Severe mental illness (SMI), which includes schizophrenia, schizoaffective disorder and bipolar disorder, has profound health impacts, even in the elderly.

To evaluate relative risk of hospital admission and length of hospital stay for physical illness in elders with SMI.

To construct a population-based retrospective cohort observed from April 2007 to March 2016, data from a case registry with full but de-identified electronic health records were retrieved for patients of the South London and Maudsley NHS Foundation Trust, the single secondary mental healthcare service provider in south-east London. We compared participants with SMI aged >60 years old with the general population of the same age and residing in the same areas through data linkage by age-, sex- and fiscal-year-standardised admission ratios (SARs) for primary diagnoses at hospital discharge. Furthermore, we compared the duration of hospital stay with an age-, sex- and cause-of-admission-matched random group by linear regression for major causes of admission.

In total, records for 4175 older people with SMI were obtained, relating to 10 342 admission episodes, showing an overall SAR for all physical illnesses of 5.15 (95% CI: 5.05, 5.25). Among the top causes of admission, SARs ranged from 3.87 for circulatory system disorders (ICD-10 codes: I00–I99) to 6.99 for genitourinary system or urinary conditions (N00–N39). Specifically, the diagnostic group of ‘symptoms, signs and findings, not elsewhere classified’ (R00–R99) had an elevated SAR of 6.56 (95% CI: 6.22, 6.90). Elders with SMI also had significantly longer hospital stays than their counterparts in the general population, especially for digestive system illnesses (K00–K93), after adjusting for confounding.

Poorer overall physical health and specific patterns were identified in elders with SMI.

Studies show ethnic inequalities in rates of involuntary admission and types of clinical care (such as psychological therapies). However, few studies have investigated if there is a relationship between clinical care practices and ethnic inequalities in involuntary admission.

This study investigated the impact of ethnicity and clinical care on involuntary admission and the potential mediation effects of prior clinical care.

In this retrospective cohort study, we used data from the electronic records of the South London and Maudsley NHS Foundation Trust and identified patients with a first hospital admission between January 2008 and May 2021. Logistic regression and mediation analyses were used to investigate the association between ethnicity and involuntary admission, and whether clinical care, in the 12 months preceding admission, mediates the association.

Compared with White British people, higher odds of involuntary admission were observed among 10 of 14 minority ethnic groups; with more than twice the odds observed among people of Asian Chinese, of Asian Bangladeshi and of any Black background. There were some ethnic differences in clinical care prior to admission, but these had a minimal impact on the inequalities in involuntary admission. More out-patient appointments and home treatment were associated with higher odds of involuntary admission, whereas psychological therapies and having a care plan were associated with reduced odds of involuntary admission.

Ethnic inequalities in involuntary admission persist after accounting for potential mediating effects of several types and frequencies of clinical care. Promoting access to psychological therapies and ensuring that care plans are in place may reduce involuntary admissions.

Although no drugs are licensed for the treatment of personality disorder, pharmacological treatment in clinical practice remains common.

This study aimed to estimate the prevalence of psychotropic drug use and associations with psychological service use among people with personality disorder.

Using data from a large, anonymised mental healthcare database, we identified all adult patients with a diagnosis of personality disorder and ascertained psychotropic medication use between 1 August 2015 and 1 February 2016. Multivariable logistic regression models were constructed, adjusting for sociodemographic, clinical and service use factors, to examine the association between psychological services use and psychotropic medication prescribing.

Of 3366 identified patients, 2029 (60.3%) were prescribed some form of psychotropic medication. Patients using psychological services were significantly less likely to be prescribed psychotropic medication (adjusted odds ratio 0.48, 95% CI 0.39–0.59, P<0.001) such as antipsychotics, benzodiazepines and antidepressants. This effect was maintained following several sensitivity analyses. We found no difference in the risk for mood stabiliser (adjusted odds ratio 0.79, 95% CI 0.57–1.10, P = 0.169) and multi-class psychotropic use (adjusted odds ratio 0.80, 95% CI 0.60–1.07, P = 0.133) between patients who did and did not use psychological services.

Psychotropic medication prescribing is common in patients with personality disorder, but significantly less likely in those who have used psychological services. This does not appear to be explained by differences in demographic, clinical and service use characteristics. There is a need to develop clear prescribing guidelines and conduct research in clinical settings to examine medication effectiveness for this population.

Clozapine, an antipsychotic with unique efficacy in treatment-resistant psychosis, is associated with increased susceptibility to infection, including pneumonia.

To investigate associations between clozapine treatment and increased risk of COVID-19 infection in patients with schizophrenia-spectrum disorders who are receiving antipsychotic medications in a geographically defined population in London, UK.

Using information from South London and Maudsley NHS Foundation Trust (SLAM) clinical records, via the Clinical Record Interactive Search system, we identified 6309 individuals who had an ICD-10 diagnosis of schizophrenia-spectrum disorders and were taking antipsychotics at the time of the COVID-19 pandemic onset in the UK. People who were on clozapine treatment were compared with those on any other antipsychotic treatment for risk of contracting COVID-19 between 1 March and 18 May 2020. We tested associations between clozapine treatment and COVID-19 infection, adjusting for gender, age, ethnicity, body mass index (BMI), smoking status and SLAM service use.

Of 6309 participants, 102 tested positive for COVID-19. Individuals who were on clozapine had increased risk of COVID-19 infection compared with those who were on other antipsychotic medication (unadjusted hazard ratio HR = 2.62, 95% CI 1.73–3.96), which was attenuated after adjusting for potential confounders, including clinical contact (adjusted HR = 1.76, 95% CI 1.14–2.72).

These findings provide support for the hypothesis that clozapine treatment is associated with an increased risk of COVID-19 infection. Further research will be needed in other samples to confirm this association. Potential clinical implications are discussed.

Internet-delivered treatments for depression have proved successful, with supported programs offering the potential for improved adherence and outcomes. Internet interventions are particularly interesting in the context of increasing access to interventions, and delivering interventions population-wide.

Investigate the potential feasibility and effectiveness of an online intervention for depression in the community.

Establish the effectiveness of a supported online delivered cognitive behavioural intervention for symptoms of depression in adults in the community.

The study was a randomized controlled trial of an 8-module internet-delivered cognitive behavioral therapy (iCBT) program for adults with depressive symptoms (n = 96) compared to a waiting-list control group (n = 92). Participants received weekly support from a trained supporter. The primary outcome was depressive symptoms as measured by the Beck Depression Inventory (BDI-II). The program was made available nationwide from an established and recognized charity for depression.

For the treatment group, post-treatment effect sizes reported were large for the primary outcome measure (d = 0.91). The between-group effects were moderate to large and statistically significant for the primary outcomes (d = 0.50) favoring the treatment group. Gains were maintained at 6-month follow-up.

The study has demonstrated the efficacy of the online delivered space from depression treatment. Participants demonstrated reliable and statistically significant changes in symptoms from pre- to post-intervention. The study supports a model for delivering online depression interventions population-wide using trained supporters.

The authors have not supplied their declaration of competing interest.

Common mental health problems affect a quarter of the population. Online cognitive–behavioural therapy (CBT) is increasingly used, but the factors modulating response to this treatment modality remain unclear.

This study aims to explore the demographic and clinical predictors of response to one-to-one CBT delivered via the internet.

Real-world clinical outcomes data were collected from 2211 NHS England patients completing a course of CBT delivered by a trained clinician via the internet. Logistic regression analyses were performed using patient and service variables to identify significant predictors of response to treatment.

Multiple patient variables were significantly associated with positive response to treatment including older age, absence of long-term physical comorbidities and lower symptom severity at start of treatment. Service variables associated with positive response to treatment included shorter waiting times for initial assessment and longer treatment durations in terms of the number of sessions.

Knowledge of which patient and service variables are associated with good clinical outcomes can be used to develop personalised treatment programmes, as part of a quality improvement cycle aiming to drive up standards in mental healthcare. This study exemplifies translational research put into practice and deployed at scale in the National Health Service, demonstrating the value of technology-enabled treatment delivery not only in facilitating access to care, but in enabling accelerated data capture for clinical research purposes.

A.C., S.B., V.T., K.I., S.F., A.R., A.H. and A.D.B. are employees or board members of the sponsor. S.R.C. consults for Cambridge Cognition and Shire. Keywords: Anxiety disorders; cognitive behavioural therapies; depressive disorders; individual psychotherapy

Serious mental illness (SMI, including schizophrenia, schizoaffective disorder, and bipolar disorder) is associated with worse general health. However, admissions to general hospitals have received little investigation. We sought to delineate frequencies of and causes for non-psychiatric hospital admissions in SMI and compare with the general population in the same area.

Records of 18 380 individuals with SMI aged ⩾20 years in southeast London were linked to hospitalisation data. Age- and gender-standardised admission ratios (SARs) were calculated by primary discharge diagnoses in the 10th edition of the World Health Organization International Classification of Diseases (ICD-10) codes, referencing geographic catchment data.

Commonest discharge diagnosis categories in the SMI cohort were urinary conditions, digestive conditions, unclassified symptoms, neoplasms, and respiratory conditions. SARs were raised for most major categories, except neoplasms for a significantly lower risk. Hospitalisation risks were specifically higher for poisoning and external causes, injury, endocrine/metabolic conditions, haematological, neurological, dermatological, infectious and non-specific (‘Z-code’) causes. The five commonest specific ICD-10 diagnoses at discharge were ‘chronic renal failure’ (N18), a non-specific code (Z04), ‘dental caries’ (K02), ‘other disorders of the urinary system’ (N39), and ‘pain in throat and chest’ (R07), all of which were higher than expected (SARs ranging 1.57–6.66).

A range of reasons for non-psychiatric hospitalisation in SMI is apparent, with self-harm, self-neglect and/or reduced healthcare access, and medically unexplained symptoms as potential underlying explanations.