Cohort profile of the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLaM BRC) Case Register: current status and recent enhancement of an Electronic Mental Health Record-derived data resource

Universal Credit receipt among working-age patients who are accessing specialist mental health services: results from a novel data linkage study

BACKGROUND

In 2013, Universal Credit (UC) was introduced by the UK Government. Understanding of how UC provision is allocated among people with mental disorders, and its intersection with protected characteristics is limited. This study aimed to explore (1) how UC receipt, including UC conditionality regime, varied among users of specialist mental health services between 2013 and 2019 and (2) associations between sociodemographic and diagnostic patient characteristics and UC receipt.

METHODS

Working-age individuals who had accessed specialist mental health services were included if they had their mental health record data successfully linked with administrative benefits data. Associations between sociodemographic, diagnostic patient characteristics and UC receipt were explored using logistic regression models.

RESULTS

Of the 143 715 patients, 26.9% had received UC between 2013 and 2019. Four in five patients were allocated to the searching for work conditionality regime during their time on UC. Females were less likely to have received UC (adjusted OR (AOR) 0.87, 95% CI 0.85 to 0.89) than males, and UC receipt decreased with age. Black patients (AOR 1.39, 95% CI 1.34 to 1.44) and patients from mixed and multiple ethnic backgrounds (AOR 1.27, 95% CI 1.18 to 1.38) had a higher likelihood of UC receipt than White patients. UC receipt was lower among patients diagnosed with severe mental illness compared with other psychiatric diagnoses (AOR 0.74, 95% CI 0.71 to 0.77).

CONCLUSION

One in four specialist mental health service users had received UC and a large majority were subject to conditionality. The temporality of UC conditionality and mental health service presentation needs further exploration.

The cumulative impact of attention deficit hyperactivity disorder, autism and intellectual disability for young people

BACKGROUND

Neurodevelopmental conditions frequently co-occur. The aim of this paper was to determine whether there is a cumulative association between (1) the number of neurodevelopmental conditions, specifically hyperkinetic disorder (hereafter referred to as attention deficit hyperactivity disorder), autism spectrum disorder (hereafter referred to as autism) and intellectual disability, and (2) behavioural and socio-emotional problems and the level of clinician-rated functioning for young males and females.

METHODS

In this cross-sectional study, diagnostic information, caregiver-rated behavioural and socio-emotional data (as conceptualised by the Strengths and Difficulties Questionnaire) and clinician-rated functioning scores (as conceptualised by the Children's Global Assessment Scale) were extracted from electronic patient records for 2768 young people aged 3-17 years (mean = 11.55, SD = 3.46). All data were extracted at baseline, that is, at the time the young person was diagnosed with attention deficit hyperactivity disorder, autism and/or an intellectual disability. Ordinal regression analyses tested associations between the number of neurodevelopmental conditions met (i.e. 1, 2 or 3) and behavioural and socio-emotional outcomes and functioning.

RESULTS

After controlling for age and biological sex, the number of neurodevelopmental conditions was associated with higher levels of inattention/hyperactivity and peer problems, lower levels of prosocial behaviour and poorer clinician-rated functioning. Although these findings were consistent for males, a cumulative association was not identified for females, except for clinician-rated functioning.

CONCLUSIONS

For young people, the presence of multiple neurodevelopmental conditions may have a cumulative impact across domains, but this may differ between males and females.

Diagnosis and pharmacological management of attention deficit hyperactivity disorder in adults with and without intellectual disability: cohort study using electronic health records

BACKGROUND

Attention deficit hyperactivity disorder (ADHD) is increasingly diagnosed in adults. People with intellectual disability have higher rates of ADHD yet there is little evidence on the presentation and pharmacological treatment of ADHD in this population or how this differs from the general population.

METHODS

Retrospective cohort study using data from electronic health records. Adults with intellectual disability newly diagnosed with ADHD between 2007 and 2022 were matched to adults with ADHD without intellectual disability and their clinical features and treatments were compared.

RESULTS

A total of 159 adults with ADHD and intellectual disability and 648 adults with ADHD without intellectual disability formed the dataset. Adults with intellectual disability had higher rates of psychiatric co-morbidity and spent more time under mental health services than those without intellectual disability. They were more likely to have recorded agitation, aggression, hostility, and mood instability, and less likely to have poor concentration recorded in the 12 months prior to the diagnosis of ADHD. Following diagnosis, people with intellectual disability were significantly less likely to be prescribed any medication for ADHD than controls without intellectual disability (adjusted odds ratio 0.60, 95% confidence interval 0.38-0.91), and were less likely to be prescribed stimulants (27.7% v 46.0%, p < 0.001).

CONCLUSIONS

The presence of behaviors that challenge in adults with intellectual disability may indicate co-occurring ADHD. Further work to define the safety and efficacy of medication for ADHD in adults with intellectual disability is needed to understand differences in prescription rates and to avoid inequities in care outcomes.

Optimising the use of electronic medical records for large scale research in psychiatry

The explosion and abundance of digital data could facilitate large-scale research for psychiatry and mental health. Research using so-called "real world data"-such as electronic medical/health records-can be resource-efficient, facilitate rapid hypothesis generation and testing, complement existing evidence (e.g. from trials and evidence-synthesis) and may enable a route to translate evidence into clinically effective, outcomes-driven care for patient populations that may be under-represented. However, the interpretation and processing of real-world data sources is complex because the clinically important 'signal' is often contained in both structured and unstructured (narrative or "free-text") data. Techniques for extracting meaningful information (signal) from unstructured text exist and have advanced the re-use of routinely collected clinical data, but these techniques require cautious evaluation. In this paper, we survey the opportunities, risks and progress made in the use of electronic medical record (real-world) data for psychiatric research.

Predicting patients with dementia most at risk of needing psychiatric in-patient or enhanced community care using routinely collected clinical data: retrospective multi-site cohort study

BACKGROUND

Dementia is a common and progressive condition whose prevalence is growing worldwide. It is challenging for healthcare systems to provide continuity in clinical services for all patients from diagnosis to death.

AIMS

To test whether individuals who are most likely to need enhanced care later in the disease course can be identified at the point of diagnosis, thus allowing the targeted intervention.

METHOD

We used clinical information collected routinely in de-identified electronic patient records from two UK National Health Service (NHS) trusts to identify at diagnosis which individuals were at increased risk of needing enhanced care (psychiatric in-patient or intensive (crisis) community care).

RESULTS

We examined the records of a total of 25 326 patients with dementia. A minority (16% in the Cambridgeshire trust and 2.4% in the London trust) needed enhanced care. Patients who needed enhanced care differed from those who did not in age, cognitive test scores and Health of the Nation Outcome Scale scores. Logistic regression discriminated risk, with an area under the receiver operating characteristic curve (AUROC) of up to 0.78 after 1 year and 0.74 after 4 years. We were able to confirm the validity of the approach in two trusts that differed widely in the populations they serve.

CONCLUSIONS

It is possible to identify, at the time of diagnosis of dementia, individuals most likely to need enhanced care later in the disease course. This permits the development of targeted clinical interventions for this high-risk group.

Estimating demand for potential disease-modifying therapies for Alzheimer's disease in the UK

BACKGROUND

Phase three trials of the monoclonal antibodies lecanemab and donanemab, which target brain amyloid, have reported statistically significant differences in clinical end-points in early Alzheimer's disease. These drugs are already in use in some countries and are going through the regulatory approval process for use in the UK. Concerns have been raised about the ability of healthcare systems, including those in the UK, to deliver these treatments, considering the resources required for their administration and monitoring.

AIMS

To estimate the scale of real-world demand for monoclonal antibodies for Alzheimer's disease in the UK.

METHOD

We used anonymised patient record databases from two National Health Service trusts for the year 2019 to collect clinical, demographic, cognitive and neuroimaging data for these cohorts. Eligibility for treatment was assessed using the inclusion criteria from the clinical trials of donanemab and lecanemab, with consideration given to diagnosis, cognitive performance, cerebrovascular disease and willingness to receive treatment.

RESULTS

We examined the records of 82 386 people referred to services covering around 2.2 million people. After applying the trial criteria, we estimate that a maximum of 906 people per year would start treatment with monoclonal antibodies in the two services, equating to 30 200 people if extrapolated nationally.

CONCLUSIONS

Monoclonal antibody treatments for Alzheimer's disease are likely to present a significant challenge for healthcare services to deliver in terms of the neuroimaging and treatment delivery. The data provided here allows health services to understand the potential demand and plan accordingly.

Suicidal thoughts and behaviours in body dysmorphic disorder: Prevalence and correlates in a sample of mental health service users in the UK

BACKGROUND

Previous research indicates an association of body dysmorphic disorder (BDD) with suicidal thoughts and behaviours, but has largely relied on small cohorts drawn from specialist clinics.

METHODS

Anonymised health-records from the South London and Maudsley NHS Foundation Trust between 2007 and 2019 were systematically searched using the Clinical Record Interactive Search data system.

RESULTS

298 patients diagnosed with BDD between age 12 and 65 years were identified. 206 (69 %) had experienced lifetime suicidal ideation. 149 (50 %) had recorded lifetime acts of self-harm or suicide attempts, most commonly involving cutting and self-poisoning. Rates of self-harm/suicide attempts were similar in those diagnosed before or after 18 years. Comorbid depression was associated with suicidal ideation (OR: 4.26 95% CI 2.07-9.72). Additionally, comorbid depression, OCD and anxiety were all associated with self-harm/suicide attempts (OR: 1.94 95% CI 1.15-3.31, OR: 1.99 95% CI 1.09-3.73, and OR: 1.93 95% CI 1.09-3.45, respectively). The presence of two or more psychiatric comorbidities was associated with a significantly elevated likelihood of suicidal ideation (OR: 7.06 95% CI 2.80-21.7) and self-harm/suicide attempts (OR: 4.62 95% CI 2.32-9.62).

LIMITATIONS

It is likely that BDD was under-diagnosed in the cohort, and those identified may not be representative. Additionally, the frequency and detail with which suicidal thoughts and behaviours were assessed varied and may also represent underestimates.

CONCLUSIONS

Suicidal ideation and self-harm/suicide attempts are common among individuals with BDD accessing mental health services. Psychiatric comorbidity and suicidal ideation should be assessed in all BDD patients.

Cohort profile: The Social media, smartphone use and Self-harm in Young People (3S-YP) study-A prospective, observational cohort study of young people in contact with mental health services

OBJECTIVES

The Social media, Smartphone use and Self-Harm (3S-YP) study is a prospective observational cohort study to investigate the mechanisms underpinning associations between social media and smartphone use and self-harm in a clinical youth sample. We present here a comprehensive description of the cohort from baseline data and an overview of data available from baseline and follow-up assessments.

METHODS

Young people aged 13-25 years were recruited from a mental health trust in England and followed up for 6 months. Self-report data was collected at baseline and monthly during follow-up and linked with electronic health records (EHR) and user-generated data.

FINDINGS

A total of 362 young people enrolled and provided baseline questionnaire data. Most participants had a history of self-harm according to clinical (n = 295, 81.5%) and broader definitions (n = 296, 81.8%). At baseline, there were high levels of current moderate/severe anxiety (n = 244; 67.4%), depression (n = 255; 70.4%) and sleep disturbance (n = 171; 47.2%). Over half used social media and smartphones after midnight on weekdays (n = 197, 54.4%; n = 215, 59.4%) and weekends (n = 241, 66.6%; n = 263, 72.7%), and half met the cut-off for problematic smartphone use (n = 177; 48.9%). Of the cohort, we have questionnaire data at month 6 from 230 (63.5%), EHR data from 345 (95.3%), social media data from 110 (30.4%) and smartphone data from 48 (13.3%).

CONCLUSION

The 3S-YP study is the first prospective study with a clinical youth sample, for whom to investigate the impact of digital technology on youth mental health using novel data linkages. Baseline findings indicate self-harm, anxiety, depression, sleep disturbance and digital technology overuse are prevalent among clinical youth. Future analyses will explore associations between outcomes and exposures over time and compare self-report with user-generated data in this cohort.

Rates of venous thromboembolism associated with acute psychiatric admission: A retrospective cohort study

The present study aimed to identify rates of venous thromboembolism (VTE) amongst patients treated in inpatient mental health units using linked primary care and mental health care records. Patients resident in the London Borough of Lambeth admitted to mental health units in Southeast London between January 2008 and March 2019 were included, as well as a control group of patients being treated in the community for mental illness. The primary outcome measure was a diagnosis of VTE being recorded in GP records during or within 3 months of an admission to a mental health unit. For 7,198 psychiatric inpatient admissions, 11 episodes of VTE (1.5/1,000 admissions) were identified, with no VTE cases identified in 4,561 patients being treated in the community for mental illness during an equivalent window. This finding indicates that VTE rates following psychiatric inpatient admission might be similar to those following unselected acute medical admission. Larger scale studies are required to confirm the estimated incidence of VTE in patients with mental health conditions and the contribution of acute psychiatry hospitalisation to VTE risk.

Distributions of recorded pain in mental health records: a natural language processing based study

OBJECTIVE

The objective of this study is to determine demographic and diagnostic distributions of physical pain recorded in clinical notes of a mental health electronic health records database by using natural language processing and examine the overlap in recorded physical pain between primary and secondary care.

DESIGN, SETTING AND PARTICIPANTS

The data were extracted from an anonymised version of the electronic health records of a large secondary mental healthcare provider serving a catchment of 1.3 million residents in south London. These included patients under active referral, aged 18+ at the index date of 1 July 2018 and having at least one clinical document (≥30 characters) between 1 July 2017 and 1 July 2019. This cohort was compared with linked primary care records from one of the four local government areas.

OUTCOME

The primary outcome of interest was the presence of recorded physical pain within the clinical notes of the patients, not including psychological or metaphorical pain.

RESULTS

A total of 27 211 patients were retrieved. Of these, 52% (14,202) had narrative text containing relevant mentions of physical pain. Older patients (OR 1.17, 95% CI 1.15 to 1.19), females (OR 1.42, 95% CI 1.35 to 1.49), Asians (OR 1.30, 95% CI 1.16 to 1.45) or black (OR 1.49, 95% CI 1.40 to 1.59) ethnicities, living in deprived neighbourhoods (OR 1.64, 95% CI 1.55 to 1.73) showed higher odds of recorded pain. Patients with severe mental illnesses were found to be less likely to report pain (OR 0.43, 95% CI 0.41 to 0.46, p<0.001). 17% of the cohort from secondary care also had records from primary care.

CONCLUSION

The findings of this study show sociodemographic and diagnostic differences in recorded pain. Specifically, lower documentation across certain groups indicates the need for better screening protocols and training on recognising varied pain presentations. Additionally, targeting improved detection of pain for minority and disadvantaged groups by care providers can promote health equity.

Associations between recorded loneliness and adverse mental health outcomes among patients receiving mental healthcare in South London: a retrospective cohort study

PURPOSE

Loneliness disproportionately affects people with mental disorders, but associations with mental health outcomes in groups affected remain less well understood.

METHOD

A cohort of patients receiving mental healthcare on 30th June 2012 was assembled from a large mental health records database covering a south London catchment area. Recorded loneliness within the preceding 2 years was extracted using natural language processing and outcomes were measured between 30th June 2012 until 30th December 2019, except for survival which applied a censoring point of 6th December 2020 according to data available at the time of extraction. The following mental healthcare outcomes: (i) time to first crisis episode; (ii) time to first emergency presentation; (iii) all-cause mortality; (iv) days active to service per year; and (v) face-to-face contacts per year.

RESULTS

Loneliness was recorded in 4,483 (16.7%) patients in the study population and fully adjusted models showed associations with subsequent crisis episode (HR 1.17, 95% CI 1.07-1.29), emergency presentation (HR 1.30, 1.21-1.40), days active per year (IRR 1.04, 1.03-1.05), and face-to-face contacts per year (IRR 1.28, 1.27-1.30). Recorded loneliness in patients with substance misuse problems was particularly strongly associated with adverse outcomes, including risk of emergency presentation (HR 1.68, 1.29-2.18) and mortality (HR 1.29, 1.01-1.65).

CONCLUSION

Patients receiving mental healthcare who are recorded as lonely have a higher risk of several adverse outcomes which may require a need for higher service input.

Implementation of an Electronic Clinical Decision Support System for the Early Recognition and Management of Dysglycemia in an Inpatient Mental Health Setting Using CogStack: Protocol for a Pilot Hybrid Type 3 Effectiveness-Implementation Randomized Controlled Cluster Trial

BACKGROUND

Severe mental illnesses (SMIs), including schizophrenia, bipolar affective disorder, and major depressive disorder, are associated with an increased risk of physical health comorbidities and premature mortality from conditions including cardiovascular disease and diabetes. Digital technologies such as electronic clinical decision support systems (eCDSSs) could play a crucial role in improving the clinician-led management of conditions such as dysglycemia (deranged blood sugar levels) and associated conditions such as diabetes in people with a diagnosis of SMI in mental health settings.

OBJECTIVE

We have developed a real-time eCDSS using CogStack, an information retrieval and extraction platform, to automatically alert clinicians with National Health Service Trust-approved, guideline-based recommendations for dysglycemia monitoring and management in secondary mental health care. This novel system aims to improve the management of dysglycemia and associated conditions, such as diabetes, in SMI. This protocol describes a pilot study to explore the acceptability, feasibility, and evaluation of its implementation in a mental health inpatient setting.

METHODS

This will be a pilot hybrid type 3 effectiveness-implementation randomized controlled cluster trial in inpatient mental health wards. A ward will be the unit of recruitment, where it will be randomly allocated to receive either access to the eCDSS plus usual care or usual care alone over a 4-month period. We will measure implementation outcomes, including the feasibility and acceptability of the eCDSS to clinicians, as primary outcomes, alongside secondary outcomes relating to the process of care measures such as dysglycemia screening rates. An evaluation of other implementation outcomes relating to the eCDSS will be conducted, identifying facilitators and barriers based on established implementation science frameworks.

RESULTS

Enrollment of wards began in April 2022, after which clinical staff were recruited to take part in surveys and interviews. The intervention period of the trial began in February 2023, and subsequent data collection was completed in August 2023. Data are currently being analyzed, and results are expected to be available in June 2024.

CONCLUSIONS

An eCDSS can have the potential to improve clinician-led management of dysglycemia in inpatient mental health settings. If found to be feasible and acceptable, then, in combination with the results of the implementation evaluation, the system can be refined and improved to support future successful implementation. A larger and more definitive effectiveness trial should then be conducted to assess its impact on clinical outcomes and to inform scalability and application to other conditions in wider mental health care settings.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04792268; https://clinicaltrials.gov/study/NCT04792268.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/49548.

Effects of Benzodiazepine Exposure on Real-World Clinical Outcomes in Individuals at Clinical High Risk for Psychosis

BACKGROUND AND HYPOTHESIS

Animal models indicate GABAergic dysfunction in the development of psychosis, and that benzodiazepine (BDZ) exposure can prevent the emergence of psychosis-relevant phenotypes. However, whether BDZ exposure influences real-world clinical outcomes in individuals at clinical high risk for psychosis (CHR-P) is unknown.

STUDY DESIGN

This observational cohort study used electronic health record data from CHR-P individuals to investigate whether BDZ exposure (including hypnotics, eg, zopiclone) reduces the risk of developing psychosis and adverse clinical outcomes. Cox proportional-hazards models were employed in both the whole-unmatched sample, and a propensity score matched (PSM) subsample.

STUDY RESULTS

567 CHR-P individuals (306 male, mean[±SD] age = 22.3[±4.9] years) were included after data cleaning. The BDZ-exposed (n = 105) and BDZ-unexposed (n = 462) groups differed on several demographic and clinical characteristics, including psychotic symptom severity. In the whole-unmatched sample, BDZ exposure was associated with increased risk of transition to psychosis (HR = 1.61; 95% CI: 1.03-2.52; P = .037), psychiatric hospital admission (HR = 1.93; 95% CI: 1.13-3.29; P = .017), home visit (HR = 1.64; 95% CI: 1.18-2.28; P = .004), and Accident and Emergency department attendance (HR = 1.88; 95% CI: 1.31-2.72; P < .001). However, after controlling for confounding-by-indication through PSM, BDZ exposure did not modulate the risk of any outcomes (all P > .05). In an analysis restricted to antipsychotic-naïve individuals, BDZ exposure reduced the risk of transition to psychosis numerically, although this was not statistically significant (HR = 0.59; 95% CI: 0.32-1.08; P = .089).

CONCLUSIONS

BDZ exposure in CHR-P individuals was not associated with a reduction in the risk of psychosis transition or adverse clinical outcomes. Results in the whole-unmatched sample suggest BDZ prescription may be more likely in CHR-P individuals with higher symptom severity.

Understanding social and clinical associations with unemployment for people with schizophrenia and bipolar disorders: large-scale health records study

PURPOSE

People with severe mental illness (SMI) experience high levels of unemployment. We aimed to better understand the associations between clinical, social, and demographic inequality indicators and unemployment.

METHODS

Data were extracted from de-identified health records of people with SMI in contact with secondary mental health services in south London, UK. A Natural Language Processing text-mining application was applied to extract information on unemployment in the health records. Multivariable logistic regression was used to assess associations with unemployment, in people with SMI.

RESULTS

Records from 19,768 service users were used for analysis, 84.9% (n = 16,778) had experienced unemployment. In fully adjusted models, Black Caribbean and Black African service users were more likely to experience unemployment compared with White British service users (Black Caribbean: aOR 1.62, 95% CI 1.45-1.80; Black African: 1.32, 1.15-1.51). Although men were more likely to have experienced unemployment relative to women in unadjusted models (OR 1.36, 95% CI 1.26-1.47), differences were no longer apparent in the fully adjusted models (aOR 1.05, 95% CI 0.97-1.15). The presence of a non-affective (compared to affective) diagnosis (1.24, 1.13-1.35), comorbid substance use (2.02, 1.76-2.33), previous inpatient admissions (4.18, 3.71-4.70), longer inpatient stays (78 + days: 7.78, 6.34-9.54), and compulsory admissions (3.45, 3.04-3.92) were associated with unemployment, in fully adjusted models.

CONCLUSION

People with SMI experience high levels of unemployment, and we found that unemployment was associated with several clinical and social factors. Interventions to address low employment may need to also address these broader inequalities.

Improving our understanding of the social determinants of mental health: a data linkage study of mental health records and the 2011 UK census

OBJECTIVES

To address the lack of individual-level socioeconomic information in electronic healthcare records, we linked the 2011 census of England and Wales to patient records from a large mental healthcare provider. This paper describes the linkage process and methods for mitigating bias due to non-matching.

SETTING

South London and Maudsley NHS Foundation Trust (SLaM), a mental healthcare provider in Southeast London.

DESIGN

Clinical records from SLaM were supplied to the Office of National Statistics for linkage to the census through a deterministic matching algorithm. We examined clinical (International Classification of Disease-10 diagnosis, history of hospitalisation, frequency of service contact) and socio-demographic (age, gender, ethnicity, deprivation) information recorded in Clinical Record Interactive Search (CRIS) as predictors of linkage success with the 2011 census. To assess and adjust for potential biases caused by non-matching, we evaluated inverse probability weighting for mortality associations.

PARTICIPANTS

Individuals of all ages in contact with SLaM up until December 2019 (N=459 374).

OUTCOME MEASURES

Likelihood of mental health records' linkage to census.

RESULTS

220 864 (50.4%) records from CRIS linked to the 2011 census. Young adults (prevalence ratio (PR) 0.80, 95% CI 0.80 to 0.81), individuals living in more deprived areas (PR 0.78, 95% CI 0.78 to 0.79) and minority ethnic groups (eg, Black African, PR 0.67, 0.66 to 0.68) were less likely to match to census. After implementing inverse probability weighting, we observed little change in the strength of association between clinical/demographic characteristics and mortality (eg, presence of any psychiatric disorder: unweighted PR 2.66, 95% CI 2.52 to 2.80; weighted PR 2.70, 95% CI 2.56 to 2.84).

CONCLUSIONS

Lower response rates to the 2011 census among people with psychiatric disorders may have contributed to lower match rates, a potential concern as the census informs service planning and allocation of resources. Due to its size and unique characteristics, the linked data set will enable novel investigations into the relationship between socioeconomic factors and psychiatric disorders.

Emotional and behavioral outcomes among youths with mental disorders during the first Covid lockdown and school closures in England: a large clinical population study using health care record integrated surveys

PURPOSE

Emotional and behavioral problems in children and young people (CYP) have increased over the pandemic. Those with pre-existing mental disorders are more vulnerable but have been understudied. We investigated emotional and behavioral outcomes in this population; differences across diagnostic groups; and social, educational, and clinical determinants.

METHODS

We invited 5386 caregivers and CYP (aged 5-17) under child mental health services pre-pandemic to complete an online survey on CYP's emotional/behavioral symptoms and pandemic-related circumstances, and integrated responses with clinicodemographic information extracted from electronic health records. We compared four parent-rated outcomes (total emotional/behavioral scores and emotional/behavioral changes as compared to before the pandemic) across the three most common diagnostic groups in our population (Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD) and emotional disorders (EmD)). We then estimated the association of clinicodemographic and pandemic-related characteristics with emotional/behavioral outcomes.

RESULTS

A total of 1741 parents (32.3%) completed the survey. Parents of CYP with ADHD or ASD reported more behavioral difficulties (t(591) = 5.618 (0.001); t(663) = 6.527 (0.001)); greater emotional deterioration (t(591) = 2.592 (0.009); t(664) = 4.670 (< 0.001); and greater behavioral deterioration (t(594) = 4.529 (< 0.001); t(664) = 5.082 (< 0.001)) as compared to the EmD group. Those with ASD and EmD showed more emotional difficulties than ADHD (t(891) = - 4.431 (< 0.001); t(590) = - 3.254 (0.001)). Across diagnoses, poor parental mental health and challenges with education were most strongly associated with worse outcomes.

CONCLUSIONS

Within our clinical population, CYP with ADHD/ASD were the most adversely affected during lockdown. Enhancing clinical service provision that tackles parental stress and supports education may help mitigate the impact of future restrictions.

Sociodemographic and clinical predictors of delay to and length of stay with early intervention for psychosis service: findings from the CRIS-FEP study

PURPOSE

We investigated the influence of sociodemographic and clinical characteristics on delay to early intervention service (EIS) and the length of stay (LOS) with EIS.

METHODS

We used incidence data linked to the Clinical Record Interactive Search-First Episode Psychosis (CRIS-FEP) study. We followed the patients from May 2010 to March 2016. We performed multivariable Cox regression to estimate hazard ratios of delay to EIS. Negative binomial regression was used to determine LOS with EIS by sociodemographic and clinical characteristics, controlling for confounders.

RESULTS

343 patients were eligible for an EIS, 34.1% of whom did not receive the service. Overall, the median delay to EIS was 120 days (IQR; 15-1668); and the median LOS was 130.5 days (IQR 0-663). We found that women (adj.HR 0.58; 95%C I 0.42-0.78), living alone (adj.HR: 0.63; 95% CI 0.43-0.92) and ethnicity ('Other': adj.HR 0.47; 95% CI 0.23-0.98) were associated with prolonged delay to EIS. However, family involvement in help-seeking for psychosis (adj.HR 1.37; 95% CI 1.01-1.85) was strongly associated with a shorter delay to EIS. Patients who have used mental health services previously also experienced long delays to EIS.

CONCLUSIONS

Our analyses highlight the link between sociodemographic status, help-seeking behaviours, and delay to EIS. Our findings also show the vulnerability faced by those with a previous mental health problem who later develop psychosis in receiving specialist treatment for psychosis. Initiatives that ameliorate indicators of social disadvantage are urgently needed to reduce health inequalities and improve clinical outcomes.

Identifying features of risk periods for suicide attempts using document frequency and language use in electronic health records

Background

Individualising mental healthcare at times when a patient is most at risk of suicide involves shifting research emphasis from static risk factors to those that may be modifiable with interventions. Currently, risk assessment is based on a range of extensively reported stable risk factors, but critical to dynamic suicide risk assessment is an understanding of each individual patient's health trajectory over time. The use of electronic health records (EHRs) and analysis using machine learning has the potential to accelerate progress in developing early warning indicators.

Setting

EHR data from the South London and Maudsley NHS Foundation Trust (SLaM) which provides secondary mental healthcare for 1.8 million people living in four South London boroughs.

Objectives

To determine whether the time window proximal to a hospitalised suicide attempt can be discriminated from a distal period of lower risk by analysing the documentation and mental health clinical free text data from EHRs and (i) investigate whether the rate at which EHR documents are recorded per patient is associated with a suicide attempt; (ii) compare document-level word usage between documents proximal and distal to a suicide attempt; and (iii) compare n-gram frequency related to third-person pronoun use proximal and distal to a suicide attempt using machine learning.

Methods

The Clinical Record Interactive Search (CRIS) system allowed access to de-identified information from the EHRs. CRIS has been linked with Hospital Episode Statistics (HES) data for Admitted Patient Care. We analysed document and event data for patients who had at some point between 1 April 2006 and 31 March 2013 been hospitalised with a HES ICD-10 code related to attempted suicide (X60-X84; Y10-Y34; Y87.0/Y87.2).

Findings

n = 8,247 patients were identified to have made a hospitalised suicide attempt. Of these, n = 3,167 (39.8%) of patients had at least one document available in their EHR prior to their first suicide attempt. N = 1,424 (45.0%) of these patients had been "monitored" by mental healthcare services in the past 30 days. From 60 days prior to a first suicide attempt, there was a rapid increase in the monitoring level (document recording of the past 30 days) increasing from 35.1 to 45.0%. Documents containing words related to prescribed medications/drugs/overdose/poisoning/addiction had the highest odds of being a risk indicator used proximal to a suicide attempt (OR 1.88; precision 0.91 and recall 0.93), and documents with words citing a care plan were associated with the lowest risk for a suicide attempt (OR 0.22; precision 1.00 and recall 1.00). Function words, word sequence, and pronouns were most common in all three representations (uni-, bi-, and tri-gram).

Conclusion

EHR documentation frequency and language use can be used to distinguish periods distal from and proximal to a suicide attempt. However, in our study 55.0% of patients with documentation, prior to their first suicide attempt, did not have a record in the preceding 30 days, meaning that there are a high number who are not seen by services at their most vulnerable point.

The effectiveness of sexual assault referral centres with regard to mental health and substance use: a national mixed-methods study - the MiMoS Study

Background

Sexual assault referral centres have been established to provide an integrated service that includes forensic examination, health interventions and emotional support. However, it is unclear how the mental health and substance use needs are being addressed.

Aim

To identify what works for whom under what circumstances for people with mental health or substance use issues who attend sexual assault referral centres.

Setting and sample

Staff and adult survivors in English sexual assault referral centres and partner agency staff.

Design

A mixed-method multistage study using realist methodology comprising five work packages. This consisted of a systematic review and realist synthesis (work package 1); a national audit of sexual assault referral centres (work package 2); a cross-sectional prevalence study of mental health and drug and alcohol needs (work package 3); case studies in six sexual assault referral centre settings (work package 4), partner agencies and survivors; and secondary data analysis of outcomes of therapy for sexual assault survivors (work package 5).

Findings

There is a paucity of evidence identified in the review to support specific ways of addressing mental health and substance use. There is limited mental health expertise in sexual assault referral centres and limited use of screening tools based on the audit. In the prevalence study, participants (n = 78) reported high levels of psychological distress one to six weeks after sexual assault referral centre attendance (94% of people had symptoms of post-traumatic stress disorder). From work package 4 qualitative analysis, survivors identified how trauma-informed care potentially reduced risk of re-traumatisation. Sexual assault referral centre staff found having someone with mental health expertise in the team helpful not only in helping plan onward referrals but also in supporting staff. Both sexual assault referral centre staff and survivors highlighted challenges in onward referral, particularly to NHS mental health care, including gaps in provision and long waiting times. Work package 5 analysis demonstrated that people with recorded sexual assault had higher levels of baseline psychological distress and received more therapy but their average change scores at end point were similar to those without sexual trauma.

Limitations

The study was adversely affected by the pandemic. The data were collected during successive lockdowns when services were not operating as usual, as well as the overlay of anxiety and isolation due to the pandemic.

Conclusions

People who attend sexual assault centres have significant mental health and substance use needs. However, sexual assault referral centres vary in how they address these issues. Access to follow-up support from mental health services needs to be improved (especially for those deemed to have 'complex' needs) and there is some indication that co-located psychological therapies provision improves the survivor experience. Routine data analysis demonstrated that those with sexual assault can benefit from therapy but require more intensity than those without sexual assault.

Future work

Further research is needed to evaluate the effectiveness and cost-effectiveness of providing co-located psychological therapy in the sexual assault referral centres, as well as evaluating the long-term needs and outcomes of people who attend these centres.

Funding

This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (16/117/03) and is published in full in Health and Social Care Delivery Research; Vol. 11, No. 21.

Trial registration

This trial is registered as PROSPERO 2018 CRD42018119706 and ISRCTN 18208347.

The association between early childhood onset epilepsy and attention-deficit hyperactivity disorder (ADHD) in 3237 children and adolescents with Autism Spectrum Disorder (ASD): a historical longitudinal cohort data linkage study

Children and young people with Autism Spectrum Disorder (ASD) have an increased risk of comorbidities, such as epilepsy and Attention-Deficit/Hyperactivity Disorder (ADHD). However, little is known about the relationship between early childhood epilepsy (below age 7) and later ADHD diagnosis (at age 7 or above) in ASD. In this historical cohort study, we examined this relationship using an innovative data source, which included linked data from routinely collected acute hospital paediatric records and childhood community and inpatient psychiatric records. In a large sample of children and young people with ASD (N = 3237), we conducted a longitudinal analysis to examine early childhood epilepsy as a risk factor for ADHD diagnosis while adjusting for potential confounders, including socio-demographic characteristics, intellectual disability, family history of epilepsy and associated physical conditions. We found that ASD children and young people diagnosed with early childhood epilepsy had nearly a twofold increase in risk of developing ADHD later in life, an association which persisted after adjusting for potential confounders (adjusted OR = 1.72, CI95% = 1.13-2.62). This study suggests that sensitive monitoring of ADHD symptoms in children with ASD who have a history of childhood epilepsy may be important to promote early detection and treatment. It also highlights how linked electronic health records can be used to examine potential risk factors over time for multimorbidity in neurodevelopmental conditions.

Identifying individuals with intellectual disability who access mental health support and are at high risk for adverse clinical outcomes: cohort study

BACKGROUND

People with intellectual disability often experience aggressive challenging behaviour and mental health issues. It can be difficult to identify those who are at higher risk of adverse clinical outcomes when in clinical care.

AIMS

To characterise potential subgroups in adults with intellectual disability referred to mental health services in those presenting with aggressive behaviour or common mental disorders (CMDs).

METHOD

There were 836 adults (≥18 years) with intellectual disability and a record of aggressive challenging behaviour, and 205 patients with intellectual disability and CMDs, who were seen in specialist mental health services over a 5-year period. Cluster analysis was used to define patient characteristics associated with clinical outcome.

RESULTS

Distinct patient groups with differentiated profiles were observed in people with intellectual disability displaying aggressive challenging behaviour, and in those presenting with CMDs. Characteristics of the aggressive behaviour group who experienced adverse outcomes included being <30 years old, being male, more mentions of aggression and agitation in their clinical record, a diagnosis of pervasive developmental disorder and prescription of psychotropic medication. Characteristics of the CMD cluster that experienced adverse clinical outcomes were being older, being a White male, having a mild intellectual disability and physical health concerns.

CONCLUSIONS

People with intellectual disability who experience adverse clinical outcomes can be identified with a cluster analysis approach of common features, but differ by clinical presentation. This could be used not only to stratify this clinically heterogeneous population in terms of response to interventions, but also improve precision in the development of tailored interventions.

Early neutrophil trajectory following clozapine may predict clozapine response - Results from an observational study using electronic health records

BACKGROUND

Clozapine has unique effectiveness in treatment-resistant schizophrenia and is known to cause immunological side-effects. A transient spike in neutrophils commonly occurs in the first weeks of clozapine therapy. There is contradictory evidence in the literature as to whether neutrophil changes with clozapine are linked to treatment response.

AIMS

The current study aims to further examine the neutrophil changes in response to clozapine and explore any association between neutrophil trajectory and treatment response.

METHODS

A retrospective cohort study of patients undergoing their first treatment with clozapine and continuing for at least 2 years identified 425 patients (69% male/31% female). Neutrophil counts at baseline, 3 weeks and 1 month were obtained predominantly by linkage with data from the clozapine monitoring service. Clinical Global Impression- Severity (CGI-S) was rated from case notes at the time of clozapine initiation and at 2 years. Latent class growth analysis (LCGA) was performed to define distinct trajectories of neutrophil changes during the first month of treatment. Logistic regression was then conducted to investigate for association between the trajectory of neutrophil count changes in month 1 and clinical response at 2 years as well as between baseline neutrophil count and response.

RESULTS

Of the original cohort, 397 (93%) patients had useable neutrophil data during the first 6 weeks of clozapine treatment. LCGA revealed significant differences in neutrophil trajectories with a three-class model being the most parsimonious. The classes had similar trajectory profiles but differed primarily on overall neutrophil count: with low, high-normal and high neutrophil classes, comprising 52%, 40% and 8% of the sample respectively. Membership of the high-normal group was associated with significantly increased odds of a positive response to clozapine, as compared to the low neutrophil group [Odds ratio (OR) = 2.10, p-value = 0.002; 95% confidence interval (95% CI) = 1.31-3.36]. Baseline neutrophil count was a predictor of response to clozapine at 2 years, with counts of ≥5 × 109/l significantly associated with positive response (OR = 1.60, p-value = 0.03; 95% CI = 1.03-2.49).

CONCLUSIONS

Our data are consistent with the hypothesis that patients with low-level inflammation, reflected in a high-normal neutrophil count, are more likely to respond to clozapine, raising the possibility that clozapine exerts its superior efficacy via immune mechanisms.

Unraveling ethnic disparities in antipsychotic prescribing among patients with psychosis: A retrospective cohort study based on electronic clinical records

BACKGROUND

Previous studies have shown mixed evidence on ethnic disparities in antipsychotic prescribing among patients with psychosis in the UK, partly due to small sample sizes. This study aimed to examine the current state of antipsychotic prescription with respect to patient ethnicity among the entire population known to a large UK mental health trust with non-affective psychosis, adjusting for multiple potential risk factors.

METHODS

This retrospective cohort study included all patients (N = 19,291) who were aged 18 years or over at their first diagnoses of non-affective psychosis (identified with the ICD-10 codes of F20-F29) recorded in electronic health records (EHRs) at the South London and Maudsley NHS Trust until March 2021. The most recently recorded antipsychotic treatments and patient attributes were extracted from EHRs, including both structured fields and free-text fields processed using natural language processing applications. Multivariable logistic regression models were used to calculate the odds ratios (OR) for antipsychotic prescription according to patient ethnicity, adjusted for multiple potential contributing factors, including demographic (age and gender), clinical (diagnoses, duration of illness, service use and history of cannabis use), socioeconomic factors (level of deprivation and own-group ethnic density in the area of residence) and temporal changes in clinical guidelines (date of prescription).

RESULTS

The cohort consisted of 43.10 % White, 8.31 % Asian, 40.80 % Black, 2.64 % Mixed, and 5.14 % of patients from Other ethnicity. Among them, 92.62 % had recorded antipsychotic receipt, where 24.05 % for depot antipsychotics and 81.72 % for second-generation antipsychotic (SGA) medications. Most ethnic minority groups were not significantly different from White patients in receiving any antipsychotic. Among those receiving antipsychotic prescribing, Black patients were more likely to be prescribed depot (adjusted OR 1.29, 95 % confidence interval (CI) 1.14-1.47), but less likely to receive SGA (adjusted OR 0.85, 95 % CI 0.74-0.97), olanzapine (OR 0.82, 95 % CI 0.73-0.92) and clozapine (adjusted OR 0.71, 95 % CI 0.6-0.85) than White patients. All the ethnic minority groups were less likely to be prescribed olanzapine than the White group.

CONCLUSIONS

Black patients with psychosis had a distinct pattern in antipsychotic prescription, with less use of SGA, including olanzapine and clozapine, but more use of depot antipsychotics, even when adjusting for the effects of multiple demographic, clinical and socioeconomic factors. Further research is required to understand the sources of these ethnic disparities and eliminate care inequalities.

Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review

Digital data play an increasingly important role in advancing health research and care. However, most digital data in healthcare are in an unstructured and often not readily accessible format for research. Unstructured data are often found in a format that lacks standardization and needs significant preprocessing and feature extraction efforts. This poses challenges when combining such data with other data sources to enhance the existing knowledge base, which we refer to as digital unstructured data enrichment. Overcoming these methodological challenges requires significant resources and may limit the ability to fully leverage their potential for advancing health research and, ultimately, prevention, and patient care delivery. While prevalent challenges associated with unstructured data use in health research are widely reported across literature, a comprehensive interdisciplinary summary of such challenges and possible solutions to facilitate their use in combination with structured data sources is missing. In this study, we report findings from a systematic narrative review on the seven most prevalent challenge areas connected with the digital unstructured data enrichment in the fields of cardiology, neurology and mental health, along with possible solutions to address these challenges. Based on these findings, we developed a checklist that follows the standard data flow in health research studies. This checklist aims to provide initial systematic guidance to inform early planning and feasibility assessments for health research studies aiming combining unstructured data with existing data sources. Overall, the generality of reported unstructured data enrichment methods in the studies included in this review call for more systematic reporting of such methods to achieve greater reproducibility in future studies.

Ethnic differences in physical and mental multimorbidity in working age adults with a history of depression and/or anxiety

BACKGROUND

The current study used data from an ethnically diverse population from South London to examine ethnic differences in physical and mental multimorbidity among working age (18-64 years) adults in the context of depression and anxiety.

METHOD

The study included 44 506 patients who had previously attended Improving Access to Psychological Therapies services in the London Borough of Lambeth. Multinomial logistic regression examined cross-sectional associations between ethnicity with physical and mental multimorbidity. Patterns of multimorbidity were identified using hierarchical cluster analysis.

RESULTS

Within 44 056 working age adults with a history of depression or anxiety from South London there were notable ethnic differences in physical multimorbidity. Adults of Black Caribbean ethnicity were more likely to have physical multimorbidity [adjusted relative risk ratio (aRRR) = 1.25, 95% confidence interval (CI) 1.15-1.36] compared to adults of White ethnicity. Relative to adults of White ethnicity, adults of Asian ethnicity were more likely to have physical multimorbidity at higher thresholds only (e.g. 4 + conditions; aRRR = 1.53, 95% CI 1.17-2.00). Three physical (atopic, cardiometabolic, mixed) and three mental (alcohol/substance use, common/severe mental illnesses, personality disorder) multimorbidity clusters emerged. Ethnic minority groups with multimorbidity had a higher probability of belonging to the cardiometabolic cluster.

CONCLUSION

In an ethnically diverse population with a history of common mental health disorders, we found substantial between- and within-ethnicity variation in rates of physical, but not mental, multimorbidity. The findings emphasised the value of more granular definitions of ethnicity when examining the burden of physical and mental multimorbidity.

Evaluation of a Pathway team for homeless mental health in-patients

AIMS AND METHOD

The Pathway model is an enhanced care coordination model for homeless people in hospital. We aimed to evaluate the first attempt to apply it on psychiatric wards, which started in 2015 in South London. We developed a logic model which expressed how the Pathway approach might work. Two predictions from this model were tested, using propensity scores and regression to estimate the effect of the intervention among people who were eligible for it.

RESULTS

The Pathway team theorised that their interventions would reduce length of stay, improve housing outcomes and optimise the use of primary care - and, more tentatively, reduce readmission and emergency presentations. We were able to estimate effects on length of stay (-20.3 days; 95% CI -32.5 to -8.1; P = 0.0012) and readmission (a non-significant reduction).

CLINICAL IMPLICATIONS

The marked reduction in length of stay, explicable in terms of the logic model, constitutes preliminary support for the Pathway model in mental health services.

Survival and critical care use among people with dementia in a large English cohort

BACKGROUND

Admitting people with dementia to critical care units may not always lead to a clear survival benefit. Critical care admissions of people with dementia vary across countries. Little is known about the use and trends of critical care admissions of people with dementia in England.

OBJECTIVE

To investigate critical care use and survival among people with dementia in a large London catchment area.

METHODS

A retrospective cohort study using data from dementia assessment services in south London, UK (2007-20) linked with national hospitalisation data to ascertain critical care admissions. Outcomes included age-sex-standardised critical care use and 1-year post-critical care admission survival by dementia severity (binary: mild versus moderate/severe). We used logistic regression and Kaplan-Meier survival plots for investigating 1-year survival following a critical care admission and linear regressions for time trends.

RESULTS

Of 19,787 people diagnosed with dementia, 726 (3.7%) had ≥1 critical care admission at any time after receiving their dementia diagnosis. The overall 1-year survival of people with dementia, who had a CCA, was 47.5% (n = 345). Dementia severity was not associated with 1-year survival following a critical care admission (mild dementia versus moderate-severe dementia odds of 1-year mortality OR: 0.90, 95% CI [0.66-1.22]). Over the 12-year period from 2008 to 2019, overall critical care use decreased (β = -0.05; 95% CI = -0.01, -0.0003; P = 0.03), while critical care admissions occurring during the last year of life increased (β = 0.11, 95% CI = 0.01, 0.20, P = 0.03).

CONCLUSIONS

In this cohort, while critical care use among people with dementia declined overall, its use increased among those in their last year of life. Survival remains comparable to that observed in general older populations.

Effects of Benzodiazepine Exposure on Real-World Clinical Outcomes in Individuals at Clinical High-Risk for Psychosis

Background

Animal models indicate GABAergic dysfunction in the development of psychosis, and that benzodiazepine (BDZ) exposure can prevent the emergence of psychosis-relevant phenotypes. However, whether BDZ exposure influences the risk of psychosis in humans is unknown.

Methods

This observational-cohort study used electronic health record data from 818 individuals at clinical high-risk for psychosis (CHR-P) to investigate whether BDZ exposure (including hypnotics e.g., zopiclone) reduces the risk of developing psychosis and adverse clinical outcomes. Cox proportional-hazards models were employed in both the whole-unmatched sample, and a propensity score matched (PSM) subsample.

Results

567 CHR-P individuals were included after data cleaning (105 BDZ-exposed, 462 BDZ-unexposed). 306 (54%) individuals were male, and the mean age was 22.3 years (SD 4.9). The BDZ-exposed and BDZ-unexposed groups differed on several demographic and clinical characteristics, including psychotic symptom severity. In the whole-unmatched sample, BDZ exposure was associated with increased risk of transition to psychosis (HR=1.61; 95%CI:1.03-2.52; P=0.037), psychiatric hospital admission (HR=1.93; 95%CI:1.13-3.29; P=0.017), home visit (HR=1.64; 95%CI:1.18-2.28; P=0.004), and A&E attendance (HR=1.88; 95%CI:1.31-2.72; P<0.001). However, after controlling for confounding-by-indication through PSM, BDZ exposure did not modulate the risk of any outcomes (all P>0.05). In analysis restricted to antipsychotic-naïve individuals, BDZ exposure reduced the risk of transition to psychosis at trend-level (HR=0.59; 95%CI:0.32-1.08; P=0.089).

Conclusions

BDZ exposure in CHR-P individuals was not associated with a reduction in the risk of psychosis transition or other adverse clinical outcomes. Results in the whole-unmatched sample suggest BDZ prescription may be more likely in CHR-P individuals with higher symptom severity.

Establishment of a Public Mental Health Database for Research Purposes in the Ferrara Province: Development and Preliminary Evaluation Study

Background

The immediate use of data exported from electronic health records (EHRs) for research is often limited by the necessity to transform data elements into an actual data set.

Objective

This paper describes the methodology for establishing a data set that originated from an EHR registry that included clinical, health service, and sociodemographic information.

Methods

The Extract, Transform, Load process was applied to raw data collected at the Integrated Department of Mental Health and Pathological Addictions in Ferrara, Italy, from 1925 to February 18, 2021, to build the new, anonymized Ferrara-Psychiatry (FEPSY) database. Information collected before the first EHR was implemented (ie, in 1991) was excluded. An unsupervised cluster analysis was performed to identify patient subgroups to support the proof of concept.

Results

The FEPSY database included 3,861,432 records on 46,222 patients. Since 1991, each year, a median of 1404 (IQR 1117.5-1757.7) patients had newly accessed care, and a median of 7300 (IQR 6109.5-9397.5) patients were actively receiving care. Among 38,022 patients with a mental disorder, 2 clusters were identified; the first predominantly included male patients who were aged 25 to 34 years at first presentation and were living with their parents, and the second predominantly included female patients who were aged 35 to 44 years and were living with their own families.

Conclusions

The process for building the FEPSY database proved to be robust and replicable with similar health care data, even when they were not originally conceived for research purposes. The FEPSY database will enable future in-depth analyses regarding the epidemiology and social determinants of mental disorders, access to mental health care, and resource utilization.

A multisite comparison using electronic health records and natural language processing to identify the association between suicidality and hospital readmission amongst patients with eating disorders

OBJECTIVES

To describe and compare the association between suicidality and subsequent readmission for patients hospitalized for eating disorder treatment, within 2 years of discharge, at two large academic medical centers in two different countries.

METHODS

Over an 8-year study window from January 2009 to March 2017, we identified all inpatient eating disorder admissions at Weill Cornell Medicine, New York, USA (WCM) and South London and Maudsley Foundation NHS Trust, London, UK (SLaM). To establish each patient's-suicidality profile, we applied two natural language processing (NLP) algorithms, independently developed at the two institutions, and detected suicidality in clinical notes documented in the first week of admission. We calculated the odds ratios (OR) for any subsequent readmission within 2 years postdischarge and determined whether this was to another eating disorder unit, other psychiatric unit, a general medical hospital admission or emergency room attendance.

RESULTS

We identified 1126 and 420 eating disorder inpatient admissions at WCM and SLaM, respectively. In the WCM cohort, evidence of above average suicidality during the first week of admission was significantly associated with an increased risk of noneating disorder-related psychiatric readmission (OR 3.48 95% CI = 2.03-5.99, p-value < .001), but a similar pattern was not observed in the SLaM cohort (OR 1.34, 95% CI = 0.75-2.37, p = .32), there was no significant increase in risk of admission. In both cohorts, personality disorder increased the risk of any psychiatric readmission within 2 years.

DISCUSSION

Patterns of increased risk of psychiatric readmission from above average suicidality detected via NLP during inpatient eating disorder admissions differed in our two patient cohorts. However, comorbid diagnoses such as personality disorder increased the risk of any psychiatric readmission across both cohorts.

PUBLIC SIGNIFICANCE

Suicidality amongst is eating disorders is an extremely common presentation and it is important we further our understanding of identifying those most at risk. This research also provides a novel study design, comparing two NLP algorithms on electronic health record data based in the United States and United Kingdom on eating disorder inpatients. Studies researching both UK and US mental health patients are sparse therefore this study provides novel data.

Socioemotional profiles of autism spectrum disorders, attention deficit hyperactivity disorder, and disinhibited and reactive attachment disorders: a symptom comparison and network approach

Children with autism spectrum disorders (ASDs), attention deficit hyperactivity disorder (ADHD) and disinhibited and reactive attachment disorders (RAD/DAD) often experience socioemotional problems. Elucidating a clear picture of these profiles is essential. Strengths and Difficulties Questionnaires (SDQs) were analysed from cohort of children with ASD (n = 1430), ADHD (n = 1193), and RAD/DAD (n = 39). Kruskal-Wallis Tests and network analytic techniques were used to investigate symptom profiles. Children with ASD experienced more emotional problems, peer problems and fewer prosocial behaviours. Children with ADHD and RAD/DAD had higher levels of hyperactivity and conduct problems. Overall, ASD and ADHD networks were highly correlated (rs = 0.82), and we did not observe a statistically significant difference in terms of global Strength.

HbA1c recording in patients following a first diagnosis of serious mental illness: the South London and Maudsley Biomedical Research Centre case register

OBJECTIVES

To investigate factors associated with the recording of glycated haemoglobin (HbA1c) in people with first diagnoses of serious mental illness (SMI) in a large mental healthcare provider, and factors associated with HbA1c levels, when recorded. To our knowledge this is the first such investigation, although attention to dysglycaemia in SMI is an increasing priority in mental healthcare.

DESIGN

The study was primarily descriptive in nature, seeking to ascertain the frequency of HbA1c recording in the mental healthcare sector for people following first SMI diagnosis.

SETTINGS

A large mental healthcare provider, the South London and Maudsley National Health Service Trust.

PARTICIPANTS

Using electronic mental health records data, we ascertained patients with first SMI diagnoses (schizophrenia, schizoaffective disorder, bipolar disorder) from 2008 to 2018.

OUTCOME MEASURES

Recording or not of HbA1c level was ascertained from routine local laboratory data and supplemented by a natural language processing (NLP) algorithm for extracting recorded values in text fields (precision 0.89%, recall 0.93%). Age, gender, ethnic group, year of diagnosis, and SMI diagnosis were investigated as covariates in relation to recording or not of HbA1c and first recorded levels.

RESULTS

Of 21 462 patients in the sample (6546 bipolar disorder; 14 916 schizophrenia or schizoaffective disorder; mean age 38.8 years, 49% female), 4106 (19.1%) had at least one HbA1c result recorded from laboratory data, increasing to 6901 (32.2%) following NLP. HbA1c recording was independently more likely in non-white ethnic groups (black compared with white: OR 2.45, 95% CI 2.29 to 2.62), and was negatively associated with age (OR per year increase 0.93, 0.92-0.95), female gender (0.83, 0.78-0.88) and bipolar disorder (0.49, 0.45-0.52).

CONCLUSIONS

Over a 10-year period, relatively low level of recording of HbA1c was observed, although this has increased over time and ascertainment was increased with text extraction. It remains important to improve the routine monitoring of dysglycaemia in these at-risk disorders.

Antipsychotic polypharmacy and adverse drug reactions among adults in a London mental health service, 2008-2018

BACKGROUND

Antipsychotic polypharmacy (APP) occurs commonly but it is unclear whether it is associated with an increased risk of adverse drug reactions (ADRs). Electronic health records (EHRs) offer an opportunity to examine APP using real-world data. In this study, we use EHR data to identify periods when patients were prescribed 2 + antipsychotics and compare these with periods of antipsychotic monotherapy. To determine the relationship between APP and subsequent instances of ADRs: QT interval prolongation, hyperprolactinaemia, and increased body weight [body mass index (BMI) ⩾ 25].

METHODS

We extracted anonymised EHR data. Patients aged 16 + receiving antipsychotic medication at Camden & Islington NHS Foundation Trust between 1 January 2008 and 31 December 2018 were included. Multilevel mixed-effects logistic regression models were used to elucidate the relationship between APP and the subsequent presence of QT interval prolongation, hyperprolactinaemia, and/or increased BMI following a period of APP within 7, 30, or 180 days respectively.

RESULTS

We identified 35 409 observations of antipsychotic prescribing among 13 391 patients. Compared with antipsychotic monotherapy, APP was associated with a subsequent increased risk of hyperprolactinaemia (adjusted odds ratio 2.46; 95% CI 1.87-3.24) and of registering a BMI > 25 (adjusted odds ratio 1.75; 95% CI 1.33-2.31) in the period following the APP prescribing.

CONCLUSIONS

Our observations suggest that APP should be carefully managed with attention to hyperprolactinaemia and obesity.

Multimorbidity clusters among people with serious mental illness: a representative primary and secondary data linkage cohort study

BACKGROUND

People with serious mental illness (SMI) experience higher mortality partially attributable to higher long-term condition (LTC) prevalence. However, little is known about multiple LTCs (MLTCs) clustering in this population.

METHODS

People from South London with SMI and two or more existing LTCs aged 18+ at diagnosis were included using linked primary and mental healthcare records, 2012-2020. Latent class analysis (LCA) determined MLTC classes and multinominal logistic regression examined associations between demographic/clinical characteristics and latent class membership.

RESULTS

The sample included 1924 patients (mean (s.d.) age 48.2 (17.3) years). Five latent classes were identified: 'substance related' (24.9%), 'atopic' (24.2%), 'pure affective' (30.4%), 'cardiovascular' (14.1%), and 'complex multimorbidity' (6.4%). Patients had on average 7-9 LTCs in each cluster. Males were at increased odds of MLTCs in all four clusters, compared to the 'pure affective'. Compared to the largest cluster ('pure affective'), the 'substance related' and the 'atopic' clusters were younger [odds ratios (OR) per year increase 0.99 (95% CI 0.98-1.00) and 0.96 (0.95-0.97) respectively], and the 'cardiovascular' and 'complex multimorbidity' clusters were older (ORs 1.09 (1.07-1.10) and 1.16 (1.14-1.18) respectively). The 'substance related' cluster was more likely to be White, the 'cardiovascular' cluster more likely to be Black (compared to White; OR 1.75, 95% CI 1.10-2.79), and both more likely to have schizophrenia, compared to other clusters.

CONCLUSION

The current study identified five latent class MLTC clusters among patients with SMI. An integrated care model for treating MLTCs in this population is recommended to improve multimorbidity care.

Associations between air pollution and mental health service use in dementia: a retrospective cohort study

BACKGROUND

Little is known about the role of air pollution in how people with dementia use mental health services.

OBJECTIVE

We examined longitudinal associations between air pollution exposure and mental health service use in people with dementia.

METHODS

In 5024 people aged 65 years or older with dementia in South London, high resolution estimates of nitrogen dioxide (NO2) and particulate matter (PM2.5 and PM10) levels in ambient air were linked to residential addresses. Associations between air pollution and Community Mental Health Team (CMHT) events (recorded over 9 years) were examined using negative binomial regression models. Cognitive function was measured using the Mini Mental State Examination (MMSE) and health and social functioning was measured using the Health of the Nation Outcomes Scale (HoNOS65+). Associations between air pollution and both MMSE and HoNOS65+ scores were assessed using linear regression models.

FINDINGS

In the first year of follow-up, increased exposure to all air pollutants was associated with an increase in the use of CMHTs in a dose-response manner. These associations were strongest when we compared the highest air pollution quartile (quartile 4: Q4) with the lowest quartile (Q1) (eg, NO2: adjusted incidence rate ratio (aIRR) 1.27, 95% CI 1.11 to 1.45, p<0.001). Dose-response patterns between PM2.5 and CMHT events remained at 5 and 9 years. Associations were strongest for patients with vascular dementia. NO2 levels were linked with poor functional status, but not cognitive function.

CONCLUSIONS

Residential air pollution exposure is associated with increased CMHT usage among people with dementia.

CLINICAL IMPLICATIONS

Efforts to reduce pollutant exposures in urban settings might reduce the use of mental health services in people with dementia, freeing up resources in already considerably stretched psychiatric services.

Antidepressant drug prescription and incidence of COVID-19 in mental health outpatients: a retrospective cohort study

BACKGROUND

Currently, the main pharmaceutical intervention for COVID-19 is vaccination. While antidepressant (AD) drugs have shown some efficacy in treatment of symptomatic COVID-19, their preventative potential remains largely unexplored. Analysis of association between prescription of ADs and COVID-19 incidence in the population would be beneficial for assessing the utility of ADs in COVID-19 prevention.

METHODS

Retrospective study of association between AD prescription and COVID-19 diagnosis was performed in a cohort of community-dwelling adult mental health outpatients during the 1st wave of COVID-19 pandemic in the UK. Clinical record interactive search (CRIS) was performed for mentions of ADs within 3 months preceding admission to inpatient care of the South London and Maudsley (SLaM) NHS Foundation Trust. Incidence of positive COVID-19 tests upon admission and during inpatient treatment was the primary outcome measure.

RESULTS

AD mention was associated with approximately 40% lower incidence of positive COVID-19 test results when adjusted for socioeconomic parameters and physical health. This association was also observed for prescription of ADs of the selective serotonin reuptake inhibitor (SSRI) class.

CONCLUSIONS

This preliminary study suggests that ADs, and SSRIs in particular, may be of benefit for preventing COVID-19 infection spread in the community. The key limitations of the study are its retrospective nature and the focus on a mental health patient cohort. A more definitive assessment of AD and SSRI preventative potential warrants prospective studies in the wider demographic.

Borrowing strength from clinical trials in analysing longitudinal data from a treated cohort: investigating the effectiveness of acetylcholinesterase inhibitors in the management of dementia

BACKGROUND

Health care professionals seek information about effectiveness of treatments in patients who would be offered them in routine clinical practice. Electronic medical records (EMRs) and randomized controlled trials (RCTs) can both provide data on treatment effects; however, each data source has limitations when considered in isolation.

METHODS

A novel modelling methodology which incorporates RCT estimates in the analysis of EMR data via informative prior distributions is proposed. A Bayesian mixed modelling approach is used to model outcome trajectories among patients in the EMR dataset receiving the treatment of interest. This model incorporates an estimate of treatment effect based on a meta-analysis of RCTs as an informative prior distribution. This provides a combined estimate of treatment effect based on both data sources.

RESULTS

The superior performance of the novel combined estimator is demonstrated via a simulation study. The new approach is applied to estimate the effectiveness at 12 months after treatment initiation of acetylcholinesterase inhibitors in the management of the cognitive symptoms of dementia in terms of Mini-Mental State Examination scores. This demonstrated that estimates based on either trials data only (1.10, SE = 0.316) or cohort data only (1.56, SE = 0.240) overestimated this compared with the estimate using data from both sources (0.86, SE = 0.327).

CONCLUSIONS

It is possible to combine data from EMRs and RCTs in order to provide better estimates of treatment effectiveness.

Investigating online activity in UK adolescent mental health patients: a feasibility study using a natural language processing approach for electronic health records

OBJECTIVES

To assess the feasibility of using a natural language processing (NLP) application for extraction of free-text online activity mentions in adolescent mental health patient electronic health records (EHRs).

SETTING

The Clinical Records Interactive Search system allows detailed research based on deidentified EHRs from the South London and Maudsley NHS Foundation Trust, a large south London Mental Health Trust providing secondary and tertiary mental healthcare.

PARTICIPANTS AND METHODS

We developed a gazetteer of online activity terms and annotation guidelines, from 5480 clinical notes (200 adolescents, aged 11-17 years) receiving specialist mental healthcare. The preprocessing and manual curation steps of this real-world data set allowed development of a rule-based NLP application to automate identification of online activity (internet, social media, online gaming) mentions in EHRs. The context of each mention was also recorded manually as: supportive, detrimental or neutral in a subset of data for additional analysis.

RESULTS

The NLP application performed with good precision (0.97) and recall (0.94) for identification of online activity mentions. Preliminary analyses found 34% of online activity mentions were considered to have been documented within a supportive context for the young person, 38% detrimental and 28% neutral.

CONCLUSION

Our results provide an important example of a rule-based NLP methodology to accurately identify online activity recording in EHRs, enabling researchers to now investigate associations with a range of adolescent mental health outcomes.

Predictors of hospital readmission for patients diagnosed with delirium: An electronic health record data analysis

INTRODUCTION

Delirium is an acute and fluctuating change in attention and cognition that increases the risk of functional decline, institutionalisation and death in hospitalised patients. After delirium, patients have a significantly higher risk of readmission to hospital. Our aim was to investigate factors associated with hospital readmission in people with delirium.

METHODS

We carried out an observational retrospective cohort study using linked mental health care and hospitalisation records from South London. Logistic regression models were used to predict the odds of 30-day readmission and Cox proportional hazard models to calculate readmission risks when not restricting follow-up time.

RESULTS

Of 2814 patients (mean age 78.9 years SD ±11.8) discharged from hospital after an episode of delirium, 823 (29.3%) were readmitted within 30 days. Depressed mood (odds ratio (OR) 1.34 (95% confidence interval (CI) 1.08-1.66)), moderate-to-severe physical health problems (OR 1.67 (95% CI 1.18-2.2.36)) and a history of serious circulatory disease (OR 1.29 (95% CI 1.07-1.55)) were associated with higher odds of hospital readmission, whereas a diagnosis of delirium superimposed on dementia (OR 0.67 (95% CI 0.53-0.84)) and problematic alcohol/substance (OR 0.54 (95% CI 0.33-0.89)) use were associated with lower odds. Cox proportionate hazard models showed similar results.

CONCLUSION

Almost one-third of patients with delirium were readmitted within a short period of time, a more detailed understanding of the underlying risk factors could help prevent readmissions. Our findings indicate that the aetiology (as alcohol-related delirium), the recognition that delirium occurred in the context of dementia, as well as potentially modifiable factors, as depressed mood affect readmission risk, and should be assessed in clinical settings.

Prevalence of HIV in mental health service users: a retrospective cohort study

OBJECTIVE

To examine the prevalence of HIV in a cohort of people who have used secondary mental health services in the UK.

DESIGN

Retrospective cohort study.

SETTING

Routinely collected clinical data from secondary mental health services in South London, UK available for research through the Clinical Record Interactive Search tool at the National Institute for Health and Care Research Maudsley Biomedical Research Centre were matched with pseudonymised national HIV surveillance data held by the UK Health Security Agency using a deterministic matching algorithm.

PARTICIPANTS

All adults aged 16+ who presented for the first time to mental health services in the South London and Maudsley (SLaM) National Health Service Trust between 1 January 2007 and 31 December 2018 were included.

PRIMARY OUTCOME

Point prevalence of HIV.

RESULTS

There were 181 177 people who had contact with mental health services for the first time between 2007 and 2018 in SLaM. Overall, 2.47% (n=4481) of those had a recorded HIV diagnosis in national HIV surveillance data at any time (before, during or after contact with mental health services), 24.73 people per 1000. HIV point prevalence was highest in people with a diagnosed substance use disorder at 3.77% (n=784). A substantial percentage of the sample did not have a formal mental health diagnosis (27%), but even with those excluded, the point prevalence remained high at 2.31%. Around two-thirds of people had their diagnosis of HIV before contact with mental health services (67%; n=1495).

CONCLUSIONS

The prevalence of HIV in people who have had contact with mental health services was approximately 2.5 times higher than the general population in the same geographical area. Future work should investigate risk factors and disparities in HIV outcomes between those with and without mental health service contact.

Predicting type 2 diabetes prevalence for people with severe mental illness in a multi-ethnic East London population

BACKGROUND AND AIMS

Prevalence of type two diabetes mellitus (T2DM) in people with severe mental illness (SMI) is 2-3 times higher than in general population. Predictive modelling has advanced greatly in the past decade, and it is important to apply cutting-edge methods to vulnerable groups. However, few T2DM prediction models account for the presence of mental illness, and none seemed to have been developed specifically for people with SMI. Therefore, we aimed to develop and internally validate a T2DM prevalence model for people with SMI.

METHODS

We utilised a large cross-sectional sample representative of a multi-ethnic population from London (674,000 adults); 10,159 people with SMI formed our analytical sample (1,513 T2DM cases). We fitted a linear logistic regression and XGBoost as stand-alone models and as a stacked ensemble. Age, sex, body mass index, ethnicity, area-based deprivation, past hypertension, cardiovascular diseases, prescribed antipsychotics, and SMI illness were the predictors.

RESULTS

Logistic regression performed well while detecting T2DM presence for people with SMI: area under the receiver operator curve (ROC-AUC) was 0.83 (95 % CI 0.79-0.87). XGBoost and LR-XGBoost ensemble performed equally well, ROC-AUC 0.83 (95 % CI 0.79-0.87), indicating a negligible contribution of non-linear terms to predictive power. Ethnicity was the most important predictor after age. We demonstrated how the derived models can be utilised and estimated a 2.14 % (95 %CI 2.03 %-2.24 %) increase in T2DM prevalence in East London SMI population in 20 years' time, driven by the projected demographic changes.

CONCLUSIONS

Primary care data, the setting where prediction models could be most fruitfully used, provide enough information for well-performing T2DM prevalence models for people with SMI. We demonstrated how thorough internal cross-validation of an ensemble of a linear and machine-learning model can quantify the predictive value of non-linearity in the data.

Documenting maternal and childcare information of mothers presented to substance use treatment services: A qualitative study of reports in a clinical case register

BACKGROUND

Mothers compose a significant proportion of women in substance use treatment services. These women have needs that, if not addressed, can negatively impact their capacity to parent. This study explores the feasibility of using free-text notes from electronic health records (EHRs) to identify factors that impair mothers' ability to care for their children.

METHOD

This study is a qualitative analysis of EHRs of 50 women attending substance use services in Southeast London who were parents of dependent children (defined as mothers of children aged <18 years independent of living together). We developed a sampling stratification process to ensure an adequate volume of data were available and analyzed per case. The study identified and tested search terms. We extracted data from clinical notes and letters of communication with other services/agencies (free-text notes) using the identified search terms and conducted deductive thematic analysis.

RESULTS

The mean number of documents per case was 92.17 (SD = 18.51). The study identified five themes with subthemes: childcare arrangements, family context, safeguarding issues, factors that might impact the treatment plan and care of the child, and communication between the health care and child welfare systems.

CONCLUSION

The study demonstrates a novel approach for exploring parenting-related characteristics of mothers in substance use treatment. Despite a range of maternal and childcare-related information available on EHRs, the type of treatment and support being offered to patients in response to the reported information is less well documented. Findings highlight the need for further investments in implementing effective family centered strategies within substance use services.

Ethnicity and survival after a dementia diagnosis: a retrospective cohort study using electronic health record data

BACKGROUND

Individuals from minority ethnic groups in the UK are thought to be at higher risk of developing dementia while facing additional barriers to receiving timely care. However, few studies in the UK have examined if there are ethnic disparities in survival once individuals receive a dementia diagnosis.

METHODS

We conducted a retrospective cohort study using electronic health record data of individuals diagnosed with dementia from a large secondary mental healthcare provider in London. Patients from Black African, Black Caribbean, South Asian, White British, and White Irish ethnic backgrounds were followed up for a 10-year period between 01 January 2008 and 31 December 2017. Data were linked to death certificate data from the Office of National Statistics to determine survival from dementia diagnosis. Standardised mortality ratios were calculated to estimate excess deaths in each ethnicity group as compared to the gender- and age-standardised population of England and Wales. We used Cox regression models to compare survival after dementia diagnosis across each ethnicity group.

RESULTS

Mortality was elevated at least twofold across all ethnicity groups with dementia compared to the general population in England and Wales. Risk of death was lower in Black Caribbean, Black African, White Irish, and South Asian groups as compared to the White British population, even after adjusting for age, gender, neighbourhood-level deprivation, indicators of mental and physical comorbidities. Risk of death remained lower after additionally accounting for those who emigrated out of the cohort.

CONCLUSIONS

While mortality in dementia is elevated across all ethnic groups as compared to the general population, reasons for longer survival in minority ethnic groups in the UK as compared to the White British group are unclear and merit further exploration. Implications of longer survival, including carer burden and costs, should be considered in policy and planning to ensure adequate support for families and carers of individuals with dementia.

Adverse outcomes associated with recorded victimization in mental health electronic records during the first UK COVID-19 lockdown

PURPOSE

The impact of COVID-19 pandemic policies on vulnerable groups such as people with mental health problems who experience violence remains unknown. This study aimed to investigate the prevalence of victimization recorded in mental healthcare records during the first UK lockdown, and associations with subsequent adverse outcomes.

METHODS

Using a large mental healthcare database, we identified all adult patients receiving services between 16.12.2019 and 15.06.2020 and extracted records of victimisation between 16.03.2020 and 15.06.2020 (first UK COVID-19 lockdown). We investigated adverse outcomes including acute care, emergency department referrals and all-cause mortality in the year following the lockdown (16.06.2020- 01.11.2021). Multivariable Cox regressions models were constructed, adjusting for socio-demographic, socioeconomic, clinical, and service use factors.

RESULTS

Of 21,037 adults receiving mental healthcare over the observation period, 3,610 (17.2%) had victimisation mentioned between 16.03.2020 and 15.06.2020 (first UK COVID-19 lockdown). Service users with mentions of victimisation in their records had an elevated risk for all outcomes: acute care (adjusted HR: 2.1; 95%CI 1.9-2.3, p < 0.001), emergency department referrals (aHR: 2.0; 95%CI 1.8-2.2; p < 0.001), and all-cause mortality (aHR: 1.5; 95%CI 1.1-1.9; p = 0.003), when compared to service users with no recorded victimisation. We did not observe a statistically significant interaction with gender; however, after adjusting for possible confounders, men had slightly higher hazard ratios for all-cause mortality and emergency department referrals than women.

CONCLUSION

Patients with documented victimisation during the first UK lockdown were at increased risk for acute care, emergency department referrals and all-cause mortality. Further research is needed into mediating mechanisms.

Recording of intellectual disability in general hospitals in England 2006-2019: Cohort study using linked datasets

BACKGROUND

Accurate recognition and recording of intellectual disability in those who are admitted to general hospitals is necessary for making reasonable adjustments, ensuring equitable access, and monitoring quality of care. In this study, we determined the rate of recording of intellectual disability in those with the condition who were admitted to hospital and factors associated with the condition being unrecorded.

METHODS AND FINDINGS

Retrospective cohort study using 2 linked datasets of routinely collected clinical data in England. We identified adults with diagnosed intellectual disability in a large secondary mental healthcare database and used general hospital records to investigate recording of intellectual disability when people were admitted to general hospitals between 2006 and 2019. Trends over time and factors associated with intellectual disability being unrecorded were investigated. We obtained data on 2,477 adults with intellectual disability who were admitted to a general hospital in England at least once during the study period (total number of admissions = 27,314; median number of admissions = 5). People with intellectual disability were accurately recorded as having the condition during 2.9% (95% CI 2.7% to 3.1%) of their admissions. Broadening the criteria to include a nonspecific code of learning difficulty increased recording to 27.7% (95% CI 27.2% to 28.3%) of all admissions. In analyses adjusted for age, sex, ethnicity, and socioeconomic deprivation, having a mild intellectual disability and being married were associated with increased odds of the intellectual disability being unrecorded in hospital records. We had no measure of quality of hospital care received and could not relate this to the presence or absence of a record of intellectual disability in the patient record.

CONCLUSIONS

Recognition and recording of intellectual disability in adults admitted to English general hospitals needs to be improved. Staff awareness training, screening at the point of admission, and data sharing between health and social care services could improve care for people with intellectual disability.

Associations of remote mental healthcare with clinical outcomes: a natural language processing enriched electronic health record data study protocol

INTRODUCTION

People often experience significant difficulties in receiving mental healthcare due to insufficient resources, stigma and lack of access to care. Remote care technology has the potential to overcome these barriers by reducing travel time and increasing frequency of contact with patients. However, the safe delivery of remote mental healthcare requires evidence on which aspects of care are suitable for remote delivery and which are better served by in-person care. We aim to investigate clinical and demographic associations with remote mental healthcare in a large electronic health record (EHR) dataset and the degree to which remote care is associated with differences in clinical outcomes using natural language processing (NLP) derived EHR data.

METHODS AND ANALYSIS

Deidentified EHR data, derived from the South London and Maudsley (SLaM) National Health Service Foundation Trust Biomedical Research Centre (BRC) Case Register, will be extracted using the Clinical Record Interactive Search tool for all patients receiving mental healthcare between 1 January 2019 and 31 March 2022. First, data on a retrospective, longitudinal cohort of around 80 000 patients will be analysed using descriptive statistics to investigate clinical and demographic associations with remote mental healthcare and multivariable Cox regression to compare clinical outcomes of remote versus in-person assessments. Second, NLP models that have been previously developed to extract mental health symptom data will be applied to around 5 million documents to analyse the variation in content of remote versus in-person assessments.

ETHICS AND DISSEMINATION

The SLaM BRC Case Register and Clinical Record Interactive Search (CRIS) tool have received ethical approval as a deidentified dataset (including NLP-derived data from unstructured free text documents) for secondary mental health research from Oxfordshire REC C (Ref: 18/SC/0372). The study has received approval from the SLaM CRIS Oversight Committee. Study findings will be disseminated through peer-reviewed, open access journal articles and service user and carer advisory groups.