The development of a questionnaire to assess the attitudes of older people to end-of-life issues (AEOLI)

Objectives: To develop an end-of-life attitudes questionnaire for use in a large community-based sample of older people. Design: Nominal groups and standardization of questions. Participants: Eighteen older people, ten academics and five specialist palliative care health professionals were involved in nominal groups. Thirty older people took part in initial pilot work and a further 50 were involved in reliability testing. Results: A 27-item attitudes of older people to end-of-life issues (AEOLI) questionnaire. Discussion: In modern times, death and dying predominantly occurs among older people and yet we know very little about older people's attitudes to end-of-life care. The AEOLI questionnaire can be used in large scale surveys to elicit attitudes on end-of life issues considered important by older people and health care professionals

Can the Spitzer Quality of Life Index help to reduce prognostic uncertainty in terminal care?

Data from an on-going trial of co-ordinating care for terminally ill cancer patients are used to investigate whether the Spitzer Quality of Life (QL) Index can be used to reduce prognostic uncertainty in terminal care. Four questions are addressed. First, can doctors and nurses distinguish between patients with a prognosis of more or less than 1 year? Second, do the medical and nursing staff differ in their ability to estimate prognosis? Third, are there differences in the length of life remaining between groups of patients with different QL Index scores? Fourth, how well does the QL Index predict the likelihood of individual patients dying within 6 months of assessment? Doctors and nurses assigned between 17 and 25% of patients to the wrong prognostic group and were as likely to over-estimate as to under-estimate life expectancy. Medical and nursing staff did not differ in their ability to make prognostic judgements. Patients with a low QL Index score were more likely to die within 6 months than those with higher scores, but scores on the Index were not strong predictors of 6-month survival in individual patients. The Index is not accurate enough to be used to predict what sort of treatment terminally ill patients will require in the future and for how long. Nevertheless, it may prove valuable for those planning services for terminally ill cancer patients who require information on the levels of need in a population

Substance use in clinical high risk for psychosis: a review of the literature

AimIn the literature, there is evidence suggesting an association between substance use and psychosis. However, little is known about substance use in those who may be in the pre-psychotic phase, that is, those who are putatively prodromal are considered to be at clinical high risk (CHR) of developing psychosis. MethodsWe conducted a review of publications measuring patterns and rates of substance use in CHR for psychosis individuals and the effects on the transition to psychosis. ResultsOf 5527 potentially relevant research papers, 10 met inclusion criteria of CHR subjects and specifically mentioned substance use in the sample. The results of these studies varied. Cannabis, alcohol and tobacco/nicotine were reported as the most commonly used substances. There was limited information on the changes in patterns of use over time. Two out of the ten studies found a significant association between the use of substances and subsequent transition to psychosis. In one of these studies, substance abuse was a predictor of psychosis when included as a variable in a prediction algorithm. In the other study, the abuse of cannabis and nicotine was associated with transition to psychosis. ConclusionsWe found limited evidence to suggest that increased rates of substance use may be associated with transition to psychosis. However, further prospective research examining the association between substance use and transition to psychosis is required before any firm conclusions can be made

Evaluating Depressive Symptoms in Schizophrenia: A Psychometric Comparison of the Calgary Depression Scale for Schizophrenia and the Hamilton Depression Rating Scale

Background: The aim of this study was to compare two measures of depression in patients with schizophrenia and schizophrenia spectrum disorder, including patients with delusional and schizoaffective disorder, to conclude implications for their application. Sampling and Methods: A total of 278 patients were assessed using the Calgary Depression Scale for Schizophrenia (CDSS) and the Hamilton Depression Rating Scale (HAMD-17). The Positive and Negative Syndrome Scale (PANSS) was also applied. At admission and discharge, a principal component analysis was performed with each depression scale. The two depression rating scales were furthermore compared using correlation and regression analyses. Results: Three factors were revealed for the CDSS and HAMD-17 factor component analysis. A very similar item loading was found for the CDSS at admission and discharge, whereas results of the loadings of the HAMD-17 items were less stable. The first two factors of the CDSS revealed correlations with positive, negative and general psychopathology. In contrast, multiple significant correlations were found for the HAMD-17 factors and the PANSS sub-scores. Multiple regression analyses demonstrated that the HAMD-17 accounted more for the positive and negative symptom domains than the CDSS. Conclusions:The present results suggest that compared to the HAMD-17, the CDSS is a more specific instrument to measure depressive symptoms in schizophrenia and schizophrenia spectrum disorder, especially in acutely ill patients. Copyright (c) 2012 S. Karger AG, Base

Effectiveness and cost-effectiveness of body psychotherapy in the treatment of negative symptoms of schizophrenia - a multi-centre randomised controlled trial

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Cancer Trajectories at the End of Life: is there an effect of age and gender?

<p>Abstract</p> <p>Background</p> <p>Few empirical data show the pattern of functional decline at the end of life for cancer patients, especially among older patients.</p> <p>Methods</p> <p>In a mortality follow-back survey (the Italian Survey of the Dying of Cancer – ISDOC) a random sample of 1,271 lay caregivers were interviewed, at a mean of 234 days after bereavement. The main outcome was number of days before death when the patient experienced a permanent functional decline.</p> <p>Results</p> <p>1,249 (98%) caregivers answered the question about patient's function. The probability to be free from a functional disability was high (94%) 52 weeks before death, but was lower for older age groups (15% for those aged 85 or more) and women (8%). It remained stable until 18 weeks before death, then fell to 63% at 12 weeks and 49% at 6 weeks before death (among those aged 85 or more the figures were 50% and 41%). The pattern was consistent across sub-groups, except for patients affected by Central Nervous System tumors who experienced a longer, slower functional decline.</p> <p>Conclusion</p> <p>This study provides empirical support for the declining trajectory in cancer, and suggests that the decline commences at around 12 weeks in all age groups, even among patients over 85 years.</p

Variations in the quality and costs of end-of-life care, preferences and palliative outcomes for cancer patients by place of death: the QUALYCARE study

<p>Abstract</p> <p>Background</p> <p>Emerging trends and new policies suggest that more cancer patients might die at home in the future. However, not all have equal chances of achieving this. Furthermore, there is lack of evidence to support that those who die at home experience better care and a better death than those who die as inpatients. The QUALYCARE study aims to examine variations in the quality and costs of end-of-life care, preferences and palliative outcomes associated with dying at home or in an institution for cancer patients.</p> <p>Methods/Design</p> <p>Mortality followback survey (with a nested case-control study of home vs. hospital deaths) conducted with bereaved relatives of cancer patients in four Primary Care Trusts in London. Potential participants are identified from death registrations and approached by the Office for National Statistics in complete confidence. Data are collected via a postal questionnaire to identify the informal and formal care received in the three months before death and the associated costs, relatives' satisfaction with care, and palliative outcomes for the patients and their relatives. A well-established questionnaire to measure relatives' views on the care integrates four brief and robust tools - the Client Service Receipt Inventory, the Palliative Outcome Scale, the EQ-5 D and the Texas Revised Inventory of Grief. Further questions assess patients and relatives' preferences for place of death. The survey aims to include 500 bereaved relatives (140 who experienced a home death, 205 a hospital death, 115 a hospice death and 40 a nursing home death). Bivariate and multivariate analyses will explore differences in place of death and place of end-of-life care, in preferences for place of death, patients' palliative outcomes and relatives' bereavement outcomes, in relation to place of death. Factors influencing death at home and the costs of end-of-life care by place of death will be identified.</p> <p>Discussion</p> <p>Collecting data on end-of-life care retrospectively from bereaved relatives has ethical, practical and scientific challenges. QUALYCARE has been carefully designed to address these challenges in a robust and ethically sound population-based survey. By discovering variations in the underlying individual reality of place of death for people dying from cancer and their families, this study will advance our understanding of end-of-life care and, we hope, improve care for cancer patients and their families in the future.</p> <p>Trial registration</p> <p>National Institute of Health Research (NIHR) Clinical Research Network Portfolio. UKCRN7041.</p