from the University of Bristol study team gathered views about long COVID in children between 9 March and 30 April 2021 from:

  • seven young people from the NIHR Bristol Biomedical Research Centre Young People’s Advisory Group (YPAG)
  • five families whose children have long COVID or suspected long COVID, recruited through two online UK campaign groups for long COVID, and
  • a survey completed by four GPs and one paediatrician, and an online meeting with two paediatricians.

It is important to note that the opinions gathered were based on small samples which may not be representative.

Through the meeting and survey with the doctors, the study team found that clinical understanding of long COVID in children is currently very limited.

The doctors said that it may be hard to distinguish between long COVID and other conditions with similar symptoms. Many of the symptoms of long COVID, like fatigue and feeling sick, aren’t very specific, and are common to many different conditions. Long COVID in children currently lacks a clinical definition, making diagnosis difficult. It isn’t yet properly understood whether long COVID is a new condition in itself, or a group of conditions like post viral fatigue, which is already recognised.

Young people, and families of children with long COVID or suspected long COVID, who were also asked for their opinion, said that feeling sick or stomach pain, extreme tiredness, and headaches were the symptoms they would rank as most ‘harmful’. For young people, this was based on them imagining having the symptoms. For the families, this was based on their first-hand experience.

The families also said that the symptoms their children were experiencing were numerous, often very severe, and more wide-ranging than those currently listed on the NHS website for long COVID. It is not yet clear what is causing the unusual symptoms.

The families said that they had struggled to get a diagnosis and treatment for their children. They also said that long COVID symptoms were having a significant impact on their children’s day-to-day lives both physically and psychologically, and that some of the children had missed school because of the symptoms. Some of the families also found fevers difficult to manage because their children had to miss school to self-isolate every time they had a fever. They wanted to know why the set of symptoms were being experienced, and why their children in particular had developed them.

It is not known how many children have or will develop long COVID. So far, studies which have tried to measure the rate of long COVID in children suggest it is rare. However, quantifying the number of cases is made difficult by a lack of clinical understanding of long COVID including the lack of an agreed clinical definition. The opinions collected suggest that relying on clinical diagnoses alone will under-estimate cases. On the other hand, there needs to be a cautious approach to estimating the number of cases based on non-specific symptoms, as other conditions which cause similar symptoms may be counted as well.

Caroline Relton, Professor of Epigenetic Epidemiology and Director of the Bristol Population Health Science Institute at the University of Bristol, joint lead for CoMMinS and one of the lead authors of the report, said: “The opinions we gathered further highlight that it is difficult to count the number of children with long COVID on the basis of diagnoses alone while long COVID in children remains poorly defined.

“There are added complications of studying long COVID in children, when it is sometimes difficult to disentangle what might be the result of experiencing infection from what might result from the wider impact of experiencing the pandemic. Isolation, school closures, disrupted education and other influences on family life could all have health consequences. Defining the extent of the problem in children and the root causes will be essential to helping provide the right treatment and to aid the recovery of young people who are suffering.”

The findings highlight that examining GP and hospital visits, and school attendance, might currently be a more useful and feasible way of assessing how COVID-19 has affected children, rather than relying only on diagnoses of long COVID. However, the study researchers also need to be aware how often healthcare is accessed according to need, and absence from school due to self-isolation, which will affect what is being measured.

Feeling sick or stomach pain, extreme tiredness, and headaches will be important symptoms to consider in the study.

Read the full report

Find the full report on the CoMMinS study news page.

 

Most of the world is missing out on the genomics revolution: why this is bad for science

Yoonsu Cho, Bryony Hayes and Daniel Lawson

As we usher in the era of precision medicine – healthcare tailored to the individual – genetic information is being used to design drugs, tests and medical procedures. While this approach enables physicians to better predict the needs of patients and quickly adopt the most suitable treatment, it should be acknowledged that what is suitable for many, is not suitable for all. Appropriate medical care is linked with ancestry – for example, healthy people with African ancestry naturally exhale less air than reference samples of Europeans, leading to mis-diagnosis for Asthma. For people from Black and Minority Ethnicities, the potential impact of ethnicity is intensely debated in Cancer treatment. Research suggests that lack of BME participation in medical research will lead to poor medication choices, genetic tests being less useful, and any COVID-19 treatments being less well tested.

The World Health Organization has stated that “everyone should have a fair opportunity to attain their full health potential and that no one should be disadvantaged from achieving their potential”. However, systematic discrimination & socio-economic-related disadvantages such as lower education and difficulty accessing high quality jobs are overwhelmingly experienced by non-white people, with statistics showing that 80% of Black African and Caribbean communities are living in England’s most deprived areas (as defined by the Neighbourhood Renewal Fund). These factors contribute to people from those communities receiving worse medical care overall. Beyond this, poor representation in research now can only lead to systematically poorer healthcare in the years to come. In 2009, only 4% of genetic association studies used samples with non-European ancestry. Whilst this rose to almost 20% by 2016, this improvement was largely due to East Asian nations such as Korea, China and Japan initiating their own biobank projects, leaving many ethnicities under-represented. Hence, from a medical genetics perspective, “Black and Minority Ethnicity” (BME) is well defined as “ethnicities without a rich nation to back a representative genetic biobank” and includes African ancestry.

Improving participation of underrepresented populations in Biobanks should make science more useful for all.

Why does biobank representation matter?

Epidemiological comparisons – that is, comparing large numbers of people who develop disease and those that do not – often rely on genetics to infer which behaviours and conditions are causes and which are effects. These analyses use a technique called Mendelian Randomization (MR). MR  has demonstrated, for example, that alcohol consumption causally increases body mass and made it clear that even moderate alcohol intake has no beneficial effect on health outcomes. Causal hypotheses are a critical pathway to drug discovery and public health intervention, but are based almost entirely on European populations. Since there are many genes that affect most disease risk and these are of different importance across ancestries, we cannot be certain that the associations found apply to other populations. This urgently needs to be addressed in order to:

  • promote representative translational research that is relevant to all
  • reduce bias in the consideration of new health policies that may negatively impact minority populations.

Some populations have increased risk from specific diseases, and many people have ancestry from all over the world, making the categorisation of ‘race’ in medicine of some value but increasingly problematic. The IEU leads work on measuring this ancestry variation, which is important for individuals’ health. Getting at the cause of disease is key for understanding the effects of genes on disease risk and traits. Data on varied ethnicities is valuable for science, simply by showing us more variation. Traits such as height, weight and pre-inclination for education may not be directly related to ethnicity, but data from varied ancestries still helps to separate genetic cause from effect. Paradoxically, the least available data on African ancestry is particularly valuable scientifically, due to the lack of variation in the population that came out-of-africa around 50,000 years ago.

Science and the public improving representation together

Acknowledgement of this deficit is becoming more widespread, and the Black Lives Matter movement has refocused attention on representation in science, but the solution remains undetermined. How do we in the science and research community push for better diversity and representation in our resources? Biobanks operate on a consensual ‘opt in, opt out’ system and tend to favour certain groups. In 2016 the Financial Times generalised the participants of UK Biobank and “healthy, wealthy and white”, but why do so many more individuals from this demographic ‘opt in’? In 2018 Prictor et al theorised that BME groups may experience more barriers to participation such as location, cultural sensitivities around human tissue, and issues of literacy and language. However, given the history of the relationship between the research community and minority groups, seen in cases such as the Tuskegee Study, it is easy to see why BME populations might be less inclined to participate, if invited.

Although there is still need for considerable change, several recent developments will help, including the China Kadoori Biobank, the ancestrally diverse US-based Million Veterans program, and many others. However, given restrictions on privacy and reporting methods, these biobanks are hard to compare. Currently the IEU is part of a multi-national effort to develop tools to get the best science possible out of these comparisons, whilst simultaneously respecting privacy and data security issues. The IEU has been collaborating with various research groups across the world to make our research more reproducible. Building tools that work at scale is a challenge encompassing Mathematics, Statistics, Computer Science, Engineering, Genomics and Epidemiology, but this work is paving the way to promoting representative research that is inclusive and applicable for all.