Using linked NHS data to understand SARS-CoV-2 outcomes in children in England

Background:
The World Health Organization identified children as a potentially vulnerable population for SARS-CoV-2. Most children are now known to have a milder infection than adults or are asymptomatic, but some have severe sequelae in the form of a multisystem inflammatory syndrome (MIS-C), which remains incompletely understood (1,2). There are also many unanswered questions regarding post-infection sequelae (‘long COVID’) in children (3). The efficacy and safety of COVID vaccines in childhood are currently unknown, although trials are ongoing. New SARS-CoV-2 variants associated with increased transmissibility have contributed to surging global infections. It is unknown if “variants of concern” are better able to infect children, and the role of children in transmission (e.g. in households and schools) is unclear. As the pandemic matures, the increased transmissibility and pathogenicity of some new variants require a better assessment of the impact of SARS-CoV-2 on children.

Aims/Objectives:
To understand the potential differential effects of SARS CoV-2 variants on severity of COVID-19 disease, on post-infection sequelae and on treatment responses in children in England.

Methods:
UCL have developed a cohort of all children born in English National Health Service (NHS) hospitals - 97% of all births in England, based on linked, national hospital and mortality data. The cohort includes 13.5 million children born between 2003 and March 2021, and is updated quarterly. Children are linked to their mothers’ and siblings’ hospital and mortality data via their birth record. Children are followed up longitudinally via hospital outpatient and emergency department visits, admissions, and national mortality data. The cohort has been further linked to national surveillance data on SARS-CoV-2 tests and results in the community and hospitals.. This project will involve examining outcomes of interest (e.g. SARS-CoV-2 tests and results, COVID-related emergency department attendances and admissions, including for MIS-C, and mortality) for children at fine spatio-temporal scale (from 2020-2021), allowing examination of the potential differential effects of different variants whilst adjusting for a wide range of confounding variables The longitudinal nature of the dataset allows for taking into account risk factors such as birth characteristics (e.g. preterm births) and underlying long-term conditions.

For research on outcomes that do not require hospital contact or dispensed medicines (including long COVID), the student will use the Clinical Practice Research Datalink, which holds longitudinal primary care records, on clinician coded diagnoses, prescribed medicines, vaccinations, test results and referrals from over 2000 primary care practices across the UK, and is linked to national COVID surveillance datasets.

This is an excellent opportunity for a student with a strong qualitative background (e.g. MSc in statistics, epidemiology or demography) and experience of statistical programming to gain skills in using large, real-world NHS data to better understand the burden of SARS-CoV-2 infection in children. The student will be jointly based in the Child Health Informatics Group, a large, diverse and friendly group of child health data science researchers and the Infections group, with research programmes on established and re-emerging infections including HIV, viral hepatitis, syphilis, and Zika virus. (https://www.ucl.ac.uk/child-health/research/population-policy-and-practice-research-and-teaching-department/cenb-clinical-epidemiology)

References:
1. Abrams JY et al. Multisystem inflammatory syndrome in children associated with severe acute respiratory syndrome coronavirus 2: a systematic review. J Pediatr 2020;226:45-54.
2. Gotzinger F et al. COVID-19 in children and adolescents in Europe: a multinational, multicentre cohort study. Lancet Child Adolesc Health 2020; 4:653-61.
3. Thompson H. Children with long COVID. New Scientist. 2021; 249(3323): 10–11.