Abstract Study Objective Tools proposed to triage patient acuity in COVID-19 infection have only been validated in hospital populations. We estimated the accuracy of five risk-stratification tools recommended to predict severe illness and compare accuracy to existing clinical decision-making in a pre-hospital setting. Methods An observational cohort study using linked ambulance service data for patients attended by EMS crews in the Yorkshire and Humber region of England between 18th March 2020 and 29th June 2020 was conducted to assess performance of the PRIEST tool, NEWS2, the WHO algorithm, CRB-65 and PMEWS in patients with suspected COVID-19 infection. The primary outcome was death or need for organ support. Results Of 7549 patients in our cohort, 17.6% (95% CI:16.8% to 18.5%) experienced the primary outcome. The NEWS2, PMEWS, PRIEST tool and WHO algorithm identified patients at risk of adverse outcomes with a high sensitivity (>0.95) and specificity ranging from 0.3 (NEWS2) to 0.41 (PRIEST tool). The high sensitivity of NEWS2 and PMEWS was achieved by using lower thresholds than previously recommended. On index assessment, 65% of patients were transported to hospital and EMS decision to transfer patients achieved a sensitivity of 0.84 (95% CI 0.83 to 0.85) and specificity of 0.39 (95% CI 0.39 to 0.40). Conclusion Use of NEWS2, PMEWS, PRIEST tool and WHO algorithm could improve sensitivity of EMS triage of patients with suspected COVID-19 infection. Use of the PRIEST tool would improve sensitivity of triage without increasing the number of patients conveyed to hospital.
This study explores the correlation between residents’ subjective assessments of urban neighbourhoods, obtained through virtual walkthroughs, and objective measures of deprivation. Our study was set within a specific city in the United Kingdom, with neighbourhoods selected based on Indices of Multiple Deprivation (IMD). We invited residents in the UK through Prolific, a crowdsourcing platform. Employing complete case analysis, TF-IDF keyword extraction, the Kruskal–Wallis test, and Spearman’s rank-order correlation, our study examines the alignment between subjective assessments and existing deprivation measures (IMD). The results reveal a nuanced relationship, suggesting potential subjective biases influencing residents’ perceptions. Despite these complexities, the study highlights the value of virtual walkthroughs in offering a holistic overview of neighbourhoods. While acknowledging the limitations posed by subjective biases, we argue that virtual walkthroughs provide insights into residents’ experiences that potentially complement traditional objective measures of deprivation. By capturing the intricacies of residents’ perceptions, virtual walkthroughs contribute to a more comprehensive understanding of neighbourhood deprivation. This research informs future endeavours to integrate subjective assessments with objective measures for robust neighbourhood evaluations.
Abstract Knowledge sharing in rural agricultural communities is vital to the success of farmers and sustaining high yields. A range of actors in the knowledge landscape participate in knowledge sharing, and with this, a variety of complexities are introduced. In this paper, we report on a set of field visits, interviews and focus groups in various settings to understand this complex nature of the knowledge landscape. Our study was set within multiple locations within 20 miles North‐East of Dhaka, the capital city of Bangladesh. Our findings highlight the high level of interconnectedness of different actors in the agricultural communities and the complexities involved in establishing trust of information. We report on the importance of fostering successful relationships within the communities and the growing strains of climate change.
Accurate, long-term data are needed in order to determine trends in active travel, to examine the effectiveness of any interventions and to quantify the health, social and economic consequences of active travel. However, most studies of individual travel behaviour have either used self-report (which is limited in detail and open to bias), or provided logging devices for short periods, so lack the ability to monitor long-term trends. We have developed apps using participants' own smartphones (Android or iOS) that monitor and feed-back individual user's physical activity whilst the phone is carried or worn. The nature, time and location of any physical activity are uploaded to a secure survey and allow researchers to identify large scale behaviour. Pilot data from almost 2000 users have been logged and are reported. This constitutes a natural experiment, collecting long-term physical activity, transport mode and route choice information across a large cross-section of users.
The prevalence of Social Media in sharing day to day in-
formation regarding all aspects of our life is ever increasing. More so,
with access to cheap Internet-enabled devices and proliferation of Social Media applications. Among the variety of information shared, the
most relevant, in the context of this paper is how individuals assess their
surroundings and how they or their loved ones are affected by adverse
events, disasters and crises. Traditional channels of communication often
fall behind in providing timely information for emergency responders to
formulate an accurate picture of the situation on the ground. The role
of Social Media in complimenting such sources of information is thus
invaluable and Social Media has been recognised as a key element of
assessing evolving situations. Timely, accurate and efficient means to explore and query Social Media is essential for an effective response during
emergencies, and hence this gives rise to a Knowledge Management issue.
Our paper presents our approach to analysing Real-Time Social Media
data streams using Visual Analytic techniques. We discuss the highly
visual and interactive approach we employ to provide emergency responders means to access data of interest, supporting different information
seeking paradigms.
Supplemental material, sj-pdf-1-bjo-10.1177_0308022620921111 for Remote Home Visit: Exploring the feasibility, acceptability and potential benefits of using digital technology to undertake occupational therapy home assessments by Jennifer Read, Natalie Jones, Colette Fegan, Peter Cudd, Emma Simpson, Suvodeep Mazumdar and Fabio Ciravegna in British Journal of Occupational Therapy
The essence of a city is its citizens and communities. A city’s infrastructure and associated services play a vital role in citizens' day-to-day living and their overall quality of life. Traditionally, services are deployed in a top-down approach where authorities, councils and public bodies take a reactive approach to address community needs and concerns. In this paper, we propose our ‘Citizen Observatory’ approach to enable citizens to take a proactive role in the management of their local communities and environment by supporting their engagement in the decision-making process. We discuss how to empower citizens and communities to engage with and assist authorities to establish a more informed understanding of residents’ needs and the status of their local environments. Through the WeSenseIt project, we employ a location-based crowdsourcing and communication strategy to develop a resilient, efficient and collaborative information ecosystem for decision-making in urban and rural areas.
In recent years, deep learning has been increasingly used for several applications such as object analysis, feature extraction and image classification. This paper explores the use of deep learning in a flood monitoring application in the context of an EC-funded project, Smart Cities and Open Data REuse (SCORE). IoT sensors for detecting blocked gullies and drainages are notoriously hard to build, hence we propose a novel technique to utilise deep learning for building an IoT-enabled smart camera to address this need. In our work, we apply deep leaning to classify drain blockage images to develop an effective image classification model for different severity of blockages. Using this model, an image can be analysed and classified in number of classes depending upon the context of the image. In building such model, we explored the use of filtering in terms of segmentation as one of the approaches to increase the accuracy of classification by concentrating only into the area of interest within the image. Segmentation is applied in data pre-processing stage in our application before the training. We used crowdsourced publicly available images to train and test our model. Our model with segmentation showed an improvement in the classification accuracy.
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