Trust, guilds and kinship in London, 1330-1680

How was trust created and reinforced between the inhabitants of medieval and early modern cities? And how did the social foundations of trusting relationships change over time? Current research highlights the role of kinship, neighbourhood and associations, particularly guilds, in creating ‘relationships of trust’ and social capital in the face of high levels of migration, mortality and economic volatility, but tells us little about their relative importance or how they developed. We uncover a profound shift in the contribution of family and guilds to trust networks among the middling and elite of one of Europe’s major cities, London, over three centuries, from the 1330s to the 1680s. We examine the networks of sureties created to secure the inheritances of children whose fathers died while they were minors, surviving in the records of London’s Orphans Court. Our analysis of almost fifteen thousand networks evaluates the presence of trusting relationships connected with guild membership, family and place over several centuries. We show a profound increase in the role of kinship – a re-embedding of trust within the family - and a decline of the importance of shared guild membership in connecting Londoner’s who secured orphans’ inheritances together. We suggest these developments are best explained as a result of the impact of the Reformation on the form and intensity of sociability fostered by guilds and the enormous growth of the metropolis

Trust, guilds and kinship in London, 1330-1680

How was trust created and reinforced between the inhabitants of medieval and early modern cities? And how did the social foundations of trusting relationships change over time? Current research highlights the role of kinship, neighbourhood and associations, particularly guilds, in creating ‘relationships of trust’ and social capital in the face of high levels of migration, mortality and economic volatility, but tells us little about their relative importance or how they developed. We uncover a profound shift in the contribution of family and guilds to trust networks among the middling and elite of one of Europe’s major cities, London, over three centuries, from the 1330s to the 1680s. We examine almost 15,000 networks of sureties created to secure orphans’ inheritances to measure the presence of trusting relationships connected by guild membership, family and place. We uncover a profound increase in the role of kinship – a re-embedding of trust within the family - and a decline of the importance of shared guild membership in connecting Londoner’s who secured orphans’ inheritances together. These developments indicate a profound transformation in the social fabric of urban society

A rare complication of acute appendicitis – case presentation

Introduction. Considered by many authors a vestigial structure, the appendix is a small dimensions organ with mostly unknown functions. Acute appendicitis is the most common condition of the ileocecal appendix, having multifaceted clinical manifestations, often masquerading as various unrelated syndromes, but causing increased morbidity, especially when diagnosed late. Although the disease could manifest at any age, there is a progressive increase of its incidence from birth, with a maximum between 10 to 40 years. Case presentation. We report the case of an 84 years-old female patient, who presented with significant abdominal pain in the lower quadrants, mainly in the right iliac fossa and in whom the CT examination was suggestive of a utero-appendicular fistula, a very rare complication in daily practice and even more seldomly encountered in the elderly. Conclusions. Acute appendicitis remains a condition for which surgery is still the optimum treatment, especially in the case of an elderly patient having a radiologically suspected fistula with the uterus that could easily lead to septic gynecological complications and possibly progression to multiple organ failure

Assessing The Factual Accuracy of Generated Text

We propose a model-based metric to estimate the factual accuracy of generated text that is complementary to typical scoring schemes like ROUGE (Recall-Oriented Understudy for Gisting Evaluation) and BLEU (Bilingual Evaluation Understudy). We introduce and release a new large-scale dataset based on Wikipedia and Wikidata to train relation classifiers and end-to-end fact extraction models. The end-to-end models are shown to be able to extract complete sets of facts from datasets with full pages of text. We then analyse multiple models that estimate factual accuracy on a Wikipedia text summarization task, and show their efficacy compared to ROUGE and other model-free variants by conducting a human evaluation study

Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis

Background: Infections due to antibiotic-resistant bacteria are threatening modern health care. However, estimating their incidence, complications, and attributable mortality is challenging. We aimed to estimate the burden of infections caused by antibiotic-resistant bacteria of public health concern in countries of the EU and European Economic Area (EEA) in 2015, measured in number of cases, attributable deaths, and disability-adjusted life-years (DALYs). Methods: We estimated the incidence of infections with 16 antibiotic resistance–bacterium combinations from European Antimicrobial Resistance Surveillance Network (EARS-Net) 2015 data that was country-corrected for population coverage. We multiplied the number of bloodstream infections (BSIs) by a conversion factor derived from the European Centre for Disease Prevention and Control point prevalence survey of health-care-associated infections in European acute care hospitals in 2011–12 to estimate the number of non-BSIs. We developed disease outcome models for five types of infection on the basis of systematic reviews of the literature. Findings: From EARS-Net data collected between Jan 1, 2015, and Dec 31, 2015, we estimated 671 689 (95% uncertainty interval [UI] 583 148–763 966) infections with antibiotic-resistant bacteria, of which 63·5% (426 277 of 671 689) were associated with health care. These infections accounted for an estimated 33 110 (28 480–38 430) attributable deaths and 874 541 (768 837–989 068) DALYs. The burden for the EU and EEA was highest in infants (aged <1 year) and people aged 65 years or older, had increased since 2007, and was highest in Italy and Greece. Interpretation: Our results present the health burden of five types of infection with antibiotic-resistant bacteria expressed, for the first time, in DALYs. The estimated burden of infections with antibiotic-resistant bacteria in the EU and EEA is substantial compared with that of other infectious diseases, and has increased since 2007. Our burden estimates provide useful information for public health decision-makers prioritising interventions for infectious diseases

Is the Concept of a Green Economy a Useful Way of Framing Policy Discussions and Policymaking to Promote Sustainable Development?

In this article, the authors discuss the use of green economy to promote sustainable development. Research and Partnerships Unit Head Sheng Fulai states that sustainable development is composed of economic, social and environmental development. Furthermore, it features Research and Partnerships associate Gary Flomenhoft who believes that green economy is useful when it deals with factors such as distribution of wealth and throughput of materials and energy

COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

BACKGROUND: Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. METHODS: In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. FINDINGS: Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. INTERPRETATION: Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. FUNDING: British Heart Foundation Data Science Centre, led by Health Data Research UK