Poster #86 FIRST EPISODE OF PSYCHOSIS (FEP): AN INCIDENCE PILOT STUDY USING THE SOUTH LONDON AND MAUDSLEY NHS FOUNDATION TRUST BIOMEDICAL RESEARCH CENTRE (SLAM BRC) CASE REGISTER

Background with rationaleEvery year all 32 local authorities in Scotland provide information on looked after children in their area to the Scottish Government. This forms the basis for the annual Children Looked After Statistics (CLAS). Information is also collected by Scottish Children’s Reporter Administration (SCRA) on all children who are involved in the Children’s Hearings System. Until now these two data sets had never been linked. Main Aim To test the feasibility and success of the linkage on the basis that these datasets had not previously been linked, and if linkage was possible, use this data to enhance our understanding of the child and process factors associated with pathways to permanence or lack of permanence. Methods/ApproachVeterans were identified using the South London and Maudsley Biomedical Research Centre (SLaM) case register – a database holding secondary mental health care electronic records for the South London and Maudsley National Health Service Trust of 300,000 patients. We developed two methods. An NLP and machine learning tool were developed to automatically evaluate personal history statements written by clinicians. ResultsFor the first time, as part of the Permanently Progressing? Building secure futures for children in Scotland study, these two data sets were linked safely and successfully for 1,000 children who became looked after in 2012-13 when they were aged five and under. The linkage provided important new information for practitioners and policymakers. In this presentation we will focus on the key findings, such as what it told us about previous referrals and methodological insights regarding these data sets and their linkage. ConclusionThe data linkage process was complex and time-consuming but possible. The data we were able to link provided valuable information that enhanced our understanding of child and process factors.

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Childhood cognitive ability and the use of long-term care in later life

International Journal for Population Data Science ◽

10.23889/ijpds.v4i3.1181 ◽

2019 ◽

Vol 4 (3) ◽

Author(s):

Matthew Iveson ◽

Drew Altschul ◽

Ian Deary

Keyword(s):

Cognitive Ability ◽

Early Life ◽

Long Term Care ◽

Later Life ◽

Research Centre ◽

The South ◽

Term Care ◽

Case Register ◽

Life Circumstances

Background As the population ages the demand for care is predicted to increase. Previous studies have reported that individuals with poorer post-morbid cognitive ability are at higher risk of entering long-term care, both institutionalised care and home-based care. Given that post-morbid cognitive ability is sensitive to the type and severity of morbidity, it remains unclear whether higher cognitive ability, as a trait-level measure of individual differences, contributes to care usage. Some success has been observed using non-cognitive early-life circumstances such as socioeconomic circumstances as pre-morbid predictors of care risk. However, the contribution of early-life cognitive ability has yet to be examined. Main Aim We investigate the association between early-life circumstances, particularly cognitive ability, and the risk of entry into long-term care in later life (age 65+). Methods Veterans were identified using the South London and Maudsley Biomedical Research Centre (SLaM) case register – a database holding secondary mental health care electronic records for the South London and Maudsley National Health Service Trust of 300,000 patients. We developed two methods. An NLP and machine learning tool were developed to automatically evaluate personal history statements written by clinicians. Results This study uses a large sample of individuals born in Scotland in 1936 and who took part in the Scottish Mental Survey 1947. It links research data from childhood to routinely-collected administrative and healthcare records from across the life course. Conclusion We demonstrate the importance of early-life factors for predicting care usage in later life and how this role differs between types of long-term care. The implications of the results for research and policy will be discussed.

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