Tackling barriers to COVID-19 vaccine uptake in London: a mixed-methods evaluation.

BACKGROUND: In response to the COVID-19 pandemic, the first vaccine was administered in December 2020 in England. However, vaccination uptake has historically been lower in London than in other English regions. METHODS: Mixed-methods: This comprised an analysis of cumulative percentage uptake across London between 8 December 2020 and 6 June 2021 by vaccine priority cohorts and ethnicity. We also undertook thematic analyses of uptake barriers, interventions to tackle these and key learning from a qualitative survey of 27 London local authority representatives, vaccine plans from London's five Integrated Care Systems and interviews with 38 London system representatives. RESULTS: Vaccine uptake was lower in Black ethnic (57-65% uptake) compared with the White British group (90% uptake). Trust was a critical issue, including mistrust in the vaccine itself and in authorities administering or promoting it. The balance between putative costs and benefits of vaccination created uptake barriers for zero-hour and shift workers. Intensive, targeted and 'hyper-local' initiatives, which sustained community relationships and were not constrained by administrative boundaries, helped tackle these barriers. CONCLUSIONS: The success of the national vaccination programme depended on conceding local autonomy, investing in responsive and long-term partnerships to engender trust through in-depth understanding of communities' beliefs.

The patient safety collaborative programme: opportunities for physician engagement.

Driving improvements in patient safety has been a core goal of the Academic Health Science Networks (AHSNs) in England since their inception in 2013. The National Patient Safety Collaborative Programme, nested within the 15 geographically located AHSNs, was established in 2014 in response to the Berwick review. In 2019, the new NHS national patient safety strategy was published, which placed the AHSNs as a key vehicle for delivering its ambitions. This paper explores the achievements of, and opportunities presented by, the collaborative in addressing some of the key patient safety challenges facing physicians and their wider teams. Case studies illustrate the AHSNs' contribution to support national ambitions, including the adoption of the National Early Warning Score (NEWS) 2, and the impact of regionally-led work on patient outcomes, such as reducing mortality from sepsis and acute kidney injury. We set out current activities, opportunities for physician engagement and plans for future work.

Overcoming Challenges In Codifying And Replicating Complex Health Care Interventions.

The complex nature of many health care interventions poses challenges for successful replication. This article presents insights on tackling these challenges primarily drawn from recent research and programs in the UK. These insights include the need to codify complex interventions in ways that reflect their social, context-sensitive, and dynamic nature; to capture learning as the intervention is implemented in new contexts; and to design programs in ways that respect adopters' role in the spread process. We argue that program leaders should have familiarity with theoretical approaches for conceptualizing complex interventions, that a discrete testing-and-revision phase should be recognized as part of the spread process, and that programs should be designed in ways that build and sustain adopter commitment. These perspectives complement the traditional focus on the innovator in models of spread by highlighting the role adopters play in adapting interventions and generating learning, and they have implications for the design of programs to spread innovation.

Developing and testing a framework to measure and monitor safety in healthcare.

The NHS excels at measuring incidences of past harm - whether it is falls or hospital-acquired infections - but research undertaken by Charles Vincent, Jane Carthey and Susan Burnett for the Health Foundation suggests past harm is only one element of what is needed to understand how safe care is. The researchers developed a framework to incorporate other necessary elements, such as anticipating and preparing for risks before they lead to harm to patients. In 2013, the Health Foundation road-tested this framework with staff in three NHS organisations and held a two-day summit with leaders from across the healthcare system to get feedback on its potential. This article presents the findings of this phase of work and sets it in the context of recent changes in the policy and regulatory landscape for patient safety in England. It concludes that the framework offers a great deal of potential for supporting organisations to understand the safety of their services. The framework could be most effective when used to identify the relative strengths and weaknesses of current safety measures, and when staff are given sufficient time, resource and support to consider the complex issues surfaced by the questions in the framework. This needs to be matched by a system of regulation which is aligned and mature, and an approach from NHS Trust Boards which welcomes information about the risks of its services.

Domain Anomaly Detection in Machine Perception: A System Architecture and Taxonomy.

We address the problem of anomaly detection in machine perception. The concept of domain anomaly is introduced as distinct from the conventional notion of anomaly used in the literature. We propose a unified framework for anomaly detection which exposes the multifaceted nature of anomalies and suggest effective mechanisms for identifying and distinguishing each facet as instruments for domain anomaly detection. The framework draws on the Bayesian probabilistic reasoning apparatus which clearly defines concepts such as outlier, noise, distribution drift, novelty detection (object, object primitive), rare events, and unexpected events. Based on these concepts we provide a taxonomy of domain anomaly events. One of the mechanisms helping to pinpoint the nature of anomaly is based on detecting incongruence between contextual and noncontextual sensor(y) data interpretation. The proposed methodology has wide applicability. It underpins in a unified way the anomaly detection applications found in the literature. To illustrate some of its distinguishing features, in here the domain anomaly detection methodology is applied to the problem of anomaly detection for a video annotation system.

Action recognition using mined hierarchical compound features.

The field of Action Recognition has seen a large increase in activity in recent years. Much of the progress has been through incorporating ideas from single-frame object recognition and adapting them for temporal-based action recognition. Inspired by the success of interest points in the 2D spatial domain, their 3D (space-time) counterparts typically form the basic components used to describe actions, and in action recognition the features used are often engineered to fire sparsely. This is to ensure that the problem is tractable; however, this can sacrifice recognition accuracy as it cannot be assumed that the optimum features in terms of class discrimination are obtained from this approach. In contrast, we propose to initially use an overcomplete set of simple 2D corners in both space and time. These are grouped spatially and temporally using a hierarchical process, with an increasing search area. At each stage of the hierarchy, the most distinctive and descriptive features are learned efficiently through data mining. This allows large amounts of data to be searched for frequently reoccurring patterns of features. At each level of the hierarchy, the mined compound features become more complex, discriminative, and sparse. This results in fast, accurate recognition with real-time performance on high-resolution video. As the compound features are constructed and selected based upon their ability to discriminate, their speed and accuracy increase at each level of the hierarchy. The approach is tested on four state-of-the-art data sets, the popular KTH data set to provide a comparison with other state-of-the-art approaches, the Multi-KTH data set to illustrate performance at simultaneous multiaction classification, despite no explicit localization information provided during training. Finally, the recent Hollywood and Hollywood2 data sets provide challenging complex actions taken from commercial movie sequences. For all four data sets, the proposed hierarchical approach outperforms all other methods reported thus far in the literature and can achieve real-time operation.

Decentralized sensor fusion for Ubiquitous Networking Robotics in Urban Areas.

In this article we explain the architecture for the environment and sensors that has been built for the European project URUS (Ubiquitous Networking Robotics in Urban Sites), a project whose objective is to develop an adaptable network robot architecture for cooperation between network robots and human beings and/or the environment in urban areas. The project goal is to deploy a team of robots in an urban area to give a set of services to a user community. This paper addresses the sensor architecture devised for URUS and the type of robots and sensors used, including environment sensors and sensors onboard the robots. Furthermore, we also explain how sensor fusion takes place to achieve urban outdoor execution of robotic services. Finally some results of the project related to the sensor network are highlighted.

Using points at infinity for parameter decoupling in camera calibration.

The majority of camera calibration methods, including the Gold Standard algorithm, use point-based information and simultaneously estimate all calibration parameters. In contrast, we propose a novel calibration method that exploits line orientation information and decouples the problem into two simpler stages. We formulate the problem as minimization of the lateral displacement between single projected image lines and their vanishing points. Unlike previous vanishing point methods, parallel line pairs are not required. Additionally, the invariance properties of vanishing points mean that multiple images related by pure translation can be used to increase the calibration data set size without increasing the number of estimated parameters. We compare this method with vanishing point methods and the Gold Standard algorithm and demonstrate that it has comparable performance.