Hospital admission and community treatment of mental disorders in England from 1998 to 2012

Objective

The number of psychiatric hospital beds in England has declined since the 1950s. Since the early 2000s, mental health staff increasingly work in community treatment teams.We analysed recent trends in hospital and community treatment in England for eight mental health diagnoses.

Method

We obtained data from the UK Government Health and Social Care Information Centre covering the period 1998 to 2012. We analysed hospital admissions and length of stay for each diagnosis each year using linear regression. We studied associations among admissions, community treatment and hospital bed availability each year using structural equation modeling.

Results

The number of mental health beds fell 39%, from 37,000 in 1998 to 22,300 in 2012.Hospital admissions for five diagnoses declined significantly (depression, bipolar disorder, schizophrenia, dementia and obsessive compulsive disorder, P< .01 or P< .001). The strongest decline for depression involved 1000 fewer admissions each year. Admissions for three disorders increased significantly (posttraumatic stress disorder, eating disorders and alcohol-related disorders, P< .01 or P< .001). Alcohol-related admissions increased most strongly, by more than 1700 a year, and were significantly associated with increasing liver fibrosis and cirrhosis admissions (Pearson's r=0.89, P< .001) across the National Health Service (NHS) and the affordability of alcohol (Pearson's r=0.76, P< .01).The median length of stay declined significantly for four diagnoses (P< .001); the other four diagnoses did not change significantly. Depression had the steepest decline of almost 1 less day in hospital per admission per year.Almost 300 more patients were sectioned under the Mental Health Act each year.Community activity had relatively little effect on admissions, and its direct effect was not significantly different from zero. Years with more psychiatric beds had more admissions.

Conclusions

Mental health bed numbers have declined significantly in England. Annual admissions and lengths of stay declined for a range of severe mental disorders including schizophrenia, bipolar disorder and depression.The fall in available beds can account for much of the decline in admissions. National reports of crisis team activity are not associated with declines in hospital admissions.There may be significant needs, especially of depressive patients, not being met by secondary community services, such as 24-hour observation and care. This calls for policy review and further epidemiological study of morbidity, mortality and health needs associated with mental disorder in the community.     

对SPSS统计软件中Mcnemar检验结果的讨论

目的：比较手工计算与SPSS软件计算Mcnemar检验的结果,分析结果不一样的原因。方法选取相关书籍上配对设计资料的应用实例,用两种方法计算：一是利用Mcnemar检验公式进行手工计算,二是用SPSS统计软件计算,比较两种计算方法的结果有何不同。结果手工计算的结果为P=0.001,SPSS软件计算的结果为P=0.002,两种方法的结果有出入。结论SPSS统计软件对配对设计四格表资料进行计算时采用的是一种精确检验,运用的是二项分布原理,虽然与手工计算的结果有些出入,但无论用哪种方法都不会影响到最终的统计推断。     

Assessing the goodness of fit of personal risk models

We describe a flexible family of tests for evaluating the goodness of fit (calibration) of a pre‐specified personal risk model to the outcomes observed in a longitudinal cohort. Such evaluation involves using the risk model to assign each subject an absolute risk of developing the outcome within a given time from cohort entry and comparing subjects’ assigned risks with their observed outcomes. This comparison involves several issues. For example, subjects followed only for part of the risk period have unknown outcomes. Moreover, existing tests do not reveal the reasons for poor model fit when it occurs, which can reflect misspecification of the model's hazards for the competing risks of outcome development and death. To address these issues, we extend the model‐specified hazards for outcome and death, and use score statistics to test the null hypothesis that the extensions are unnecessary. Simulated cohort data applied to risk models whose outcome and mortality hazards agreed and disagreed with those generating the data show that the tests are sensitive to poor model fit, provide insight into the reasons for poor fit, and accommodate a wide range of model misspecification. We illustrate the methods by examining the calibration of two breast cancer risk models as applied to a cohort of participants in the Breast Cancer Family Registry. The methods can be implemented using the Risk Model Assessment Program, an R package freely available at http://stanford.edu/~ggong/rmap/ . Copyright © 2014 John Wiley & Sons, Ltd.     

Having your cake and eating it too: Flexibility and power with mass univariate statistics for ERP data

ERP studies produce large spatiotemporal data sets. These rich data sets are key to enabling us to understand cognitive and neural processes. However, they also present a massive multiple comparisons problem, potentially leading to a large number of studies with false positive effects (a high Type I error rate). Standard approaches to ERP statistical analysis, which average over time windows and regions of interest, do not always control for Type I error, and their inflexibility can lead to low power to detect true effects. Mass univariate approaches offer an alternative analytic method. However, they have thus far been viewed as appropriate primarily for exploratory statistical analysis and only applicable to simple designs. Here, we present new simulation studies showing that permutation-based mass univariate tests can be employed with complex factorial designs. Most importantly, we show that mass univariate approaches provide slightly greater power than traditional spatiotemporal averaging approaches when strong a priori time windows and spatial regions are used. Moreover, their power decreases only modestly when more exploratory spatiotemporal parameters are used. We argue that mass univariate approaches are preferable to traditional spatiotemporal averaging analysis approaches for many ERP studies.     

Non-Gaussian water diffusion in aging white matter

Age-associated white matter degeneration has been well documented and is likely an important mechanism contributing to cognitive decline in older adults. Recent work has explored a range of noninvasive neuroimaging procedures to differentially highlight alterations in the tissue microenvironment. Diffusional kurtosis imaging (DKI) is an extension of diffusion tensor imaging (DTI) that accounts for non-Gaussian water diffusion and can reflect alterations in the distribution and diffusion properties of tissue compartments. We used DKI to produce whole-brain voxel-based maps of mean, axial, and radial diffusional kurtoses, quantitative indices of the tissue microstructure's diffusional heterogeneity, in 111 participants ranging from the age of 33 to 91 years. As suggested from prior DTI studies, greater age was associated with alterations in white-matter tissue microstructure, which was reflected by a reduction in all 3 DKI metrics. Prominent effects were found in prefrontal and association white matter compared with relatively preserved primary motor and visual areas. Although DKI metrics co-varied with DTI metrics on a global level, DKI provided unique regional sensitivity to the effects of age not available with DTI. DKI metrics were additionally useful in combination with DTI metrics for the classification of regions according to their multivariate “diffusion footprint”, or pattern of relative age effect sizes. It is possible that the specific multivariate patterns of age-associated changes measured are representative of different types of microstructural pathology. These results suggest that DKI provides important complementary indices of brain microstructure for the study of brain aging and neurologic disease.