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

A model of the determinants of expenditure on children's personal social services

Every year the United Kingdom central government assesses the relative spending needs of English local authorities in respect of the services for which is it responsible. This is done by estimating a Standard Spending Assessment (SSA) for each service, which is intended to indicate the spending requirements of an authority if it were to adopt a standard level of services, given the circumstances in its area. In practice, statistical methods are used to develop SSAs for most services. This report describes the findings of a study designed to review the methods for setting SSAs for a single service: personal social services (PSS) for children, which in 1995/96 accounting for about £1.8 billion of expenditure (4.4% of total local government expenditure). The study was commissioned by the Department of Health and undertaken by a consortium which comprised The University of York, MORI and the National Children’s Bureau. The study was guided by a technical advisory group, comprising representatives from the local authority associations and the Department of Health. In seeking to limit the length of the report, the authors have necessarily omitted a great deal of the technical material produced in the course of the study. We understand that the Department of Health is willing to make this material and the data used in the study available to interested parties, subject to certain confidentiality restrictions. Existing methodology for constructing SSAs had been the subject of some criticism, both in general and specifically in respect of children’s PSS. This document reports the results of a study designed to apply a radically new statistical approach to estimating the SSA for children’s PSS. Previous methods were based on statistical analysis of local authority aggregate data. In contrast, this study is based on an analysis of PSS spending in 1,036 small areas (with populations of about 10,000) within 25 local authorities. A relatively new statistical method known as multilevel modelling, which was originally developed in the educational sector, was used for this purpose.children, SSA, social services

Income, relative income, and self-reported health in Britain 1979-2000

According to the relative income hypothesis, an individual’s health depends on the distribution of income in a reference group, as well as on the income of the individual. We use data on 231,208 individuals in Great Britain from 19 rounds of the General Household Survey between 1979 and 2000 to test alternative specifications of the hypothesis with different measures of relative income, national and regional reference groups, and two measures of self assessed health. All models include individual education, social class, housing tenure, age, gender and income. The estimated effects of relative income measures are usually weaker with regional reference groups and in models with time trends. There is little evidence for an independent effect of the Gini coefficient once time trends are allowed for. Deprivation relative to mean income and the Hey-Lambert-Yitzhaki measures of relative deprivation are generally negatively associated with individual health, though most such models do not perform better on the Bayesian Information Criterion than models without relative income. The only model which performs better than the model without relative income and which has a positive estimated effect of absolute income on health has relative deprivation measured as income proportional to mean income. In this model the increase in the probability of good health from a ceteris paribus reduction in relative deprivation from the upper quartile to zero is 0.010, whereas as an increase in income from the lower to the upper quartile increases the probability by 0.056. Measures of relative deprivation constructed by comparing individual income with incomes within a regional or national reference group will always be highly correlated with individual income, making identification of the separate effects of income and relative deprivation problematic.relative income, relative deprivation, income inequality, health.

Income, Relative Income, and Self-Reported Health in Britain 1979-2000

According to the relative income hypothesis, an individual's health depends on the distribution of income in a reference group, as well as on the income of the individual. We use data on 231,208 individuals in Great Britain from 19 rounds of the General Household Survey between 1979 and 2000 to test alternative specifications of the hypothesis with different measures of relative income, national and regional reference groups, and two measures of self assessed health. All models include individual education, social class, housing tenure, age, gender and income. The estimated effects of relative income measures are usually weaker with regional reference groups and in models with time trends. There is little evidence for an independent effect of the Gini coefficient once time trends are allowed for. Deprivation relative to mean income and the Hey-Lambert-Yitzhaki measures of relative deprivation are generally negatively associated with individual health, though most such models do not perform better on the Bayesian Information Criterion than models without relative income. The only model which performs better than the model without relative income and which has a positive estimated effect of absolute income on health has relative deprivation measured as income proportional to mean income. In this model the increase in the probability of good health from a ceteris paribus reduction in relative deprivation from the upper quartile to zero is 0.010, whereas as an increase in income from the lower to the upper quartile increases the probability by 0.056. Measures of relative deprivation constructed by comparing individual income with incomes within a regional or national reference group will always be highly correlated with individual income, making identification of the separate effects of income and relative deprivation problematic.Relative income, relative deprivation, income inequality, health

Security and confidentiality approach for the Clinical E-Science Framework (CLEF)

CLEF is an MRC sponsored project in the E-Science programme that aims to establish policies and infrastructure for the next generation of integrated clinical and bioscience research. One of the major goals of the project is to provide a pseudonymised repository of histories of cancer patients that can be accessed by researchers. Robust mechanisms and policies are needed to ensure that patient privacy and confidentiality are preserved while delivering a repository of such medically rich information for the purposes of scientific research. This paper summarises the overall approach adopted by CLEF to meet data protection requirements, including the data flows and pseudonymisation mechanisms that are currently being developed. Intended constraints and monitoring policies that will apply to research interrogation of the repository are also outlined. Once evaluated, it is hoped that the CLEF approach can serve as a model for other distributed electronic health record repositories to be accessed for research

HD Physiology Project-Japanese efforts to promote multilevel integrative systems biology and physiome research.

The HD Physiology Project is a Japanese research consortium that aimed to develop methods and a computational platform in which physiological and pathological information can be described in high-level definitions across multiple scales of time and size. During the 5 years of this project, an appropriate software platform for multilevel functional simulation was developed and a whole-heart model including pharmacokinetics for the assessment of the proarrhythmic risk of drugs was developed. In this article, we outline the description and scientific strategy of this project and present the achievements and influence on multilevel integrative systems biology and physiome research

Practical methodology for missing data handling in interrupted time series analysis

Interrupted time series (ITS) is a quasi-experimental design for evaluating the effect of an intervention or treatment by comparing the outcome trajectory over time before and after initiation of the intervention. ITS became popular for evaluating interventions at the population level (e.g. policies); thus, the development of statistical methods was mainly orientated to modelling population-level data. This thesis aims to explore the issues that emerge when population-level ITS analyses are applied to incomplete individual-level data in health research, proposing alternative analysis methods. First, I performed a scoping review to demonstrate how the issues of missing data at the individual level have rarely been addressed in most recent ITS studies. Despite its limitations, complete case analysis is the most frequently used method for handling missing data. Individual-level data are usually transformed into population-level time-specific summaries before fitting ITS models. This method can lead to bias. Mixed effect models (MEM) can solve this, but my review demonstrates few studies have done so in the past. I then fitted MEM to study body weight gain induced by the initiation of antipsychotics using an ITS design on electronic health records. MEM allowed fully observed covariates to inform the implicit imputation of the outcome. ITS facilitated new clinical evidence: in the long-term, typical patients do not lose the weight they gained during the first six weeks of treatment. However, the MEM alone was not ideal for handling missing covariates (i.e. dosage). Thereafter, I used simulation studies to evaluate the performance of aggregate-level segmented regression (SR), MEM and multilevel multiple imputation (MI-JOMO) for handling data missing at random (MAR) in ITS analysis. I showed that the aggregate-level SR can over or underestimate the ITS effect. MEM is effective for handling outcomes MAR, but it should be combined with MI-JOMO when covariates are also MAR. Finally, I applied MEM with MI-JOMO to assess how dose and age modify the antipsychotic-induced weight gain. Interaction terms in MEM helped to evaluate differences in weight trajectories over time between groups by dose or age, using MI-JOMO for handling missing dose. Clinically, older people’s weight is less affected by the initiation of antipsychotic treatment than younger people’s

Why do older adults seek emergency care? The impact of contextual factors, care, health, and social relations

Background: Emergency department (ED) visits are becoming more prevalent globally. EDs provide care for acute health conditions, but some of these visits are driven by needs unmet by primary health care and social care for older adults, indicating ineffective social care and healthcare systems. The ED is often an inappropriate setting for older adults because of the lack of interdisciplinary teams with clinical competence in the care of an increasingly ageing population and because of poor continuity of care which entails the risk of adverse health outcomes. The Andersen model of health services use proposes contextual and individual factors to understand health care utilisation better. However, there are knowledge gaps in research on ED care in relation to contextual factors, home help receipt, and aspects related to inadequate informal care. Moreover, selection bias often limits previous research on ED care. Aim: The overall aim was to study factors associated with ED care use in older adults. Design: Prospective cohort study Study sample: All adults ≥65 years with ED visits in 2014 living in two Swedish regions (Dalarna, N=16 688 and Stockholm, N=101 017) participated in study I. The study population in study II was all community-living older-old adults (≥80 years) who were registered residents of Dalarna on 31 December 2014, excluding those who moved into residential care facilities during 2015 (N=16 543). In study III, the participants were adults ≥60 years who participated in the Swedish National Study on Aging and Care-Kungsholmen (SNAC-K). The data were pooled from three waves (W) of SNAC: W1= 2001-2003, W3=2007-2009, and W5=2013-2015. Persons living in residential care facilities were excluded from study III providing an analytical sample of N=3 066 at W1, N=1 885 at W3, and N=1 208 at W5. In study IV, adults ≥66 years who participated in the SNAC-Blekinge study (W3: 2007-2009) and who provided information on the exposure variable were included (N=673). Data sources: The four studies of this thesis were based on national and regional registers and survey data. The registers were the Longitudinal Integration Database for Health Insurance and Labour Market, the Social Services Register, the Swedish Prescribed Drug Register, the National Patient Register, and the health care databases of Region Blekinge, Dalarna, and Stockholm. Municipal-level data were accessed from Kolada, a publicly accessible, comprehensive national database. Survey data were based on the Swedish National study on Aging and Care in Blekinge and Kungsholmen. Dependent variables: The dependent variables were ED visits, at least one ED revisit within 30 days of an initial ED visit, and frequent ED use. Independent variables: The independent variables included contextual factors (the proportion of adults aged ≥80 years in the total population, annual social care expenditures per person aged ≥80 years, home help quality, median days in residential care, and distance to the ED), individual-level predisposing factors (age, gender, and education), individual-level enabling resources (living arrangements, social connections, social support, and informal care), individual-level need factors (subjective and objective health status), and health care and social care use (primary health care visits, specialist care visits, hospital admissions, ED visits in the previous year, disposition at initial ED visit (admission to inpatient care/discharged home), residential care receipt, and home help receipt). Data analyses: Logistic regression models were used to analyse the associations between independent variables and dichotomous dependent variables (ED visits, ED revisits, frequent ED use). Cox regression models were computed to determine the association between independent variables and time to the first ED visit. Associations between independent variables and the number of ED visits were assessed using generalised estimating equations with negative binomial regressions. In studies III and IV, all analyses were stratified by age group (Study III: younger-old, 80 years). Results: Analysis of contextual factors showed that the proportion of adults aged ≥80 years in the total population and shorter distance to the ED were associated with ED visits in older-old adults (Study II). There were mixed findings on age, gender, and education level for individual-level predisposing factors. Regarding individual-level enabling resources, higher levels of social support were negatively associated with ED visits but only in older-old adults (Study III). In relation to the need for care factors, poor health status was associated with ED visits (Studies II-IV), ED revisits (Study I), and frequent ED use (Study IV). Concerning the utilisation of care, primary health care visits in the previous 12 months were associated with ED visits (Study II) and ED revisits (Study I). Hospital admissions and ED visits 12 months before the initial ED visit were associated with ED revisits (Studies I & II). Older-old adults admitted to inpatient care at the initial ED visits were 29% less likely to revisit an ED within 30 days of the initial ED visit than those discharged home (Study II). Older-old adults receiving home help for instrumental services and personal care were 148% more likely to visit an ED compared to those not receiving home help. This group with intensive home help also had a 30% higher likelihood of an ED revisit within 30 days of the initial ED visit (Study II). Conclusions: Contextual factors contribute to understanding ED care use in older adults. Our findings on poor health status suggest that the need for care determines ED care use in older adults. However, factors other than health status also explain the use of ED care. For example, social support indicates inequalities and suggests investing in public health resources to address these risk factors. Discharge to home from the ED and risk of an ED revisit could indicate that health care and post-discharge care are not meeting the needs of older patients. Findings on the home help receipt and ED care use illustrate the vulnerability of this group and highlight the importance of future research on self-reported unmet needs of home help and the effect of unmet needs on the use of ED care