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Detecting multiple spatial disease clusters: information criterion and scan statistic approach

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journal contribution
posted on 04.09.2020, 16:00 by Kunihiko Takahashi, Hideyasu ShimadzuHideyasu Shimadzu
Background: Detecting the geographical tendency for the presence of a disease or incident is, particularly at an early stage, a key challenge for preventing severe consequences. Given recent rapid advancements in information technologies, it is required a comprehensive framework that enables simultaneous detection of multiple spatial clusters, whether disease cases are randomly scattered or clustered around specifc epicenters on a larger scale. We develop a new methodology that detects multiple spatial disease clusters and evaluates its performance compared to existing other methods.
Methods: A novel framework for spatial multiple-cluster detection is developed. The framework directly stands on the integrated bases of scan statistics and generalized linear models, adopting a new information criterion that selects the appropriate number of disease clusters. We evaluated the proposed approach using a real dataset, the hospital admission for chronic obstructive pulmonary disease (COPD) in England, and simulated data, whether the approach tends to select the correct number of clusters.
Results: A case study and simulation studies conducted both confrmed that the proposed method performed better compared to conventional cluster detection procedures, in terms of higher sensitivity.
Conclusions: We proposed a new statistical framework that simultaneously detects and evaluates multiple disease clusters in a large study space, with high detection power compared to conventional approaches.

Funding

JSPS KAKENHI Grant Numbers: JP17K00046 and JP19K21569.

History

School

  • Science

Department

  • Mathematical Sciences

Published in

International Journal of Health Geographics

Volume

19

Issue

1

Publisher

BioMed Central

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Acceptance date

25/08/2020

Publication date

2020-09-02

Copyright date

2020

eISSN

1476-072X

Language

en

Depositor

Dr Hideyasu Shimadzu. Deposit date: 2 September 2020

Article number

33