Linking two administrative datasets about looked after children: testing feasibility and enhancing understanding

Abstract

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.