| Detection of aberrations in the occurrence of notifiable diseases surveillance data. | |
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MedLine Citation:
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PMID: 2540519 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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The detection of unusual patterns in the occurrence of diseases and other health events presents an important challenge to public health surveillance. This paper discusses three analytic methods for identifying aberrations in underlying distributions. The methods are illustrated on selected infectious diseases included in the National Notifiable Diseases Surveillance System of the Centers for Disease Control. Results suggest the utility of such an analytic approach. Further work will determine the sensitivity of such methods to variations in the occurrence of disease. These methods are useful for evaluating and monitoring public health surveillance data. |
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Authors:
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D F Stroup; G D Williamson; J L Herndon; J M Karon |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Statistics in medicine Volume: 8 ISSN: 0277-6715 ISO Abbreviation: Stat Med Publication Date: 1989 Mar |
Date Detail:
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Created Date: 1989-05-30 Completed Date: 1989-05-30 Revised Date: 2004-11-17 |
Medline Journal Info:
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Nlm Unique ID: 8215016 Medline TA: Stat Med Country: ENGLAND |
Other Details:
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Languages: eng Pagination: 323-9; discussion 331-2 Citation Subset: IM |
Affiliation:
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Division of Surveillance and Epidemiologic Studies, Centers for Disease Control, Atlanta, Georgia 30333. |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Centers for Disease Control and Prevention (U.S.) Communicable Diseases / epidemiology* Data Interpretation, Statistical* Humans Population Surveillance Space-Time Clustering United States |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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