SenseMaker: Co-creating Sensors for Journalism

SenseMaker started with a simple question: what happens when journalists, engineers and communities come together to design and build sensors for storytelling? The project was underpinned by the desire to create new ‘sensing’ devices. These sensors would have a tangible benefit for journalists and storytellers who seek to capture data to reveal, drive or enhance stories in their communities. The core enquiry sought to understand how open collaborations, centred on research and development, can create new editorial opportunities, particularly when data, sensors and narratives combine. In short, the core to SenseMaker was: • To co-design journalism sensors • To use them to create, prompt, underpin and develop content • To provoke, prompt or challenge public debate Partners included the Media Innovation Studio and Engineering Innovation Centre teams at the University of Central Lancashire (UCLan) and Reach PLC, specifically with the involvement of the Manchester Evening News (MEN). This is our final project report

The Roles Of State And Non-State Actors In Early Warning And Early Action

In early warning and early action (EWEA), the active participation of non-state actors is imperative to the design and dissemination of effective warnings and in ensuring that life-saving preparedness measures are actioned when alerts are issued. Regrettably, there is often a lack of collaboration between state and non-state actors in the development and coordination of early action plans for extreme weather and climate-related events. This document provides an overview of the state and non-state actors involved in building effective, people-centred, inclusive and resilient early warning systems (EWS) at the local, national, and international levels (as depicted in Figure 1). It also outlines their specific roles and contributions across the four key areas of the early warning system value chain (as illustrated in Figure 2). Additionally, this document encompasses eight essential recommendations (R1-R8) aimed at international organisations, national governments and other actors involved in developing and providing early warnings and/or early action to improve the willingness and efficacy of EWEA actors to collaborate and ensure a comprehensive approach to disaster risk reduction and response. These recommendations are accompanied by a set of actions designed for various stakeholders engaged in early warning systems EWS. The actions are intended to facilitate the broader adoption, utilisation, and sustainability of the recommendation

Genomic microbial epidemiology is needed to comprehend the global problem of antibiotic resistance and to improve pathogen diagnosis

Contamination of waste effluent from hospitals and intensive food animal production with antimicrobial residues is an immense global problem. Antimicrobial residues exert selection pressures that influence the acquisition of antimicrobial resistance and virulence genes in diverse microbial populations. Despite these concerns there is only a limited understanding of how antimicrobial residues contribute to the global problem of antimicrobial resistance. Furthermore, rapid detection of emerging bacterial pathogens and strains with resistance to more than one antibiotic class remains a challenge. A comprehensive, sequence-based genomic epidemiological surveillance model that captures essential microbial metadata is needed, both to improve surveillance for antimicrobial resistance and to monitor pathogen evolution. Escherichia coli is an important pathogen causing both intestinal [intestinal pathogenic E. coli (IPEC)] and extraintestinal [extraintestinal pathogenic E. coli (ExPEC)] disease in humans and food animals. ExPEC are the most frequently isolated Gram negative pathogen affecting human health, linked to food production practices and are often resistant to multiple antibiotics. Cattle are a known reservoir of IPEC but they are not recognized as a source of ExPEC that impact human or animal health. In contrast, poultry are a recognized source of multiple antibiotic resistant ExPEC, while swine have received comparatively less attention in this regard. Here, we review what is known about ExPEC in swine and how pig production contributes to the problem of antibiotic resistance

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

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

Blood glucose as a predictor of mortality in children admitted to the hospital with febrile illness in Tanzania.

Data from a prospective study of 3,319 children ages 2 months to 5 years admitted with febrile illness to a Tanzanian district hospital were analyzed to determine the relationship of blood glucose and mortality. Hypoglycemia (blood sugar 5 mmol/L, the adjusted odds of dying were 3.3 (95% confidence interval = 2.1-5.2) and 9.8 (95% confidence interval = 5.1-19.0) among children with admission blood glucose 2.5-5 and < 2.5 mmol/L, respectively. Receiver operating characteristic (ROC) analysis suggested an optimal cutoff for admission blood sugar of < 5 mmol/L in predicting mortality (sensitivity = 57.7%, specificity = 75.2%). A cutoff for admission blood glucose of < 5 mmol/L represents a simple and clinically useful predictor of mortality in children admitted with severe febrile illness to hospital in resource-poor settings

Differences in the recurrence and mortality outcomes rates of incidental and nonincidental papillary thyroid microcarcinoma: a systematic review and meta-analysis of 21 329 person-years of follow-up

Context: There is controversy as to whether papillary thyroid microcarcinoma (PTMC) represents more than one disease entity with different outcomes, requiring different treatment. Objectives: To compare characteristics, outcomes, and factors associated with prognosis of incidental and nonincidental PTMC. Setting and Design: Two reviewers performed searches of online databases (1966–2012), reference lists, and conference abstract books. Longitudinal studies of subjects >16 years old receiving any treatments for papillary thyroid cancer ≤10 mm in size were included. Two reviewers independently screened abstracts and articles, extracted data, and assessed quality of studies using National Institute of Clinical Excellence and PRISMA criteria. Results: Of 1102 abstracts identified, 262 studies were reviewed and 17 studies included, comprising 3523 subjects, with mean follow-up of 70 months and total follow-up of 21 329 person-years. This included 854 subjects with incidental PTMC (follow-up, 4800 person-years; mean tumor size, 4.6 mm [range 3.3–6.7 mm]) and 2669 nonincidental PTMC cases (follow-up, 16 529 person-years; mean tumor size, 6.9 mm [range 5.6–8.0 mm]). The recurrence rate in the incidental group (0.5%; 95% confidence interval [CI], 0–1%, P < .001) was significantly lower than that in the nonincidental group PTMC (7.9%; 95% CI, 5–11%), with an OR of recurrence of 14.7 (95% CI, 5.6–54.8, P < .001) for nonincidental PTMC, compared with incidental PTMC. Lymph nodes were involved in 80% (126/157) of recurrences. On meta-regression, age, sex, size, tumor multifocality, lymph node involvement, and treatment modality were not significantly associated with recurrence. Conclusions: Our meta-analysis strongly suggests the existence of at least two distinct entities of PTMC. Incidental PTMC has different clinical characteristics and a much lower recurrence rate than nonincidental PTMC, suggesting that management protocols should be re-considered. Additional studies with standardized data collection are required to explore potential differences between subgroups of nonincidental PTMC