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Multiple-cluster detection test for purely temporal disease clustering: integration of scan statistics and generalized linear models

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posted on 23.11.2018, 14:27 by Kunihiko Takahashi, Hideyasu ShimadzuHideyasu Shimadzu
The spatial scan statistic is commonly used to detect spatial and/or temporal disease clusters in epidemiological studies. Although multiple clusters in the study space can be thus identified, current theoretical developments are mainly based on detecting a ‘single’ cluster. The standard scan statistic procedure enables the detection of multiple clusters, recursively identifying additional ‘secondary’ clusters. However, their p-values are calculated one at a time, as if each cluster is a primary one. Therefore, a new procedure that can accurately evaluate multiple clusters as a whole is needed. The present study focuses on purely temporal cases and then proposes a new test procedure that evaluates the p-value for multiple clusters, combining generalized linear models with an information criterion approach. This framework encompasses the conventional, currently widely used detection procedure as a special case. An application study adopting the new framework is presented, analysing the Japanese daily incidence of out-of-hospital cardiac arrest cases. The analysis reveals that the number of the incident increases around New Year’s Day in Japan. Further, simulation studies undertaken confirm that the proposed method possesses a consistency property that tends to select the correct number of clusters when the truth is known.

History

School

  • Science

Department

  • Mathematical Sciences

Published in

PLoS ONE

Volume

13

Issue

11

Citation

TAKAHASHI, K. and SHIMADZU, H., 2018. Multiple-cluster detection test for purely temporal disease clustering: integration of scan statistics and generalized linear models. PLoS ONE, 13(11): e0207821.

Publisher

© The Authors. Published by Public Library of Science (PLoS)

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/ by/4.0/

Acceptance date

09/11/2018

Publication date

2018-11-21

Notes

This is an Open Access Article. It is published by Public Library of Science (PLoS) under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

ISSN

1932-6203

Language

en

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