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Methods for eliciting aetiological clues from geographically clustered cases of disease, with application to leukaemia–lymphoma data
Author(s) -
Williams Joan R.,
Alexander Freda E.,
Cartwright Ray A.,
McNally Richard J. Q.
Publication year - 2001
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/1467-985x.00185
Subject(s) - cluster analysis , disease , etiology , correlation , cluster (spacecraft) , geography , computer science , medicine , pathology , artificial intelligence , mathematics , geometry , programming language
The nearest neighbour analysis method has been developed to determine whether a disease case may be regarded as being unusually close to other neighbouring cases of the same disease. Using this method, each disease case is classified as spatially ‘clustered’ or ‘non‐clustered’. The method is also used to provide a test for global clustering. ‘Clusters’ are constructed by amalgamating geographically neighbouring clustered cases into one contiguous ‘cluster area’. This paper describes a method for studying differences between clustered and non‐clustered cases, in terms of case ‘attributes’. These attributes may be person related, such as age and sex, or area based, such as geographical isolation. The area‐based variables are subject to geographical correlation. The comparison of clustered and non‐clustered cases may reveal similarities or differences, which may, in turn, give clues to disease aetiology. A method for studying ‘linkage’ or similarities in attributes between cases that occur in the same clusters is also described. The methods are illustrated by application to incidence data for leukaemias and lymphomas.