Predictors of uncertainty and unwillingness to receive the COVID-19 booster vaccine in a sample of 22,139 fully vaccinated adults in the UK

Summary Background The continued success of the COVID-19 vaccination programme in the UK will depend on widespread uptake of booster vaccines. However, there is evidence of hesitancy and unwillingness to receive the booster vaccine, even in fully vaccinated adults. Identifying factors associated with COVID-19 booster vaccine intentions specifically in this population is therefore critical. Methods We used data from 22,139 fully vaccinated adults who took part in the UCL COVID-19 Social Study. Multinomial logistic regression examined longitudinal predictors of uncertainty and unwillingness (versus willingness) to receive a COVID-19 booster vaccine (measured 22 November 2021 to 6 December 2021), including (i) socio-demographic factors, (ii) COVID-19 related factors (e.g., having been infected with COVID-19), and (iii) initial intent to receive a COVID-19 vaccine in the four months following the announcement in the UK that the vaccines had been approved (2 December 2020 to 31 March 2021). Findings 4% of the sample reported that they were uncertain about receiving a COVID-19 booster vaccine, and a further 4% unwilling. Initial uncertainty and unwillingness to accept the first COVID-19 vaccine in 2020-21 were each associated with over five times the risk of being uncertain about and unwilling to accept a booster vaccine. Healthy adults (those without a pre-existing physical health condition) were also more likely to be uncertain or unwilling to receive a booster vaccine. In addition, low levels of current stress about catching or becoming seriously ill from COVID-19, consistently low compliance with COVID-19 government guidelines during periods of strict restrictions (e.g., lockdowns), lower levels of educational qualification, lower socio-economic position and age below 45 years were all associated with uncertainty and unwillingness. Interpretation Our findings highlight that there are a range of factors that predict booster intentions, with the strongest predictor being previous uncertainty and unwillingness. Two other concerning patterns also emerged from our results. First, administration of booster vaccinations may increase social inequalities in experiences of COVID-19 as adults from lower socio-economic backgrounds are also most likely to be uncertain or unwilling to accept a booster vaccine as well as most likely to be seriously affected by the virus. Second, some of those most likely to spread COVID-19 (i.e., those with poor compliance with guidelines) are most likely to be uncertain and unwilling. Public health messaging should be tailored specifically to these groups.

81 The Use of Online Care-Maps for Children with Medical Complexity

Primary Subject area Complex Care Background Children with medical complexity (CMC) are a highly medicalized population of children who require specialized care across various settings including the hospital, home and community, making care coordination challenging. Care-maps, a visual representation of the people and places involved in a patient’s care, are one such tool to facilitate care coordination (Figure 1). To date, care-maps have not yet been used in a clinical environment, examined in real time or used via a standardized approach. Objectives The aims of our study were to develop a shareable standardized online tool that supports the parental creation of a care-map, and to assess the utility of care-maps in clinical care from a parent, health care provider (HCP), and community perspective. Design/Methods Parents of CMC were invited to use a standardized online care platform called Connecting2gether for 6-months and create online care-maps that could be shared with their HCPs and other community members (i.e., teachers, secondary caregivers). Demographics and internet usage surveys were completed at baseline and an acceptability survey was completed at 6-months. Surveys were analyzed using descriptive methods and care-maps were analyzed via descriptive visual analysis. Results Thirty-seven parents enrolled on the platform and 25 (70%) created a care-map and used it for the duration of the study. Of the 25, 14 (66%) went back and made revisions and 17 (80%) reported using it in clinic, home or school. Visual analysis demonstrated 11 categories (bubbles) that were commonly included. All care-maps included a Medical Team, School/Daycare and Family and Friends category, which automatically populated. The majority of care-maps included a central child bubble with the child’s photo (92%), and Community Medical Services (i.e. rehab centers) (60%). Less frequent categories included Home Care (28%), Goals (16%), and 12% included What I Like, Funding, and Community/Foundation individual bubbles. Some parents reported initial uncertainty, but at end-of-study, some reported care-maps as the most useful feature of the platform. Fifty seven percent (12/23) of HCPs viewed the created care-map and only 20% used it in the child’s care. The majority (83%) of HCPs specifically valued seeing the big picture of the child’s care, found it easy to navigate and the detail it provided. Conclusion The ability of care-maps to illustrate the intricate web of medical and non-medical care supporting CMCs in their daily life provides insight and value for parents, HCPs and non-HCPs. Care-maps were found to be valuable from the perspective of HCPs. Parents reported initial uncertainty, highlighting the importance of the HCP promoting the use of care-maps with their patients and families.

How predictability affects habituation to novelty

One becomes accustomed to repeated exposures, even for a novel event. In the present study, we investigated how predictability affects habituation to novelty by applying a mathematical model of arousal that we previously developed, and through the use of psychophysiological experiments to test the model’s prediction. We formalized habituation to novelty as a decrement in Kullback-Leibler divergence from Bayesian prior to posterior (i.e., information gain) representing arousal evoked from a novel event through Bayesian update. The model predicted an interaction effect between initial uncertainty and initial prediction error (i.e., predictability) on habituation to novelty: the greater the initial uncertainty, the faster the decrease in information gain (i.e., the sooner habituation occurs). This prediction was supported by experimental results using subjective reports of surprise and event-related potential (P300) evoked by visual-auditory incongruity. Our findings suggest that in highly uncertain situations, repeated exposure to stimuli can enhance habituation to novel stimuli.

A case of Capnocytophaga canimorsus endocarditis in a non-immunosuppressed host: the value of 16S PCR for diagnosis

Capnocytophaga canimorsus is a rare cause of endocarditis and is particularly unusual in non-immunosuppressed hosts. It is associated with animal bites, particularly those from dogs. This case describes a healthy 59-year-old woman, with no identifiable risk factors or dog bite history, who presented with fever of unknown origin. Echocardiography demonstrated an aortic valve mass and root abscess, in keeping with endocarditis, requiring urgent valve replacement surgery. Eight sets of blood cultures were drawn in total; after prolonged incubation, one set grew C. canimorusus. There was initial uncertainty over this being the causative organism, given the lack of immunosuppression or dog bite history, but 16S PCR of the valve identified the same organism, permitting targeted treatment. This case highlights the value of valve 16S PCR as a diagnostic tool in endocarditis.

CONSTRUCTION OF EXPERT-STATISTICAL MODELS FROM INCOMPLETE DATA

The article deals with the problem of constructing a linear regression model based on incomplete data containing gaps, using statistical and expert information. The reasons for the gaps in the data can be, in particular, a temporary malfunction (failure) of the measuring equipment when taking various technical characteristics, or negligence in the work of statistical services when fixing the reporting indicators. Very often, gaps arise when processing various kinds of sociological information in the form of questionnaires, when respondents refuse to answer a specific question (but answer others) or give an inadmissible, in particular, evasive answer. The approach proposed in the work involves filling the gaps with intervals, the boundaries of which are formed by experts, guided by both their experience and knowledge about the object of research, and using the well-known methods of point filling in the gaps. After that, the estimation of the parameters of the model, depending on the nature of the initial uncertainty in the data, is reduced to solving problems of linear or partially Boolean linear programming. The case is considered when the solution of the formalizing uncertainty in the initial data of the interval system of linear algebraic equations is not unique. The problem of constructing a linear regression equation for the influence of the volume of export of large-tonnage containers and the freight turnover of the PRC railway transport on the volume of import of large-capacity containers at the Zabaikalsk-Manchuria railway checkpoint is solved.

Strengthening Graduate Programs by Engaging Students as Co-Designers

This chapter will tell the story of how a cohort of master's students were engaged in a project-based course in which their problem to be tackled was the re-design of their larger program curriculum. In addition to gaining transferable skills from this co-designer's experience, these students left with a sense of legacy and the satisfaction that the program would better serve future cohorts. The authors discuss how students-as-partners approaches are informed by tenets of project-based learning and inquiry-based learning. They explore how students navigate frustrations within the ambiguity of these learning environments, and how the navigation of this initial uncertainty may contribute to the development of greater agency. Finally, the authors offer a consideration of how these teaching approaches may help fulfill the goals of critical pedagogy.

Modelling acceptable novelty transitions with emotional habituation: Effects of uncertainty and prediction error on preference changes

Previous mathematical models developed to optimize the degree of novelty in product design have represented novelty by emotional dimensions such as arousal (surprise) and valence (positivity and negativity). Formalizing arousal as Bayesian information gain and valence as a function of arousal based on Berlyne’s arousal potential theory, the model indicates that novelty becomes acceptable with repeated exposure and changes the preferences of the users. Hence, acceptable novelty transitions are important in the design of long-term product experience. We propose a mathematical model of acceptable novelty transitions with emotional habituation based on the emotional dimension model. We formalized valence as a function of information-theoretic free-energy and expressed free-energy as a function of three parameters: initial prediction error, initial uncertainty, and noise of sensory stimulus. To verify whether the transition speed of acceptable novelty depends on the initial uncertainty in our model, we analysed the responses of participants to historic artworks; we manipulated the uncertainty level by varying the obscurity of the presented pieces and the prediction error by rendering them in different artistic styles. We used the subjective reports of valence in response to the samples as measures of valence levels. The experimental results support our hypothesis.

Impact probability computation of near-Earth objects using Monte Carlo line sampling and subset simulation

This work introduces two Monte Carlo (MC)-based sampling methods, known as line sampling and subset simulation, to improve the performance of standard MC analyses in the context of asteroid impact risk assessment. Both techniques sample the initial uncertainty region in different ways, with the result of either providing a more accurate estimate of the impact probability or reducing the number of required samples during the simulation with respect to standard MC techniques. The two methods are first described and then applied to some test cases, providing evidence of the increased accuracy or the reduced computational burden with respect to a standard MC simulation. Finally, a sensitivity analysis is carried out to show how parameter setting affects the accuracy of the results and the numerical efficiency of the two methods.

Transitive inference after minimal training in rhesus macaques (Macaca mulatta)

Rhesus macaques, trained for several hundred trials on adjacent items in an ordered list (e.g. A>B, B>C, C>D, etc.), are able to make accurate transitive inferences (TI) about previously untrained pairs (e.g. A>C, B>D, etc.). How that learning unfolds during training, however, is not well understood. We sought to measure the relationship between the amount of training and the resulting response accuracy in four rhesus macaques, including the absolute minimal case of seeing each of the six adjacent pairs only once prior to testing. We also ran conditions with 24 and 114 trials. In general, learning effects were small, but they varied in proportion to the square root of the amount of training. These results suggest that subjects learned serial order in an incremental fashion. Thus, rather than performing transitive inference by a logical process, serial learning in rhesus macaques proceeds in a manner more akin to a statistical inference, with an initial uncertainty about list position that becomes gradually more accurate as evidence accumulates.