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Multimorbidity and adverse outcomes following emergency department attendance: population based cohort study
Get PDFObjectives: To describe the effect of multimorbidity on adverse patient centred outcomes in people attending emergency department. Design: Population based cohort study. Setting: Emergency departments in NHS Lothian in Scotland, from 1 January 2012 to 31 December 2019. Participants: Adults (≥18 years) attending emergency departments. Data sources: Linked data from emergency departments, hospital discharges, and cancer registries, and national mortality data. Main outcome measures: Multimorbidity was defined as at least two conditions from the Elixhauser comorbidity index. Multivariable logistic or linear regression was used to assess associations of multimorbidity with 30 day mortality (primary outcome), hospital admission, re-attendance at the emergency department within seven days, and time spent in emergency department (secondary outcomes). Primary analysis was stratified by age (<65 v ≥65 years). Results: 451 291 people had 1 273 937 attendances to emergency departments during the study period. 43 504 (9.6%) had multimorbidity, and people with multimorbidity were older (median 73 v 43 years), more likely to arrive by emergency ambulance (57.8% v 23.7%), and more likely to be triaged as very urgent (23.5% v 9.2%) than people who do not have multimorbidity. After adjusting for other prognostic covariates, multimorbidity, compared with no multimorbidity, was associated with higher 30 day mortality (8.2% v 1.2%, adjusted odds ratio 1.81 (95% confidence interval (CI) 1.72 to 1.91)), higher rate of hospital admission (60.1% v 20.5%, 1.81 (1.76 to 1.86)), higher reattendance to an emergency department within seven days (7.8% v 3.5%, 1.41 (1.32 to 1.50)), and longer time spent in the department (adjusted coefficient 0.27 h (95% CI 0.26 to 0.27)). The size of associations between multimorbidity and all outcomes were larger in younger patients: for example, the adjusted odds ratio of 30 day mortality was 3.03 (95% CI 2.68 to 3.42) in people younger than 65 years versus 1.61 (95% CI 1.53 to 1.71) in those 65 years or older. Conclusions: Almost one in ten patients presenting to emergency department had multimorbidity using Elixhauser index conditions. Multimorbidity was strongly associated with adverse outcomes and these associations were stronger in younger people. The increasing prevalence of multimorbidity in the population is likely to exacerbate strain on emergency departments unless practice and policy evolve to meet the growing demand.Additional authors: Bruce Guthrie; Nazir I Lon
Invertebrate responses to rewilding: a monitoring framework for practitioners
Get PDFRewilding presents a unique opportunity to better understand the processes influencing ecological communities and how they function. Although empirical evidence on the effects of rewilding is growing rapidly, knowledge gain is unbalanced, particularly for invertebrates, despite this group representing a large proportion of biodiversity and being fundamental to key ecosystem processes. Here, we advocate for more targeted systematic monitoring and experimental research, providing a site-based framework for practitioners to evaluate project effects on invertebrate biodiversity. This framework utilizes taxonomic indicators of change, representative of processes important to ecosystem functioning. Implementation of this framework and the associated opportunities and challenges for practitioners are discussed. Adopting this framework would broaden the taxonomic groups and ecosystem processes evaluated by rewilding projects, transform the sector from opinion-based to evidence-based, and help address some of the most pressing ecological and conservation questions of the twenty-first century
Promoting social justice through dramatizing children's literature: Lessons from EFL classrooms in Türkiye
Get PDFSocial justice language education (SJLE) explores the ways in which language classrooms can be transformed to disrupt the existing oppressive policies and practices in schools and the society at large (Ortaçtepe Hart & Martel, 2020; Ortaçtepe Hart, 2023; Ortega, 2021). As an approach within SJLE, dramatizing children's literature can raise the awareness of learners of English as a foreign language (EFL) of social injustices across the world, help them voice their own experiences in the class, and contribute to their language development (Caldas, 2018; García-Mateus, 2021; Gualdron & Castillo, 2018; Koss & Daniel, 2018). Focusing on the intersections of drama, children's literature, and SJLE, this qualitative case study explored a) a preservice EFL teacher's trajectory as a social justice educator, and b) the affordances of dramatizing children's literature on developing young learners' English language skills and awareness of social justice issues. Three picture storybooks, Paper Bag Princess, William's Doll, and Amazing Grace, were chosen and scripted for drama. Data were elicited through preservice teachers' observation notes and reflections as well as through semi-structured interviews with students. The results showed that dramatizing children's literature helped EFL young learners challenge their stereotypical beliefs regarding gender roles, gender inequalities, and racism. It also fostered their language development, especially in pronunciation (e.g., producing sounds), speaking (e.g., pitch and melody), and vocabulary by creating an entertaining and safe environment in which they could engage in contextualized language use. The study provides pedagogical implications in relation to how dramatizing children's literature can help disrupt social and educational injustices, transform students' stereotypical beliefs and biases, and promote empathy and critical awareness at large
Outcomes of home design to support healthy cognitive ageing: modified e-Delphi exercise with older people and housing-related professionals
Get PDFBackground There is emerging agreement that living in a home designed to support healthy cognitive ageing can enable people to live better with dementia and cognitive change. However, existing literature has used a variety of outcome measures that have infrequently been informed by the perspectives of older people or of professional in design and supply of housing. The DesHCA (Designing Homes for Healthy Cognitive Ageing) study aimed to identify outcomes that were meaningful for these groups and to understand their content and meanings. Methods A presurvey of older people and housing professionals (n = 62) identified potential outcomes. These were then used in three rounds of a modified e-Delphi exercise with a panel of older people and housing professionals (n = 74) to test meanings and identify areas of agreement and disagreement. Descriptive statistics were used to present findings from previous rounds. Results The survey confirmed a wide range of possible outcomes considered important. Through the e-Delphi rounds, panellists prioritised outcomes relating to living at home that could be influenced by design, and clarified their understanding of the meanings of outcomes. In subsequent rounds, they commented on earlier results. The exercise enabled five key outcome areas to be identified-staying independent, feeling safe, living in an adaptable home, enabling physical activity and enabling enjoyed activities-which were then tested for their content and applicability in panellists' views. Conclusion The five key outcome areas appeared meaningful to panellists, whilst also demonstrating nuanced meanings. They indicate useful outcomes for future research, though will require careful definition in each case to become measures. Importantly, they are informed by the views of those most immediately affected by better or poorer home design.Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material
Health and wellbeing (dis)benefits of accessing inland blue spaces over the course of the COVID-19 pandemic
Get PDFThe COVID-19 pandemic led to widespread repercussions, affecting all aspects of society, from global economics to everyday social interactions. Due to the significant uncertainty caused by the pandemic, many individuals sought solace from nature. Freshwater environments, or inland blue spaces, are one type of natural environment that may have acted as a vital public health resource for communities during the pandemic. This research used semi-structured interviews combined with narrative analysis to capture detailed insight into the impact of, and nuanced benefits and challenges associated with, accessing inland blue spaces over the course of the COVID-19 pandemic. Participants from a range of backgrounds across Scotland were involved to determine the influence of their health and ‘shielding’ status on inland blue space experiences. In the initial stages of the pandemic, those who were taking shielding precautions described experiencing a heightened awareness of, and anxiety towards, other users of inland blue spaces. However, across the sample, individuals emphasised the overall beneficial impact of accessing freshwater areas for maintaining mental and physical wellbeing levels during the pandemic. Positive health outcomes were achieved through participating in a wide range of leisure and recreational opportunities at inland blue spaces. The research further justifies the value of accessing inland blue spaces and demonstrates the benefits of integrating access and exposure to natural environments into future pandemic response strategies. The qualitative insight also highlights the need for context-specific landscape management strategies to promote blue space access across user groups and address existing environmental inequalities
Elements of episodic memory: lessons from 40 years of research
Get PDF40 years ago, Endel Tulving published his hugely influential Elements of Episodic Memory (Oxford: Clarendon Press, 1983). For the first time, this discussed the details of episodic memory (i.e. the ability to remember personal past events), including a specific conscious experience. Ten years later, Tulving defined the ability to mentally project oneself in time to be the critical feature distinguishing episodic from semantic memory (‘What is episodic memory?’ Curr. Dir. Psychol. Sci. 2, 67–70, doi:10.1111/1467-8721.ep10770899). In this conception, the conscious experience of episodic memory captures the experience of reliving a personal event as it was experienced in the past, while the same ability allows a potential symmetry between remembering the past, and our ability to project into an imagined future. With the recent passing of Endel Tulving, this theme issue offers an opportunity to question our understanding of mental time travel in full
Explaining evolutionary feature selection via local optima networks
Get PDFWe analyse tness landscapes of evolutionary feature selection to obtain information about feature importance in supervised machine learning. Local optima networks (LONs) are a compact representation of a landscape, and can potentially be adapted for use in explainable artiicial intelligence (XAI). This work examines their applicability for discerning feature importance in supervised machine learning datasets. We visualise aspects of feature selection LONs for a breast cancer prediction dataset as case study, and this process reveals information about the composition of feature sets for the underlying ML models. The estimations of feature importance obtained from LONs are compared with the coeecients extracted from logistic regression models (interpretable AI), and also against feature importances obtained through an established XAI technique: SHAP (explainable AI). We nd that the features present in the LON are not strongly correlated with the model coeecients and SHAP values derived from a model trained prior to feature selection, nor are they strongly correlated within similar groups of local optima after feature selection, calling into question the eeects of constraining the feature space for wrapper-based techniques based on such ranking metrics
Why are socioeconomic health inequalities unacceptable? Studying the influence of explanatory framings on cognitive appraisals
Get PDFStudies of aversion to health inequality have found that this is often greater when health outcomes are presented as varying with socioeconomic conditions. We sought to understand better why this is by studying the cognitive appraisals made about health inequality when presented with distinct explanatory framings. Across two pre-registered studies (N = 1321), UK and US participants judged the acceptability of life expectancy differences attributed to distinct framings: income, education, social class, neighborhood, lifestyle choices, and genetics. Health inequality was least acceptable when attributed to the four socioeconomic framings, and most acceptable for lifestyle choices and genetics. Six appraisal dimensions—complexity, malleability, inevitability, and extent driven by biological, psychological, and sociocultural causes—varied with framing and predicted views on health inequality. These dimensions could explain most of the drop in acceptability for health inequality attributed to socioeconomic factors relative to a condition with no framing. This work illustrates for the first time the cognitive appraisals and causal intuitions that link different explanatory framings to views on health inequality. These framings are viewed as least acceptable because they reduce the perceived involvement of biological causes while increasing the perception that sociocultural and psychological factors contribute to health inequality
Mapping and Monitoring Of Water Hyacinth In Lake Victoria Using Polarimetric Radar Data
Get PDFWater hyacinth, an invasive species originating from South America, has become a significant concern since its introduction in Lake Victoria (Kenya), particularly in the Winam Gulf, where large annual blooms are observed. Monitoring the occurrence and location using in situ methods is expensive and challenging due to the lake's vastness. Remote sensing monitoring methods offer an alternate option due to the ability to cover vast areas. This study explores the potential of polarimetric Synthetic Aperture Radar (PolSAR), specifically utilising Sentinel-1 VV-VH data to map and monitor water hyacinth cover. The change detection method based on Optimisation of Power Difference (OPDiff) and minimum eigenvalue selection achieves a remarkable accuracy of 98.89% in separating clear and water hyacinth-infested water. Using polarimetric data offered better separability, enabling spatial and temporal monitoring. The analysis reveals that in 2018 water hyacinth cover peaked, spanning over 200 km 2 . Temporal variability showcases a seasonal rise and peak from September to December. This research demonstrates the capability of using PolSAR data to accurately map and monitor water hyacinth's spatial and temporal dynamics, offering valuable insights for effective management strategies
SF-ICNN: Spectral–Fractal Iterative Convolutional Neural Network for Classification of Hyperspectral Images
Get PDFOne primary concern in the field of remote-sensing image processing is the precise classification of hyperspectral images (HSIs). Lately, deep-learning models have demonstrated cutting-edge results in HSI classification. Despite this, researchers continue to study and propose simpler, more robust models. This study presents a novel deep-learning approach, the iterative convolutional neural network (ICNN), which combines spectral–fractal features and classifier probability maps iteratively, aiming to enhance the HSI classification accuracy. Experiments are conducted to prove the accuracy enhancement of the proposed method using HSI benchmark datasets of Indian pine (IP) and the University of Pavia (PU) to evaluate the performance of the proposed technique. The final results show that the proposed approach reaches overall accuracies of 99.16% and 95.5% on the IP and PU datasets, respectively, which are better than some basic methods. Additionally, the end findings demonstrate that greater accuracy levels might be achieved using a primary CNN network that employs the iteration loop than with certain current state-of-the-art spatial–spectral HSI classification techniques
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