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BackgroundThe transition between hospital and home is a vulnerable period for acutely hospitalized older patients during which they are at increased risk for adverse health outcomes. Yet, studies describing experiences of a geriatric patient population postdischarge from a patient perspective are missing.ObjectiveTo characterize patient experiences with recovering at home after acute hospitalization.Design and MethodsQualitative semi-structured interviews, involving 20 recently discharged patients (age ≥ 70), were conducted. A thematic analysis of the content was performed.ResultsTen categories were identified as recurring topics, which were grouped into 3 major themes. The first theme describes the effect of hospitalization on daily life. Participants described that the problems they encountered were mostly caused by symptoms they experienced since discharge, which were grouped into 2 themes: (a) physical consequences of hospitalization and (b) motivational and psychological problems after hospitalization. Among most frequently presented symptoms were fatigue, apathy, unsteadiness while standing, and fear of falling.ImplicationsImpairment in mobility and instrumental activities of daily living was the dominant experience for interviewed patients. This disability was mainly attributed to four main symptoms: fatigue, apathy, unsteadiness while standing, and fear of falling. We propose a tentative model that summarizes the role of posthospital symptoms in the recovery process and the potential syndromes (frailty, posthospital syndrome, palliative symptoms, and sickness behavior) that could underlie these shared symptoms which may act as a conceptual framework for future research. Our study points at the relevance of symptom experiences in functional recovery postdischarge.

Purpose of reviewThe unmet palliative care needs of patients with chronic heart failure (CHF) are well known. Palliative care needs assessment is paramount for timely provision of palliative care. The present review provides an overview of palliative care needs assessment in patients with CHF: the role of prognostic tools, the role of the surprise question, and the role of palliative care needs assessment tools.Recent findingsMultiple prognostic tools are available, but offer little guidance for individual patients. The surprise question is a simple tool to create awareness about a limited prognosis, but the reliability in CHF seems less than in oncology and further identification and assessment of palliative care needs is required. Several tools are available to identify palliative care needs. Data about the ability of these tools to facilitate timely initiation of palliative care in CHF are lacking.SummarySeveral tools are available aiming to facilitate timely introduction of palliative care. Focus on identification of needs rather than prognosis appears to be more fitting for people with CHF. Future studies are needed to explore whether and to what extent these tools can help in addressing palliative care needs in CHF in a timely manner.

Purpose of review The unmet palliative care needs of patients with chronic heart failure (CHF) are well known. Palliative care needs assessment is paramount for timely provision of palliative care. The present review provides an overview of palliative care needs assessment in patients with CHF: the role of prognostic tools, the role of the surprise question, and the role of palliative care needs assessment tools. Recent findings Multiple prognostic tools are available, but offer little guidance for individual patients. The surprise question is a simple tool to create awareness about a limited prognosis, but the reliability in CHF seems less than in oncology and further identification and assessment of palliative care needs is required. Several tools are available to identify palliative care needs. Data about the ability of these tools to facilitate timely initiation of palliative care in CHF are lacking. Summary Several tools are available aiming to facilitate timely introduction of palliative care. Focus on identification of needs rather than prognosis appears to be more fitting for people with CHF. Future studies are needed to explore whether and to what extent these tools can help in addressing palliative care needs in CHF in a timely manner.

BackgroundMany countries developed supportive measures for palliative home care, such as financial incentives or multidisciplinary palliative home care teams. For policy makers, it is important to evaluate the use of these national palliative home care supportive measures on a population level.Methods and findingsUsing routinely-collected data on all deaths in Belgium in 2012 (n = 107,847) we measured the use of four statutory supportive measures, specifically intended for patients who have obtained the legal palliative status, and three non-statutory supportive measures. Factors associated with uptake were analysed using multivariable logistic regression. Of all deaths of adult home-dwelling persons in Belgium (n = 87,007), 17.9 percent used at least one statutory supportive measure and 51.5 percent used at least one non-statutory supportive measure. In those who died of an illness indicative of palliative care needs 33.1 percent used at least one statutory supportive measure and 62.2 percent used at least one non-statutory supportive measure. Younger people and persons dying from cancer were more likely to use a statutory policy measure. Older people and persons dying from COPD were most likely to use a non-statutory policy measure. Women, non-single people, and those living in less urbanised areas were most likely to use any supportive measure.ConclusionsStatutory supportive measures for palliative home care are underused, even in a subpopulation of persons with potential palliative care needs. Policy makers should stimulate an equitable uptake, and reducing the observed inequalities is an important focus for health care policy.

The inclusion of medical costs in life years gained in economic evaluations of health care technologies has long been controversial. Arguments in favour of the inclusion of such costs are gaining support, which shifts the question from whether to how to include these costs. This paper elaborates on the issue how to include cost in life years gained in cost effectiveness analysis given the current practice of economic evaluations in which costs of related diseases are included. We combine insights from the theoretical literature on the inclusion of unrelated medical costs in life years gained with insights from the so-called 'red herring' literature. It is argued that for most interventions it would be incorrect to simply add all medical costs in life years gained to an ICER, even when these are corrected for postponement of the expensive last year of life. This is the case since some of the postponement mechanism is already captured in the unadjusted ICER by modelling the costs of related diseases. Using the example of smoking cessation, we illustrate the differences and similarities between different approaches. The paper concludes with a discussion about the proper way to account for medical costs in life years gained in economic evaluations. Copyright (C) 2010 John Wiley & Sons, Ltd.

A shortcoming of many economic evaluations is that they do not include all medical costs in life-years gained (also termed indirect medical costs). One of the reasons for this is the practical difficulties in the estimation of these costs. While some methods have been proposed to estimate indirect medical costs in a standardized manner, these methods fail to take into account that not all costs in life-years gained can be estimated in such a way. Costs in life-years gained caused by diseases related to the intervention are difficult to estimate in a standardized manner and should always be explicitly modelled. However, costs of all other (unrelated) diseases in life-years gained can be estimated in such a way. We propose a conceptual model of how to estimate costs of unrelated diseases in life-years gained in a standardized manner. Furthermore, we describe how we estimated the parameters of this conceptual model using various data sources and studies conducted in the Netherlands. Results of the estimates are embedded in a software package called 'Practical Application to Include future Disease costs' (PAID 1.0). PAID 1.0 is available as a Microsoft (R) Excel tool (available as Supplemental Digital Content via a link in this article) and enables researchers to 'switch off' those disease categories that were already included in their own analysis and to estimate future healthcare costs of all other diseases for incorporation in their economic evaluations. We assumed that total healthcare expenditure can be explained by age, sex and time to death, while the relationship between costs and these three variables differs per disease. To estimate values for age-and sex-specific per capita health expenditure per disease and healthcare provider stratified by time to death we used Dutch cost-of-illness (COI) data for the year 2005 as a backbone. The COI data consisted of age-and sex-specific per capita health expenditure uniquely attributed to 107 disease categories and eight healthcare provider categories. Since the Dutch COI figures do not distinguish between costs of those who die at a certain age (decedents) and those who survive that age (survivors), we decomposed average per capita expenditure into parts that are attributable to decedents and survivors, respectively, using other data sources.

Since there is still a dearth of information about the end stage of chronic obstructive pulmonary disease (COPD), the main aim of this study was to examine the development of health-related quality of life (HRQoL) and functional status over time in COPD patients in Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage IV.82 Dutch COPD patients completed the St George's Respiratory Questionnaire (SGRQ) for HRQoL and the Groningen Activities for Daily Living Restriction Scale (GARS) for functional status every 3 months during the year following enrolment. Survival was followed up to 5 yrs after enrolment. Data were analysed by stratifying the study population into severity subgroups according to the lowest, intermediate and highest tertile of SGRQ and GARS at baseline. Outcome measures were change in SGRQ and GARS scores over time and survival time.In the majority of patients, scores on the SGRQ and GARS declined gradually over time. In the subgroup of 32 patients that died within 2 yrs of enrolment, these scores also declined gradually, without steep deteriorations.In patients with end-stage COPD, HRQoL and functional status deteriorated gradually over time, indicating that clinicians did not gain much additional support for differentiating the end stage of COPD by considering HRQoL and functional status using the SGRQ and GARS.

This short paper describes our three submissions to the 2015 edition of the MIREX Symbolic Melodic Similarity task. They all rely on a geometric model that represents melodies as spline curves in the pitch-time plane. The similarity between two melodies is then computed with a sequence alignment algorithm between sequences of spline spans: the more similar the shape of the curves, the more similar the melodies they represent. All submissions are the same we submitted last year, so they can serve as baseline to new submissions. This work was supported by an A4U postdoctoral grant and a Juan de la Cierva postdoctoral fellowship.

It has been demonstrated repeatedly that time to death is a much better predictor of health care expenditures than age. This is known as the 'red herring' hypothesis. In this article, we investigate whether this is also the case regarding disease-specific hospital expenditures. Longitudinal data samples from the Dutch hospital register (n=11 253 455) were used to estimate 94 disease-specific two-part models. Based on these models, Monte Carlo simulations were used to assess the predictive value of proximity to death and age on disease-specific expenditures. Results revealed that there was a clear effect of proximity of death on health care expenditures. This effect was present for most diseases and was strongest for most cancers. However, even for some less fatal diseases, proximity to death was found to be an important predictor of expenditures. Controlling for proximity to death, age was found to be a significant predictor of expenditures for most diseases. However, its impact is modest when compared to proximity to death. Considering the large variation in the degree to which proximity to death and age matter for each specific disease, we may speak not only of age as a 'red herring' but also of a 'carpaccio of red herrings'.

A shortcoming of many economic evaluations is that they do not include all medical costs in life-years gained (also termed indirect medical costs). One of the reasons for this is the practical difficulties in the estimation of these costs. While some methods have been proposed to estimate indirect medical costs in a standardized manner, these methods fail to take into account that not all costs in life-years gained can be estimated in such a way. Costs in life-years gained caused by diseases related to the intervention are difficult to estimate in a standardized manner and should always be explicitly modelled. However, costs of all other (unrelated) diseases in life-years gained can be estimated in such a way. We propose a conceptual model of how to estimate costs of unrelated diseases in life-years gained in a standardized manner. Furthermore, we describe how we estimated the parameters of this conceptual model using various data sources and studies conducted in the Netherlands. Results of the estimates are embedded in a software package called 'Practical Application to Include future Disease costs' (PAID 1.0). PAID 1.0 is available as a Microsoft (R) Excel tool (available as Supplemental Digital Content via a link in this article) and enables researchers to 'switch off' those disease categories that were already included in their own analysis and to estimate future healthcare costs of all other diseases for incorporation in their economic evaluations. We assumed that total healthcare expenditure can be explained by age, sex and time to death, while the relationship between costs and these three variables differs per disease. To estimate values for age-and sex-specific per capita health expenditure per disease and healthcare provider stratified by time to death we used Dutch cost-of-illness (COI) data for the year 2005 as a backbone. The COI data consisted of age-and sex-specific per capita health expenditure uniquely attributed to 107 disease categories and eight healthcare provider categories. Since the Dutch COI figures do not distinguish between costs of those who die at a certain age (decedents) and those who survive that age (survivors), we decomposed average per capita expenditure into parts that are attributable to decedents and survivors, respectively, using other data sources.