Using surveillance data to determine treatment rates and outcomes for patients with chronic hepatitis C virus infection

The aim of this work was to develop and validate an algorithm to monitor rates of, and response to, treatment of patients infected with hepatitis C virus (HCV) across England using routine laboratory HCV RNA testing data. HCV testing activity between January 2002 and December 2011 was extracted from the local laboratory information systems of a sentinel network of 23 laboratories across England. An algorithm based on frequency of HCV RNA testing within a defined time period was designed to identify treated patients. Validation of the algorithm was undertaken for one center by comparison with treatment data recorded in a clinical database managed by the Trent HCV Study Group. In total, 267,887 HCV RNA test results from 100,640 individuals were extracted. Of these, 78.9% (79,360) tested positive for viral RNA, indicating an active infection, 20.8% (16,538) of whom had a repeat pattern of HCV RNA testing suggestive of treatment monitoring. Annual numbers of individuals treated increased rapidly from 468 in 2002 to 3,295 in 2009, but decreased to 3,110 in 2010. Approximately two thirds (63.3%; 10,468) of those treated had results consistent with a sustained virological response, including 55.3% and 67.1% of those with a genotype 1 and non-1 virus, respectively. Validation against the Trent clinical database demonstrated that the algorithm was 95% sensitive and 93% specific in detecting treatment and 100% sensitive and 93% specific for detecting treatment outcome. Conclusions: Laboratory testing activity, collected through a sentinel surveillance program, has enabled the first country-wide analysis of treatment and response among HCV-infected individuals. Our approach provides a sensitive, robust, and sustainable method for monitoring service provision across Englan

Gattini : a multisite campaign for the measurement of sky brightness in Antarctica

We present the Gattini project: a multisite campaign to measure the optical sky properties above the two high altitude Antarctic astronomical sites of Dome C and Dome A. The Gattini-DomeC project, part of the IRAIT site testing campaign and ongoing since January 2006, consists of two cameras for the measurement of optical sky brightness, large area cloud cover and auroral detection above the DomeC site, home of the French-Italian Concordia station. The cameras are transit in nature and are virtually identical except for the nature of the lenses. The cameras have operated successfully throughout the past two Antarctic winter seasons and here we present the first results obtained from the returned 2006 dataset. The Gattini-DomeA project will place a similar site testing facility at the highest point on the Antarctic plateau, Dome A, with observations commencing in 2008. The project forms a small part of a much larger venture coordinated by the Polar Research Institute of China as part of the International Polar Year whereby an automated site testing facility called PLATO will be traversed into the DomeA site. The status of this exciting and ambitious project with regards to the Gattini-DomeA cameras will be presented

Gattini: A multisite campaign for the measurement of sky brightness in Antarctica

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The value of open-source clinical science in pandemic response: lessons from ISARIC

International audienc

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use