Automated Detection of Infectious Disease Outbreaks in Hospitals: A Retrospective Cohort Study
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{"title"=>"Automated detection of infectious disease outbreaks in hospitals: A retrospective cohort study", "type"=>"journal", "authors"=>[{"first_name"=>"Susan S.", "last_name"=>"Huang", "scopus_author_id"=>"7405417069"}, {"first_name"=>"Deborah S.", "last_name"=>"Yokoe", "scopus_author_id"=>"7003941779"}, {"first_name"=>"John", "last_name"=>"Stelling", "scopus_author_id"=>"7004626939"}, {"first_name"=>"Hilary", "last_name"=>"Placzek", "scopus_author_id"=>"35760451700"}, {"first_name"=>"Martin", "last_name"=>"Kulldorff", "scopus_author_id"=>"7004233391"}, {"first_name"=>"Ken", "last_name"=>"Kleinman", "scopus_author_id"=>"7006424133"}, {"first_name"=>"Thomas F.", "last_name"=>"O'Brien", "scopus_author_id"=>"56562380600"}, {"first_name"=>"Michael S.", "last_name"=>"Calderwood", "scopus_author_id"=>"35760229000"}, {"first_name"=>"Johanna", "last_name"=>"Vostok", "scopus_author_id"=>"26536892500"}, {"first_name"=>"Julie", "last_name"=>"Dunn", "scopus_author_id"=>"8089995400"}, {"first_name"=>"Richard", "last_name"=>"Platt", "scopus_author_id"=>"7102023384"}], "year"=>2010, "source"=>"PLoS Medicine", "identifiers"=>{"doi"=>"10.1371/journal.pmed.1000238", "scopus"=>"2-s2.0-77649153367", "pui"=>"358373282", "isbn"=>"1549-1676 (Electronic) 1549-1277 (Linking)", "pmid"=>"20186274", "issn"=>"15491277", "sgr"=>"77649153367"}, "id"=>"94a733e7-26b0-3a49-892b-72bb4ff7967b", "abstract"=>"Detection of outbreaks of hospital-acquired infections is often based on simple rules, such as the occurrence of three new cases of a single pathogen in two weeks on the same ward. These rules typically focus on only a few pathogens, and they do not account for the pathogens' underlying prevalence, the normal random variation in rates, and clusters that may occur beyond a single ward, such as those associated with specialty services. Ideally, outbreak detection programs should evaluate many pathogens, using a wide array of data sources.", "link"=>"http://www.mendeley.com/research/automated-detection-infectious-disease-outbreaks-hospitals-retrospective-cohort-study-6", "reader_count"=>47, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Librarian"=>1, "Researcher"=>14, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>1, "Student > Master"=>5, "Other"=>2, "Student > Bachelor"=>3, "Lecturer > Senior Lecturer"=>2, "Professor"=>3}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Librarian"=>1, "Researcher"=>14, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>1, "Student > Master"=>5, "Other"=>2, "Student > Bachelor"=>3, "Lecturer > Senior Lecturer"=>2, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Engineering"=>1, "Mathematics"=>2, "Medicine and Dentistry"=>27, "Agricultural and Biological Sciences"=>10, "Computer Science"=>4, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>27}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>10}, "Computer Science"=>{"Computer Science"=>4}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>2}}, "reader_count_by_country"=>{"New Zealand"=>1, "United States"=>2, "United Kingdom"=>2, "Kenya"=>1, "Germany"=>1}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/862321"], "description"=>"a<p>Number of days from the first culture associated with the cluster and the date of the first alert.</p>b<p>Number of days between the first and the last alert for a cluster.</p>c<p>Reflects the frequency (d) in which such as cluster is expected to occur by chance alone. Only clusters meeting a threshold recurrence interval of ≥365 d are provided.</p>d–e<p>Indicates same cluster identified by more than one signal type.</p><p>N, no; Y, yes.</p>", "links"=>[], "tags"=>["hospital-associated", "clusters", "whonet-satscan", "automated"], "article_id"=>532769, "categories"=>["Mathematics", "Medicine", "Virology"], "users"=>["Susan S. Huang", "Deborah S. Yokoe", "John Stelling", "Hilary Placzek", "Martin Kulldorff", "Ken Kleinman", "Thomas F. O'Brien", "Michael S. Calderwood", "Johanna Vostok", "Julie Dunn", "Richard Platt"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000238.t003", "stats"=>{"downloads"=>5, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Potential_hospital_associated_clusters_detected_using_WHONET_SaTScan_automated_system_2002_8211_2006_/532769", "title"=>"Potential hospital-associated clusters detected using WHONET-SaTScan automated system, 2002–2006.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 02:35:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/862175"], "description"=>"<p>Significant differences among organism type and cluster size were noted when assessing the likelihood of triggering an intervention (Fisher exact tests). A trend toward a significant difference was found among cluster types. Among organism type, the likelihood of a cluster triggering an intervention was: gram-positive (43%), gram-negative (13%), fungal (14%). Among cluster size, the likelihood of a cluster triggering an intervention was: 2–5 (13%), 6–10 (45%), 10+ (44%). Among recurrence interval, the likelihood of a cluster triggering an intervention was: 365–999 (20%), 1,000–5,000 (20%), >5,000 (36%). Among cluster type, the likelihood of a cluster was: hospital (27%), antibiotic profile (12%), ward (38%), and service (50%).</p>", "links"=>[], "tags"=>["survey-based", "whonet-satscan"], "article_id"=>532627, "categories"=>["Mathematics", "Medicine", "Virology"], "users"=>["Susan S. Huang", "Deborah S. Yokoe", "John Stelling", "Hilary Placzek", "Martin Kulldorff", "Ken Kleinman", "Thomas F. O'Brien", "Michael S. Calderwood", "Johanna Vostok", "Julie Dunn", "Richard Platt"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000238.g002", "stats"=>{"downloads"=>3, "page_views"=>39, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Graph_showing_survey_based_Infection_Control_response_by_type_of_WHONET_SaTScan_cluster_/532627", "title"=>"Graph showing survey-based Infection Control response by type of WHONET-SaTScan cluster.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 02:34:55"}
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Relative Metric

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