Suchergebnisse

ObjectivesCriminal offending and re-offending comes at a significant social and economic cost. Offending prevention therefore presents a high priority policy area. Low educational attainment is a known risk factor for offending, but little is understood about how changes in school performance over time might be associated with offending.
MethodsWe investigated this in a large sample of n~4.3 million pupils using an administrative data linkage between two routinely-collected national datasets: the National Pupil Database (NPD) and the Police National Computer (PNC). First, we conducted growth mixture modelling using NPD data over three statutory testing years (School Years 2, 6 and 11). We then investigated the association between membership of these trajectories and subsequent conviction or caution for any criminal offence between Year 11 and age 21.
ResultsWe derived five school performance trajectories: (1) Average Consistent (n=3,497,167, 81.0%), (2) Average Increasing (n=66,383, 1.5%), (3) Average Declining (n=373,117, 8.6%), (4) Low Increasing (n=98,805, 2.3%), and (5) Low Consistent (n=281,964, 6.5%). The Average Declining group had the highest proportion of individuals who went on to be convicted or cautioned for any first offence up to age 21 (9.8%), followed by the Low Consistent (8.5%), Low Increasing (5.6%), Average Consistent (4.2%) and Average Increasing (1.5%) groups. Furthermore, as the number of offending days between Year 11 and age 21 increased (indicating repeat offending), the likelihood of having been in the Average Declining or Low Consistent groups also increased. We will also present findings from multilevel models accounting for school clustering, different offence types, and interactions.
ConclusionTentatively, findings suggest that changes in school performance could help to identify pupils who are struggling and at increased risk of criminal justice involvement, and therefore might be in need of additional support.

Background Research has shown depression to result in poorer educational attainment among children. However, comparatively little is known about the relationship between depression and attainment over time, and the role that children's socio-demographic characteristics might play.
Aim To describe the educational attainment trajectory profiles of children with a clinical diagnosis of depression, and to explore whether these trajectory profiles vary by gender, ethnicity, area-level deprivation and age at diagnosis.
Methods This is a historical, longitudinal cohort study, making use of two linked administrative datasets. First, a cohort of children (<18 years) were identified who had received a first diagnosis of depression from the South London and Maudsley NHS Foundation Trust between 2007 and 2013 (n=1490). Their pseudonymised secondary mental healthcare records were extracted via the Clinical Record Interactive Search application at the Trust's Biomedical Research Centre. These records were then linked to England's National Pupil Database, which contains attainment data for these children at three timepoints of interest: Key Stage 1 (ages 5 to 7 years), Key Stage 2 (7 to 11 years) and Key Stage 4 (14 to 16 years). Longitudinal mixed models for repeated measures will be conducted to model changes in attainment over these three timepoints according to gender, ethnicity, area-level deprivation and age at diagnosis.
Results Preliminary results are suggestive of attainment trajectories in this cohort varying according to their socio-demographic characteristics. Analysis will be completed August 2019.
Conclusion The findings from this study will identify whether some groups of depressed children experience better or worse patterns of educational attainment over time. This could highlight priorities for intervention in England's ongoing efforts to improve child mental health provision in schools.

ABSTRACT
ObjectivesTo compare sample biases when linking education data to external datasets using opt in and opt out consent models for the Millennium Cohort Study (MCS) – opt in, and the Case Register Interactive Search Child and Adolescent Mental Health sample (CRIS CAMHS) –opt out.
ApproachWe compared demographic factors in the linked and unlinked populations when two cohorts, the MCS, and CRIS CAMHS data were linked to the National Pupil Database. The MCS is a birth cohort collecting prospective data on the social, economic and health-related circumstances of children surveyed at roughly two-yearly intervals from the age of 9 months. At age 7, parents were asked to consent for information from their child's education records (via the UK Department for Education's National Pupil Database, NPD) to linked to the MCS. Of the 9000 adults with parental responsibility, 8448 (93.9%) consented. The CRIS CAMHS sample is the UK's largest, anonymised clinical database for children and adolescents referred to South London child and adolescent mental health services, which is collected using opt out consent. All 35, 426 children were eligible for linkage to their educational data.
ResultsFor MCS, 7446 (82.7% of the population) eligible children were successfully linked to NPD following opt in consent. After stratification into distinct geographical regions, London children in the most deprived quartile of neighbourhood deprivation were over twice as likely (O.R 2.5, C.I 1.18-5.3) not to have their educational records linked. For the opt out CRIS CAMHS cohort, 30,178 (85.1%) were linked, with no significant differences in linkage rates between children in the highest and lowest quartiles of deprivation (O.R 1.05, C.I 0.93-1.18). Relative to children of White ethnicity, Asian, Black African and Mixed ethnic groups were significantly less likely to be matched in both studies. Black Caribbean ethnicity was significantly associated with non-linkage in the MCS cohort (O.R 3.0, C.I 1.49-6.01), but not in CRIS CAMHS (0.96, C.I 0.83-1.12).
ConclusionRecord linkages are a valuable enhancement to child-based longitudinal studies and clinical registries, allowing evaluation of questions relevant to public health and social care policy. Opt out consent approaches improve representation of more socially disadvantaged populations. Nevertheless, whether using opt in and opt out consent process, possible biases due to linkage error can be important and need to be addressed when analysing and interpreting results.

IntroductionThe use of linked data and non-consent methodologies is a rapidly growing area of health research due to the increasing detail, availability and scope of routinely collected electronic health records data. However, gaining the necessary legal and governance approvals to undertake data linkage is a complex process in England.
ObjectivesWe reflect on our own experience of establishing lawful basis for data linkage through Section 251 approval, with the intention to build a knowledgebase of practical advice for future applicants.
MethodsThematic analysis was conducted on a corpus of Section 251 feedback reports from the NHS Health Research Authority Confidentiality Advisory Group.
ResultsFour themes emerged from the feedback. These were: (a) Patient and Public Involvement, (b)~Establishing Rationale, (c) Data maintenance and contingency, and the need to gain (d) Further Permissions from external authorities prior to full approval.
ConclusionsSecuring Section 251 approval poses ethical, practical and governance challenges. However, through a comprehensive, planned approach Section 251 approval is possible, enabling researchers to unlock the potential of linked data for the purposes of health research.

ObjectivesTo present an overview of a cohort of working age adults accessing secondary mental healthcare services and benefits related to unemployment, sickness, disability, or income support and describe the different benefit types received across diagnostic and sociodemographic groups.
MethodsUsing a novel data linkage containing electronic secondary mental health care records from the South London and Maudsley (SLaM) NHS Foundation Trust and benefits data from the Department for Work and Pensions (DWP), we present descriptive statistics on a cohort of working age adults. The data window covers the period January 2007-June 2020.
ResultsWe identified n=150,348 patients of working age (18-65 years), who had attended SLaM secondary mental health care services, 78.3% of which had received a benefit relating to unemployment, sickness, disability, or income support. Of this group, 68% had a recorded primary psychiatric diagnosis. We found that a much higher percentage of those with a primary psychiatric diagnosis received more than one benefit (69.4%) compared to those who had not received a primary psychiatric diagnosis (30.6%).
ConclusionsWe showed types of benefits received among working age adults accessing secondary mental health care services. This cohort will be further examined to explore trajectories of mental health care and benefit receipt and provide evidence that will help to inform both DWP policies and mental health care delivery.

IntroductionMaternal mental health problems and substance misuse are key risk factors for child neglect or abuse and court-mandated placement into care. Linkage between mental health records and family court data could raise awareness about parent mental health needs and inform approaches to address them.
ObjectivesTo evaluate data linkage between administrative family court data and electronic mental health records for a population-based mental health service for 1.3 million people in South London.
MethodsWe deterministically linked administrative family court data for women (n=5463) involved in care proceedings in South London with service user records from the South London and Maudsley NHS Mental Health Trust (SLaM). We restricted the cohort to women involved in proceedings between 2007 and 2019, in local authorities where SLaM solely provides secondary/tertiary mental health services and the Improving Access to Psychological Therapies (IAPT) (n=3226). We analysed the associations between match status and sociodemographic/case characteristics using multivariable logistic regression.
ResultsTwo-thirds (2317/3226; 66%) of women linked to a SLaM service user record at some point; most (91%) who linked accessed secondary/tertiary mental health services, indicating serious mental illness. Accounting for possible missed matches, we estimated that 70-83% of women accessed SLaM services at some point. Older women at index proceedings (>35yrs OR: 0.69, 95%CI: 0.54-0.88vs<25yrs) and Black women or women from other ethnic groups (Black ethnic groups 0.65, 0.50-0.83; other ethnicity 0.59, 0.43-0.81 vs White ethnic groups) had lower odds of linking. Odds of linking were higher for women with an infant in proceedings (1.42, 1.18-1.71), or with curtailed/terminated parental responsibility (1.44, 1.20-1.73).
ConclusionsOur linkage supports growing evidence of a high burden of mental health problems and substance misuse among women whose children enter care in England, compared to the general population. Research using this linkage should inform strategies to address the considerable mental health needs of vulnerable women and their children.

IntroductionIn the UK, mental disorders are one of the most common reasons for claiming a benefit relating to unemployment, income, sickness and disability. Limited information exists regarding the demographic characteristics and psychiatric profiles of working age individuals claiming benefits in London. Until recently, detailed data on both mental disorders and benefit receipt were unavailable.
ObjectivesTo establish and describe a cohort of working age adults accessing secondary mental health services and benefits related to unemployment, income, sickness, and disability.
MethodsUsing a novel data linkage containing electronic secondary mental health records from the South London and Maudsley (SLaM) NHS Foundation Trust and benefits data from the Department for Work and Pensions (DWP), we present descriptive statistics on sociodemographics, psychiatric diagnoses, and benefits received among a cohort of working age adults. The DWP benefits data window covers the period January 2007-June 2020, the SLaM data window covers the period January 2007-June 2019.
ResultsWe identified n = 150,348 patients (18-65 years), who had attended SLaM secondary mental health services, 78.3% of which had received a benefit relating to unemployment, income, sickness and disability. Of this group, 68% had a recorded primary psychiatric diagnosis. We found that a much higher percentage of those with a primary psychiatric diagnosis received more than one benefit (69.4%) compared to those who had not received a primary psychiatric diagnosis (30.6%). Almost 70% of claimants who obtained more than one benefit were identified as living within the two quintiles representing the highest levels of deprivation in the South-east London boroughs served by SLaM.
ConclusionsWe showed types of benefits received among working age adults accessing secondary mental health services. This cohort will be further examined to explore trajectories of mental health care and benefit receipt and provide evidence that will help to inform both DWP policies and mental health care delivery.

LAY SUMMARY Limited research has explored the relationships between paternal mental health, adolescent offspring mental health, and family functioning in United Kingdom military populations. The authors investigated this in a study of 105 serving and ex-serving members of the United Kingdom Armed Forces, with adolescent offspring ages 11 to 17 years. It was found military fathers with symptoms of PTSD or depression had more family difficulties, particularly around communication. Their adolescent offspring were also more likely to meet criteria for mental health disorders. These findings demonstrate the importance of supporting military fathers and their families with mental health and well-being.

Risk assessment of suicidal behavior is a time-consuming but notoriously inaccurate activity formental health services globally. In the last 50 years a large number of tools have been designed for suicide risk assessment, and tested in a wide variety of populations, but studies show that these tools suffer fromlow positive predictive values.More recently, advances in research fields such as machine learning and natural language processing applied on large datasets have shown promising results for health care, and may enable an important shift in advancing precision medicine. In this conceptual review, we discuss established risk assessment tools and examples of novel data-driven approaches that have been used for identification of suicidal behavior and risk. We provide a perspective on the strengths and weaknesses of these applications to mental health-related data, and suggest research directions to enable improvement in clinical practice ; This manuscript was written as a result of a workshop that was held at the Institute of Psychiatry, Psychology and Neuroscience, King's College London, financially supported by the European Science Foundation (ESF) Research Networking Programme Evaluating Information Access Systems: http://elias-network.eu/. SV is supported by the Swedish Research Council (2015-00359) and the Marie Skłodowska Curie Actions, Cofund, Project INCA 600398. EB-G is partially supported by grants from Instituto de Salud Carlos III (ISCIII PI13/02200; PI16/01852), Delegación del Gobierno para el Plan Nacional de Drogas (20151073); American Foundation for Suicide Prevention (AFSP) (LSRG- 1-005-16). NW is supported by the UCLH NIHR Biomedical Research Centre. DN is supported by The Alan Turing Institute under the EPSRC grant EP/N510129/1, with an Alan Turing Institute Fellowship (TU/A/000006). RP has received support from a Medical Research Council (MRC) Health Data Research UK Fellowship (MR/S003118/1) and a Starter Grant for Clinical Lecturers (SGL015/1020) supported by the Academy of Medical Sciences, The Wellcome Trust, MRC, British Heart Foundation, Arthritis Research UK, the Royal College of Physicians and Diabetes UK. DL is supported by the UK Medical Research Council under grant MR/N028244/2 and the King's Centre for Military Health Research. JD is supported by a Medical Research Council (MRC) Clinical Research Training Fellowship (MR/L017105/1). RD is funded by a Clinician Scientist Fellowship (research project e-HOST-IT) from the Health Foundation in partnership with the Academy of Medical Sciences

The UK's National Health Service (NHS) generates uniquely rich data that should be rapidly deployed for policy and service improvement, yet researchers report difficulties in accessing these data. Paradoxically, these restrictions are occurring at the same time as the open science movement, which encourages data sharing to improve the rigour, transparency, and replicability of research. We describe the urgency of improvements to data access and propose solutions from a mental health research perspective, although the issues discussed extend to all areas in which analysis and linkage of health data support policy and practice. Actions are needed at every level, from data users and data custodians to government (panel).