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Background databases of electronic health records are powerful tools for dementia research, but data can be influenced by incomplete recording. We examined whether people with dementia recorded in a specialist database (from a mental health and dementia care service) differ from those recorded in primary care. Methods a retrospective cohort study of the population covered by Lambeth DataNet (primary care electronic records) between 2007 and 2019. Documentation of dementia diagnosis in primary care coded data and linked records in a specialist database (Clinical Records Interactive Search) were compared. Results 3,859 people had dementia documented in primary care codes and 4,266 in the specialist database, with 2,886/5,239 (55%) documented in both sources. Overall, 55% were labelled as having Alzheimer’s dementia and 29% were prescribed dementia medication, but these proportions were significantly higher in those documented in both sources. The cohort identified from the specialist database were less likely to live in a care home (prevalence ratio 0.73, 95% confidence interval 0.63–0.85), have multimorbidity (0.87, 0.77–0.98) or consult frequently (0.91, 0.88–0.95) than those identified through primary care codes, although mortality did not differ (0.98, 0.91–1.06). Discussion there is under-recording of dementia diagnoses in both primary care and specialist databases. This has implications for clinical care and for generalizability of research. Our results suggest that using a mental health database may under-represent those patients who have more frailty, reflecting differential referral to mental health services, and demonstrating how the patient pathways are an important consideration when undertaking database studies.

PurposeThere is a dearth of literature examining the impact of the COVID-19 pandemic on older people with dual diagnosis referred to mental health services. The purpose of this study was to compare dual diagnosis before and after lockdown in people aged between 55 and 74 with alcohol use.Design/methodology/approachData were collected for people referred to mental health services using an anonymised database of de-identified records to identify people with both substance use disorder alone, or accompanied by co-existing mental disorders.FindingsIn total, 366 older people were assessed with the Alcohol Use Disorders Identification Test (AUDIT), 185 before and 181 after lockdown. People with dual diagnosis were more likely to be referred than those without, after compared to before lockdown (13 and 6%, respectively, p < 0.05). People with any substance use disorder with and without dual diagnosis showed an even greater likelihood of referral after, compared with before, lockdown (61 and 34%, respectively, p < 0.0001). Opioid use more than once a month was more likely to be reported after, compared with before, lockdown (66 and 36%, respectively, p < 0.005).Research limitations/implicationsThe finding of a higher likelihood of opioid use after compared with before lockdown during the COVID-19 pandemic warrants further exploration. There is also further scope for further studies that involve older non-drinkers.Originality/valueA greater likelihood of both dual diagnosis and substance use disorder alone after, compared with before lockdown has implications for both mental health and addiction service provision during a pandemic.

Background: There has been little previous research within mental health services exploring changes in drinking behavior among older people during the COVID-19 pandemic. This study compared alcohol...

Purpose The World Health Organisation (WHO) recently ranked air pollution as the major environmental cause of premature death. However, the significant potential health and societal costs of poor mental health in relation to air quality are not represented in the WHO report due to limited evidence. We aimed to test the hypothesis that long-term exposure to air pollution is associated with poor mental health. Methods A prospective longitudinal population-based mental health survey was conducted of 1698 adults living in 1075 households in South East London, from 2008 to 2013. High-resolution quarterly average air pollution concentrations of nitrogen dioxide (NO2) and oxides (NOx), ozone (O3), particulate matter with an aerodynamic diameter 10) and 2.5) were linked to the home addresses of the study participants. Associations with mental health were analysed with the use of multilevel generalised linear models, after adjusting for large number of confounders, including the individuals’ socioeconomic position and exposure to road-traffic noise. Results We found robust evidence for interquartile range increases in PM2.5, NOx and NO2 to be associated with 18–39% increased odds of common mental disorders, 19–30% increased odds of poor physical symptoms and 33% of psychotic experiences only for PM10. These longitudinal associations were more pronounced in the subset of non-movers for NO2 and NOx. Conclusions The findings suggest that traffic-related air pollution is adversely affecting mental health. Whilst causation cannot be proved, this work suggests substantial morbidity from mental disorders could be avoided with improved air quality.

ObjectivesSickness absence is strongly associated with poor mental health, and mental disorders often go untreated. In this population-based cohort study, we identified people receiving fit notes from their general practitioner (GP) and determined access to mental health treatment stratified by health complaint and demographic variables.DesignLongitudinal study of health records.SettingPrimary care and secondary mental health care in the borough of Lambeth, South London. Forty-five GP practices in Lambeth and the local secondary mental healthcare trust.ParticipantsThe analytical sample included 293 933 working age adults (16–60 years) registered at a Lambeth GP practice between 1 January 2014 and 30 April 2016.Primary and secondary outcome measuresThree indicators of mental healthcare in the year after first fit note were antidepressant prescription, contact with Improving Access to Psychological Therapy (IAPT) services and contact with secondary mental health services.Results75% of people with an identified mental health condition at first fit note had an indicator of mental healthcare in the following year. Black Caribbean and Black African groups presenting with mental disorders were less likely to have a mental healthcare indicator compared with White British groups.ConclusionsThe majority of those with an identified mental health need receive some treatment in the year following a fit note; however, our results suggest Black African and Black Caribbean groups with an identified mental healthcare need have less complete access compared to the White British group.

ObjectivesTo investigate changes in daily mental health (MH) service use and mortality in response to the introduction and the lifting of the COVID-19 ‘lockdown’ policy in Spring 2020.DesignA regression discontinuity in time (RDiT) analysis of daily service-level activity.Setting and participantsMental healthcare data were extracted from 10 UK providers.Outcome measuresDaily (weekly for one site) deaths from all causes, referrals and discharges, inpatient care (admissions, discharges, caseloads) and community services (face-to-face (f2f)/non-f2f contacts, caseloads): Adult, older adult and child/adolescent mental health; early intervention in psychosis; home treatment teams and liaison/Accident and Emergency (A&E). Data were extracted from 1 Jan 2019 to 31 May 2020 for all sites, supplemented to 31 July 2020 for four sites. Changes around the commencement and lifting of COVID-19 ‘lockdown’ policy (23 March and 10 May, respectively) were estimated using a RDiT design with a difference-in-difference approach generating incidence rate ratios (IRRs), meta-analysed across sites.ResultsPooled estimates for the lockdown transition showed increased daily deaths (IRR 2.31, 95% CI 1.86 to 2.87), reduced referrals (IRR 0.62, 95% CI 0.55 to 0.70) and reduced inpatient admissions (IRR 0.75, 95% CI 0.67 to 0.83) and caseloads (IRR 0.85, 95% CI 0.79 to 0.91) compared with the pre lockdown period. All community services saw shifts from f2f to non-f2f contacts, but varied in caseload changes. Lift of lockdown was associated with reduced deaths (IRR 0.42, 95% CI 0.27 to 0.66), increased referrals (IRR 1.36, 95% CI 1.15 to 1.60) and increased inpatient admissions (IRR 1.21, 95% CI 1.04 to 1.42) and caseloads (IRR 1.06, 95% CI 1.00 to 1.12) compared with the lockdown period. Site-wide activity, inpatient care and community services did not return to pre lockdown levels after lift of lockdown, while number of deaths did. Between-site heterogeneity most often indicated variation in size rather than direction of effect.ConclusionsMH service delivery underwent sizeable changes during the first national lockdown, with as-yet unknown and unevaluated consequences.

ObjectivesThe recent COVID-19 pandemic has disrupted mental healthcare delivery, with many services shifting from in-person to remote patient contact. We investigated the impact of the pandemic on the use of remote consultation and on the prescribing of psychiatric medications.Design and settingThe Clinical Record Interactive Search tool was used to examine deidentified electronic health records of people receiving mental healthcare from the South London and Maudsley (SLaM) NHS Foundation Trust. Data from the period before and after the onset of the pandemic were analysed using linear regression, and visualised using locally estimated scatterplot smoothing.ParticipantsAll patients receiving care from SLaM between 7 January 2019 and 20 September 2020 (around 37 500 patients per week).Outcome measures(i) The number of clinical contacts (in-person, remote or non-attended) with mental healthcare professionals per week.(ii) Prescribing of antipsychotic and mood stabiliser medications per week.ResultsFollowing the onset of the pandemic, the frequency of in-person contacts was significantly reduced compared with that in the previous year (β coefficient: −5829.6 contacts, 95% CI −6919.5 to −4739.6, pConclusionsThe COVID-19 pandemic has been associated with a marked increase in remote consultation, particularly among younger patients. However, there was no evidence that this has led to changes in psychiatric prescribing. Nevertheless, further work is needed to ensure that older patients are able to access mental healthcare remotely.

The lockdown and social distancing policy imposed due to the COVID-19 pandemic is likely to have had a widespread impact on mental healthcare service provision and use. Previous reports from the South London and Maudsley NHS Trust (SLaM; a large mental health service provider for 1.2m residents in South London) highlighted a shift to virtual contacts among those accessing community mental health and home treatment teams and an increase in deaths over the pandemic’s first wave. However, there is a need to quantify this for individuals with particular vulnerabilities, including those with learning disabilities and other neurodevelopmental disorders. Taking advantage of the Clinical Record Interactive Search (CRIS) data resource with 24-hourly updates of electronic mental health records data, this paper describes daily caseloads and contact numbers (face-to-face and virtual) for individuals with potential neurodevelopmental disorders across community, specialist, crisis and inpatient services. The report focussed on the period 1st January to 31st July 2020. We also report on daily accepted and discharged trust referrals, total trust caseloads and daily inpatient admissions and discharges for individuals with potential neurodevelopmental disorders. In addition, daily deaths are described for all current and previous SLaM service users with potential neurodevelopmental disorders over this period. In summary, comparing periods before and after 16th March 2020 there was a shift from face-to-face contacts to virtual contacts across all teams. The largest declines in caseloads and total contacts were seen in Home Treatment Team, Liaison/A&E and Older Adult teams. Reduced accepted referrals and inpatient admissions were observed and there was an 103% increase in average daily deaths in the period after 16th March, compared to the period 1st January to 15th March (or a 282% increase if the 2-month period from 16th March to 15th May was considered alone).

This study sought to provide an early description of mental health service activity before and after national implementation of social distancing for COVID-19. A time series analysis was carried out of daily service-level activity on data from a large mental healthcare provider in southeast London, from 01.02.2020 to 31.03.2020, comparing activity before and after 16.03.2020: i) inpatient admissions, discharges and numbers, ii) contact numbers and daily caseloads (Liaison, Home Treatment Teams, Community Mental Health Teams); iii) numbers of deaths for past and present patients. Daily face-to-face contact numbers fell for liaison, home treatment and community services with incomplete compensatory rises in non-face-to-face contacts. Daily caseloads fell for all services, apart from working age and child/adolescent community teams. Inpatient numbers fell 13.6% after 16th March, and daily numbers of deaths increased by 61.8%.

The lockdown and social distancing policy imposed due to the COVID-19 pandemic is likely to have a widespread impact on mental healthcare for both services themselves and the people accessing those services. Previous reports from the South London and Maudsley NHS Trust (SLaM; a large mental health service provider for 1.2m residents in South London) highlighted a shift to virtual contacts among those accessing community mental health and home treatment teams and an increase in deaths over the pandemic’s first wave. However, there is a need to understand this further for specific groups, including those diagnosed with a personality disorder who might have particular vulnerabilities. Taking advantage of the Clinical Record Interactive Search (CRIS) data resource with 24-hourly updates of electronic mental health records data, this paper describes daily caseloads and contact numbers (face-to-face and virtual) for individuals with personality disorders across community, specialist, crisis and inpatient services. The report focussed on the period 1st January to 31st May 2020. We also report on daily accepted and discharged trust referrals, total trust caseloads and daily inpatient admissions and discharges for individuals with personality disorders. In addition, daily deaths are described for all current and previous SLaM service users with personality disorder over this period. In summary, comparing periods before and after 16th March 2020 there was a shift from face-to-face contacts to virtual contacts across all teams. Liaison and Older Adult teams showed the largest drop in caseloads, whereas Early Intervention in Psychosis service caseloads remained the same. Reduced accepted referrals and inpatient admissions were observed and there was a 28% increase in average daily deaths in the period after 16th March, compared to the period 1st January to 15th March.

Background How neighbourhood characteristics affect the physical safety of people with mental illness is unclear. Aims To examine neighbourhood effects on physical victimisation towards people using mental health services. Method We developed and evaluated a machine-learning-derived free-text-based natural language processing (NLP) algorithm to ascertain clinical text referring to physical victimisation. This was applied to records on all patients attending National Health Service mental health services in Southeast London. Sociodemographic and clinical data, and diagnostic information on use of acute hospital care (from Hospital Episode Statistics, linked to Clinical Record Interactive Search), were collected in this group, defined as ‘cases’ and concurrently sampled controls. Multilevel logistic regression models estimated associations (odds ratios, ORs) between neighbourhood-level fragmentation, crime, income deprivation, and population density and physical victimisation. Results Based on a human-rated gold standard, the NLP algorithm had a positive predictive value of 0.92 and sensitivity of 0.98 for (clinically recorded) physical victimisation. A 1 s.d. increase in neighbourhood crime was accompanied by a 7% increase in odds of physical victimisation in women and an 13% increase in men (adjusted OR (aOR) for women: 1.07, 95% CI 1.01–1.14, aOR for men: 1.13, 95% CI 1.06–1.21, P for gender interaction, 0.218). Although small, adjusted associations for neighbourhood fragmentation appeared greater in magnitude for women (aOR = 1.05, 95% CI 1.01–1.11) than men, where this association was not statistically significant (aOR = 1.00, 95% CI 0.95–1.04, P for gender interaction, 0.096). Neighbourhood income deprivation was associated with victimisation in men and women with similar magnitudes of association. Conclusions Neighbourhood factors influencing safety, as well as individual characteristics including gender, may be relevant to understanding pathways to physical victimisation towards people with mental illness.

The lockdown and social distancing policy response to the COVID-19 pandemic in the UK has a potentially important impact on provision of mental healthcare; however, there has been relatively little quantification of this. Taking advantage of the Clinical Record Interactive Search (CRIS) data resource with 24-hourly updates of electronic mental health records data, this paper describes daily caseloads and contact numbers (face-to-face and virtual) for home treatment teams (HTTs) and working age adult community mental health teams (CMHTs) from 1st February to 15th May 2020 at the South London and Maudsley NHS Trust (SLaM), a large mental health service provider for 1.2m residents in south London. In addition daily deaths are described for all current and previous SLaM service users over this period and the same dates in 2019. In summary, comparing periods before and after 16th March 2020 the CMHT sector showed relatively stable caseloads and total contact numbers, but a substantial shift from face-to-face to virtual contacts, while HTTs showed the same changeover but reductions in caseloads and total contacts (although potentially an activity rise again during May). Number of deaths for the two months between 16th March and 15th May were 2.4-fold higher in 2020 than 2019, with 958 excess deaths.

Objective Mining the data contained within Electronic Health Records (EHRs) can potentially generate a greater understanding of medication effects in the real world, complementing what we know from Randomised control trials (RCTs). We Propose a text mining approach to detect adverse events and medication episodes from the clinical text to enhance our understanding of adverse effects related to Clozapine, the most effective antipsychotic drug for the management of treatment-resistant schizophrenia, but underutilised due to concerns over its side effects. Material and methods We used data from de-identified EHRs of three mental health trusts in the UK (>50 million documents, over 500,000 patients, 2835 of which were prescribed Clozapine). We explored the prevalence of 33 adverse effects by age, gender, ethnicity, smoking status and admission type three months before and after the patients started Clozapine treatment. Where possible, we compared the prevalence of adverse effects with those reported in the Side Effects Resource (SIDER). Results Sedation, fatigue, agitation, dizziness, hypersalivation, weight gain, tachycardia, headache, constipation and confusion were amongst the highest recorded Clozapine adverse effect in the three months following the start of treatment. Higher percentages of all adverse effects were found in the first month of Clozapine therapy. Using a significance level of (p< 0.05) our chi-square tests show a significant association between most of the ADRs and smoking status and hospital admission, and some in gender, ethnicity and age groups in all trusts hospitals. Later we combined the data from the three trusts hospitals to estimate the average effect of ADRs in each monthly interval. In gender and ethnicity, the results show significant association in 7 out of 33 ADRs, smoking status shows significant association in 21 out of 33 ADRs and hospital admission shows the significant association in 30 out of 33 ADRs. Conclusion A better understanding of how drugs work in the real world can complement clinical trials.

Background. Mood instability and sleep disturbance are common symptoms in people with mental illness. Both features are clinically important and associated with poorer illness trajectories. We compared clinical outcomes in people presenting to secondary mental health care with mood instability and/or sleep disturbance with outcomes in people without either mood instability or sleep disturbance. Methods. Data were from electronic health records of 31,391 patients ages 16–65 years presenting to secondary mental health services between 2008 and 2016. Mood instability and sleep disturbance were identified using natural language processing. Prevalence of mood instability and sleep disturbance were estimated at baseline. Incidence rate ratios were estimates for clinical outcomes including psychiatric diagnoses, prescribed medication, and hospitalization within 2-years of presentation in persons with mood instability and/or sleep disturbance compared to individuals without either symptom. Results. Mood instability was present in 9.58%, and sleep disturbance in 26.26% of patients within 1-month of presenting to secondary mental health services. Compared with individuals without either symptom, those with mood instability and sleep disturbance showed significantly increased incidence of prescription of any psychotropic medication (incidence rate ratios [IRR] = 7.04, 95% confidence intervals [CI] 6.53–7.59), and hospitalization (IRR = 5.32, 95% CI 5.32, 4.67–6.07) within 2-years of presentation. Incidence rates of most clinical outcomes were considerably increased among persons with both mood instability and sleep disturbance, relative to persons with only one symptom. Conclusions. Mood instability and sleep disturbance are present in a wide range of mental disorders, beyond those in which they are conventionally considered to be symptoms. They are associated with poor outcomes, particularly when they occur together. The poor prognosis associated with mood instability and sleep disorder may be, in part, because they are often treated as secondary symptoms. Mood instability and sleep disturbance need better recognition as clinical targets for treatment in their own right.

Obsessive–compulsive disorder (OCD) and autism spectrum disorders (ASD) commonly co-occur and are considered challenging to manage when they co-occur in youth. However, clinical characteristics and prognosis of this group remain poorly understood. This study examined the prevalence, clinical correlates and outcomes of paediatric OCD co-occurring with ASD (OCD + ASD) in a large clinical cohort. Data were extracted from electronic clinical records of young people aged 4–17 years who had attended a mental health trust in South London, United Kingdom. We identified young people with diagnoses of OCD + ASD (n = 335), OCD without ASD (n = 1010), and ASD without OCD (n = 6577). 25% of youth with OCD had a diagnosis of ASD, while 5% of those with ASD had a diagnosis of OCD. At diagnosis, youth with OCD + ASD had lower psychosocial functioning scores on the clinician-rated Child Global Assessment Scale (CGAS) compared to those with either OCD or ASD. Youth with OCD + ASD were equally likely to receive CBT compared to those with OCD but were more likely to be prescribed medication and use services for longer than either comparison group. Youth with OCD + ASD showed significant improvements in functioning (CGAS scores) after service utilisation but their gains were smaller than those with OCD. OCD + ASD commonly co-occur, conferring substantial impairment, although OCD may be underdiagnosed in youth with ASD. Young people with co-occurring OCD + ASD can make significant improvements in functioning with routine clinical care but are likely to remain more impaired than typically developing youth with OCD, indicating a need for longer-term support for these young people. Electronic supplementary material The online version of this article (10.1007/s00787-020-01478-8) contains supplementary material, which is available to authorized users.

Background The density of information in digital health records offers new potential opportunities for automated prediction of cost-relevant outcomes. Aims We investigated the extent to which routinely recorded data held in the electronic health record (EHR) predict priority service outcomes and whether natural language processing tools enhance the predictions. We evaluated three high priority outcomes: in-patient duration, readmission following in-patient care and high service cost after first presentation. Method We used data obtained from a clinical database derived from the EHR of a large mental healthcare provider within the UK. We combined structured data with text-derived data relating to diagnosis statements, medication and psychiatric symptomatology. Predictors of the three different clinical outcomes were modelled using logistic regression with performance evaluated against a validation set to derive areas under receiver operating characteristic curves. Results In validation samples, the full models (using all available data) achieved areas under receiver operating characteristic curves between 0.59 and 0.85 (in-patient duration 0.63, readmission 0.59, high service use 0.85). Adding natural language processing-derived data to the models increased the variance explained across all clinical scenarios (observed increase in r2 = 12–46%). Conclusions EHR data offer the potential to improve routine clinical predictions by utilising previously inaccessible data. Of our scenarios, prediction of high service use after initial presentation achieved the highest performance.

Objectives Creation of linked mental health, social and education records for research to support evidence-based practice for regional mental health services. Setting The Clinical Record Interactive Search (CRIS) system was used to extract personal identifiers who accessed psychiatric services between September 2007 and August 2013. Participants A clinical cohort of 35 509 children and young people (aged 4–17 years). Design Multiple government and ethical committees approved the link of clinical mental health service data to Department for Education (DfE) data on education and social care services. Under robust governance protocols, fuzzy and deterministic approaches were used by the DfE to match personal identifiers (names, date of birth and postcode) from National Pupil Database (NPD) and CRIS data sources. Outcome measures Risk factors for non-matching to NPD were identified, and the potential impact of non-match biases on International Statistical Classification of Diseases, 10th Revision (ICD-10) classifications of mental disorder, and persistent school absence (

Summary Background Crisis resolution and home treatment teams (CRTs) offer an alternative to hospital admission for patients undergoing mental health crises in the UK. Few studies have been done to examine predictors of relapse and readmission after contact with CRTs. Methods We used the Clinical Record Interactive Search to identify all patients receiving care from CRTs in two National Health Service (NHS) mental health trusts in London: Camden and Islington NHS Foundation Trust and South London and Maudsley NHS Foundation Trust. We used Cox regression models to examine rates and predictors of admission to acute mental health services within 1 year of contact with CRTs. Sex, age, ethnicity, marital status, social deprivation, severity of psychopathology, duration of index CRT episode, first contact with services, and diagnosis were extracted and examined as predictors of admission. Findings Between Jan 1, 2008, and Aug 31, 2014, 17 666 patients were treated by CRTs—8759 patients in the Camden and Islington trust and 8907 patients in the South London and Maudsley trust. 53·9 patients per 100 person-years (95% CI 52·1–55·8) in Camden and Islington and 51·3 patients per 100 person-years (95% CI 49·6–53·1) in South London and Maudsley were admitted to acute services within 1 year of seeing the CRT. In both cohorts, non-affective psychotic disorders (adjusted hazard ratio [HR] 1·25, 95% CI 1·09–1·44 in Camden and Islington; 1·27, 1·17–1·38 in South London and Maudsley) and age older than 65 years (1·18, 1·01–1·37 in Camden and Islington; 1·32, 1·12–1·56 in South London and Maudsley) were associated with increased risk of admission, whereas first contact with services (0·57, 0·52–0·62 in Camden and Islington; 0·69, 0·63–0·75 in South London and Maudsley), anxiety disorders (0·81, 0·69–0·96 in Camden and Islington; 0·77, 0·67–0·87 in South London and Maudsley), and longer index CRT episodes (adjusted HR per day 0·996, 0·994–0·998 in Camden and Islington; 0·989, 0·987–0·991 in South London and Maudsley) were associated with reduced risk of admission. Interpretation Past use of mental health services and a diagnosis of non-affective psychosis, which are markers of severity of mental illness, and older age, which is a marker of chronicity, are all risk factors for future relapse after interactions with CRTs. These findings might help clinicians and policy makers to offer more targeted and cost-effective services to reduce relapse rates. Funding None.

Objectives/AimsCatatonia is an important neuropsychiatric disorder with a high morbidity and mortality. However, due to a perception that it is very infrequent and because of the acuity of the patients, it has remained poorly studied and research has often been confined to small groups.We hypothesised that: catatonia would remain a significant clinical problem; catatonic patients would have a longer duration of admission and higher mortality; serum iron would be reduced, reflecting a systemic inflammatory response, and high rates of NMDA receptor antibody serum positivity would be observed.MethodsThis was a prospective cohort study that included patients in a large mental health trust who were diagnosed with catatonia between 2007 and 2016. We used the Clinical Records Interactive Search (CRIS) system hosted at the NIHR Maudsley Biomedical Research Centre to search the clinical records for patients with catatonia. An initial free-text search was refined by use of a natural language processing app. The results of the app were validated by three of the authors, who included patients in the analysis only if a clinician had made a diagnosis of catatonia and two or more items of the Bush-Francis Catatonia Screening Instrument were in evidence.Demographic, clinical and blood-based markers could then be extracted for these patients and compared, where relevant, to non-catatonic psychiatric patients.Results1,456 patients with catatonia (of whom 787 were psychiatric inpatients) and 37,456 psychiatric inpatient controls were identified. There was no evidence for a reduction in the rate of catatonia over time. Patients with catatonia were younger than the controls by 2.52 years (95% CI 1.30 to 3.73) and similar in gender composition. Patients with catatonia were more likely to be black (53.5% vs 24.5%, pConclusionsThis is the largest study of catatonia to date. There is evidence that catatonia is not dying out and confers high morbidity but without affecting mortality. The innate immune system does not seem to be activated, but NMDA receptor antibodies are present at higher rates than in psychiatric controls. We demonstrate that catatonia remains an important clinical problem and may be associated with neuroimmunological dysfunction.

ObjectivesLinkage of electronic health records (EHRs) to Hospital Episode Statistics (HES)-Office for National Statistics (ONS) mortality data has provided compelling evidence for lower life expectancy in people with severe mental illness. However, linkage error may underestimate these estimates. Using a clinical sample (n=265 300) of individuals accessing mental health services, we examined potential biases introduced through missed matching and examined the impact on the association between clinical disorders and mortality.SettingThe South London and Maudsley NHS Foundation Trust (SLaM) is a secondary mental healthcare provider in London. A deidentified version of SLaM’s EHR was available via the Clinical Record Interactive Search system linked to HES-ONS mortality records.ParticipantsRecords from SLaM for patients active between January 2006 and December 2016.Outcome measuresTwo sources of death data were available for SLaM participants: accurate and contemporaneous date of death via local batch tracing (gold standard) and date of death via linked HES-ONS mortality data. The effect of linkage error on mortality estimates was evaluated by comparing sociodemographic and clinical risk factor analyses using gold standard death data against HES-ONS mortality records.ResultsOf the total sample, 93.74% were successfully matched to HES-ONS records. We found a number of statistically significant administrative, sociodemographic and clinical differences between matched and unmatched records. Of note, schizophrenia diagnosis showed a significant association with higher mortality using gold standard data (OR 1.08; 95% CI 1.01 to 1.15; p=0.02) but not in HES-ONS data (OR 1.05; 95% CI 0.98 to 1.13; p=0.16). Otherwise, little change was found in the strength of associated risk factors and mortality after accounting for missed matching bias.ConclusionsDespite significant clinical and sociodemographic differences between matched and unmatched records, changes in mortality estimates were minimal. However, researchers and policy analysts using HES-ONS linked resources should be aware that administrative linkage processes can introduce error.

Objective: To determine the frequency of all-cause general hospital admissions for individuals with personality disorder (PD) in a large clinical population using linked secondary mental healthcare and hospitalisation data.Method: A retrospective cohort study, using anonymised electronic mental health records from South London and Maudsley NHS Foundation Trust (SLaM), linked to Hospital Episodes Statistics in England. People with PD aged 15 years or older, receiving care within SLaM between April 2007 and March 2013, were identified and compared to residents from the local catchment area. Standardised admission ratios (SARs) were calculated for all major categories of causes of general hospital admission for this defined group, with local residents in 2011 UK Census as the standard population.Results: For the 7,677 people identified with PD, SAR for all causes of admission was 2.75 (95% CI: 2.70, 2.81). Both men and women with PD had increased SARs across multiple ICD-10 categories, including circulatory, respiratory, digestive, nervous, and musculoskeletal system disorders, and endocrine, blood and infectious disorders. Sensitivity analysis (removing the impact of repeated admissions by same individual for same diagnosis in the same year) yielded similar findings.Conclusions: By comparison with members of the general population, individuals with a diagnosis of personality disorder are at significantly higher risk of hospital admission resulting from a wide range of physical health problems.

Summary Background Evidence regarding the mental health needs of trafficked people is limited; however, prevalence of depression and post-traumatic stress disorder is high among trafficked people who are in contact with shelter services. We aimed to investigate the sociodemographic and clinical characteristics of trafficked people with severe mental illness. Methods We did a historical cohort study of trafficked people in contact with secondary mental health services in South London, UK, between Jan 1, 2006, and July 31, 2012. We searched and retrieved comprehensive clinical electronic health records for over 200 000 patients from the Case Register Interactive Search database to identify trafficked patients. A matched cohort of non-trafficked adults was generated by simple computer-generated random selection of potential controls for each case within the parameters of matching criteria. We extracted data on sociodemographic and clinical characteristics and abuse history, and used multiple imputation to deal with missing data. We fitted logistic regression models to compare trafficked and non-trafficked patients. Findings We identified 133 trafficked patients, including 37 children. 78 (81%) of 96 adults and 25 (68%) of 37 children were female. 19 (51%) of 37 children were trafficked for sexual exploitation. Among both adults and children, the most commonly recorded diagnoses were post-traumatic stress disorder, severe stress, or adjustment disorder (27 adults [28%] and ten children [27%]) and affective disorders (33 adults [34%] and ten children [27%]). Records documented childhood physical or sexual abuse among trafficked adults (41 [43%]) and children (28 [76%]), and adulthood abuse among trafficked adults (58 [60%]). Trafficked adults were more likely to be compulsorily admitted as a psychiatric inpatient than non-trafficked adults (adjusted odds ratio 7·61, 95% CI 2·18–26·60; p=0·002) and had longer admissions (1·48, 1·01–2·15; p=0·045). No association was found between trafficking status and either adverse pathway into care (adjusted odds ratio 0·91, 95% CI 0·40–2·05; p=0·82) or substance misuse problems (0·55, 0·27–1·17; p=0·12). Interpretation Severe mental illness in trafficked people is associated with longer admissions and high levels of abuse before and after trafficking. Evidence is needed on the effectiveness of interventions to promote recovery for this vulnerable group. Funding Department of Health Policy Research Programme.

Background: Intellectual disability (ID) carries a high impact on need for care, health status, and premature mortality. Respiratory system diseases contribute a major part of mortality among people with ID, but remain under-investigated as consequent morbidities.Methods: Anonymised electronic mental health records from the South London and Maudsley Trust (SLaM) were linked to national acute medical care data. Using retrospective cohort and matched case-control study designs, adults with ID receiving SLaM care between 1 January 2008 and 31 March 2013 were identified and compared to local catchment residents for respiratory system disease admissions. Standardised admission ratios (SARs) were first calculated, followed by a comparison of duration of hospitalisation with respiratory system disease between people with ID and age- and gender-matched random counterparts modelled using linear regression. Finally, the risk of readmission for respiratory system disease was analysed using Cox models.Results: For the 3,138 adults with ID identified in SLaM, the SAR for respiratory system disease admissions was 4.02 (95% CI: 3.79, 4.26). Compared to adults without ID, duration of hospitalisation was significantly longer by 2.34 days (95% CI: 0.03, 4.64) and respiratory system disease readmission was significantly elevated (hazard ratio=1.35; 95% CI: 1.17, 1.56) after confounding adjustment.Conclusions: Respiratory system disease admissions in adults with ID are more frequent, of longer duration, and have a higher likelihood of recurring. Development and evaluation of potential interventions to the preventable causes of respiratory diseases should be prioritised.

Mental health diagnoses are largely based on clinical observation and symptom reports, rather than biological evidence. A biobank that collects biological samples should allow the identification of disease mechanisms and more effective treatments.

The UK government has recently recognised the need to improve mental health services in the country. Electronic health records provide a rich source of patient data which could help policymakers to better understand needs of the service users. The main objective of this study is to unveil statistics of diagnoses recorded in the Case Register of the South London and Maudsley NHS Foundation Trust, one of the largest mental health providers in the UK and Europe serving a source population of over 1.2 million people residing in south London. Based on over 500,000 diagnoses recorded in ICD10 codes for a cohort of approximately 200,000 mental health patients, we established frequency rate of each diagnosis (the ratio of the number of patients for whom a diagnosis has ever been recorded to the number of patients in the entire population who have made contact with mental disorders). We also investigated differences in diagnoses prevalence between subgroups of patients stratified by gender and ethnicity. The most common diagnoses in the considered population were (recurrent) depression (ICD10 codes F32-33;16.4% of patients), reaction to severe stress and adjustment disorders (F43;7.1%), mental/behavioural disorders due to use of alcohol (F10;6.9%), and schizophrenia (F20;5.6%). We also found many diagnoses which were more likely to be recorded in patients of a certain gender or ethnicity. For example, mood (affective) disorders (F31-F39); neurotic, stress-related and somatoform disorders (F40-F48, except F42); and eating disorders (F50) were more likely to be found in records of female patients, while males were more likely to be diagnosed with mental/behavioural disorders due to psychoactive substance use (F10-F19). Furthermore, mental/behavioural disorders due to use of alcohol and opioids were more likely to be recorded in patients of white ethnicity, and disorders due to use of cannabinoids in those of black ethnicity.

Background: Modeling trajectories of decline can help describe the variability in progression of cognitive impairment in dementia. Better characterisation of these trajectories has significant implications for understanding disease progression, trial design and care planning.Methods: Patients with at least three Mini-mental State Examination (MMSE) scores recorded in the South London and Maudsley NHS Foundation Trust Electronic Health Records, UK were selected (N = 3441) to form a retrospective cohort. Trajectories of cognitive decline were identified through latent class growth analysis of longitudinal MMSE scores. Demographics, Health of Nation Outcome Scales and medications were compared across trajectories identified.Results: Four of the six trajectories showed increased rate of decline with lower baseline MMSE. Two trajectories had similar initial MMSE scores but different rates of decline. In the faster declining trajectory of the two, a higher incidence of both behavioral problems and sertraline prescription were present.Conclusions: We find suggestive evidence for association of behavioral problems and sertraline prescription with rate of decline. Further work is needed to determine whether trajectories replicate in other datasets.

Aims and methodTo assess the usefulness of the electronic patient record, we used the search engine Clinical Record Interactive Search (CRIS) to scan all acute admissions during 2008 for possible substance use disorders. In addition, screening interviews were undertaken with 75 in-patients, and documentation in their files was compared with results of screening interviews.ResultsOf 839 acute admissions during 2008, 47% of males and 29% of females had reference to a substance misuse problem in their file. Documentation was unsystematic and inconsistent and mostly occurred in progress notes rather than in structured questionnaires. Screening interviews and manual review of files of 75 current in-patients confirmed that substance use disorders were common, but poorly documented.Clinical implicationsThe study highlights the power of search engines in scanning electronic clinical records, but also identified the limitations of unsystematic documentation in research and practice. Mental health staff were reluctant to diagnose or rate severity of substance misuse problems.

Objectives To examine links between clinical and other characteristics of people with Alzheimer9s disease living in the community, likelihood of care home or hospital admission, and associated costs. Design Observational data extracted from clinical records using natural language processing and Hospital Episode Statistics. Statistical analyses examined effects of cognition, physical health, mental health, sociodemographic factors and living circumstances on risk of admission to care home or hospital over 6 months and associated costs, adjusting for repeated observations. Setting Catchment area for South London and Maudsley National Health Service Foundation Trust, provider for 1.2 million people in Southeast London. Participants Every individual with diagnosis of Alzheimer9s disease seen and treated by mental health services in the catchment area, with at least one rating of cognition, not resident in care home at time of assessment (n=3075). Interventions Usual treatment. Main outcome measures Risk of admission to, and days spent in three settings during 6-month period following routine clinical assessment: care home, mental health inpatient care and general hospital inpatient care. Results Predictors of probability of care home or hospital admission and/or associated costs over 6 months include cognition, functional problems, agitation, depression, physical illness, previous hospitalisations, age, gender, ethnicity, living alone and having a partner. Patterns of association differed considerably by destination. Conclusions Most people with dementia prefer to remain in their own homes, and funding bodies see this as cheaper than institutionalisation. Better treatment in the community that reduces health and social care needs of Alzheimer9s patients would reduce admission rates. Living alone, poor living circumstances and functional problems all raise admission rates, and so major cuts in social care budgets increase the risk of high-cost admissions which older people do not want. Routinely collected data can be used to reveal local patterns of admission and costs.

Objectives: To investigate the role of risk assessment in predicting suicide in patients with schizophrenia spectrum disorders (SSDs) receiving secondary mental healthcare. We postulated that risk assessment plays a limited role in predicting suicide in these patients. Design: Retrospective case–control study. Setting: Anonymised electronic mental health record data from the South London and Maudsley National Health Service (NHS) Foundation Trust (SLaM) (London, UK) linked with national mortality data. Participants: In 242 227 SLaM service users up to 31 December 2013, 635 suicides were identified. 96 (15.1%) had a SSD diagnosis. Those who died before 1 January 2007 (n=25) were removed from the analyses. Thus, 71 participants with SSD who died from suicide over the study period (cases) were compared with 355 controls. Main outcome measure: Risk of suicide in relation to risk assessment ratings. Results: Cases were younger at first contact with services (mean±SD 34.5±12.6 vs 39.2±15.2) and with a higher preponderance of males (OR=2.07, 95% CI 1.18 to 3.65, p=0.01) than controls. Also, suicide occurred within 10 days after last contact with services in half of cases, with the most common suicide methods being hanging (14) and jumping (13). Cases were more likely to have the following ‘risk assessment’ items previously recorded: suicidal history (OR=4.42, 95% CI 2.01 to 9.65, p

Purpose: The South London and Maudsley National Health Service (NHS) Foundation Trust Biomedical Research Centre (SLaM BRC) Case Register and itsClinical Record Interactive Search (CRIS) application were developed in 2008, generating a research repository of real-time, anonymised, structured andopen-text data derived from the electronic health record system used by SLaM, a large mental healthcare provider in southeast London. In this paper, we updatethis register’s descriptive data, and describe the substantial expansion and extension of the data resource since its original development.Participants: Descriptive data were generated from the SLaM BRC Case Register on 31 December 2014. Currently, there are over 250 000 patient records accessed through CRIS.Findings to date: Since 2008, the most significant developments in the SLaM BRC Case Register have been the introduction of natural language processing toextract structured data from open-text fields, linkages to external sources of data, and the addition of a parallel relational database (Structured QueryLanguage) output. Natural language processing applications to date have brought in new and hitherto inaccessible data on cognitive function, education,social care receipt, smoking, diagnostic statements and pharmacotherapy. In addition, through external data linkages, large volumes of supplementary information have been accessed on mortality, hospital attendancesand cancer registrations.Future plans: Coupled with robust data security and governance structures, electronic health records provide potentially transformative information onmental disorders and outcomes in routine clinical care. The SLaM BRC Case Register continues to grow as a database, with approximately 20 000 new cases added each year, in addition to extension of follow-up for existing cases. Data linkages and natural language processing present important opportunities toenhance this type of research resource further, achieving both volume and depth of data. However, research projects still need to be carefully tailored, so that they take into account the nature and quality of the source information.

Background Human trafficking is a global crime and human rights violation. Although research has demonstrated a high prevalence of mental disorder among trafficked people and that trafficked people are in contact with mental health services, little is known about mental health professionals’ experiences of identifying and providing care for trafficked people. This study aimed to understand how people are identified as trafficked within mental health services and the challenges professionals experience in responding to trafficked people’s mental health needs. Method Qualitative study of electronic health records of trafficked people in contact with secondary mental health services in South East London, England. Comprehensive clinical electronic health records for over 200,000 patients in contact with secondary mental health services in South London were searched and retrieved to identify trafficked patients. Content analysis was used to establish how people were identified as trafficked, and thematic analysis was used to explore the challenges experienced in responding to mental health needs. Results The sample included 130 trafficked patients, 95 adults and 35 children. In 43 % (41/95) of adult cases and 63 % (22/35) child cases, mental health professionals were informed that their patient was a potential victim of trafficking by another service involved in their patient’s care. Cases were also identified through patients disclosing their experiences of exploitation and abuse. Key challenges faced by staff included social and legal instability, difficulties ascertaining history, patients’ lack of engagement, availability of services, and inter-agency working. Conclusions Training to increase awareness, encourage helpful responses, and inform staff about the available support options would help to ensure the mental health needs of trafficked people are met. Further research is needed to establish if these challenges are similar in other health settings. Electronic supplementary material The online version of this article (doi:10.1186/s12888-015-0679-3) contains supplementary material, which is available to authorized users.

Highlights • Junior psychiatrists notes were compared before and after the use of a template. • Template notes were rated as more thorough, organized, useful and comprehensible. • An audit revealed template notes contained more clinically relevant information., Objectives The mental state examination (MSE) provides crucial information for healthcare professionals in the assessment and treatment of psychiatric patients as well as potentially providing valuable data for mental health researchers accessing electronic health records (EHRs). We wished to establish if improvements could be achieved in the documenting of MSEs by junior doctors within a large United Kingdom mental health trust following the introduction of an EHR based semi-structured MSE assessment template (OPCRIT+). Methods First, three consultant psychiatrists using a modified version of the Physician Documentation Quality Instrument-9 (PDQI-9) blindly rated fifty MSEs written using OPCRIT+ and fifty normal MSEs written with no template. Second, we conducted an audit to compare the frequency with which individual components of the MSE were documented in the normal MSEs compared with the OPCRIT + MSEs. Results PDQI-9 ratings indicated that the OPCRIT + MSEs were more ‘Thorough’, ‘Organized’, ‘Useful’ and ‘Comprehensible’ as well as being of an overall higher quality than the normal MSEs. The audit identified that the normal MSEs contained fewer mentions of the individual components of ‘Thought content’, ‘Anxiety’ and ‘Cognition & Insight’. Conclusions These results indicate that a semi-structured assessment template significantly improves the quality of MSE recording by junior doctors within EHRs. Future work should focus on whether such improvements translate into better patient outcomes and have the ability to improve the quality of information available on EHRs to researchers.

Objectives Electronic healthcare records (EHRs) are a rich source of information, with huge potential for secondary research use. The aim of this study was to develop an application to identify instances of Adverse Drug Events (ADEs) from free text psychiatric EHRs. Methods We used the GATE Natural Language Processing (NLP) software to mine instances of ADEs from free text content within the Clinical Record Interactive Search (CRIS) system, a de-identified psychiatric case register developed at the South London and Maudsley NHS Foundation Trust, UK. The tool was built around a set of four movement disorders (extrapyramidal side effects [EPSEs]) related to antipsychotic therapy and rules were then generalised such that the tool could be applied to additional ADEs. We report the frequencies of recorded EPSEs in patients diagnosed with a Severe Mental Illness (SMI) and then report performance in identifying eight other unrelated ADEs. Results The tool identified EPSEs with >0.85 precision and >0.86 recall during testing. Akathisia was found to be the most prevalent EPSE overall and occurred in the Asian ethnic group with a frequency of 8.13%. The tool performed well when applied to most of the non-EPSEs but least well when applied to rare conditions such as myocarditis, a condition that appears frequently in the text as a side effect warning to patients. Conclusions The developed tool allows us to accurately identify instances of a potential ADE from psychiatric EHRs. As such, we were able to study the prevalence of ADEs within subgroups of patients stratified by SMI diagnosis, gender, age and ethnicity. In addition we demonstrated the generalisability of the application to other ADE types by producing a high precision rate on a non-EPSE related set of ADE containing documents. Availability The application can be found at http://git.brc.iop.kcl.ac.uk/rmallah/dystoniaml.

ObjectiveStandardised mortality ratios are twice the population average in the year following a mental health admission, yet there is a relative paucity of research on uptake of general medical care in psychiatric inpatients.MethodsA retrospective database analysis was performed to ascertain the frequency of acute medical care usage by psychiatric inpatients. Data was gathered through a static linkage between anonymised clinical records in a large UK Mental Health Provider and the national hospital activity database (Hospital Episode Statistics (HES)) over one year from 2010 to 2011.ResultsOver the year, 10.4% of the 8023 psychiatric admission episodes included at least one night in a general hospital during that psychiatric inpatient stay, while 12.0% of psychiatry admission episodes entailed an Emergency Department (ED) visit. Over the course of the full year, of the 4,674 people admitted to the Mental Health Provider at least once, 16.0% were admitted to a general hospital while registered as a mental health inpatient and 18.0% were seen in the Emergency Department (ED). Patients were simultaneously registered as occupying beds in both general and psychiatric hospitals for a total of 5163 bed days at a cost of £2.4 million over the year.ConclusionThis large population based linkage study indicates a high rate of general hospital utilisation by psychiatric inpatients in an independent mental health provider. The need for combined, flexible and practical approaches to the medical care of psychiatric inpatients is highlighted, to reduce unplanned care and provide treatment in the site best suited to the patient’s needs.

Objective Our primary objective was to identify cognitive behavioural therapy (CBT) delivery for people with psychosis (CBTp) using an automated method in a large electronic health record database. We also examined what proportion of service users with a diagnosis of psychosis were recorded as having received CBTp within their episode of care during defined time periods provided by early intervention or promoting recovery community services for people with psychosis, compared with published audits and whether demographic characteristics differentially predicted the receipt of CBTp. Methods Both free text using natural language processing (NLP) techniques and structured methods of identifying CBTp were combined and evaluated for positive predictive value (PPV) and sensitivity. Using inclusion criteria from two published audits, we identified anonymised cross-sectional samples of 2579 and 2308 service users respectively with a case note diagnosis of schizophrenia or psychosis for further analysis. Results The method achieved PPV of 95% and sensitivity of 96%. Using the National Audit of Schizophrenia 2 criteria, 34.6% service users were identified as ever having received at least one session and 26.4% at least two sessions of CBTp; these are higher percentages than previously reported by manual audit of a sample from the same trust that returned 20.0%. In the fully adjusted analysis, CBTp receipt was significantly (p

Background Lower levels of cognitive function have been found to be associated with higher mortality in older people, particularly in dementia, but the association in people with other mental disorders is still inconclusive. Methods and Findings Data were analysed from a large mental health case register serving a geographic catchment of 1.23 million residents, and associations were investigated between cognitive function measured by the Mini-Mental State Examination (MMSE) and survival in patients aged 65 years old and over. Cox regressions were carried out, adjusting for age, gender, psychiatric diagnosis, ethnicity, marital status, and area-level socioeconomic index. A total of 6,704 subjects were involved, including 3,368 of them having a dementia diagnosis and 3,336 of them with depression or other diagnoses. Descriptive outcomes by Kaplan-Meier curves showed significant differences between those with normal and impaired cognitive function (MMSE score

Progress in personalised psychiatry is dependent on researchers having access to systematic and accurately acquired symptom data across clinical diagnoses. We have developed a structured psychiatric assessment tool, OPCRIT+, that is being introduced into the electronic medical records system of the South London and Maudsley NHS Foundation Trust which can help to achieve this. In this report we examine the utility of the symptom data being collected with the tool. Cross-sectional mental state data from a mixed-diagnostic cohort of 876 inpatients was subjected to a principal components analysis (PCA). Six components, explaining 46% of the variance in recorded symptoms, were extracted. The components represented dimensions of mania, depression, positive symptoms, anxiety, negative symptoms and disorganization. As indicated by component scores, different clinical diagnoses demonstrated distinct symptom profiles characterized by wide-ranging levels of severity. When comparing the predictive value of symptoms against diagnosis for a variety of clinical outcome measures (e.g. 'Overactive, aggressive behaviour'), symptoms proved superior in five instances (R(2) range: 0.06-0.28) whereas diagnosis was best just once (R(2):0.25). This report demonstrates that symptom data being routinely gathered in an NHS trust, when documented on the appropriate tool, have considerable potential for onward use in a variety of clinical and research applications via representation as dimensions of psychopathology.

IntroductionMood instability is an important problem but has received relatively little research attention. Natural language processing (NLP) is a novel method, which can used to automatically extract clinical data from electronic health records (EHRs).AimsTo extract mood instability data from EHRs and investigate its impact on people with mental health disorders.MethodsData on mood instability were extracted using NLP from 27,704 adults receiving care from the South London and Maudsley NHS Foundation Trust (SLaM) for affective, personality or psychotic disorders. These data were used to investigate the association of mood instability with different mental disorders and with hospitalisation and treatment outcomes.ResultsMood instability was documented in 12.1% of people included in the study. It was most frequently documented in people with bipolar disorder (22.6%), but was also common in personality disorder (17.8%) and schizophrenia (15.5%). It was associated with a greater number of days spent in hospital (B coefficient 18.5, 95% CI 12.1–24.8), greater frequency of hospitalisation (incidence rate ratio 1.95, 1.75–2.17), and an increased likelihood of prescription of antipsychotics (2.03, 1.75–2.35).ConclusionsUsing NLP, it was possible to identify mood instability in a large number of people, which would otherwise not have been possible by manually reading clinical records. Mood instability occurs in a wide range of mental disorders. It is generally associated with poor clinical outcomes. These findings suggest that clinicians should screen for mood instability across all common mental health disorders. The data also highlight the utility of NLP for clinical research.Disclosure of interestThe authors have not supplied their declaration of competing interest.

Background Case registers have been used extensively in mental health research. Recent developments in electronic medical records, and in computer software to search and analyse these in anonymised format, have the potential to revolutionise this research tool. Methods We describe the development of the South London and Maudsley NHS Foundation Trust (SLAM) Biomedical Research Centre (BRC) Case Register Interactive Search tool (CRIS) which allows research-accessible datasets to be derived from SLAM, the largest provider of secondary mental healthcare in Europe. All clinical data, including free text, are available for analysis in the form of anonymised datasets. Development involved both the building of the system and setting in place the necessary security (with both functional and procedural elements). Results Descriptive data are presented for the Register database as of October 2008. The database at that point included 122,440 cases, 35,396 of whom were receiving active case management under the Care Programme Approach. In terms of gender and ethnicity, the database was reasonably representative of the source population. The most common assigned primary diagnoses were within the ICD mood disorders (n = 12,756) category followed by schizophrenia and related disorders (8158), substance misuse (7749), neuroses (7105) and organic disorders (6414). Conclusion The SLAM BRC Case Register represents a 'new generation' of this research design, built on a long-running system of fully electronic clinical records and allowing in-depth secondary analysis of both numerical, string and free text data, whilst preserving anonymity through technical and procedural safeguards.

Objectives: To assess the stage at cancer diagnosis and survival after cancer diagnosis among people served by secondary mental health services, compared with other local people.Setting: Using the anonymised linkage between a regional monopoly secondary mental health service provider in southeast London of four London boroughs, Croydon, Lambeth, Lewisham and Southwark, and a population-based cancer register, a historical cohort study was constructed.Participants: A total of 28 477 cancer cases aged 15+ years with stage of cancer recorded at diagnosis were identified. Among these, 2206 participants had been previously assessed or treated in secondary mental healthcare before their cancer diagnosis and 125 for severe mental illness (schizophrenia, schizoaffective or bipolar disorders).Primary and secondary outcome measures: Stage when cancer was diagnosed and all-cause mortality after cancer diagnosis among cancer cases registered in the geographical area of southeast London.Results: Comparisons between people with and without specific psychiatric diagnosis in the same residence area for risks of advanced stage of cancer at diagnosis and general survival after cancer diagnosed were analysed using logistic and Cox models. No associations were found between specific mental disorder diagnoses and beyond local spread of cancer at presentation. However, people with severe mental disorders, depression, dementia and substance use disorders had significantly worse survival after cancer diagnosis, independent of cancer stage at diagnosis and other potential confounders.Conclusions: Previous findings of associations between mental disorders and cancer mortality are more likely to be accounted for by differences in survival after cancer diagnosis rather than by delayed diagnosis.

Introduction A recent systematic review found a high prevalence of violence and mental distress among women trafficked for sexual exploitation; no data were identified for trafficked men and children. Objectives To describe the clinical characteristics of trafficked people in contact with a large inner city mental health service compared with a non-trafficked cohort. Aims To investigate whether, compared with a non-trafficked cohort, trafficked people would be significantly more likely to have co-morbid disorders and have significantly smaller improvements in functioning at the end of an episode of care. Methods Study population: mental health service users who had been trafficked for exploitation and a non-trafficked service user cohort matched for gender and age. Data source: The South London and Maudsley NHS Trust (SLaM) Biomedical Research Centre Case Register Interactive Search (CRIS) database of anonymised full patient records (2006–2012). Results We identified case records of 135 people who had been trafficked. 104 (77%) were female; age at first SLaM contact ranged from 8 to 49 years (mean 23.6, SD 8.0). 38 (28%) of the trafficked service users received psychiatric care from an emergency department. Depression (28.1%, n = 38), PTSD (19.3%, n = 26), non-affective psychoses (12.6%, n = 17) were the most frequently recorded diagnoses among trafficked service users. Further analysis is in progress and scheduled for completion by March 2013. Conclusions Significant numbers of trafficked people were seen in an inner-city mental health service; services therefore need to understand their complex needs.

Background Serious mental illness can affect many aspects of an individual’s ability to function in daily life. The aim of this investigation was to determine if the environmental and functional status of people with serious mental illness contribute to the high mortality risk observed in this patient group. Methods We identified cases of schizophrenia, schizoaffective and bipolar disorder aged ≥15 years in a large secondary mental healthcare case register linked to national mortality tracing. We modelled the effect of activities of daily living (ADLs), living conditions, occupational and recreational activities and relationship factors (Health of the Nation Outcome Scale [HoNOS] subscales) on all-cause mortality over a 4-year observation period (2007–10) using Cox regression. Results We identified 6,880 SMI cases (242 deaths) in the observation period. ADL impairment was associated with an increased risk of all-cause mortality (adjusted HR 1.9; 95% CI 1.3–2.8; p = 0.001, p for trend across ADL categories = 0.001) after controlling for a broad range of covariates (including demographic factors, physical health, mental health symptoms and behaviours, socio-economic status and mental health service contact). No associations were found for the other three exposures. Stratification by age indicated that ADLs were most strongly associated with mortality in the youngest (15 to

Introduction Longitudinal cognitive change before and after acetyl cholinesterase inhibitor (AChEI) treatment initiation in Alzheimer's disease has never been described previously in a representative clinical population. Objectives To model longitudinal changes in cognitive function for before and after AChEI prescription. Aims To further investigate differences in response by cognitive function at treatment initiation. Method A retrospective longitudinal analysis was carried out of all 1843 patients from the South London and Maudsley NHS Foundation Trust (a large mental health provider to a catchment population of approximately 1.2 m) who were prescribed AChEIs between 2003–10 and had a minimum of one MMSE score within 1 year before treatment initiation and one MMSE score within 3 years after this. Manually extracted MMSE scores were analyzed over this period using three-piece linear mixed models. Results Rates of MMSE change were −1.9 (95% CI −2.3,−1.4) in the year before treatment initiation, +1.3 (0.9,1.7) in the 6 months after treatment initiation, and −2.4 (−2.6,−2.3) from 6 months to 3 years. The difference between pre-treatment and 6-month-post-treatment slopes was −0.6 (−1.8,0.6) at baseline (treatment initiation) MMSE of 25 or over, +2.7 (1.7,3.7) at MMSE 21–24, and +4.6 (3.6,5.7) at MMSE 10–20. Conclusion In this naturalistic sample, a clear cognitive response to AChEI treatment was observed over the first six months followed by an unchanged decline. Response was substantially higher for patients with lower MMSE scores at treatment initiation.

PurposeLinked maternity, neonatal and maternal mental health records were created to support research into the early life origins of physical and mental health, in mothers and children. The Early Life Cross Linkage in Research (eLIXIR) Partnership was developed in 2018, generating a repository of real-time, pseudonymised, structured data derived from the electronic health record systems of two acute and one Mental Health Care National Health Service (NHS) Provider in South London. We present early descriptive data for the linkage database and the robust data security and governance structures, and describe the intended expansion of the database from its original development. Additionally, we report details of the accompanying eLIXIR Research Tissue Bank of maternal and neonatal blood samples.ParticipantsDescriptive data were generated from the eLIXIR database from 1 October 2018 to 30 June 2019. Over 17 000 electronic patient records were included.Findings to date10 207 women accessed antenatal care from the 2 NHS maternity services, with 8405 deliveries (8772 infants). This diverse, inner-city maternity service population was born in over 170 countries with an ethnic profile of 46.1% white, 19.1% black, 7.0% Asian, 4.1% mixed and 4.1% other. Of the 10 207 women, 11.6% had a clinical record in mental health services with 3.0% being treated during their pregnancy. This first data extract included 947 infants treated in the neonatal intensive care unit, of whom 19.1% were postnatal transfers from external healthcare providers.Future plansElectronic health records provide potentially transformative information for life course research, integrating physical and mental health disorders and outcomes in routine clinical care. The eLIXIR database will grow by ~14 000 new maternity cases annually, in addition to providing child follow-up data. Additional datasets will supplement the current linkage from other local and national resources, including primary care and hospital inpatient data for mothers and their children.