Ethnic variations in the experiences of mental health service users in England: results of a national patient survey programme

Background: Minority ethnic groups in the UK are reported to have a poor experience of mental health services, but comparative information is scarce. Aims: To examine ethnic differences in patients’ experience of community mental health services. Method: Trusts providing mental health services in England conducted surveys in 2004 and 2005 of users of community mental health services. Multiple regression was used to examine ethnic differences in responses. Results: About 27 000 patients responded to each of the surveys, of whom 10% were of minority ethnic origin. In the 2004 survey, age, living alone, the 2004 survey, age, living alone, detention and hospital admissions were stronger predictors of patient experience than ethnicity. Self-reported mental health status had the strongest explanatory effect. In the 2005 survey, the main negative differences relative to the White British were for Asians. Conclusions: Ethnicity had a smaller effect on patient experience than other variables. Relative to the White British, the Black group did not report negative experiences whereas the Asian group were most likely to respond negatively. However, there is a need for improvements in services for minority ethnic groups, including access to talking therapies and better recording of ethnicity.

The impact of the nursing workforce on patient outcomes in intensive care

Introduction: Evidence from studies conducted mainly in the US and mainland Europe suggests that characteristics of the workforce, such as nurse patient ratios and workload (measured in a number of different ways) may be linked to variations in patient outcomes across health care settings (Carmel and Rowan 2001). Few studies have tested this relationship in the UK thus questions remain about whether we are justified in extrapolating evidence from studies conducted in very different health care systems. Objectives: To investigate whether characteristics of the nursing workforce affect patient mortality UK Intensive Care Units. Data: Patient data came from the case mix programme, Intensive Care National Audit and Research Centre (ICNARC), while information about the units came from a survey of all ICUs in England (Audit Comission 1998). The merged data set contained information on 43,859 patients in 69 units across England. ICNARC also supplied a risk adjustment variable to control for patient characteristics that are often the most important determinants of survival. Methods: Multivariate multilevel logistic regression. Findings: Higher numbers of direct care nurses and lower scores on measures of workload(proportion of occupied beds at the time the patient was admitted and mean daily transfers into the unit) were associated with lower mortality rates. Furthermore, the effect of the number of direct care nurses was greatest on the life chances of the patients who were most at risk of dying. Implications: This study has wide implications for workforce policy and planning because it shows that the size of the nursing workforce is associated with mortality (West et al 2006). Few studies have demonstrated this relationship in the UK. This study has a number of strengths and weaknesses and further research is required to determine whether this relationship between the nursing workforce and patient outcomes is causal.

Addressing Bed Costs for the Elderly: A New Methodology for Modelling Patient Outcomes and Length of Stay

The proportion of elderly in the population has dramatically increased and will continue to do so for at least the next 50 years. Medical resources throughout the world are feeling the added strain of the increasing proportion of elderly in the population. The effective care of elderly patients in hospitals may be enhanced by accurately modelling the length of stay of the patients in hospital and the associated costs involved. This paper examines previously developed models for patient length of stay in hospital and describes the recently developed conditional phase-type distribution (C-Ph) to model patient duration of stay in relation to explanatory patient variables. The Clinics data set was used to demonstrate the C-Ph methodology. The resulting model highlighted a strong relationship between Barthel grade, patient outcome and length of stay showing various groups of patient behaviour. The patients who stay in hospital for a very long time are usually those that consume the largest amount of hospital resources. These have been identified as the patients whose resulting outcome is transfer. Overall, the majority of transfer patients spend a considerably longer period of time in hospital compared to patients who die or are discharged home. The C-Ph model has the potential for considering costs where different costs are attached to the various phases or subgroups of patients and the anticipated cost of care estimated in advance. It is hoped that such a method will lead to the successful identification of the most cost effective case-mix management of the hospital ward.