Existing studies that use only one point in time have shown that ‘children looked after’ by the local authority (CLA) have poorer educational and health outcomes than the general population. Pre-care experiences, such as physical abuse, parental mental health illness and parental alcohol misuse, are common reasons for becoming looked after. These experiences also predict poorer health, education and social outcomes in young people who are not in care. For these reasons, it is difficult to understand whether poorer health and educational outcomes for CLA are because of differences in pre-care experiences, or of care itself.
The research has three objectives:
First, it will address the lack of large-scale, longitudinal quantitative studies in the UK examining the role of CLA status in predicting education and healthcare usage outcomes. This will be achieved by drawing on the resources of the Administrative Data Network (ADRN), who have approved this project, to combine the Wales Electronic Cohort for Children (WECC; a Wales-wide, inter-disciplinary dataset on education and healthcare usage), with national, routinely collected data on young people’s receipt of social services intervention, including histories of care for CLA. This will create a longitudinal cohort of young people, who completed Key Stage 4 in 2015 or 2016.
Second, it will reduce ambiguity over the extent to which poor outcomes among CLA are a result of pre-care childhood experiences, or negative experiences associated with being in care. This will be achieved firstly by comparing CLA with children who receive social services intervention but are not CLA (henceforth referred to as Children In Need, but Not Looked After (NLA)), in addition to general population comparisons. Whilst there are likely to be differences between these two groups that predict CLA status, there is increasing recognition within the literature that NLA may provide a more valid comparator to CLA than general population samples. Further, we will assess the extent to which between group differences are attenuated by a number of common predictors of receipt of social services intervention and entry to care (physical abuse, parental mental health illness and parental alcohol misuse), defined within recent research using routinely collected healthcare data within WECC. For the CLA vs NLA comparison (i.e. comparisons excluding the general population
sample), via routinely collected services intervention data, we will also examine the role of an additional experience, i.e. domestic abuse.
Third, whilst CLA status is often based on the assumption that removing young people from adversity will move them toward better life trajectories, this study will be the first to longitudinally examine the role of care in moderating effects of a range of pre-care experiences on education and healthcare usage outcomes. This will be achieved through investigating whether the relationship between adverse experiences and subsequent education and healthcare outcomes are intensified, or weakened by being taken into care. We will also contribute to the evidence base on how variability in experiences of care shapes outcomes.
The research will address the following questions:
1. Is CLA status associated with higher or lower education attainment and healthcare usage in comparison to i) NLA; ii) the
general population?
2. To what extent is the association of CLA status with education attainment and healthcare usage explained by prior
childhood experiences?
3. Does CLA status moderate the association of childhood experiences with educational attainment and healthcare usage?
4. Among CLA, how do experiences of care (i.e. age of entry to care, length of time in care, stability of care placements) impact on educational attainment and healthcare usage?
Oct-Nov 2019: Consultation events to refine research questions, discuss policy and practice implications, and devise dissemination and engagement strategy with representatives from all investigators and the AG.
Oct-Dec 2019: The main dataset will have already been prepared in a provisional format by ADRN prior to the start of the project, thus, data editing will begin immediately. This period will involve delivery of specification by ADRC and SAIL analysts, including running the algorithms to derive the variables required to perform analyses.
Dec-Feb 2020: Preparation of dataset (Grade 6 statistician). This will include cleaning, quality checking and integrating datasets ready for analyses.
Feb –Jun 2020: Data analysis (DFa, grade 6 statistician). Report writing (SL and Co-Is) will happen in parallel.
May 2020: Request to data guardians for clearance to disseminate initial findings, draft reports and hold further meetings with the Advisory Group to identify modifications and refinements to analyses and reports.
Jul-Sep 2020: Finalise analyses and request final clearance of the results (DFa, SL). Dissemination and stakeholder workshops on preliminary findings; production of research briefings (health, education and social care via Cardiff University’s ExChange Network events); dissemination among Advisory Group (comprising: Voices from Care; A foster carer, recruited via The Fostering Network;Local Authority Social Care representatives including Tanya Evans, Head of Children’s Services at Blaenau Gwent County Borough Council; A social work practitioner recruited via Cardiff University MA Social Work practice learning networks; The Fostering Network and a member of its staff, Maria Boffey; Natalie Avery, Head of Family Justice, Welsh Government; Dr Dominic McSherry, Queens Belfast University; Dr Nikki Luke, Oxford University REES centre) and investigator networks. Continue with report writing and dissemination.
Sep-Dec 2020: Report writing phase for submission to peer reviewed journals and reports for use by non-academic partners (SL and Co-Is).
Jan-Mar 2021: Further stakeholder workshops to assist with dissemination and the design and production of research briefings.
DECIPHer Blog: Does local authority care make a difference to the lives of vulnerable children? Sara Long
Exchange webinar: https://youtu.be/sBhE8BiZsp0
