Use of Space–Time Models to Investigate the Stability of Patterns of Disease | Zendy

Juan J. Abellán | Zendy; Sylvia Richardson | Zendy; Nicky Best | Zendy

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Use of Space–Time Models to Investigate the Stability of Patterns of Disease

Author(s) -

Juan J. Abellán,

Sylvia Richardson,

Nicky Best

Publication year - 2008

Publication title -

environmental health perspectives

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.257

H-Index - 282

eISSN - 1552-9924

pISSN - 0091-6765

DOI - 10.1289/ehp.10814

Subject(s) - bayesian probability , dimension (graph theory) , computer science , stability (learning theory) , bayesian hierarchical modeling , bayes' theorem , hierarchical database model , disease , data mining , bayesian inference , interpretation (philosophy) , econometrics , statistics , machine learning , artificial intelligence , mathematics , medicine , pathology , pure mathematics , programming language

The use of Bayesian hierarchical spatial models has become widespread in disease mapping and ecologic studies of health-environment associations. In this type of study, the data are typically aggregated over an extensive time period, thus neglecting the time dimension. The output of purely spatial disease mapping studies is therefore the average spatial pattern of risk over the period analyzed, but the results do not inform about, for example, whether a high average risk was sustained over time or changed over time.

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