Multiple-cluster detection test for purely temporal disease clustering: Integration of scan statistics and generalized linear models.
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 s...
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doaj-2f8cb3f1e8de4cc99931268c58e700582020-11-24T22:18:40ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-011311e020782110.1371/journal.pone.0207821Multiple-cluster detection test for purely temporal disease clustering: Integration of scan statistics and generalized linear models.Kunihiko TakahashiHideyasu ShimadzuThe 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.http://europepmc.org/articles/PMC6249023?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kunihiko Takahashi Hideyasu Shimadzu |
spellingShingle |
Kunihiko Takahashi Hideyasu Shimadzu Multiple-cluster detection test for purely temporal disease clustering: Integration of scan statistics and generalized linear models. PLoS ONE |
author_facet |
Kunihiko Takahashi Hideyasu Shimadzu |
author_sort |
Kunihiko Takahashi |
title |
Multiple-cluster detection test for purely temporal disease clustering: Integration of scan statistics and generalized linear models. |
title_short |
Multiple-cluster detection test for purely temporal disease clustering: Integration of scan statistics and generalized linear models. |
title_full |
Multiple-cluster detection test for purely temporal disease clustering: Integration of scan statistics and generalized linear models. |
title_fullStr |
Multiple-cluster detection test for purely temporal disease clustering: Integration of scan statistics and generalized linear models. |
title_full_unstemmed |
Multiple-cluster detection test for purely temporal disease clustering: Integration of scan statistics and generalized linear models. |
title_sort |
multiple-cluster detection test for purely temporal disease clustering: integration of scan statistics and generalized linear models. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2018-01-01 |
description |
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. |
url |
http://europepmc.org/articles/PMC6249023?pdf=render |
work_keys_str_mv |
AT kunihikotakahashi multipleclusterdetectiontestforpurelytemporaldiseaseclusteringintegrationofscanstatisticsandgeneralizedlinearmodels AT hideyasushimadzu multipleclusterdetectiontestforpurelytemporaldiseaseclusteringintegrationofscanstatisticsandgeneralizedlinearmodels |
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