Supervisors: Dr Pia Hardelid, Dr Ania Zylbersztejn
The health of children with learning disability or autism and their families: evidence from electronic health records
Background:
Up to one in three children in England have a long-term condition or disability. Children with long-term conditions, particularly neurodevelopmental conditions such as autism or learning disabilities, are more likely to require specialised medical services and frequent contact with healthcare. Most of the care for a child with long-term conditions will be provided by the child’s family, who will also negotiate access to different NHS services, support from schools and social care. Caring for a child with long-term conditions and co-ordinating their care across multiple services can therefore be a major source of stress leading to both mental and physical health problems for parents. We know very little about the impact on siblings. Routinely collected health data provides a unique opportunity to study family health.
Aims/objectives:
This project will use real-world NHS data to examine variation in the health and wellbeing of children with learning disabilities or autism and their families. The specific objectives are to:
1. Create nationally representative cohorts of mothers, fathers and siblings using electronic primary care records.
2. Assess inequalities (by deprivation, region, ethnic group) in mental and physical health outcomes of children with learning disabilities or autism, their parents and siblings compared to the general population and families of children with another chronic condition - asthma.
3. Examine how health outcomes of families vary according to the characteristics of the child (such as age, complexity of health needs) to indicate groups that would benefit most from targeted interventions.
The findings from this project will inform policies relating to health and other services that are likely to improve the health of children affected by learning disability or autism and their families.
Methods:
The student will analyse data from a primary care database (Clinical Practice Research Datalink, CPRD, currently capturing 16 million active patients),1 linked to hospital admission records (Hospital Episode Statistics, HES).2 As GPs provide primary care (vaccinations, common medications, health check-ups) and are gatekeepers for secondary care (e.g. coordinating referrals), CPRD provides a unique opportunity to study population health. The student will work with longitudinal CPRD data on over a million young people born between 1987-2021 and evaluate information available on their families. The methods used to indicate and validate family cohorts in administrative health records will be relevant to future family health research.
This is an excellent opportunity for a student with strong quantitative skills (such as an MSc in statistics, epidemiology, demography or similar) and experience of statistical programming to build their skills and experience using real-world NHS data to inform care delivery and support for a highly vulnerable group of children and their families. This studentship will provide opportunities to gain expertise in statistical methods for longitudinal data analysis and working with big administrative health records. The student will be based within the Child Health Informatics Group, a large, diverse and friendly group of data scientists with expertise in policy-relevant research using routinely collected NHS and government data. The Group has excellent track record of policy engagement (for example via NIHR Children and Families Policy Research Unit) and international comparisons, with an established network of collaborators from Sweden, Finland, Canada and Australia, that the student will benefit from.
References:
1. Herrett E, Gallagher AM, Bhaskaran K, et al. Data Resource Profile: Clinical Practice Research Datalink (CPRD). Int J Epidemiol 2015; 44: 827–36.
2. Herbert A, Wijlaars L, Zylbersztejn A, Cromwell D, Hardelid P. Data Resource Profile: Hospital Episode Statistics Admitted Patient Care (HES APC). Int J Epidemiol 2017; 46: 1093–1093i.