Data from the two systems overlapped in more recent years, due to an improved reporting completeness. ![]() Comparing NSS with HDR data, we found an initially high underreporting of all IBDs, and particularly for Hi. The highest mean annual incidence rate was found for IBD caused by Sp (1.74 per 100,000), followed by Nm (0.42 per 100,000) and by Hi (0.21 per 100,000). Most invasive meningococcal diseases were coded as meningitis (72.3%), while Hi and Sp were more frequently coded as septicaemia (51.6% and 60.4%, respectively). Results: Over a 10-year period, 14,243 hospital discharges with diagnosis of IBD were estimated in Italy (12,671 with specified aetiology and 1,572 with imputed aetiology). A multinomial logistic regression model was used to impute the aetiology of all discharges with a diagnosis of unspecified bacterial meningitis. Methods: Our study compared the number of cases of IBDs identified from the Italian Hospital Discharge Records (HDRs), using specific diagnostic ICD-9-CM codes, with those notified to the National Surveillance System (NSS) from 2007 to 2016. Please sign up with Life Science Network to see all event details.Background: Vaccine-preventable invasive bacterial diseases (IBDs) caused by Neisseria meningitidis (Nm), Streptococcus pneumoniae (Sp), and Haemophilus influenzae (Hi) have been notified in Italy since 2007 without assessing reporting completeness. Operational responsibility of the CSDS is at the Faculty of Social Sciences. In addition, CSDS aims at increasing the collaboration between researchers and methodology experts, with the goal of improving both applied research as well as methods research. CSDS promotes open data and open science as well as the development of infrastructures for data. The aim of Centre for Social Data Science (CSDS) is to advance methodologically oriented, applied and theoretical research and teaching in the social sciences. HiDATA is a joint hub of the two participating universities University of Helsinki and Aalto University. The overarching goal of HiDATA is to leverage the synergies of the network in solving significant societal and industrial challenges related to data analysis. The centre builds on the pioneering scientific results of the community and paves way for new breakthrough results across disciplines. Data Science is an interdisciplinary field focusing on methodologies for extracting knowledge and insights from data thus contributing to different areas of science. Helsinki Centre for Data Science (HiDATA) is a world-class hub of Data Science in Helsinki. Due to a limited number of seats please sign up now: bit.ly/datasociety The event is free of charge and open to the public. More information about the programme and the speakers will be published soonat: Moderator Deputy Mayor of Helsinki City Pia Pakarinen Introduction of panelists and panel discussion The discussion will be moderated by Deputy Mayor of Helsinki City Pia Pakarinen. What kind of ethical challenges machine learning and artificial intelligence developers are facing and how are they being handled?Ĭome and find out! Meet the top researchers from the University of Helsinki centres of Data Science HiDATA and Social Data Science CSDS. ![]() How can data science methods be used in planning better social and health services?What kind of data do social media provide nowadays and how can we make use of it?What kind of new services could we create with the help of data science?How can data science support evidence-based decision-making for the government, organizations and private companies? Tiedekulma - Think Corner (Yliopistonkatu 4) We are pleased to invite you to Data Science in Society event organised by HiDATA and the Centre for Social Data Science! Come and hear how we aim to increase knowledge about the society with data science. Ti 9.00-11.00 Think Corner Yliopistonkatu 4 Helsinki Suomi Please click the Source link below to double check the Start time and Location. University of Helsinki, Helsinki, Finland
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