Variation in compulsory psychiatric inpatient admission in England:a cross-sectional, multilevel analysis

Background: Rates of compulsory admission have increased in England in recent decades, and this trend is accelerating. Studying variation in rates between people and places can help identify modifiable causes. Objectives: To quantify and model variances in the rate of compulsory admission in England at different spatial levels and to assess the extent to which this was explained by characteristics of people and places. Design: Cross-sectional analysis using multilevel statistical modelling. Setting: England, including 98% of Census lower layer super output areas (LSOAs), 95% of primary care trusts (PCTs), 93% of general practices and all 69 NHS providers of specialist mental health services. Participants: 1,287,730 patients. Main outcome measure: The study outcome was compulsory admission, defined as time spent in an inpatient mental illness bed subject to the Mental Health Act (2007) in 2010/11. We excluded patients detained under sections applying to emergency assessment only (including those in places of safety), guardianship or supervision of community treatment. The control group comprised all other users of specialist mental health services during the same period. Data sources: The Mental Health Minimum Data Set (MHMDS). Data on explanatory variables, characterising each of the spatial levels in the data set, were obtained from a wide range of sources, and were linked using MHMDS identifiers. Results: A total of 3.5% of patients had at least one compulsory admission in 2010/11. Of (unexplained) variance in the null model, 84.5% occurred between individuals. Statistically significant variance occurred between LSOAs [6.7%, 95% confidence interval (CI) 6.2% to 7.2%] and provider trusts (6.9%, 95% CI 4.3% to 9.5%). Variances at these higher levels remained statistically significant even after adjusting for a large number of explanatory variables, which together explained only 10.2% of variance in the study outcome. The number of provider trusts whose observed rate of compulsory admission differed from the model average to a statistically significant extent fell from 45 in the null model to 20 in the fully adjusted model. We found statistically significant associations between compulsory admission and age, gender, ethnicity, local area deprivation and ethnic density. There was a small but statistically significant association between (higher) bed occupancy and compulsory admission, but this was subsequently confounded by other covariates. Adjusting for PCT investment in mental health services did not improve model fit in the fully adjusted models. Conclusions: This was the largest study of compulsory admissions in England. While 85% of the variance in this outcome occurred between individuals, statistically significant variance (around 7% each) occurred between places (LSOAs) and provider trusts. This higher-level variance in compulsory admission remained largely unchanged even after adjusting for a large number of explanatory variables. We were constrained by data available to us, and therefore our results must be interpreted with caution. We were also unable to consider many hypotheses suggested by the service users, carers and professionals who we consulted. There is an imperative to develop and evaluate interventions to reduce compulsory admission rates. This requires further research to extend our understanding of the reasons why these rates remain so high. Funding: The National Institute for Health Research Health Services and Delivery Research programme

Mental health: Future challenges [Mental Capital and Wellbeing: Making the most of ourselves in the 21st century]

The aim of the Foresight Project on Mental Capital and Wellbeing (www.foresight.gov.uk) is to advise the Government on how to achieve the best possible mental development and mental wellbeing for everyone in the UK in the future. The starting point of the Project was to generate an understanding of the science of mental capital and wellbeing (MCW) and to develop a vision for how the size and nature of the challenges exposed by the Project could evolve over the next 20 years. To make this analysis tractable, the work was divided into five broad areas: Mental capital through life; Learning through life; Mental health; Wellbeing and work; and Learning difficulties. This report presents the findings for Mental health and draws upon a comprehensive assessment of the scientific state-of-the art: overall, around 80 reviews have been commissioned across the five areas

Use of community treatment orders and their outcomes: an observational study

Background Community treatment orders are widely used in England. It is unclear whether their use varies between patients, places and services, or if they are associated with better patient outcomes. Objectives To examine variation in the use of community treatment orders and their associations with patient outcomes and health-care costs. Design Secondary analysis using multilevel statistical modelling. Setting England, including 61 NHS mental health provider trusts. Participants A total of 69,832 patients eligible to be subject to a community treatment order. Main outcome measures Use of community treatment orders and time subject to community treatment order; re-admission and total time in hospital after the start of a community treatment order; and mortality. Data sources The primary data source was the Mental Health Services Data Set. Mental Health Services Data Set data were linked to mortality records and local area deprivation statistics for England. Results There was significant variation in community treatment order use between patients, provider trusts and local areas. Most variation arose from substantially different practice in a small number of providers. Community treatment order patients were more likely to be in the ‘severe psychotic’ care cluster grouping, male or black. There was also significant variation between service providers and local areas in the time patients remained on community treatment orders. Although slightly more community treatment order patients were re-admitted than non-community treatment order patients during the study period (36.9% vs. 35.6%), there was no significant difference in time to first re-admission (around 32 months on average for both). There was some evidence that the rate of re-admission differed between community treatment order and non-community treatment order patients according to care cluster grouping. Community treatment order patients spent 7.5 days longer, on average, in admission than non-community treatment order patients over the study period. This difference remained when other patient and local area characteristics were taken into account. There was no evidence of significant variation between service providers in the effect of community treatment order on total time in admission. Community treatment order patients were less likely to die than non-community treatment order patients, after taking account of other patient and local area characteristics (odds ratio 0.69, 95% credible interval 0.60 to 0.81). Limitations Confounding by indication and potential bias arising from missing data within the Mental Health Services Data Set. Data quality issues precluded inclusion of patients who were subject to community treatment orders more than once. Conclusions Community treatment order use varied between patients, provider trusts and local areas. Community treatment order use was not associated with shorter time to re-admission or reduced time in hospital to a statistically significant degree. We found no evidence that the effectiveness of community treatment orders varied to a significant degree between provider trusts, nor that community treatment orders were associated with reduced mental health treatment costs. Our findings support the view that community treatment orders in England are not effective in reducing future admissions or time spent in hospital. We provide preliminary evidence of an association between community treatment order use and reduced rate of death. Future work These findings need to be replicated among patients who are subject to community treatment order more than once. The association between community treatment order use and reduced mortality requires further investigation. Study registration The study was approved by the University of Warwick’s Biomedical and Scientific Research Ethics Committee (REGO-2015-1623). Funding This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research; Vol. 8, No. 9. See the NIHR Journals Library website for further project information

Understanding increasing rates of psychiatric hospital detentions in England : development and preliminary testing of an explanatory model

Background The steep rise in the rate of psychiatric hospital detentions in England is poorly understood. Aims To identify explanations for the rise in detentions in England since 1983; to test their plausibility and support from evidence; to develop an explanatory model for the rise in detentions. Method Hypotheses to explain the rise in detentions were identified from previous literature and stakeholder consultation. We explored associations between national indicators for potential explanatory variables and detention rates in an ecological study. Relevant research was scoped and the plausibility of each hypothesis was rated. Finally, a logic model was developed to illustrate likely contributory factors and pathways to the increase in detentions. Results Seventeen hypotheses related to social, service, legal and data-quality factors. Hypotheses supported by available evidence were: changes in legal approaches to patients without decision-making capacity but not actively objecting to admission; demographic changes; increasing psychiatric morbidity. Reductions in the availability or quality of community mental health services and changes in police practice may have contributed to the rise in detentions. Hypothesised factors not supported by evidence were: changes in community crisis care, compulsory community treatment and prescribing practice. Evidence was ambiguous or lacking for other explanations, including the impact of austerity measures and reductions in National Health Service in-patient bed numbers. Conclusions Better data are needed about the characteristics and service contexts of those detained. Our logic model highlights likely contributory factors to the rise in detentions in England, priorities for future research and potential policy targets for reducing detentions

Effects of ethnic density on the risk of compulsory psychiatric admission for individuals attending secondary care mental health services: evidence from a large-scale study in England

Background Black, Asian and minority ethnicity groups may experience better health outcomes when living in areas of high own-group ethnic density – the so-called ‘ethnic density’ hypothesis. We tested this hypothesis for the treatment outcome of compulsory admission. Methods Data from the 2010–2011 Mental Health Minimum Dataset (N = 1 053 617) was linked to the 2011 Census and 2010 Index of Multiple Deprivation. Own-group ethnic density was calculated by dividing the number of residents per ethnic group for each lower layer super output area (LSOA) in the Census by the LSOA total population. Multilevel modelling estimated the effect of own-group ethnic density on the risk of compulsory admission by ethnic group (White British, White other, Black, Asian and mixed), accounting for patient characteristics (age and gender), area-level deprivation and population density. Results Asian and White British patients experienced a reduced risk of compulsory admission when living in the areas of high own-group ethnic density [odds ratios (OR) 0.97, 95% credible interval (CI) 0.95–0.99 and 0.94, 95% CI 0.93–0.95, respectively], whereas White minority patients were at increased risk when living in neighbourhoods of higher own-group ethnic concentration (OR 1.18, 95% CI 1.11–1.26). Higher levels of own-group ethnic density were associated with an increased risk of compulsory admission for mixed-ethnicity patients, but only when deprivation and population density were excluded from the model. Neighbourhood-level concentration of own-group ethnicity for Black patients did not influence the risk of compulsory admission. Conclusions We found only minimal support for the ethnic density hypothesis for the treatment outcome of compulsory admission to under the Mental Health Act

A realist approach to the evaluation of complex mental health interventions

Conventional approaches to evidence that prioritise randomised controlled trials appear increasingly inadequate for the evaluation of complex mental health interventions. By focusing on causal mechanisms and understanding the complex interactions between interventions, patients and contexts, realist approaches offer a productive alternative. Although the approaches might be combined, substantial barriers remain

Mental health in the pandemic: a repeated cross-sectional mixed-method study protocol to investigate the mental health impacts of the coronavirus pandemic in the UK.

Funder: MQ: Transforming Mental Health; Grant(s): MQBF/3INTRODUCTION: The WHO declared a global pandemic on 11 March 2020. Since then, the world has been firmly in the grip of the COVID-19. To date, more than 211 730 035 million confirmed cases and more than 4 430 697 million people have died. While controlling the virus and implementing vaccines are the main priorities, the population mental health impacts of the pandemic are expected to be longer term and are less obvious than the physical health ones. Lockdown restrictions, physical distancing, social isolation, as well as the loss of a loved one, working in a frontline capacity and loss of economic security may have negative effects on and increase the mental health challenges in populations around the world. There is a major demand for long-term research examining the mental health experiences and needs of people in order to design adequate policies and interventions for sustained action to respond to individual and population mental health needs both during and after the pandemic. METHODS AND ANALYSIS: This repeated cross-sectional mixed-method study conducts regular self-administered representative surveys, and targeted focus groups and semi-structured interviews with adults in the UK, as well as validation of gathered evidence through citizens' juries for contextualisation (for the UK as a whole and for its four devolved nations) to ensure that emerging mental health problems are identified early on and are properly understood, and that appropriate policies and interventions are developed and implemented across the UK and within devolved contexts. STATA and NVIVO will be used to carry out quantitative and qualitative analysis, respectively. ETHICS AND DISSEMINATION: Ethics approval for this study has been granted by the Cambridge Psychology Research Ethics Committee of the University of Cambridge, UK (PRE 2020.050) and by the Health and Life Sciences Research Ethics Committee of De Montfort University, UK (REF 422991). While unlikely, participants completing the self-administered surveys or participating in the virtual focus groups, semi-structured interviews and citizens' juries might experience distress triggered by questions or conversations. However, appropriate mitigating measures have been adopted and signposting to services and helplines will be available at all times. Furthermore, a dedicated member of staff will also be at hand to debrief following participation in the research and personalised thank-you notes will be sent to everyone taking part in the qualitative research.Study findings will be disseminated in scientific journals, at research conferences, local research symposia and seminars. Evidence-based open access briefings, articles and reports will be available on our study website for everyone to access. Rapid policy briefings targeting issues emerging from the data will also be disseminated to inform policy and practice. These briefings will position the findings within UK public policy and devolved nations policy and socioeconomic contexts in order to develop specific, timely policy recommendations. Additional dissemination will be done through traditional and social media. Our data will be contextualised in view of existing policies, and changes over time as-and-when policies change

Evaluating the effects of community treatment orders (CTOs) in England using the Mental Health Services Dataset (MHSDS): protocol for a national, population-based study.

INTRODUCTION: Supervised community treatment (SCT) for people with serious mental disorders has become accepted practice in many countries around the world. In England, SCT was adopted in 2008 in the form of community treatment orders (CTOs). CTOs have been used more than expected, with significant variations between people and places. There is conflicting evidence about the effectiveness of SCT; studies based on randomised controlled trials (RCTs) have suggested few positive impacts, while those employing observational designs have been more favourable. Robust population-based studies are needed, because of the ethical challenges of undertaking further RCTs and because variation across previous studies may reflect the effects of sociospatial context on SCT outcomes. We aim to examine spatial and temporal variation in the use, effectiveness and cost of CTOs in England through the analysis of routine administrative data. METHODS AND ANALYSIS: Four years of data from the Mental Health Services Dataset (MHSDS) will be analysed using multilevel models. Models based on all patients eligible for CTOs will be used to explore variation in their use. A subset of CTO-eligible patients comprising a treatment group (CTO patients) and a matched control group (non-CTO patients) will be used to examine variation in the association between CTO use and study outcomes. Primary outcome will be total time in hospital. Secondary outcomes will include time to first readmission and mortality. Outputs from these models will be used to populate predictive models of healthcare resource use. ETHICS AND DISSEMINATION: Ethical approval has been granted by the National Health Service Data Access and Advisory Group and Warwick University. To ensure patient confidentiality and to meet data governance requirements, analyses will be carried out in a secure microdata laboratory using de-identified data. Study findings will be disseminated through academic channels and shared with mental health policy-makers and other stakeholders