Behaviour change interventions to influence antimicrobial prescribing: a cross-sectional analysis of reports from UK state-of-the-art scientific conferences

Background To improve the quality of antimicrobial stewardship (AMS) interventions the application of behavioural sciences supported by multidisciplinary collaboration has been recommended. We analysed major UK scientific research conferences to investigate AMS behaviour change intervention reporting. Methods Leading UK 2015 scientific conference abstracts for 30 clinical specialties were identified and interrogated. All AMS and/or antimicrobial resistance(AMR) abstracts were identified using validated search criteria. Abstracts were independently reviewed by four researchers with reported behavioural interventions classified using a behaviour change taxonomy. Results Conferences ran for 110 days with >57,000 delegates. 311/12,313(2.5%) AMS-AMR abstracts (oral and poster) were identified. 118/311(40%) were presented at the UK’s infectious diseases/microbiology conference. 56/311(18%) AMS-AMR abstracts described behaviour change interventions. These were identified across 12/30(40%) conferences. The commonest abstract reporting behaviour change interventions were quality improvement projects [44/56 (79%)]. In total 71 unique behaviour change functions were identified. Policy categories; “guidelines” (16/71) and “service provision” (11/71) were the most frequently reported. Intervention functions; “education” (6/71), “persuasion” (7/71), and “enablement” (9/71) were also common. Only infection and primary care conferences reported studies that contained multiple behaviour change interventions. The remaining 10 specialties tended to report a narrow range of interventions focusing on “guidelines” and “enablement”. Conclusion Despite the benefits of behaviour change interventions on antimicrobial prescribing, very few AMS-AMR studies reported implementing them in 2015. AMS interventions must focus on promoting behaviour change towards antimicrobial prescribing. Greater focus must be placed on non-infection specialties to engage with the issue of behaviour change towards antimicrobial use

Unraveling the Convoluted Biological Roles of Type I Interferons in Infection and Immunity: A Way Forward for Therapeutics and Vaccine Design

It has been well-established that type I interferons (IFN-Is) have pleiotropic effects and play an early central role in the control of many acute viral infections. However, their pleiotropic effects are not always beneficial to the host and in fact several reports suggest that the induction of IFN-Is exacerbate disease outcomes against some bacterial and chronic viral infections. In this brief review, we probe into this mystery and try to develop answers based on past and recent studies evaluating the roles of IFN-Is in infection and immunity as this is vital for developing effective IFN-Is based therapeutics and vaccines. We also discuss the biological roles of an emerging IFN-I, namely IFN-ε, and discuss its potential use as a mucosal therapeutic and/or vaccine adjuvant. Overall, we anticipate the discussions generated in this review will provide new insights for better exploiting the biological functions of IFN-Is in developing efficacious therapeutics and vaccines in the future.This work was supported by Australian National Health and Medical Research Council project grant award 525431 (Charani Ranasinghe) and ACH2 EOI grants (Charani Ranasinghe)

The Differences in Antibiotic Decision-making Between Acute Surgical and Acute Medical Teams: An Ethnographic Study of Culture and Team Dynamics

Background Cultural and social determinants influence antibiotic decision-making in hospitals. We investigated and compared cultural determinants of antibiotic decision-making in acute medical and surgical specialties. Methods An ethnographic observational study of antibiotic decision-making in acute medical and surgical teams at a London teaching hospital was conducted (August 2015–May 2017). Data collection included 500 hours of direct observations, and face-to-face interviews with 23 key informants. A grounded theory approach, aided by Nvivo 11 software, analyzed the emerging themes. An iterative and recursive process of analysis ensured saturation of the themes. The multiple modes of enquiry enabled cross-validation and triangulation of the findings. Results In medicine, accepted norms of the decision-making process are characterized as collectivist (input from pharmacists, infectious disease, and medical microbiology teams), rationalized, and policy-informed, with emphasis on de-escalation of therapy. The gaps in antibiotic decision-making in acute medicine occur chiefly in the transition between the emergency department and inpatient teams, where ownership of the antibiotic prescription is lost. In surgery, team priorities are split between 3 settings: operating room, outpatient clinic, and ward. Senior surgeons are often absent from the ward, leaving junior staff to make complex medical decisions. This results in defensive antibiotic decision-making, leading to prolonged and inappropriate antibiotic use. Conclusions In medicine, the legacy of infection diagnosis made in the emergency department determines antibiotic decision-making. In surgery, antibiotic decision-making is perceived as a nonsurgical intervention that can be delegated to junior staff or other specialties. Different, bespoke approaches to optimize antibiotic prescribing are therefore needed to address these specific challenges

Redesigning the 'choice architecture' of hospital prescription charts: a mixed methods study incorporating in situ simulation testing.

Objectives: To incorporate behavioural insights into the user-centred design of an inpatient prescription chart (Imperial Drug Chart Evaluation and Adoption Study, IDEAS chart) and to determine whether changes in the content and design of prescription charts could influence prescribing behaviour and reduce prescribing errors. Design: A mixed-methods approach was taken in the development phase of the project; in situ simulation was used to evaluate the effectiveness of the newly developed IDEAS prescription chart. Setting: A London teaching hospital. Interventions/methods: A multimodal approach comprising (1) an exploratory phase consisting of chart reviews, focus groups and user insight gathering (2) the iterative design of the IDEAS prescription chart and finally (3) testing of final chart with prescribers using in situ simulation. Results: Substantial variation was seen between existing inpatient prescription charts used across 15 different UK hospitals. Review of 40 completed prescription charts from one hospital demonstrated a number of frequent prescribing errors including illegibility, and difficulty in identifying prescribers. Insights from focus groups and direct observations were translated into the design of IDEAS chart. In situ simulation testing revealed significant improvements in prescribing on the IDEAS chart compared with the prescription chart currently in use in the study hospital. Medication orders on the IDEAS chart were significantly more likely to include correct dose entries (164/164 vs 166/174; p=0.0046) as well as prescriber's printed name (163/164 vs 0/174; p<0.0001) and contact number (137/164 vs 55/174; p<0.0001). Antiinfective indication (28/28 vs 17/29; p<0.0001) and duration (26/28 vs 15/29; p<0.0001) were more likely to be completed using the IDEAS chart. Conclusions: In a simulated context, the IDEAS prescription chart significantly reduced a number of common prescribing errors including dosing errors and illegibility. Positive behavioural change was seen without prior education or support, suggesting that some common prescription writing errors are potentially rectifiable simply through changes in the content and design of prescription charts

Weakly Supervised Visual Question Answer Generation

Growing interest in conversational agents promote twoway human-computer communications involving asking and answering visual questions have become an active area of research in AI. Thus, generation of visual questionanswer pair(s) becomes an important and challenging task. To address this issue, we propose a weakly-supervised visual question answer generation method that generates a relevant question-answer pairs for a given input image and associated caption. Most of the prior works are supervised and depend on the annotated question-answer datasets. In our work, we present a weakly supervised method that synthetically generates question-answer pairs procedurally from visual information and captions. The proposed method initially extracts list of answer words, then does nearest question generation that uses the caption and answer word to generate synthetic question. Next, the relevant question generator converts the nearest question to relevant language question by dependency parsing and in-order tree traversal, finally, fine-tune a ViLBERT model with the question-answer pair(s) generated at end. We perform an exhaustive experimental analysis on VQA dataset and see that our model significantly outperform SOTA methods on BLEU scores. We also show the results wrt baseline models and ablation study

Opportunities for system level improvement in antibiotic use across the surgical pathway

Optimizing antibiotic prescribing across the surgical pathway (before, during, and after surgery) is a key aspect of tackling important drivers of antimicrobial resistance and simultaneously decreasing the burden of infection at the global level. In the UK alone, 10 million patients undergo surgery every year, which is equivalent to 60% of the annual hospital admissions having a surgical intervention. The overwhelming majority of surgical procedures require effectively limited delivery of antibiotic prophylaxis to prevent infections. Evidence from around the world indicates that antibiotics for surgical prophylaxis are administered ineffectively, or are extended for an inappropriate duration of time postoperatively. Ineffective antibiotic prophylaxis can contribute to the development of surgical site infections (SSIs), which represent a significant global burden of disease. The World Health Organization estimates SSI rates of up to 50% in postoperative surgical patients (depending on the type of surgery), with a particular problem in low- and middle-income countries, where SSIs are the most frequently reported healthcare-associated infections. Across European hospitals, SSIs alone comprise 19.6% of all healthcare-acquired infections. Much of the scientific research in infection management in surgery is related to infection prevention and control in the operating room, surgical prophylaxis, and the management of SSIs, with many studies focusing on infection within the 30-day postoperative period. However it is important to note that SSIs represent only one of the many types of infection that can occur postoperatively. This article provides an overview of the surgical pathway and considers infection management and antibiotic prescribing at each step of the pathway. The aim was to identify the implications for research and opportunities for system improvement

Apprentissage à partir de données et de connaissances incertaines : application à la prédiction de la qualité du caoutchouc

During the learning of predictive models, the quality of available data is essential for the reliability of obtained predictions. These learning data are, in practice very often imperfect or uncertain (imprecise, noised, etc). This PhD thesis is focused on this context where the theory of belief functions is used in order to adapt standard statistical tools to uncertain data.The chosen predictive model is decision trees which are basic classifiers in Artificial Intelligence initially conceived to be built from precise data. The aim of the main methodology developed in this thesis is to generalise decision trees to uncertain data (fuzzy, probabilistic, missing, etc) in input and in output. To realise this extension to uncertain data, the main tool is a likelihood adapted to belief functions,recently presented in the literature, whose behaviour is here studied. The maximisation of this likelihood provide estimators of the trees’ parameters. This maximisation is obtained via the E2M algorithm which is an extension of the EM algorithm to belief functions.The presented methodology, the E2M decision trees, is applied to a real case : the natural rubber quality prediction. The learning data, mainly cultural and climatic,contains many uncertainties which are modelled by belief functions adapted to those imperfections. After a simple descriptiv statistic study of the data, E2M decision trees are built, evaluated and compared to standard decision trees. The taken into account of the data uncertainty slightly improves the predictive accuracy but moreover, the importance of some variables, sparsely studied until now, is highlighted.Pour l’apprentissage de modèles prédictifs, la qualité des données disponibles joue un rôle important quant à la fiabilité des prédictions obtenues. Ces données d’apprentissage ont, en pratique, l’inconvénient d’être très souvent imparfaites ou incertaines (imprécises, bruitées, etc). Ce travail de doctorat s’inscrit dans ce cadre où la théorie des fonctions de croyance est utilisée de manière à adapter des outils statistiques classiques aux données incertaines.Le modèle prédictif choisi est l’arbre de décision qui est un classifieur basique de l’intelligence artificielle mais qui est habituellement construit à partir de données précises. Le but de la méthodologie principale développée dans cette thèse est de généraliser les arbres de décision aux données incertaines (floues, probabilistes,manquantes, etc) en entrée et en sortie. L’outil central d’extension des arbres de décision aux données incertaines est une vraisemblance adaptée aux fonctions de croyance récemment proposée dans la littérature dont certaines propriétés sont ici étudiées de manière approfondie. De manière à estimer les différents paramètres d’un arbre de décision, cette vraisemblance est maximisée via l’algorithme E2M qui étend l’algorithme EM aux fonctions de croyance. La nouvelle méthodologie ainsi présentée, les arbres de décision E2M, est ensuite appliquée à un cas réel : la prédiction de la qualité du caoutchouc naturel. Les données d’apprentissage, essentiellement culturales et climatiques, présentent de nombreuses incertitudes qui sont modélisées par des fonctions de croyance adaptées à ces imperfections. Après une étude statistique standard de ces données, des arbres de décision E2M sont construits et évalués en comparaison d’arbres de décision classiques. Cette prise en compte des incertitudes des données permet ainsi d’améliorer très légèrement la qualité de prédiction mais apporte surtout des informations concernant certaines variables peu prises en compte jusqu’ici par les experts du caoutchouc

EFFECTS OF POLY-ALUMINUM CHLORIDE, STARCH , ALUM AND ROSIN ON THE ROSIN SIZING, STRENGTH AND MICROSCOPIC APPEARANCE OF PAPER PREPARED FROM OLD CORRUGATED CONTAINER (OCC) PULP

The influence of rosin (0.1-0.3%), alum (0.4-0.6%), polyaluminum chloride (0.3-0.7%), and starch (0.5-1.5%) in the making of paper from old corrugated container (OCC) pulp on the freeness, breaking length, tear index, and burst index of pulp and paper sheets was studied. Using a full factorial design to identify the optimum operating conditions, equations relating the dependent variables to the operational variables of the chemical additives were derived that reproduced the former with errors lower than 5%. Using a high starch (1.5%), high PAC (0.7%), low alum (0.4%), and low rosin (0.1%) combination led to pulp that was sufficient to obtain paper with good strength properties (breaking length 5720m; burst index: 3.1 kPam2g-1; tear index: 6.2mNm2/g; Cobb test: 94; fold endurance: 1.52) SEM analysis show increasing in bonding between fibers together at this level of additives. The influence of starch on Cobb test values was not significant

Driving sustainable change in antimicrobial prescribing practice:how can social and behavioural sciences help?

Addressing the growing threat of antimicrobial resistance is, in part, reliant on the complex challenge of changing human behaviour—in terms of reducing inappropriate antibiotic use and preventing infection. Whilst there is no ‘one size fits all’ recommended behavioural solution for improving antimicrobial stewardship, the behavioural and social sciences offer a range of theories, frameworks, methods and evidence-based principles that can help inform the design of behaviour change interventions that are context-specific and thus more likely to be effective. However, the state-of-the-art in antimicrobial stewardship research and practice suggests that behavioural and social influences are often not given due consideration in the design and evaluation of interventions to improve antimicrobial prescribing. In this paper, we discuss four potential areas where the behavioural and social sciences can help drive more effective and sustained behaviour change in antimicrobial stewardship: (i) defining the problem in behavioural terms and understanding current behaviour in context; (ii) adopting a theory-driven, systematic approach to intervention design; (iii) investigating implementation and sustainability of interventions in practice; and (iv) maximizing learning through evidence synthesis and detailed intervention reporting

Correction bayésienne de prédictions issues d'arbres de décision et évaluation crédibiliste

International audienceAs for many classifiers, decision trees predictions are naturally probabilistic, with a frequentist probability distribution on labels associated to each leaf of the tree. Those probabilities have the major drawback of being potentially unreliable in the case where they have been estimated from a limited number of examples. Empirical Bayes methods enable the updating of observed probability distributions for which the parameters of the prior distribution are estimated from the data. This paper presents an approach of correcting decision trees predictive binary probabilities with an empirical Bayes method. The update of probability distributions associated with tree leaves creates a correction concentrated on small-sized leaves, which improves the quality of probabilistic tree predictions. The amplitude of these corrections is used here to generate predictive belief functions which are finally evaluated through the extension of three evaluation indexes of predictive probabilities.Comme pour de nombreux classifieurs, les prédictions issues d'arbres de décision sont naturellement probabilistes. A chaque feuille de l'arbre est associée une distribution de probabilité sur les labels estimée de façon fréquentiste. Ces probabilités présentent ainsi l'inconvénient majeur d'être potentiellement non-fiables dans le cas où elles sont estimées à partir d'un faible nombre d'exemples. Les approches bayésiennes empiriques permettent la mise-à-jour de distributions de probabilité en fonction des effectifs observés. Cet article présente une approche de correction des probabilités prédictives binaires issues d'arbres de décision au travers l'utilisation d'une méthode bayésienne empirique. L'ajustement des probabilités prédictives des arbres est ainsi concentré sur les feuilles de petites tailles, ce qui entraîne une nette amélioration des performances prédictives. L'amplitude de ces corrections est utilisée pour générer des fonctions de croyance prédictives qui sont finalement évaluées par l'extension incertaine de trois indices d'évaluation de probabilités prédictives