Linking health and education data to plan and evaluate services for children

Educator perceptions of the complex needs of young people in Pupil Referral Units: An exploratory qualitative analysis

BACKGROUND

Alternative education provision such as Pupil Referral Units support young people who have been excluded from mainstream school settings and often from disadvantaged backgrounds. However, there is limited research to date exploring educators' perceptions of the complex needs of young people in PRUs, and the extent to which PRUs as currently configured can meet such needs.

METHODS

Between March 2019 and October 2020 twenty-two participants holding various educational roles from five different Pupil Referral Units across London and Southeast England were interviewed. The interviews aimed to explore the participants' experiences of working with students in PRU's and examine some of the challenges that they might encounter. Semi-structured interviews were analysed using Reflexive thematic analysis.

RESULTS

The three identified themes and their sub-themes highlighted the complex needs of these young people and identified significant barriers to effective service provision. The first theme 'Complexities of PRU population' highlighted the challenges that young people in PRUs face and perceived systemic short falls in addressing such complexity. The second theme 'Challenges of the PRU environment' highlights the frustrations that educators experience when it comes to providing adequate support to young people in PRU's, the absence of agency support, and the uncertainty that these educational settings can bring. The third theme 'Peer Group Influences' highlights the impact of peer groups from beyond the classroom on engagement within the classroom.

CONCLUSIONS

Despite the clear complex needs of young people in PRUs, staff reported feeling ill-equipped to support these individuals and lacked access to effective inter-agency support. Participants reported that pupils' mental health difficulties were exacerbated by exclusion and reintegration practices, an over-zealous focus on educational outcomes and the impact of gang influences on their school lives. Implications include more specific mental health training for staff working in PRU's, improved inter-agency working and the incorporation of trauma-informed approaches in educational practice.

Tracing Tomorrow: young people's preferences and values related to use of personal sensing to predict mental health, using a digital game methodology

BACKGROUND

Use of personal sensing to predict mental health risk has sparked interest in adolescent psychiatry, offering a potential tool for targeted early intervention.

OBJECTIVES

We investigated the preferences and values of UK adolescents with regard to use of digital sensing information, including social media and internet searching behaviour. We also investigated the impact of risk information on adolescents' self-understanding.

METHODS

Following a Design Bioethics approach, we created and disseminated a purpose-built digital game (www.tracingtomorrow.org) that immersed the player-character in a fictional scenario in which they received a risk assessment for depression Data were collected through game choices across relevant scenarios, with decision-making supported through clickable information points.

FINDINGS

The game was played by 7337 UK adolescents aged 16-18 years. Most participants were willing to personally communicate mental health risk information to their parents or best friend. The acceptability of school involvement in risk predictions based on digital traces was mixed, due mainly to privacy concerns. Most participants indicated that risk information could negatively impact their academic self-understanding. Participants overwhelmingly preferred individual face-to-face over digital options for support.

CONCLUSIONS

The potential of digital phenotyping in supporting early intervention in mental health can only be fulfilled if data are collected, communicated and actioned in ways that are trustworthy, relevant and acceptable to young people.

CLINICAL IMPLICATIONS

To minimise the risk of ethical harms in real-world applications of preventive psychiatric technologies, it is essential to investigate young people's values and preferences as part of design and implementation processes.

Impact of inconsistent ethnicity recordings on estimates of inequality in child health and education data: a data linkage study of Child and Adolescent Mental Health Services in South London

OBJECTIVES

Ethnicity data are critical for identifying inequalities, but previous studies suggest that ethnicity is not consistently recorded between different administrative datasets. With researchers increasingly leveraging cross-domain data linkages, we investigated the completeness and consistency of ethnicity data in two linked health and education datasets.

DESIGN

Cohort study.

SETTING

South London and Maudsley NHS Foundation Trust deidentified electronic health records, accessed via Clinical Record Interactive Search (CRIS) and the National Pupil Database (NPD) (2007-2013).

PARTICIPANTS

N=30 426 children and adolescents referred to local Child and Adolescent Mental Health Services.

PRIMARY AND SECONDARY OUTCOME MEASURES

Ethnicity data were compared between CRIS and the NPD. Associations between ethnicity as recorded from each source and key educational and clinical outcomes were explored with risk ratios.

RESULTS

Ethnicity data were available for 79.3% from the NPD, 87.0% from CRIS, 97.3% from either source and 69.0% from both sources. Among those who had ethnicity data from both, the two data sources agreed on 87.0% of aggregate ethnicity categorisations overall, but with high levels of disagreement in Mixed and Other ethnic groups. Strengths of associations between ethnicity, educational attainment and neurodevelopmental disorder varied according to which data source was used to code ethnicity. For example, as compared with White pupils, a significantly higher proportion of Asian pupils achieved expected educational attainment thresholds only if ethnicity was coded from the NPD (RR=1.46, 95% CI 1.29 to 1.64), not if ethnicity was coded from CRIS (RR=1.11, 0.98 to 1.26).

CONCLUSIONS

Data linkage has the potential to minimise missing ethnicity data, and overlap in ethnicity categorisations between CRIS and the NPD was generally high. However, choosing which data source to primarily code ethnicity from can have implications for analyses of ethnicity, mental health and educational outcomes. Users of linked data should exercise caution in combining and comparing ethnicity between different data sources.

A scoping review of fair machine learning techniques when using real-world data

OBJECTIVE

The integration of artificial intelligence (AI) and machine learning (ML) in health care to aid clinical decisions is widespread. However, as AI and ML take important roles in health care, there are concerns about AI and ML associated fairness and bias. That is, an AI tool may have a disparate impact, with its benefits and drawbacks unevenly distributed across societal strata and subpopulations, potentially exacerbating existing health inequities. Thus, the objectives of this scoping review were to summarize existing literature and identify gaps in the topic of tackling algorithmic bias and optimizing fairness in AI/ML models using real-world data (RWD) in health care domains.

METHODS

We conducted a thorough review of techniques for assessing and optimizing AI/ML model fairness in health care when using RWD in health care domains. The focus lies on appraising different quantification metrics for accessing fairness, publicly accessible datasets for ML fairness research, and bias mitigation approaches.

RESULTS

We identified 11 papers that are focused on optimizing model fairness in health care applications. The current research on mitigating bias issues in RWD is limited, both in terms of disease variety and health care applications, as well as the accessibility of public datasets for ML fairness research. Existing studies often indicate positive outcomes when using pre-processing techniques to address algorithmic bias. There remain unresolved questions within the field that require further research, which includes pinpointing the root causes of bias in ML models, broadening fairness research in AI/ML with the use of RWD and exploring its implications in healthcare settings, and evaluating and addressing bias in multi-modal data.

CONCLUSION

This paper provides useful reference material and insights to researchers regarding AI/ML fairness in real-world health care data and reveals the gaps in the field. Fair AI/ML in health care is a burgeoning field that requires a heightened research focus to cover diverse applications and different types of RWD.

Assessing machine learning for fair prediction of ADHD in school pupils using a retrospective cohort study of linked education and healthcare data

OBJECTIVES

Attention deficit hyperactivity disorder (ADHD) is a prevalent childhood disorder, but often goes unrecognised and untreated. To improve access to services, accurate predictions of populations at high risk of ADHD are needed for effective resource allocation. Using a unique linked health and education data resource, we examined how machine learning (ML) approaches can predict risk of ADHD.

DESIGN

Retrospective population cohort study.

SETTING

South London (2007-2013).

PARTICIPANTS

n=56 258 pupils with linked education and health data.

PRIMARY OUTCOME MEASURES

Using area under the curve (AUC), we compared the predictive accuracy of four ML models and one neural network for ADHD diagnosis. Ethnic group and language biases were weighted using a fair pre-processing algorithm.

RESULTS

Random forest and logistic regression prediction models provided the highest predictive accuracy for ADHD in population samples (AUC 0.86 and 0.86, respectively) and clinical samples (AUC 0.72 and 0.70). Precision-recall curve analyses were less favourable. Sociodemographic biases were effectively reduced by a fair pre-processing algorithm without loss of accuracy.

CONCLUSIONS

ML approaches using linked routinely collected education and health data offer accurate, low-cost and scalable prediction models of ADHD. These approaches could help identify areas of need and inform resource allocation. Introducing 'fairness weighting' attenuates some sociodemographic biases which would otherwise underestimate ADHD risk within minority groups.

Seasonality of Pediatric Mental Health Emergency Department Visits, School, and COVID-19

OBJECTIVES

The aim of this study was to explore how the academic calendar, and by extension school-year stressors, contributes to the seasonality of pediatric mental health emergency department (ED) visits.

METHODS

The authors reviewed all pediatric mental health ED visits at a large urban medical center from 2014 to 2019. Patients who were younger than 18 years at time of presentation, were Durham residents, and had a primary payer of Medicaid were included in the sample population, and the dates of ED visits of the sample population were compared against dates of academic semesters and summer/winter breaks of a relevant school calendar. Of patients with multiple ED visits, only the first ED presentation was included, and descriptive statistics and a rate ratio were used to describe the study group and identify the rate of ED visits during semesters compared with breaks.

RESULTS

Among the sample population from 2014 to 2019, there were 1004 first pediatric mental health ED visits. Of these ED visits, the average number of visits per week during summer/winter breaks was 2.2, and the average number of visits per week during academic semester dates was 3.4. The rate of ED visits was significantly greater during academic semesters compared with breaks (Rate Ratio, 1.6; 95% confidence interval, 1.4-2.0; P < 0.001).

CONCLUSIONS

Children may be at greater risk of behavioral health crises or having increased mental needs when school is in session. As many children's mental health has worsened during the COVID-19 (coronavirus disease 2019) pandemic, these findings highlight the need for increased mental health services in the school setting as children return to in-person learning. In addition, it may benefit health systems to plan behavioral health staffing around academic calendars.

Autism spectrum disorders as a risk factor for adolescent self-harm: a retrospective cohort study of 113,286 young people in the UK

BACKGROUND

Individuals with autism spectrum disorder (ASD) are at particularly high risk of suicide and suicide attempts. Presentation to a hospital with self-harm is one of the strongest risk factors for later suicide. We describe the use of a novel data linkage between routinely collected education data and child and adolescent mental health data to examine whether adolescents with ASD are at higher risk than the general population of presenting to emergency care with self-harm.

METHODS

A retrospective cohort study was conducted on the population aged 11-17 resident in four South London boroughs between January 2009 and March 2013, attending state secondary schools, identified in the National Pupil Database (NPD). Exposure data on ASD status were derived from the NPD. We used Cox regression to model time to first self-harm presentation to the Emergency Department (ED).

RESULTS

One thousand twenty adolescents presented to the ED with self-harm, and 763 matched to the NPD. The sample for analysis included 113,286 adolescents (2.2% with ASD). For boys only, there was an increased risk of self-harm associated with ASD (adjusted hazard ratio 2·79, 95% CI 1·40-5·57, P<0·01). Several other factors including school absence, exclusion from school and having been in foster care were also associated with a higher risk of self-harm.

CONCLUSIONS

This study provides evidence that ASD in boys, and other educational, social and clinical factors, are risk factors for emergency presentation with self-harm in adolescents. These findings are an important step in developing early recognition and prevention programmes.

Association of school absence and exclusion with recorded neurodevelopmental disorders, mental disorders, or self-harm: a nationwide, retrospective, electronic cohort study of children and young people in Wales, UK

BACKGROUND

Poor attendance at school, whether due to absenteeism or exclusion, leads to multiple social, educational, and lifelong socioeconomic disadvantages. We aimed to measure the association between a broad range of diagnosed neurodevelopmental and mental disorders and recorded self-harm by the age of 24 years and school attendance and exclusion.

METHODS

In this nationwide, retrospective, electronic cohort study, we drew a cohort from the Welsh Demographic Service Dataset, which included individuals aged 7-16 years (16 years being the school leaving age in the UK) enrolled in state-funded schools in Wales in the academic years 2012/13-2015/16 (between Sept 1, 2012, and Aug 31, 2016). Using the Adolescent Mental Health Data Platform, we linked attendance and exclusion data to national demographic and primary and secondary health-care datasets. We identified all pupils with a recorded diagnosis of neurodevelopmental disorders (ADHD and autism spectrum disorder [ASD]), learning difficulties, conduct disorder, depression, anxiety, eating disorders, alcohol or drugs misuse, bipolar disorder, schizophrenia, other psychotic disorders, or recorded self-harm (our explanatory variables) before the age of 24 years. Outcomes were school absence and exclusion. Generalised estimating equations with exchangeable correlation structures using binomial distribution with the logit link function were used to calculate odds ratios (OR) for absenteeism and exclusion, adjusting for sex, age, and deprivation.

FINDINGS

School attendance, school exclusion, and health-care data were available for 414 637 pupils (201 789 [48·7%] girls and 212 848 [51·3%] boys; mean age 10·5 years [SD 3·8] on Sept 1, 2012; ethnicity data were not available). Individuals with a record of a neurodevelopmental disorder, mental disorder, or self-harm were more likely to be absent or excluded in any school year than were those without a record. Unadjusted ORs for absences ranged from 2·1 (95% CI 2·0-2·2) for those with neurodevelopmental disorders to 6·6 (4·9-8·3) for those with bipolar disorder. Adjusted ORs (aORs) for absences ranged from 2·0 (1·9-2·1) for those with neurodevelopmental disorders to 5·5 (4·2-7·2) for those with bipolar disorder. Unadjusted ORs for exclusion ranged from 1·7 (1·3-2·2) for those with eating disorders to 22·7 (20·8-24·7) for those with a record of drugs misuse. aORs for exclusion ranged from 1·8 (1·5-2·0) for those with learning difficulties to 11·0 (10·0-12·1) for those with a record of drugs misuse.

INTERPRETATION

Children and young people up to the age of 24 years with a record of a neurodevelopmental or mental disorder or self-harm before the age of 24 years were more likely to miss school than those without a record. Exclusion or persistent absence are potential indicators of current or future poor mental health that are routinely collected and could be used to target assessment and early intervention. Integrated school-based and health-care strategies to support young peoples' engagement with school life are required.

FUNDING

The Medical Research Council, MQ Mental Health Research, and the Economic and Social Research Council.

TRANSLATION

For the Welsh translation of the abstract see Supplementary Materials section.

Estimating the impact of child and early adolescent depression on subsequent educational attainment: secondary analysis of an existing data linkage

AIMS

Depression is thought to be associated with lower subsequent educational attainment during school. But, without longitudinal studies which take account of prior attainment and other potential confounders, estimates of the impact of clinically recognised depression in childhood and early adolescence are unknown. We investigated whether a clinical diagnosis of depression is associated with lower subsequent educational attainment, and whether the association is modified by gender, ethnicity and socioeconomic status.

METHODS

We conducted a secondary analysis of an existing administrative data linkage between national educational data and a large mental healthcare provider in London, UK (2007-2013). Depression diagnosis before age 15 (exposure) was measured from electronic health records, and subsequent educational attainment at age 15-16 (outcome) was measured from educational records. We fitted logistic regression models and adjusted for gender, ethnicity, socioeconomic status, relative age in school year, neurodevelopmental disorder diagnosis and prior attainment. We investigated effect modifiers using interaction terms.

RESULTS

In total, n = 63 623 were included in analysis, of whom n = 242 had record of a depression diagnosis before age 15. Depression was associated with lower odds of subsequently achieving expected attainment levels in national exams, after adjustment for all covariates (odds ratio = 0.60, 95% confidence interval = 0.43 to 0.84, p = 0.003). There was no evidence that gender, ethnicity or socioeconomic status modified this association.

CONCLUSIONS

These findings support a relationship between depression and lower subsequent educational attainment. This highlights the need for tailored educational interventions to support children and adolescents with depression, particularly in the lead up to key educational milestones.

Impact of counselling provision in primary schools on child and adolescent mental health service referral rates: a longitudinal observational cohort study

BACKGROUND

In the United Kingdom, schools play an increasingly important role in supporting young peoples' mental health. While there is a growing evidence base to support the effectiveness of school-based interventions, less is known about how these provisions impact on local Child and Adolescent Mental Health Service (CAMHS) referral rates. There is a concern that an increase in school-based provision might lead to an increase in CAMHS referrals and overwhelm services. We aimed to examine the longitudinal association between Place2Be counselling provision in primary schools on CAMHS referral rates in South London.

METHOD

This was a retrospective cohort study using linked data from the National Pupil Database (NPD) and CAMHS referrals to the South London and Maudsley's NHS Foundation Trust (SLaM) identified through the Clinical Record Interactive Search (CRIS) tool. The cohort included a total of 285 state-maintained primary schools in four London boroughs for the academic years of 2007-2012. During the study period, 23 of these schools received school-based mental health provision from Place2Be. The primary outcome was the incident rate ratio (IRR) of school-level accepted CAMHS referrals in 2012/13 in schools with, or without, Place2Be provision.

RESULTS

There was no significant association between elevated rates of CAMHS referral and Place2Be provision, even after comprehensive adjustment for school-level and pupil characteristics (IRR 0.91 (0.67-1.23)). School-level characteristics, including higher proportion of white-British pupils (IRR 1.009 (1.002-1.02)), medical staff ratio (IRR 6.49 (2.05-20.6)) and poorer Ofsted school inspection ratings (e.g. IRR 1.58 (1.06-2.34) for 'Requires Improvement' vs. 'Outstanding') were associated with increased CAMHS referral rates.

CONCLUSIONS

Place2Be provision did not result in increased specialist mental health referrals; however, other school-level characteristics did. Future research should investigate pupils' Place2Be clinical outcomes, as well the outcomes of individuals referred to CAMHS to better understand which needs are being met by which services.

Record linkage of population-based cohort data from minors with national register data: a scoping review and comparative legal analysis of four European countries

Background: The GDPR was implemented to build an overarching framework for personal data protection across the EU/EEA. Linkage of data directly collected from cohort participants, potentially serving as a prominent tool for health research, must respect data protection rules and privacy rights. Our objective was to investigate law possibilities of linking cohort data of minors with routinely collected education and health data comparing EU/EEA member states. Methods: A legal comparative analysis and scoping review was conducted of openly accessible published laws and regulations in EUR-Lex and national law databases on GDPR's implementation in Portugal, Finland, Norway, and the Netherlands and its connected national regulations purposing record linkage for health research that have been implemented up until April 30, 2021. Results: The GDPR does not ensure total uniformity in data protection legislation across member states offering flexibility for national legislation. Exceptions to process personal data, e.g., public interest and scientific research, must be laid down in EU/EEA or national law. Differences in national interpretation caused obstacles in cross-national research and record linkage: Portugal requires written consent and ethical approval; Finland allows linkage mostly without consent through the national Social and Health Data Permit Authority; Norway when based on regional ethics committee's approval and adequate information technology safeguarding confidentiality; the Netherlands mainly bases linkage on the opt-out system and Data Protection Impact Assessment. Conclusions: Though the GDPR is the most important legal framework, national legislation execution matters most when linking cohort data with routinely collected health and education data. As national interpretation varies, legal intervention balancing individual right to informational self-determination and public good is gravely needed for health research. More harmonization across EU/EEA could be helpful but should not be detrimental in those member states which already opened a leeway for registries and research for the public good without explicit consent.

The role of mental health symptomology and quality of life in predicting referrals to special child and adolescent mental health services

BACKGROUND

Children and adolescents' mental health problems have been largely assessed with conventional symptom scales, for example, Strengths and Difficulties Questionnaire (SDQ) given that it is one of the mostly widely used measures in specialist Child and Adolescent Mental Health Services (CAMHS). However, this emphasis on symptom scales might have missed some important features of the mental health challenges that children and young people experience including day to day functioning and life satisfaction aspect (i.e. qualify of life).

METHOD

The study examined longitudinal association between a young person's self-perceptions of quality of life and mental health difficulties and referral to specialist CAMHS service using a population cohort study (Targeted Mental Health in Schools service data) nested within a large-scale linkage between school (National Pupil Data base) and child mental health service administrative data (South London and Maudsley NHS Foundation Trust children and adolescent mental health services health records). Cox proportional hazard regression to estimate crude and adjusted hazard ratios (HRs) for the association between participant psychopathology, and incidence of CAMHS referral.

RESULTS

Pupils experiencing more behavioural difficulties, had an increased incidence of CAMHS referral (adjusted hazard ratio 1.1, 95% confidence interval 1.0-1.2). However, pupils who reported higher health related quality of life had a lower incidence of CAMHS referral over the follow-up period (adjusted hazard hario 0.94, 95% confidence interval 0.9-0.98).

CONCLUSION

Children and young people's perception of their quality of life should be considered at the stages of a clinical needs assessment.

Educational attainment trajectories among children and adolescents with depression, and the role of sociodemographic characteristics: longitudinal data-linkage study

BACKGROUND

Depression is associated with lower educational attainment, but there has been little investigation of long-term educational trajectories in large cohorts with diagnosed depression.

AIMS

To describe the educational attainment trajectories of children with a depression diagnosis in secondary care, and to investigate whether these trajectories vary by sociodemographic characteristics.

METHOD

We identified new referrals to South London and Maudsley's NHS Foundation Trust between 2007 and 2013 who received a depression diagnosis at under 18 years old. Linking their health records to the National Pupil Database, we standardised their performance on three assessments (typically undertaken at ages 6-7 years (school Year 2), 10-11 (Year 6) and 15-16 (Year 11)) relative to the local reference population in each academic year. We used mixed models for repeated measures to estimate attainment trajectories.

RESULTS

In our sample of 1492 children, the median age at depression diagnosis was 15 years (interquartile range = 14-16). Their attainment showed a decline between school Years 6 and 11. Attainment was consistently lower among males and those eligible for free school meals. Black ethnic groups also showed lower attainment than White ethnic groups between Years 2 and 6, but showed a less pronounced drop in attainment at Year 11.

CONCLUSIONS

Those who receive a depression diagnosis during their school career show a drop in attainment in Year 11. Although this pattern was seen among multiple sociodemographic groups, gender, ethnicity and socioeconomic status predict more vulnerable subgroups within this clinical population who might benefit from additional educational support or more intensive treatment.

Sociodemographic factors associated with routine outcome monitoring: a historical cohort study of 28,382 young people accessing child and adolescent mental health services

BACKGROUND

Patient-reported outcome measures (PROMs) are important tools to inform patients, clinicians and policy-makers about clinical need and the effectiveness of any given treatment. Consistent PROM use can promote early symptom detection, help identify unexpected treatment responses and improve therapeutic engagement. Very few studies have examined associations between patient characteristics and PROM data collection.

METHODS

We used the electronic mental health records for 28,382 children and young people (aged 4-17 years) accessing Child and Adolescent Mental Health Services (CAMHS) across four South London boroughs between the 1st of January 2008 to the 1st of October 2017. We examined the completion rates of the caregiver Strengths and Difficulties Questionnaire (SDQ), a ubiquitous PROM for CAMHS at baseline and 6-month follow-up.

RESULTS AND CONCLUSIONS

SDQs were present for approximately 40% (n = 11,212) of the sample at baseline, and from these, only 8% (n = 928) had a follow-up SDQ. Patterns of unequal PROM collection by sociodemographic factors were identified: males were more likely (aOR 1.07, 95% CI 1.01-1.13), whilst older age (aOR 0.87, 95% CI 0.87-0.88), Black (aOR 0.79 95% CI 0.74-0.84) and Asian ethnicity (aOR 0.75 95% CI 0.66-0.86) relative to White ethnicity, and residence within the most deprived neighbourhood (aOR 0.87 95% CI 0.80-0.94) were less likely to have a record of baseline SDQ. Similar results were found in the sub-group (n = 11,212) with follow-up SDQ collection. Our findings indicate systematic differences in the currently available PROMS data and highlights which groups require increased focus if we are to gain equitable PROM collection. We need to ensure representative PROM collection for all individuals accessing treatment, regardless of ethnic or socioeconomic background; biased data have adverse ramifications for policy and service level decision-making.

Inequalities in referral pathways for young people accessing secondary mental health services in south east London

Differences in health service use between ethnic groups have been well documented, but little research has been conducted on inequalities in access to mental health services among young people. This study examines inequalities in pathways into care by ethnicity and migration status in 12-29 years old accessing health services in south east London. This study analyses anonymized electronic patient record data for patients aged 12-29 referred to a south east London mental health trust between 2008 and 2016 for an anxiety or non-psychotic depressive disorder (n = 18,931). Multinomial regression was used to examine associations between ethnicity, migration status, and both referral source and destination, stratified by age group. Young people in the Black African ethnic group were more likely to be referred from secondary health or social/criminal justice services compared to those in the White British ethnic group; the effect was most pronounced for those aged 16-17 years. Young people in the Black African ethnic group were also significantly more likely to be referred to inpatient and emergency services compared to those in the White British ethnic group. Black individuals living in south east London, particularly those who identify as Black African, are referred to mental health services via more adverse pathways than White individuals. Our findings suggest that inequalities in referral destination may be perpetuated by inequalities generated at the point of access.

Impact of matching error on linked mortality outcome in a data linkage of secondary mental health data with Hospital Episode Statistics (HES) and mortality records in South East London: a cross-sectional study

OBJECTIVES

Linkage of electronic health records (EHRs) to Hospital Episode Statistics (HES)-Office for National Statistics (ONS) mortality data has provided compelling evidence for lower life expectancy in people with severe mental illness. However, linkage error may underestimate these estimates. Using a clinical sample (n=265 300) of individuals accessing mental health services, we examined potential biases introduced through missed matching and examined the impact on the association between clinical disorders and mortality.

SETTING

The South London and Maudsley NHS Foundation Trust (SLaM) is a secondary mental healthcare provider in London. A deidentified version of SLaM's EHR was available via the Clinical Record Interactive Search system linked to HES-ONS mortality records.

PARTICIPANTS

Records from SLaM for patients active between January 2006 and December 2016.

OUTCOME MEASURES

Two sources of death data were available for SLaM participants: accurate and contemporaneous date of death via local batch tracing (gold standard) and date of death via linked HES-ONS mortality data. The effect of linkage error on mortality estimates was evaluated by comparing sociodemographic and clinical risk factor analyses using gold standard death data against HES-ONS mortality records.

RESULTS

Of the total sample, 93.74% were successfully matched to HES-ONS records. We found a number of statistically significant administrative, sociodemographic and clinical differences between matched and unmatched records. Of note, schizophrenia diagnosis showed a significant association with higher mortality using gold standard data (OR 1.08; 95% CI 1.01 to 1.15; p=0.02) but not in HES-ONS data (OR 1.05; 95% CI 0.98 to 1.13; p=0.16). Otherwise, little change was found in the strength of associated risk factors and mortality after accounting for missed matching bias.

CONCLUSIONS

Despite significant clinical and sociodemographic differences between matched and unmatched records, changes in mortality estimates were minimal. However, researchers and policy analysts using HES-ONS linked resources should be aware that administrative linkage processes can introduce error.

Pharmacoepidemiology research: delivering evidence about drug safety and effectiveness in mental health

Research that provides an evidence base for the pharmacotherapy of people with mental disorders is needed. The abundance of digital data has facilitated pharmacoepidemiology and, in particular, observational research on the effectiveness of real-world medication. Advantages of pharmacoepidemiological research are the availability of large patient samples, and coverage of under-researched subpopulations in their naturalistic conditions. Such research is also cheaper and quicker to do than randomised controlled trials, meaning that issues regarding generic medication, stopping medication (deprescribing), and long-term outcomes are more likely to be addressed. Pharmacoepidemiological methods can also be extended to pharmacovigilance and to aid the development of new purposes for existing drugs. Drawbacks of observational pharmacoepidemiological studies come from the non-randomised nature of treatment selection, leading to confounding by indication. Potential methods for managing this drawback include active comparison groups, within-individual designs, and propensity scoring. Many of the more rigorous pharmacoepidemiology studies have been strengthened through multiple analytical approaches triangulated to improve confidence in inferred causal relationships. With developments in data resources and analytical techniques, it is encouraging that guidelines are beginning to include evidence from robust observational pharmacoepidemiological studies alongside randomised controlled trials. Collaboration between guideline writers and researchers involved in pharmacoepidemiology could help researchers to answer the questions that are important to policy makers and ensure that results are integrated into the evidence base. Further development of statistical and data science techniques, alongside public engagement and capacity building (data resources and researcher base), will be necessary to take full advantage of future opportunities.

Diagnostic trajectories in child and adolescent mental health services: exploring the prevalence and patterns of diagnostic adjustments in an electronic mental health case register

Community-based epidemiological studies show transitions between psychiatric disorders are common during child development. However, little research has explored the prevalence or patterns of the diagnostic adjustments that occur in child and adolescent mental health services (CAMHS). Understanding diagnostic trajectories is necessary to inform theory development in developmental psychopathology and clinical judgements regarding risk and prognosis. In this study, data from CAMHS clinical records were extracted from a British mental health case register (N = 12,543). Analysis calculated the proportion of children whose clinical records showed a longitudinal diagnostic adjustment (i.e. addition of a subsequent diagnosis of a different diagnostic class, at > 30 days' distance from their first diagnosis). Regression analyses investigated typical diagnostic sequences and their relationships with socio-demographic variables, service use and standardised measures of mental health. Analysis found that 19.3% of CAMHS attendees had undergone a longitudinal diagnostic adjustment. Ethnicity, diagnostic class and symptom profiles significantly influenced the likelihood of a diagnostic adjustment. Affective and anxiety/stress-related disorders longitudinally predicted each other, as did hyperkinetic and conduct disorders, and hyperkinetic and pervasive developmental disorders. Results suggest that approximately one in five young service users have their original psychiatric diagnosis revised or supplemented during their time in CAMHS. By revealing the most common diagnostic sequences, this study enables policy makers to anticipate future service needs and clinicians to make informed projections about their patients' likely trajectories. Further research is required to understand how young people experience diagnostic adjustments and their psychological and pragmatic implications.

The association between depression and later educational attainment in children and adolescents: a systematic review protocol

INTRODUCTION

Depression represents a major public health concern for children and adolescents, and is thought to negatively impact subsequent educational attainment. However, the extent to which depression and educational attainment are directly associated, and whether other factors play a role, is uncertain. Therefore, we aim to systematically review the literature to provide an up-to-date estimate on the strength of this association, and to summarise potential mediators and moderators on the pathway between the two.

METHODS AND ANALYSIS

To identify relevant studies, we will systematically search Embase, PsycINFO, PubMed, Education Resources Information Centre and British Education Index, manually search reference lists and contact experts in the field. Studies will be included if they investigate and report on the association between major depression diagnosis or depressive symptoms in children and adolescents aged 4-18 years (exposure) and later educational attainment (outcome). Two independent reviewers will screen titles, abstracts and full texts according to eligibility criteria, perform data extraction and assess study quality according to a modified version of the Newcastle-Ottawa Scale. If sufficiently homogeneous studies are identified, summary effect estimates will be pooled in meta-analysis, with further tests for study heterogeneity, publication bias and the effects of moderators using meta-regression.

ETHICS AND DISSEMINATION

Because this review will make use of already published data, ethical approval will not be sought. The review will be submitted for publication in a peer-reviewed journal, presented at practitioner-facing conferences, and a lay summary will be written for non-scientific audiences such as parents, young people and teachers. The work will inform upcoming investigations on the association between child and adolescent mental health and educational attainment.

PROSPERO REGISTRATION NUMBER

CRD42019123068.

Cross-Sectoral Big Data: The Application of an Ethics Framework for Big Data in Health and Research

Discussion of uses of biomedical data often proceeds on the assumption that the data are generated and shared solely or largely within the health sector. However, this assumption must be challenged because increasingly large amounts of health and well-being data are being gathered and deployed in cross-sectoral contexts such as social media and through the internet of (medical) things and wearable devices. Cross-sectoral sharing of data thus refers to the generation, use and linkage of biomedical data beyond the health sector. This paper considers the challenges that arise from this phenomenon. If we are to benefit fully, it is important to consider which ethical values are at stake and to reflect on ways to resolve emerging ethical issues across ecosystems where values, laws and cultures might be quite distinct. In considering such issues, this paper applies the deliberative balancing approach of the Ethics Framework for Big Data in Health and Research (Xafis et al. 2019) to the domain of cross-sectoral big data. Please refer to that article for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end.

Investigating the agreement between the clinician and research diagnosis of attention deficit hyperactivity disorder and how it changes over time; a clinical cohort study

BACKGROUND

Attention Deficit Hyperactivity Disorder (ADHD) is a common reason for referral to Child and Adolescent Mental Health Services (CAMHS), but families experience long delays between first professional contact and diagnosis, which risks development of secondary impairments. This study explores the agreement between clinician and research diagnoses of ADHD among children attending CAMHS, and their access to interventions. From the limited literature, we anticipated fluctuation and delays, but no other study has focused prospectively on ADHD diagnoses.

METHODS

This was a secondary analysis of a cohort of children attending two CAMHS between 2006 and 2009. The sample included 288 consecutive referrals of children aged between 5 and 11 years. Parents and teachers completed the Development and Well-Being Assessment (DAWBA) when the child was recruited to the study, which provided the research diagnosis of ADHD from the baseline. Clinicians reported no, possible, or definite diagnosis of ADHD and interventions provided at 6-monthly intervals for up to 2 years while the child attended CAMHS. We assessed agreement between the diagnoses using Kendall's Tau-B.

RESULTS

Of the 101 children with a research diagnosis of ADHD, 26 received a definite clinician diagnosis during 2-year follow-up, and 47 received a possible clinician diagnosis. The chance-corrected agreement was poor at all time points (Kendall's Tau-B 0.14-0.48). Clinician diagnoses were unstable, particularly if possible ADHD was recorded at the initial assessment. Of those with a research diagnosis, 15 were prescribed medication and 11 were offered parent training.

CONCLUSIONS

The use of standardised diagnostic assessments could reduce diagnostic uncertainty and increase access to evidence-based interventions.

Data Resource: the National Pupil Database (NPD)

Introduction

The National Pupil Database (NPD) is a record-level administrative data resource curated by the UK government’s Department for Education that is used for funding purposes, school performance tables, policy making, and research.

Processes

Data are sourced from schools, exam awarding bodies, and local authorities who collect data on an on-going basis and submit to the Department for Education either termly or yearly.

Data contents

NPD contains child-level and school-level data on all pupils in state schools in England (6.6 million in the 2016/17 academic year). The primary module is the census, which has information on characteristics and school enrolment. Other modules include alternative provision, exam attainment, absence and exclusions. Data from children’s social care are also available on children referred for support and those who become looked after. Children’s records are linkable across different modules and across time using a nationally unique, anonymised child-level identifier. Linkage to external datasets has also been accomplished using child-level identifiers.

Conclusions

The NPD is an especially valuable data resource for researchers interested in the educational experience and outcomes of children and young people in England. Although limited by the fact that children in private schools or who are home schooled are not included, it provides a near-complete picture of school trajectories and outcomes for the majority of children. Linkage to other datasets can enhance analyses and provide answers to questions that would otherwise be costly, time consuming and difficult to find

An approach to linking education, social care and electronic health records for children and young people in South London: a linkage study of child and adolescent mental health service data

OBJECTIVES

Creation of linked mental health, social and education records for research to support evidence-based practice for regional mental health services.

SETTING

The Clinical Record Interactive Search (CRIS) system was used to extract personal identifiers who accessed psychiatric services between September 2007 and August 2013.

PARTICIPANTS

A clinical cohort of 35 509 children and young people (aged 4-17 years).

DESIGN

Multiple government and ethical committees approved the link of clinical mental health service data to Department for Education (DfE) data on education and social care services. Under robust governance protocols, fuzzy and deterministic approaches were used by the DfE to match personal identifiers (names, date of birth and postcode) from National Pupil Database (NPD) and CRIS data sources.

OUTCOME MEASURES

Risk factors for non-matching to NPD were identified, and the potential impact of non-match biases on International Statistical Classification of Diseases, 10th Revision (ICD-10) classifications of mental disorder, and persistent school absence (<80% attendance) were examined. Probability weighting and adjustment methods were explored as methods to mitigate the impact of non-match biases.

RESULTS

Governance challenges included developing a research protocol for data linkage, which met the legislative requirements for both National Health Service and DfE. From CRIS, 29 278 (82.5%) were matched to NPD school attendance records. Presenting to services in late adolescence (adjusted OR (aOR) 0.67, 95% CI 0.59 to 0.75) or outside of school census timeframes (aOR 0.15, 95% CI 0.14 to 0.17) reduced likelihood of matching. After adjustments for linkage error, ICD-10 mental disorder remained significantly associated with persistent school absence (aOR 1.13, 95% CI 1.07 to 1.22).

CONCLUSIONS

The work described sets a precedent for education data being used for medical benefit in England. Linkage between health and education records offers a powerful tool for evaluating the impact of mental health on school function, but biases due to linkage error may produce misleading results. Collaborative research with data providers is needed to develop linkage methods that minimise potential biases in analyses of linked data.

The Maudsley Biomedical Research Centre (BRC) data linkage service user and carer advisory group: creating and sustaining a successful patient and public involvement group to guide research in a complex area

BACKGROUND

Patient and Public Involvement (PPI) in health and social care research has been shown to improve the quality and relevance of research. PPI in data linkage research is important in ensuring the legitimacy of future health informatics initiatives, but remains sparse and under-developed. This article describes the setting up and evaluation of a service user and carer advisory group with the aim of providing feedback and advice to researchers developing or making use of database linkages in the field of mental health.

AIM

The aim of this study is to describe the creation and formative evaluation of the service user and carer advisory group after a trial period of 12 months.

METHOD

Six individuals were recruited to the group all of whom had personal experience of mental illness. A formative evaluation was conducted after a trial period of 12 months.

RESULTS

Evaluation revealed that the group succeeded in promoting dialogue between service users/carers and researchers. Factors that contributed to the success of the group's first year included the opportunity it provided for researchers to involve service users and carers in their projects, the training provided to group members, and the openness of researchers to receiving feedback from the group.

CONCLUSION

The group encourages the incorporation of PPI in data linkage research which helps to ensure the legitimacy of data linkage practices and governance systems whilst also improving the quality and relevance of the research being conducted using linked data.

Negative Symptoms in Early-Onset Psychosis and Their Association With Antipsychotic Treatment Failure

The prevalence of negative symptoms (NS) at first episode of early-onset psychosis (EOP), and their effect on psychosis prognosis is unclear. In a sample of 638 children with EOP (aged 10-17 y, 51% male), we assessed (1) the prevalence of NS at first presentation to mental health services and (2) whether NS predicted eventual development of multiple treatment failure (MTF) prior to the age of 18 (defined by initiation of a third trial of novel antipsychotic due to prior insufficient response, intolerable adverse-effects or non-adherence). Data were extracted from the electronic health records held by child inpatient and community-based services in South London, United Kingdom. Natural Language Processing tools were used to measure the presence of Marder Factor NS and antipsychotic use. The association between presenting with ≥2 NS and the development of MTF over a 5-year period was modeled using Cox regression. Out of the 638 children, 37.5% showed ≥2 NS at first presentation, and 124 (19.3%) developed MTF prior to the age of 18. The presence of NS at first episode was significantly associated with MTF (adjusted hazard ratio 1.62, 95% CI 1.07-2.46; P = .02) after controlling for a number of potential confounders including psychosis diagnostic classification, positive symptoms, comorbid depression, and family history of psychosis. Other factors associated with MTF included comorbid autism spectrum disorder, older age at first presentation, Black ethnicity, and family history of psychosis. In EOP, NS at first episode are prevalent and may help identify a subset of children at higher risk of responding poorly to antipsychotics.

Big data: what it can and cannot achieve

This article looks at the use of large health records datasets, typically linked with other data sources, and their use in mental health research. The most comprehensive examples of this kind of big data are typically found in Scandinavian countries however there are also many useful sources in the UK. There are a number of promising methodological innovations from studies using big data in UK mental health research, including: hybrid study designs, examples of data linkage and enhanced study recruitment. It is, though, important to be aware of the limitations of research using big data, particularly the various analysis pitfalls. We therefore caution against throwing out the methodological baby with the bathwater and argue that other data sources are equally valuable and ideally research should incorporate a range of data.

Detection of Suicidality in Adolescents with Autism Spectrum Disorders: Developing a Natural Language Processing Approach for Use in Electronic Health Records

Over 15% of young people with autism spectrum disorders (ASD) will contemplate or attempt suicide during adolescence. Yet, there is limited evidence concerning risk factors for suicidality in childhood ASD. Electronic health records (EHRs) can be used to create retrospective clinical cohort data for large samples of children with ASD. However systems to accurately extract suicidality-related concepts need to be developed so that putative models of suicide risk in ASD can be explored. We present a systematic approach to 1) adapt Natural Language Processing (NLP) solutions to screen with high sensitivity for reference to suicidal constructs in a large clinical ASD EHR corpus (230,465 documents), and 2) evaluate within a screened subset of 500 patients, the performance of an NLP classification tool for positive and negated suicidal mentions within clinical text. When evaluated, the NLP classification tool showed high system performance for positive suicidality with precision, recall, and F1 scores all > 0.85 at a document and patient level. The application therefore provides accurate output for epidemiological research into the factors contributing to the onset and recurrence of suicidality, and potential utility within clinical settings as an automated surveillance or risk prediction tool for specialist ASD services.