Measuring geographical accessibility to palliative and end of life (PEoLC) related facilities:a comparative study in an area with well-developed specialist palliative care (SPC) provision

BACKGROUND: Geographical accessibility is important in accessing healthcare services. Measuring it has evolved alongside technological and data analysis advances. High correlations between different methods have been detected, but no comparisons exist in the context of palliative and end of life care (PEoLC) studies. To assess how geographical accessibility can affect PEoLC, selection of an appropriate method to capture it is crucial. We therefore aimed to compare methods of measuring geographical accessibility of decedents to PEoLC-related facilities in South London, an area with well-developed SPC provision. METHODS: Individual-level death registration data in 2012 (n = 18,165), from the Office for National Statistics (ONS) were linked to area-level PEoLC-related facilities from various sources. Simple and more complex measures of geographical accessibility were calculated using the residential postcodes of the decedents and postcodes of the nearest hospital, care home and hospice. Distance measures (straight-line, travel network) and travel times along the road network were compared using geographic information system (GIS) mapping and correlation analysis (Spearman rho). RESULTS: Borough-level maps demonstrate similarities in geographical accessibility measures. Strong positive correlation exist between straight-line and travel distances to the nearest hospital (rho = 0.97), care home (rho = 0.94) and hospice (rho = 0.99). Travel times were also highly correlated with distance measures to the nearest hospital (rho range = 0.84–0.88), care home (rho = 0.88–0.95) and hospice (rho = 0.93–0.95). All correlations were significant at p < 0.001 level. CONCLUSIONS: Distance-based and travel-time measures of geographical accessibility to PEoLC-related facilities in South London are similar, suggesting the choice of measure can be based on the ease of calculation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12904-017-0185-0) contains supplementary material, which is available to authorized users

Urban and rural differences in geographical accessibility to inpatient palliative and end-of-life (PEoLC) facilities and place of death: a national population-based study in England, UK

Abstract Background Little is known about the role of geographic access to inpatient palliative and end of life care (PEoLC) facilities in place of death and how geographic access varies by settlement (urban and rural). This study aims to fill this evidence gap. Methods Individual-level death data in 2014 (N = 430,467, aged 25 +) were extracted from the Office for National Statistics (ONS) death registry and linked to the ONS postcode directory file to derive settlement of the deceased. Drive times from patients’ place of residence to nearest inpatient PEoLC facilities were used as a proxy estimate of geographic access. A modified Poisson regression was used to examine the association between geographic access to PEoLC facilities and place of death, adjusting for patients’ socio-demographic and clinical characteristics. Two models were developed to evaluate the association between geographic access to inpatient PEoLC facilities and place of death. Model 1 compared access to hospice, for hospice deaths versus home deaths, and Model 2 compared access to hospitals, for hospital deaths versus home deaths. The magnitude of association was measured using adjusted prevalence ratios (APRs). Results We found an inverse association between drive time to hospice and hospice deaths (Model 1), with a dose–response relationship. Patients who lived more than 10 min away from inpatient PEoLC facilities in rural areas (Model 1: APR range 0.49–0.80; Model 2: APR range 0.79–0.98) and urban areas (Model 1: APR range 0.50–0.83; Model 2: APR range 0.98–0.99) were less likely to die there, compared to those who lived closer (i.e. ≤ 10 min drive time). The effects were larger in rural areas compared to urban areas. Conclusion Geographic access to inpatient PEoLC facilities is associated with where people die, with a stronger association seen for patients who lived in rural areas. The findings highlight the need for the formulation of end of life care policies/strategies that consider differences in settlements types. Findings should feed into local end of life policies and strategies of both developed and developing countries to improve equity in health care delivery for those approaching the end of life

The role of service factors on variations in place of death: an observational study

Background: Previous studies have revealed that there is significant geographical variation in place of death in (PoD) England, with sociodemographic and clinical characteristics explaining ≤ 25% of this variation. Service factors, mostly modifiable, may account for some of the unexplained variation, but their role had never been evaluated systematically. Methods: A national population-based observational study in England, using National Death Registration Database (2014) linked to area-level service data from public domains, categorised by commissioning, type and capacity, location and workforce of the services, and the service use. The relationship between the service variables and PoD was evaluated using beta regression at the area level and using generalised linear mixed models at the patient level. The relative contribution of service factors at the area level was assessed using the per cent of variance explained, measured by R2. The total impact of service factors was evaluated by the area under the receiver operating characteristic curve (AUC). The independent effect of service variables was measured at the individual level by odds ratios (ORs). Results: Among the 431,735 adult deaths, hospitals were the most common PoD (47.3%), followed by care homes (23.1%), homes (22.5%) and hospices (6.1%). One-third (30.3%) of the deaths were due to cancer and two-thirds (69.7%) were due to non-cancer causes. Almost all service categories studied were associated with some of the area-level variation in PoD. Service type and capacity had the strongest link among all service categories, explaining 14.2–73.8% of the variation; service location explained 10.8–34.1% of the variation. The contribution of other service categories to PoD was inconsistent. At the individual level, service variables appeared to be more useful in predicting death in hospice than in hospital or care home, with most AUCs in the fair performance range (0.603–0.691). The independent effect of service variables on PoD was small overall, but consistent. Distance to the nearest care facility was negatively associated with death in that facility. At the Clinical Commissioning Group level, the number of hospices per 10,000 adults was associated with a higher chance of hospice death in non-cancer causes (OR 30.88, 99% confidence interval 3.46 to 275.44), but a lower chance of hospice death in cancer causes. There was evidence for an interaction effect between the service variables and sociodemographic variables on PoD. Limitations: This study was limited by data availability, particularly those specific to palliative and end-of-life care; therefore, the findings should be interpreted with caution. Data limitations were partly due to the lack of attention and investment in this area. Conclusion: A link was found between service factors and PoD. Hospice capacity was associated with hospice death in non-cancer cases. Distance to the nearest care facility was negatively correlated with the probability of a patient dying there. Effect size of the service factors was overall small, but the interactive effect between service factors and sociodemographic variables suggests that high-quality end-of-life care needs to be built on service-level configuration tailored to individuals’ circumstances. Future work: A large data gap was identified and data collection is required nationally on services relevant to palliative and end-of-life care. Future research is needed to verify the identified links between service factors and PoD. Funding: The National Institute for Health Research Health Services and Delivery Research programme

Additional file 1: Table S1. of Measuring geographical accessibility to palliative and end of life (PEoLC) related facilities: a comparative study in an area with well-developed specialist palliative care (SPC) provision

Sample characteristics and median distances to nearest hospital, care home and hospice. (DOCX 16 kb

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Treatment and outcomes in breast cancer patients: A cross section study from the EUSOMA breast centre network

Introduction: The present study was designed to describe tumour features and treatments for patients with breast cancer. It also aimed at assessing the risk of distant metastases in relation to biological profiles, disease stages and treatment. Methods: Data were analysed from 81,882 patients in the EUSOMA database (disease stages at diagnosis 0-IV; median age 61 years; range 20-100 years). All patients were treated between January 2016 and December 2021 in 53 Breast Centres within the EUSOMA certification process in 13 European countries. Cases were classified as HR+&nbsp;/HER2-, HR+&nbsp;/HER2&nbsp;+&nbsp;, HR-/HER2&nbsp;+&nbsp;or HR-/HER2- and data were analysed accordingly. Results: Univariable and multivariable analyses for distant metastases were conducted on a subset of 38,119 cases with information on whether or not they had developed them. Potential determinants included sub-group type, Ki67 value, disease stage, adjuvant systemic therapies and post-operative radiation therapy. In multivariable analysis, the HR-/HER2&nbsp;+&nbsp;and HR-/HER2- sub-groups were associated with a higher risk of distant metastases than HR+&nbsp;/HER2-. Ki67&nbsp;&gt;&nbsp;20 % and advanced stage disease also carried a high risk. Radiation therapy emerged as a protective factor against distant metastases. Conclusions: Present results show a large patient database offers an information stream that can be applied to reduce uncertainties in clinical practice. Database parameters need to be updated dynamically for outcome monitoring. Molecular prognostic factors, gene-expression signatures, tumour-infiltrating lymphocytes and circulating tumoral DNA should be added