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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...

Background Risk estimation models integrated into Electronic Health Records (EHRs) can deliver innovative approaches in psychiatry, but clinicians' endorsement and their real-world usability are unknown. This study aimed to investigate the real-world feasibility of implementing an individualised, transdiagnostic risk calculator to automatically screen EHRs and detect individuals at-risk for psychosis. Methods Feasibility implementation study encompassing an in-vitro phase (March 2018 to May 2018) and in-vivo phase (May 2018 to April 2019). The in-vitro phase addressed implementation barriers and embedded the risk calculator (predictors: age, gender, ethnicity, index cluster diagnosis, age*gender) into the local EHR. The in-vivo phase investigated the real-world feasibility of screening individuals accessing secondary mental healthcare at the South London and Maudsley NHS Trust. The primary outcome was adherence of clinicians to automatic EHR screening, defined by the proportion of clinicians who responded to alerts from the risk calculator, over those contacted. Results In-vitro phase: implementation barriers were identified/overcome with clinician and service user engagement, and the calculator was successfully integrated into the local EHR through the CogStack platform. In-vivo phase: 3722 individuals were automatically screened and 115 were detected. Clinician adherence was 74% without outreach and 85% with outreach. One-third of clinicians responded to the first email (37.1%) or phone calls (33.7%). Among those detected, cumulative risk of developing psychosis was 12% at six-month follow-up. Conclusion This is the first implementation study suggesting that combining precision psychiatry and EHR methods to improve detection of individuals with emerging psychosis is feasible. Future psychiatric implementation research is urgently needed.

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.

ObjectivesIntroduced in the UK in 2010, the fit note was designed to address the problem of long-term sickness absence. We explored (1) associations between demographic variables and fit note receipt, ‘maybe fit’ use and long-term conditions, (2) whether individuals with long-term conditions receive more fit notes and are more likely to have the ‘maybe fit’ option selected and (3) whether long-term conditions explained associations between demographic variables and fit note receipt.MethodsData were extracted from Lambeth DataNet, a database containing electronic medical records of all 45 general practitioner (GP) practices within the borough of Lambeth. Individual-level anonymised data on GP consultations, prescriptions, Quality and Outcomes Framework diagnostic data and demographic information were analysed using survival analysis.ResultsIn a sample of 326 415 people, 41 502 (12.7%) received a fit note. We found substantial differences in fit note receipt by gender, age, ethnicity and area-level deprivation. Chronic pain (HR 3.7 (95% CI 3.3 to 4.0)) and depression (HR 3.4 (95% CI 3.3 to 3.6)) had the highest rates for first fit note receipt. ‘Maybe fit’ recommendations were used least often in patients with epilepsy and serious mental illness. The presence of long-term conditions did not explain associations between demographic variables and fit note use.ConclusionsFor the first time, we show the relationships between fit note use and long-term conditions using individual-level primary care data from south London. Further research is required in order to evaluate this relatively new policy and to understand the needs of the population it was designed to support.

BackgroundCatatonia, a severe neuropsychiatric syndrome, has few studies of sufficient scale to clarify its epidemiology or pathophysiology. We aimed to characterise demographic associations, peripheral inflammatory markers and outcome of catatonia.MethodsElectronic healthcare records were searched for validated clinical diagnoses of catatonia. In a case–control study, demographics and inflammatory markers were compared in psychiatric inpatients with and without catatonia. In a cohort study, the two groups were compared in terms of their duration of admission and mortality.ResultsWe identified 1456 patients with catatonia (of whom 25.1% had two or more episodes) and 24 956 psychiatric inpatients without catatonia. Incidence was 10.6 episodes of catatonia per 100 000 person-years. Patients with and without catatonia were similar in sex, younger and more likely to be of Black ethnicity. Serum iron was reduced in patients with catatonia [11.6 v. 14.2 μmol/L, odds ratio (OR) 0.65 (95% confidence interval (CI) 0.45–0.95), p = 0.03] and creatine kinase was raised [2545 v. 459 IU/L, OR 1.53 (95% CI 1.29–1.81), p < 0.001], but there was no difference in C-reactive protein or white cell count. N-Methyl-d-aspartate receptor antibodies were significantly associated with catatonia, but there were small numbers of positive results. Duration of hospitalisation was greater in the catatonia group (median: 43 v. 25 days), but there was no difference in mortality after adjustment.ConclusionsIn the largest clinical study of catatonia, we found catatonia occurred in approximately 1 per 10 000 person-years. Evidence for a proinflammatory state was mixed. Catatonia was associated with prolonged inpatient admission but not with increased mortality.

BackgroundGrowing evidence suggests that air pollution exposure may adversely affect the brain and increase risk for psychiatric disorders such as schizophrenia and depression. However, little is known about the potential role of air pollution in severity and relapse following illness onset.AimsTo examine the longitudinal association between residential air pollution exposure and mental health service use (an indicator of illness severity and relapse) among individuals with first presentations of psychotic and mood disorders.MethodWe identified individuals aged ≥15 years who had first contact with the South London and Maudsley NHS Foundation Trust for psychotic and mood disorders in 2008–2012 (n = 13 887). High-resolution (20 × 20 m) estimates of nitrogen dioxide (NO2), nitrogen oxides (NOx) and particulate matter (PM2.5 and PM10) levels in ambient air were linked to residential addresses. In-patient days and community mental health service (CMHS) events were recorded over 1-year and 7-year follow-up periods.ResultsFollowing covariate adjustment, interquartile range increases in NO2, NOx and PM2.5 were associated with 18% (95% CI 5–34%), 18% (95% CI 5–34%) and 11% (95% CI 3–19%) increased risk for in-patient days after 1 year. Similarly, interquartile range increases in NO2, NOx, PM2.5 and PM10 were associated with 32% (95% CI 25–38%), 31% (95% CI 24–37%), 7% (95% CI 4–11%) and 9% (95% CI 5–14%) increased risk for CMHS events after 1 year. Associations persisted after 7 years.ConclusionsResidential air pollution exposure is associated with increased mental health service use among people recently diagnosed with psychotic and mood disorders. Assuming causality, interventions to reduce air pollution exposure could improve mental health prognoses and reduce healthcare costs.

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.

Background People with schizophrenia have shortened lives. This excess mortality seems to be related to physical health conditions that may be amenable to better primary and secondary prevention. Better continuity of care may enhance such interventions as well as help prevent death by self-injury. Aims We set out to examine the relationship between the continuity of care of patients with schizophrenia, their mortality and cause of death. Method Pseudoanonymised community data from 5551 people with schizophrenia presenting over 11 years were examined for changes in continuity of care using the numbers of community teams caring for them and the Modified Modified Continuity Index. These and demographic variables were related to death certifications of physical illness from the Office of National Statistics and mortal self-injury from clinical data. Data were analysed using generalised estimating equations. Results We found no independent relationship between levels of continuity of care and overall mortality. However, lower levels of relationship continuity were significantly and independently related to death by self-injury. Conclusions We found no evidence that continuity of care is important in the prevention of physical causes of death in schizophrenia. However, there is evidence that declining relationship continuity of care has an independent effect on deaths as a result of self-injury. We suggest that there should be more attention focused on the improvement of continuity of care for these patients.

OBJECTIVES Evidence in mouse models has found that the antidepressant trazodone may be protective against neurodegeneration. We therefore aimed to compare cognitive decline of people with dementia taking trazodone with those taking other antidepressants. METHODS Three identical naturalistic cohort studies using UK clinical registers. We included all people with dementia assessed during 2008-16 who were recorded taking trazodone, citalopram or mirtazapine for at least 6 weeks. Linear mixed models examined age, time and sex-adjusted Mini-mental state examination (MMSE) change in people with all-cause dementia taking trazodone compared with those taking citalopram and mirtazapine. In secondary analyses, we examined those with non-vascular dementia; mild dementia; and adjusted results for neuropsychiatric symptoms. We combined results from the three study sites using random-effects meta-analysis. RESULTS We included 2,199 people with dementia, including 406 taking trazodone, with mean 2.2 years follow-up. There was no difference in adjusted cognitive decline in people with all-cause or non-vascular dementia taking trazodone, citalopram or mirtazapine in any of the three study sites. When data from the three sites were combined in meta-analysis, we found greater mean MMSE decline in people with all-cause dementia taking trazodone compared to those taking citalopram (0·26 points per successive MMSE measurement, 95% CI 0·03-0·49; p = 0·03). Results in sensitivity analyses were consistent with primary analyses. CONCLUSIONS There was no evidence of cognitive benefit from trazodone compared to other antidepressants in people with dementia in three naturalistic cohort studies. Despite preclinical evidence, trazodone should not be advocated for cognition in dementia.

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.

Background Gender disparities in treatment are apparent across many areas of healthcare. There has been little research into whether clozapine prescription, the first-line treatment for treatment-resistant schizophrenia (TRS), is affected by patient gender. Methods This retrospective cohort study identified 2244 patients with TRS within the South London and Maudsley NHS Trust, by using a bespoke method validated against a gold-standard, manually coded, dataset of TRS cases. The outcome and exposures were identified from the free-text using natural language processing applications (including machine learning and rules-based approaches) and from information entered in structured fields. Multivariable logistic regression was carried out to calculate the odds ratios for clozapine prescription according to patients' gender, and adjusting for numerous potential confounders including sociodemographic, clinical (e.g., psychiatric comorbidities and substance use), neutropenia, functional factors (e.g., problems with occupation), and clinical monitoring. Results Clozapine was prescribed to 77% of the women and 85% of the men with TRS. Women had reduced odds of being prescribed clozapine as compared to men after adjusting for all factors included in the present study (adjusted OR: 0.66; 95% CI 0.44–0.97; p = 0.037). Conclusion Women with TRS are less likely to be prescribed clozapine than men with TRS, even when considering the effects of multiple clinical and functional factors. This finding suggests there could be gender bias in clozapine prescription, which carries ramifications for the relatively poorer care of women with TRS regarding many outcomes such as increased hospitalisation, mortality, and poorer quality of life.

The COVID-19 pandemic is likely to have had a particularly high impact on the health and wellbeing of people with pre-existing mental disorders. This may include higher than expected mortality rates due to severe infections themselves, due to other comorbidities, or through increased suicide rates during lockdown. However, there has been very little published information to date on causes of death in mental health service users. Taking advantage of a large mental healthcare database linked to death registrations, we describe numbers of deaths within specific underlying-cause-of-death groups for the period from 1st March to 30th June in 2020 and compare these with the same four-month periods in 2015-2019. In past and current service users, there were 2561 deaths in March-June 2020, compared to an average of 1452 for the same months in 2015-19: an excess of 1109. The 708 deaths with COVID-19 as the underlying cause in 2020 accounted for 63.8% of that excess. The remaining excess was accounted for by unnatural/unexplained deaths and by deaths recorded as due to neurodegenerative conditions, with no excess in those attributed to cancer, circulatory disorders, digestive disorders, respiratory disorders, or other disease codes. Of 295 unexplained deaths in 2020 with missing data on cause, 162 (54.9%) were awaiting a formal death notice (i.e. the group that included deaths awaiting a coroner’s inquest) – an excess of 129 compared to the average of previous years, accounting for 11.6% of the excess in total deaths.

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 (CRIS) was used to examine de-identified electronic health records (EHRs) 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 (LOESS).ParticipantsAll patients receiving care from SLaM between 7th January 2019 and 20th September 2020 (around 37,500 patients per week).Outcome measuresThe number of clinical contacts (in-person, remote or non-attended) with mental healthcare professionals per weekPrescribing of antipsychotic and mood stabiliser medications per weekResultsFollowing the onset of the pandemic, the frequency of in-person contacts was significantly reduced compared to 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).

Background Using novel data mining methods such as natural language processing (NLP) on electronic health records (EHRs) for screening and detecting individuals at risk for psychosis. Method The study included all patients receiving a first index diagnosis of nonorganic and nonpsychotic mental disorder within the South London and Maudsley (SLaM) NHS Foundation Trust between January 1, 2008, and July 28, 2018. Least Absolute Shrinkage and Selection Operator (LASSO)-regularized Cox regression was used to refine and externally validate a refined version of a five-item individualized, transdiagnostic, clinically based risk calculator previously developed (Harrell’s C = 0.79) and piloted for implementation. The refined version included 14 additional NLP-predictors: tearfulness, poor appetite, weight loss, insomnia, cannabis, cocaine, guilt, irritability, delusions, hopelessness, disturbed sleep, poor insight, agitation, and paranoia. Results A total of 92 151 patients with a first index diagnosis of nonorganic and nonpsychotic mental disorder within the SLaM Trust were included in the derivation (n = 28 297) or external validation (n = 63 854) data sets. Mean age was 33.6 years, 50.7% were women, and 67.0% were of white race/ethnicity. Mean follow-up was 1590 days. The overall 6-year risk of psychosis in secondary mental health care was 3.4 (95% CI, 3.3–3.6). External validation indicated strong performance on unseen data (Harrell’s C 0.85, 95% CI 0.84–0.86), an increase of 0.06 from the original model. Conclusions Using NLP on EHRs can considerably enhance the prognostic accuracy of psychosis risk calculators. This can help identify patients at risk of psychosis who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes.

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.

BackgroundResearch on sickness absence has typically focussed on single diagnoses, despite increasing recognition that long-term health conditions are highly multimorbid and clusters comprising coexisting mental and physical conditions are associated with poorer clinical and functional outcomes. The digitisation of sickness certification in the UK offers an opportunity to address sickness absence in a large primary care population.MethodsLambeth Datanet is a primary care database which collects individual-level data on general practitioner consultations, prescriptions, Quality and Outcomes Framework diagnostic data, sickness certification (fit note receipt) and demographic information (including age, gender, self-identified ethnicity, and truncated postcode). We analysed 326 415 people's records covering a 40-month period from January 2014 to April 2017.ResultsWe found significant variation in multimorbidity by demographic variables, most notably by self-defined ethnicity. Multimorbid health conditions were associated with increased fit note receipt. Comorbid depression had the largest impact on first fit note receipt, more than any other comorbid diagnoses. Highest rates of first fit note receipt after adjustment for demographics were for comorbid epilepsy and rheumatoid arthritis (HR 4.69; 95% CI 1.73–12.68), followed by epilepsy and depression (HR 4.19; 95% CI 3.60–4.87), chronic pain and depression (HR 4.14; 95% CI 3.69–4.65), cardiac condition and depression (HR 4.08; 95% CI 3.36–4.95).ConclusionsOur results show striking variation in multimorbid conditions by gender, deprivation and ethnicity, and highlight the importance of multimorbidity, in particular comorbid depression, as a leading cause of disability among working-age adults.

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 policy response to the COVID-19 pandemic in the UK has a potentially important impact on provision of mental healthcare with uncertain consequences over the 12 months ahead. Past activity may provide a means to predict future demand. Taking advantage of the Clinical Record Interactive Search (CRIS) data resource at the South London and Maudsley NHS Trust (SLaM; a large mental health service provider for 1.2m residents in south London), we carried out a range of descriptive analyses to inform the Trust on patient groups who might be most likely to require inpatient and home treatment team (HTT) crisis care. We considered the 12 months following UK COVID-19 lockdown policy on 16th March, drawing on comparable findings from previous years, and quantified levels of change in service delivery to those most likely to receive crisis care. For 12-month crisis days from 16th March in 2015-19, we found that most (over 80%) were accounted for by inpatient care (rather than HTT), most (around 75%) were used by patients who were current or recent Trust patients at the commencement of follow-up, and highest numbers were used by patients with a previously recorded schizophreniform disorder diagnosis. For current/recent patients on 16th March there had been substantial reductions in use of inpatient care in the following 31 days in 2020, more than previous years; changes in total non-inpatient contact numbers did not differ in 2020 compared to previous years, although there had been a marked switch from face-to-face to virtual contacts.

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.

BackgroundRemote consultation technology has been rapidly adopted due to the COVID-19 pandemic. However, some healthcare settings have faced barriers in implementation. We present a study to investigate changes in rates of remote consultation during the pandemic using a large electronic health record (EHR) dataset.MethodsThe Clinical Record Interactive Search tool (CRIS) was used to examine de-identified EHR data of people receiving mental healthcare in South London, UK. Data from around 37,500 patients were analysed for each week from 7th January 2019 and 20th September 2020 using linear regression and locally estimated scatterplot smoothing (LOESS) to investigate changes in the number of clinical contacts (in-person, remote or non-attended) with mental healthcare professionals and prescribing of antipsychotics and mood stabilisers. The data are presented in an interactive dashboard: http://rpatel.co.uk/TelepsychiatryDashboard.ResultsThe frequency of in-person contacts was substantially reduced following the onset of the pandemic (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 prescribing. Further work is needed to support older patients in accessing remote mental healthcare.DisclosureAll authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: RS has received funding from Janssen, GSK and Takeda outside the submitted work. RP has received funding from Janssen, Induction Healthcare and H

BACKGROUND People with mental disorders and intellectual disabilities experience excess mortality compared with the general population. The impact of COVID-19 on exacerbating this, and in widening ethnic inequalities, is unclear. METHODS Prospective data (N=167,122) from a large mental healthcare provider in London, UK, with deaths from 2019 to 2020, used to assess age- and gender-standardised mortality ratios (SMRs) across nine psychiatric conditions (schizophrenia-spectrum disorders, affective disorders, somatoform/ neurotic disorders, personality disorders, learning disabilities, eating disorders, substance use disorders, pervasive developmental disorders, dementia) and by ethnicity. FINDINGS Prior to the World Health Organization (WHO) declaring COVID-19 a public health emergency on 30th January 2020, all-cause SMRs across all psychiatric cohorts were more than double the general population. By the second quarter of 2020, when the UK experienced substantial peaks in COVID-19 deaths, all-cause SMRs increased further, with COVID-19 SMRs elevated across all conditions (notably: learning disabilities: SMR: 9.24 (95% CI: 5.98-13.64), pervasive developmental disorders: 5.01 (95% CI: 2.40-9.20), eating disorders: 4.81 (95% CI: 1.56-11.22), schizophrenia-spectrum disorders: 3.26 (95% CI: 2.55-4.10), dementia: 3.82 (95% CI: 3.42, 4.25) personality disorders 4.58 (95% CI: 3.09-6.53)). Deaths from other causes remained at least double the population average over the whole year. Increased SMRs were similar across ethnic groups. INTERPRETATION People with mental disorders and intellectual disabilities were at a greater risk of deaths relative to the general population before, during and after the first peak of COVID-19 deaths, with similar risks by ethnicity. Mortality from non-COVID-19/ other causes was elevated before/ during the pandemic, with higher COVID-19 mortality during the pandemic. FUNDING ESRC (JD, CM), NIHR (JD, RS, MH), Health Foundation (JD), GSK, Janssen, Takeda (RS).

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.