The University of Southampton
University of Southampton Institutional Repository

A spatial scan statistic for nonisotropic two-level risk cluster

A spatial scan statistic for nonisotropic two-level risk cluster
A spatial scan statistic for nonisotropic two-level risk cluster
Spatial scan statistic methods are commonly used for geographical disease surveillance and cluster detection. The standard spatial scan statistic does not model any variability in the underlying risks of subregions belonging to a detected cluster. For a multilevel risk cluster, the isotonic spatial scan statistic could model a centralized high-risk kernel in the cluster. Because variations in disease risks are anisotropic owing to different social, economical, or transport factors, the real high-risk kernel will not necessarily take the central place in a whole cluster area. We propose a spatial scan statistic for a nonisotropic two-level risk cluster, which could be used to detect a whole cluster and a noncentralized high-risk kernel within the cluster simultaneously. The performance of the three methods was evaluated through an intensive simulation study. Our proposed nonisotropic two-level method showed better power and geographical precision with two-level risk cluster scenarios, especially for a noncentralized high-risk kernel. Our proposed method is illustrated using the hand-foot-mouth disease data in Pingdu City, Shandong, China in May 2009, compared with two other methods. In this practical study, the nonisotropic two-level method is the only way to precisely detect a high-risk area in a detected whole cluster.
geographical disease surveillance, hand–foot–mouth disease, nonisotropic two-level risk cluster, spatial scan statistic
0277-6715
177-187
Li, Xiao-Zhou
ced34730-83c9-4b8b-9241-74da17a62c9e
Wang, Jin-Feng
b8ccd997-188b-4d55-af4b-02f6189625ba
Yang, Wei-Zhong
35daea8e-8d21-43ec-bc93-f39c1c304316
Li, Zhong-Jie
c6c1bcc6-e23f-4b30-bc15-ce18abbab914
Lai, Shengjie
b57a5fe8-cfb6-4fa7-b414-a98bb891b001
Li, Xiao-Zhou
ced34730-83c9-4b8b-9241-74da17a62c9e
Wang, Jin-Feng
b8ccd997-188b-4d55-af4b-02f6189625ba
Yang, Wei-Zhong
35daea8e-8d21-43ec-bc93-f39c1c304316
Li, Zhong-Jie
c6c1bcc6-e23f-4b30-bc15-ce18abbab914
Lai, Shengjie
b57a5fe8-cfb6-4fa7-b414-a98bb891b001

Li, Xiao-Zhou, Wang, Jin-Feng, Yang, Wei-Zhong, Li, Zhong-Jie and Lai, Shengjie (2012) A spatial scan statistic for nonisotropic two-level risk cluster. Statistics in Medicine, 31 (2), 177-187. (doi:10.1002/sim.4341). (PMID:21850654)

Record type: Article

Abstract

Spatial scan statistic methods are commonly used for geographical disease surveillance and cluster detection. The standard spatial scan statistic does not model any variability in the underlying risks of subregions belonging to a detected cluster. For a multilevel risk cluster, the isotonic spatial scan statistic could model a centralized high-risk kernel in the cluster. Because variations in disease risks are anisotropic owing to different social, economical, or transport factors, the real high-risk kernel will not necessarily take the central place in a whole cluster area. We propose a spatial scan statistic for a nonisotropic two-level risk cluster, which could be used to detect a whole cluster and a noncentralized high-risk kernel within the cluster simultaneously. The performance of the three methods was evaluated through an intensive simulation study. Our proposed nonisotropic two-level method showed better power and geographical precision with two-level risk cluster scenarios, especially for a noncentralized high-risk kernel. Our proposed method is illustrated using the hand-foot-mouth disease data in Pingdu City, Shandong, China in May 2009, compared with two other methods. In this practical study, the nonisotropic two-level method is the only way to precisely detect a high-risk area in a detected whole cluster.

This record has no associated files available for download.

More information

Accepted/In Press date: 17 May 2011
e-pub ahead of print date: 16 August 2011
Published date: 30 January 2012
Keywords: geographical disease surveillance, hand–foot–mouth disease, nonisotropic two-level risk cluster, spatial scan statistic
Organisations: Population, Health & Wellbeing (PHeW)

Identifiers

Local EPrints ID: 373608
URI: http://eprints.soton.ac.uk/id/eprint/373608
ISSN: 0277-6715
PURE UUID: c939ca49-40a6-4f4d-9370-1b774927ed0e
ORCID for Shengjie Lai: ORCID iD orcid.org/0000-0001-9781-8148

Catalogue record

Date deposited: 26 Jan 2015 13:20
Last modified: 15 Mar 2024 04:02

Export record

Altmetrics

Contributors

Author: Xiao-Zhou Li
Author: Jin-Feng Wang
Author: Wei-Zhong Yang
Author: Zhong-Jie Li
Author: Shengjie Lai ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×