Tolerance After Liver Transplantation: Where Are We?

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Public Involvement in research within care homes: Benefits and challenges in the APPROACH Study

Public involvement in research (PIR) can improve research design and recruitment. Less is known about how PIR enhances the experience of participation and enriches the data collection process. In a study to evaluate how UK care homes and primary health care services achieve integrated working to promote older people’s health, PIR was integrated throughout the research processes. Objectives This paper aims to present one way in which PIR has been integrated into the design and delivery of a multi-site research study based in care homes. Design A prospective case study design, with an embedded qualitative evaluation of PIR activity. Setting and Participants Data collection was undertaken in six care homes in three sites in England. Six PIR members participated: all had prior personal or work experience in care homes. Data Collection Qualitative data collection involved discussion groups, and site-specific meetings to review experiences of participation, benefits and challenges, and completion of structured fieldwork notes after each care home visit. Results PIR members supported: recruitment, resident and staff interviews and participated in data interpretation. Benefits of PIR work were resident engagement that minimised distress and made best use of limited research resources. Challenges concerned communication and scheduling. Researcher support for PIR involvement was resource intensive. Discussion and Conclusions Clearly defined roles with identified training and support facilitated involvement in different aspectsPublic Involvement in Research members of the research team: Gail Capstick, Marion Cowie, Derek Hope, Rita Hewitt, Alex Mendoza, John Willmott. Also the involvement of Steven Iliffe and Heather Gag

Tuberculose em Transplantados Hepáticos: Uma Série de Oito Casos Durante um Período de Cinco Anos

Introduction: Tuberculosis incidence in Portugal ranged from 20 to 22 cases per 100 000 inhabitants between 2010 and 2014. Tuberculosis incidence in liver transplant recipients is not precisely known, but it is estimated to be higher than among the general population. Tuberculosis in liver transplant recipients is particularly challenging because of the atypical clinical presentation and side effects of the antibacillary drugs and their potential interactions with immunosuppressive therapies. Material and Methods: We retrospectively reviewed the clinical records of liver transplant recipients with post-transplant tuberculosis occurring from January 2010 to December 2014 at a liver transplantation unit in Lisbon, Portugal. Demographic data, baseline and clinical features, as well as treatment regimen, toxicities and outcomes, were analyzed. Results: Among 1005 recipients, active tuberculosis was diagnosed in eight patients between January 2010 and December 2014 (frequency = 0.8%). Late onset tuberculosis was more frequent than early tuberculosis. Mycobacterium tuberculosis complex was isolated from cultures in almost every case (7; 87.5%). Extra-pulmonary involvement and disseminated tuberculosis were frequent. Two patients developed rejection without allograft loss. Crude mortality was 37.5%, with 2 deaths being related to tuberculosis. Discussion: Despite the uncertainty regarding treatment duration in liver transplant recipients, disease severity, as well as number of active drugs against TB infection, should be taken into account. There was a need for a rifampin-free regimen and immunosuppression adjustment in patients who experienced acute graf rejection. Conclusion: Although the number of cases of tuberculosis is low, its post-transplant frequency is significant and the observed mortality rate is not to be neglected. The cases of hepatotoxicity and graft rejection seen in this case series demonstrate the challenges associated with tuberculosis diagnosis in liver transplant recipients and management of the interactions between immunosuppressors and rifampin. This study strengthens the recommendation of latent tuberculosis infection screening and treatment in liver transplant candidates or recipients.info:eu-repo/semantics/publishedVersio

Machine learning to refine decision making within a syndromic surveillance service

Background: Worldwide, syndromic surveillance is increasingly used for improved and timely situational awareness and early identification of public health threats. Syndromic data streams are fed into detection algorithms, which produce statistical alarms highlighting potential activity of public health importance. All alarms must be assessed to confirm whether they are of public health importance. In England, approximately 100 alarms are generated daily and, although their analysis is formalised through a risk assessment process, the process requires notable time, training, and maintenance of an expertise base to determine which alarms are of public health importance. The process is made more complicated by the observation that only 0.1% of statistical alarms are deemed to be of public health importance. Therefore, the aims of this study were to evaluate machine learning as a tool for computer-assisted human decision-making when assessing statistical alarms. Methods: A record of the risk assessment process was obtained from Public Health England for all 67505 statistical alarms between August 2013 and October 2015. This record contained information on the characteristics of the alarm (e.g. size, location). We used three Bayesian classifiers- naïve Bayes, tree-augmented naïve Bayes and Multinets - to examine the risk assessment record in England with respect to the final ‘Decision’ outcome made by an epidemiologist of ‘Alert’, ‘Monitor’ or ‘No-action’. Two further classifications based upon tree-augmented naïve Bayes and Multinets were implemented to account for the predominance of ‘No-action’ outcomes. Results: The attributes of each individual risk assessment were linked to the final decision made by an epidemiologist, providing confidence in the current process. The naïve Bayesian classifier performed best, correctly classifying 51.5% of ‘Alert’ outcomes. If the ‘Alert’ and ‘Monitor’ actions are combined then performance increases to 82.6% correctly classified. We demonstrate how a decision support system based upon a naïve Bayes classifier could be operationalised within an operational syndromic surveillance system. Conclusions: Within syndromic surveillance systems, machine learning techniques have the potential to make risk assessment following statistical alarms more automated, robust, and rigorous. However, our results also highlight the importance of specialist human input to the process

Can syndromic surveillance help forecast winter hospital bed pressures in England?

BACKGROUND: Health care planners need to predict demand for hospital beds to avoid deterioration in health care. Seasonal demand can be affected by respiratory illnesses which in England are monitored using syndromic surveillance systems. Therefore, we investigated the relationship between syndromic data and daily emergency hospital admissions. METHODS: We compared the timing of peaks in syndromic respiratory indicators and emergency hospital admissions, between 2013 and 2018. Furthermore, we created forecasts for daily admissions and investigated their accuracy when real-time syndromic data were included. RESULTS: We found that syndromic indicators were sensitive to changes in the timing of peaks in seasonal disease, especially influenza. However, each year, peak demand for hospital beds occurred on either 29th or 30th December, irrespective of the timing of syndromic peaks. Most forecast models using syndromic indicators explained over 70% of the seasonal variation in admissions (adjusted R square value). Forecast errors were reduced when syndromic data were included. For example, peak admissions for December 2014 and 2017 were underestimated when syndromic data were not used in models. CONCLUSION: Due to the lack of variability in the timing of the highest seasonal peak in hospital admissions, syndromic surveillance data do not provide additional early warning of timing. However, during atypical seasons syndromic data did improve the accuracy of forecast intensity

Using emergency department syndromic surveillance to investigate the impact of a national vaccination program: A retrospective observational study

BackgroundRotavirus infection is a common cause of gastroenteritis in children worldwide, with a high mortality burden in developing countries, particularly during the first two years of life. Rotavirus vaccination was introduced into the United Kingdom childhood vaccination schedule in July 2013, with high coverage (>90%) achieved by June 2016. We used an emergency department (ED) syndromic surveillance system to assess the impact of the rotavirus vaccination programme, specifically through the demonstration of any immediate and continuing impact on ED gastroenteritis visits in England.MethodsThis retrospective, observational study used syndromic surveillance data collected from 3 EDs in the two years before (July 2011-June 2013) and 3 years post (July 2013-June 2016) introduction of rotavirus vaccination. The weekly levels of ED visits for gastroenteritis (by age group and in total) during the period before rotavirus vaccination was first described alongside the findings of laboratory surveillance of rotavirus during the same period. An interrupted time-series analysis was then performed to demonstrate the impact of rotavirus vaccination introduction on gastroenteritis ED visit levels.ResultsDuring the two years before vaccine introduction ED visits for gastroenteritis in total and for the 0-4 years age group were seen to rise and fall in line with the seasonal rotavirus increases reported by laboratory surveillance. ED gastroenteritis visits by young children were lower in the three years following introduction of rotavirus vaccination (reduced from 8% of visits to 6% of visits). These attendance levels in young children (0-4years) remained higher than in older age groups, however the previously large seasonal increases in children were greatly reduced, from peaks of 16% to 3-10% of ED visits per week.ConclusionsED syndromic surveillance demonstrated a reduction in gastroenteritis visits following rotavirus vaccine introduction. This work establishes ED syndromic surveillance as a platform for rapid impact assessment of future vaccine programmes

A Methodological Framework for the Evaluation of Syndromic Surveillance Systems: A Case Study of England

Background: Syndromic surveillance complements traditional public health surveillance by collecting and analysing health indicators in near real time. The rationale of syndromic surveillance is that it may detect health threats faster than traditional surveillance systems permitting more timely, and hence potentially more effective public health action. The effectiveness of syndromic surveillance largely relies on the methods used to detect aberrations. Very few studies have evaluated the performance of syndromic surveillance systems and consequently little is known about the types of events that such systems can and cannot detect. Methods: We introduce a framework for the evaluation of syndromic surveillance systems that can be used in any setting based upon the use of simulated scenarios. For a range of scenarios this allows the time and probability of to be determined and uncertainty is fully incorporated. In addition, we demonstrate how such a framework can model the benefits of increases in the number of centres reporting syndromic data and also determine the minimum size of outbreaks that can or cannot be detected. Here, we demonstrate its utility using simulations of national influenza outbreaks and localised outbreaks of cryptosporidiosis. Results: Influenza outbreaks are consistently detected with larger outbreaks being detected in a more timely manner. Small cryptosporidiosis outbreaks (<1000 symptomatic individuals) are unlikely to be detected. We also demonstrate the advantages of having multiple syndromic data streams (e.g. emergency attendance data, telephone helpline data, general practice consultation data) as different streams are able to detect different types outbreaks with different efficacy (e.g. emergency attendance data are useful for the detection of pandemic influenza but not for outbreaks of cryptosporidiosis). We also highlight that for any one disease, the utility of data streams may vary geographically, and that the detection ability of syndromic surveillance varies seasonally (e.g. an influenza outbreak starting in July is detected sooner than one starting later in the year). We argue that our framework constitutes a useful tool for public health emergency preparedness in multiple settings. Conclusions: The proposed framework allows the exhaustive evaluation of any syndromic surveillance system and constitutes a useful tool for emergency preparedness and response