Approximately 1% of UK school age children have an Autistic Spectrum Disorder (ASD). Children with ASD typically have longstanding difficulties using language, engaging in every day social interactions and having a general, flexible interest in the world around them. ASD is commonly diagnosed in the first years of primary school. The majority of children who receive an ASD diagnosis will go on to have difficulties throughout their school life, often leaving with no qualifications. This puts them at a significant disadvantage as adults and at greater risk for a host of mental and physical health problems. An important objective for UK child mental health specialists, teachers and researchers is to reduce these educational failings for children with ASD whilst at school, to ensure they have better skills and opportunities in later life.In the UK, specialist Child and Adolescent Mental Health Services (CAMHS) commonly provide ASD assessments and offer treatment. CAMHS services also treat common childhood mental health conditions including anxiety, depression, conduct and hyperactivity disorders. Children without ASD who suffer these disorders often have considerable difficulties achieving their potential at school. Fortunately there are a number of effective mental health treatments including medications that can help non ASD children get back on track. These treatments increase their chances of attending school, not getting into trouble and passing exams.Children with ASD also suffer from these disorders and at much greater rates (up to 70% in some studies) than typically developing children. ASD combined with other mental health disorders presents as a much more complex condition to manage and may significantly impact educational performance. Currently there is little evidence available to inform clinicians on how best to treat the common co-occurring conditions associated with ASD. Pharmacological treatments, developed primarly in non ASD children, may not have the same effect in improving educational performance for ASD children. Pharmacological treatments (for example stimulants, anti-depressants and anti-psychotics) are being increasingly used by CAMHS to help manage common co-occurring disorders in children with ASD. At present, little is known which disorders or additional symptoms lead clinicians to prescribe these medications to children with ASD. Also, very few experimental trials are conducted in children with ASD that are adequately designed to measure the long term effect of treatment, therefore their influence on longer term educational outcomes like overall school attendance, exclusion rates or final exam results remains unclear. With robust confidentiality and data protection measures in place, electronic health records(EHRs) can provide rich data to investigate the gaps in current knowledge. They can be used to determine which childhood factors lead to impaired educational performance in ASD, and how they may be modified by treatment. For this proposal, I will use EHRs via the Clinical Record Interactive Search system(CRIS) and link the records with the National Pupil Database(NPD). CRIS was developed by King College London to allow researchers to retrieve in depth information from the anonymised case notes of over 34,000 CAMHS patients. The NPD is a database managed by the Department for Education and contains all children academic records since enrolment in state school from 2002. As the fellowship applicant, under the supervision of Professor Hotopf and Dr Hayes at Kings College London, and Professor Ford at the University of Exeter, I will study whether additional mental health difficulties impact on educational performance and whether treatments that may reduce these difficulties could improve academic outcomes.Technical SummaryI will establish cross-sectional and longitudinal individual clinical profiles (e.g. socio-demographic, co-occurring disorders, initial symptom severity, impairment, home, neighbourhood and service use factors) for a large ASD child cohort and model clinical predictors of pharmacotherapy use (drug type, class switches, polypharmacy). Text mining algorithms and structured fields in CRIS will provide data on a range of baseline clinical factors and pharmacological treatment. I will then explore associations between clinical profiles and academic outcomes in children with ASD, which will include academic attainment, school drop-out, attendance and exclusion rates. I will estimate the risk of these outcomes according to clinical profiles, including co-occurring disorder. CRIS will be linked with the National Pupil Database, a longitudinal dataset containing educational and census information since 2002, for all children who have been enrolled in state education. This will provide individual level longitudinal education performance data. I will then model the clinical predictors, treatment exposures (drug type, duration of treatment), treatment response exposures ( effect sizes derived from validated psychometric scales and normalised against outcome data provided by CAMHS Outcome Research Consortium) against multiple education outcomes. I will estimate the risk of specific education outcomes according to ASD clinical profiles and co-occurring disorders and whether these risks are modified by medication use and treatment response.
Interagency Autism Coordinating Committee (IACC)
Autism Research Database (AFD)
