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There has been little development of surveillance procedures for epidemiological data with fine spatial resolution such as case events at residential address locations. This is often due to difficulties of access when confidentiality of medical records is an issue. However, when such data are available, it is important to be able to affect an appropriate analysis strategy. We propose a model for point events in the context of prospective surveillance based on conditional logistic modeling. A weighted conditional autoregressive model is developed for irregular lattices to account for distance effects, and a Dirichlet tessellation is adopted to define the neighborhood structure. Localized clustering diagnostics are compared including the proposed local Kullback-Leibler information criterion. A simulation study is conducted to examine the surveillance and detection methods, and a data example is provided of non-Hodgkin's lymphoma data in South Carolina.

Original publication

DOI

10.1177/0962280216660422

Type

Journal article

Journal

Statistical methods in medical research

Publication Date

08/2016

Volume

25

Pages

1101 - 1117

Addresses

Department of Public Health sciences, Medical University of South Carolina, USA chawarat.rot@biotec.or.th.

Keywords

Humans, Lymphoma, Non-Hodgkin, Cluster Analysis, Logistic Models, Bayes Theorem, Prospective Studies, ROC Curve, Computer Simulation, South Carolina, Epidemiological Monitoring, Spatio-Temporal Analysis