4 edition of Methods for investigating localized clustering of disease found in the catalog.
by International Agency for Research on Cancer, World Health Organization in Lyon, France
Written in English
Includes bibliographical references (p. 236-247).
|Statement||edited by F.E. Alexander and P. Boyle.|
|Series||IARC scientific publications ;, no. 135|
|Contributions||Alexander, F. E., Boyle, P.|
|LC Classifications||RA652.2.M3 M465 1996|
|The Physical Object|
|Pagination||247 p. :|
|Number of Pages||247|
|LC Control Number||97183624|
Hierarchical cluster analysis is a statistical method for finding relatively homogeneous clusters of cases based on dissimilarities or distances between objects. It starts with each case as a separate cluster (i.e., there are as many clusters as cases), and then combines the clusters sequentially, reducing the number of clusters at each step until only one cluster is left. Methodological considerations in the study of clusters and clustering of childhood cancer are reviewed briefly. A selection of 11 studies of individual clusters of childhood leukaemia which are either particularly notable or recent and have been reported in peer review journals is then considered. Focus is placed on sources of alerts, descriptive studies, field-work studies, conclusions and Cited by:
Apparent clusters are not valuable to guide additional intervention efforts, since these would prevent few additional cases. Our method of space-time nearest-neighborship analysis provides a sensitive novel approach to the epidemiology of meningococcal disease and possibly even other infectious diseases. Location and Disease • location = clue to cause • history (e.g., Snow’s Cholera studies) – Migrant studies (e.g., breast cancer in Japanese women who move to the US) • contemporary examples • Lyme disease, infectious diseases (H1N1 ‘09) • heart disease and “Mediterranean diet” • File Size: 1MB.
The Scien~:e of the Total Environment, () [ Elsevier Science Publishers B.V., Amsterdam Methodological problems in investigating disease clusters Daniel Wartenber)~~ and Michael Greenbergb ~Department of Environmental and Comnmnity Medicine at~d Graduate Program bt Public Health, Robert Wtr~ced J'ohnsa,n Medical School, Piscataway, NJ, USA ~Depattment of Urban Cited by: 8. Start studying Epidemology Midterm. Learn vocabulary, terms, and more with flashcards, games, and other study tools. Search. A method for providing quantitative measurements of risks to health is known as: Localized occurrence of disease. Urban diseases and causes of mortality are more likely to be those spread by.
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The identification and analysis of disease clusters can yield significant clues in epidemiologic research, and as such will continue to be an important subject of cancer research and epidemiology for the foreseeable : Paperback.
Methods for investigating generalized spatial clustering of disease in human populations have only recently become available. This volume presents the outcome of a unique practical test of these methods, in which authors of several newly-developed approaches conducted their own blind analyses of over 50 artificial datasets, some random, some generated by clustering processes.
PDF Book Methods For Investigating Localized Clustering Of Disease Iarc Scientific Publications Author: Yasuo Uchida Library Subject: Methods For Investigating Localized ePub Format Keywords: Methods,For,Investigating,Localized,Clustering,Of,Disease,Iarc,Scientific,Publications ePub Format Created Date: +02'00'.
Methods for investigating localized clustering of disease. Second-order analysis of spatial clustering. Diggle PJ(1), Morris S. Author information: (1)Department of Mathematics and Statistics, University of Cited by: 3. IARC Sci Publ.
;() Methods for investigating localized clustering of disease. The data-sets. [No authors listed] PMID: Methods for investigating localized clustering of disease.
Geostatistics for determining the risk of rare disease. Oliver MA(1), Muir KR, Parkes SE, Webster R. Author information: (1)Institute of Public and Environmental Health, School of Chemistry, University of Birmingham, Edgbaston, UK.
PMID: [Indexed for MEDLINE] MeSH termsCited by: 1. Methods for investigating localized clustering of disease. Using a geographical analysis machine to detect the presence of spatial clustering and the location of clusters in synthetic data. Openshaw S(1). Author information: (1)Department of Geography, University of Leeds, by: Testing for over-dispersion using an adapted form of the Potthoff-Whittinghill method, C.R.
Muirhead and B.K. Butland; 5. Clustering methods based on k nearest neighbour distributions, J. Cuzick and R. Edwards; 6.
Using a geographical analysis machine to detect the presence of spatial clustering and the location of clusters in synthetic data, S. Openshaw. Methods for investigating localized clustering of disease. Historical aspects of leukaemia clusters.
Boyle P(1), Walker AM, Alexander FE. Author information: (1)Division of Epidemiology and Biostatistics, European Institute of Oncology, Milan, by: 6.
Statistical Methods for Disease Clustering. The development of powerful computing environment and the geographical information system (GIS) in recent decades has thrust the analysis of geo-referenced disease incidence data into the mainstream of spatial : Toshiro Tango.
Methods for investigating localized clustering of disease. Using a geographical analysis machine to detect the presence of spatial clustering and the location of clusters in synthetic data. Openshaw S. IARC Sci Publ, (); discussion01 Jan Cited by: 0 articles | PMID: Methods for investigating localized clustering of disease.
Lyon, France: International Agency for Research on Cancer, World Health Organization, (OCoLC) Material Type: Internet resource: Document Type: Book, Internet Resource: All Authors / Contributors: F E Alexander; P Boyle.
Author: Newell JN, Journal: IARC scientific publications Methods for investigating localized clustering of disease. The detection of small-area database by: 2.
Likelihood-Based Tests for Localized Spatial Clustering of Disease Article in Environmetrics 15(8) - December with 7 Reads How we measure 'reads'. If we permit clusters to have subclusters, then we obtain a hierarchical clustering, which is a set of nested clusters that are organized as a tree.
Each node (cluster) in the tree (except for the leaf nodes) is the union of its children (subclusters), and the root of the tree is the cluster containing all the objects.
In order to test for clustering of leukemia or any other disease under this scheme, only two measurements are required for each 5-year unit: the total number of occurrences in the unit and the maximum number of occurrences in the year in which the maximum number by: There have been articles on comparing methods for global clustering evaluation and cluster detection in disease surveillance, but power and sample size (SS) requirements have not been explored for.
Many different methods have been proposed to test for the presence of geographical clusters. Two of the most popular methods are the spatial scan method proposed by Kulldorff and that using a fixed number of cases within scanning circles proposed by Besag and by: The methods have been applied to the clustering of gonorrhoea in the USA 8 and Chlamydia trachomatis in Canada 35 and the clustering of three bacterial STI infections (gonorrhea, chlamydia and syphilis) in Australia.
36 In the developing world, the methods have been used to investigate spatial patterns of childhood mortality in Burkina Faso. 37 Cited by: A Method for Testing Low-Value Spatial Clustering for Rare Disease Article in Acta Tropica 91(3) September with 14 Reads How we measure 'reads'.
A reported “cluster” of excess childhood leukemia cases and possible environmental causes in Woburn, Massachusetts, formed a key motivation for the events described in the popular book and motion picture A Civil Action. Although statistical methods to assess spatial clustering existed prior to the events in Woburn, increasing interest in environmental risk factors and recent developments Cited by: Filling the Gap: Functional Clustering of ABC Proteins for the Investigation of Hormonal Transport in Statistical Methods for Disease Clustering T.
Tango, New York, Springer x+ pp., € ISBN ‐1‐‐‐9 This is an updated book that provides statistical methods for detecting disease clustering and localized clusters. The methods provided are very useful in epidemiology where it is important to evaluate whether a disease is randomly distributed across .