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Adaptations for finding irregularly shaped disease clusters
BACKGROUND: Recent adaptations of the spatial scan approach to detecting disease clusters have addressed the problem of finding clusters that occur in non-compact and non-circular shapes – such as along roads or river networks. Some of these approaches may have difficulty defining cluster boundaries...
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2007
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1939838/ https://www.ncbi.nlm.nih.gov/pubmed/17615077 http://dx.doi.org/10.1186/1476-072X-6-28 |
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author | Yiannakoulias, Nikolaos Rosychuk, Rhonda J Hodgson, John |
author_facet | Yiannakoulias, Nikolaos Rosychuk, Rhonda J Hodgson, John |
author_sort | Yiannakoulias, Nikolaos |
collection | PubMed |
description | BACKGROUND: Recent adaptations of the spatial scan approach to detecting disease clusters have addressed the problem of finding clusters that occur in non-compact and non-circular shapes – such as along roads or river networks. Some of these approaches may have difficulty defining cluster boundaries precisely, and tend to over-fit data with very irregular (and implausible) clusters shapes. RESULTS & DISCUSSION: We describe two simple adaptations to these approaches that can be used to improve the effectiveness of irregular disease cluster detection. The first adaptation penalizes very irregular cluster shapes based on a measure of connectivity (non-connectivity penalty). The second adaptation prevents searches from combining smaller clusters into large super-clusters (depth limit). We conduct experiments with simulated data in order to observe the performance of these adaptations on a number of synthetic cluster shapes. CONCLUSION: Our results suggest that the combination of these two adaptations may increase the ability of a cluster detection method to find irregular shapes without affecting its ability to find more regular (i.e., compact) shapes. The depth limit in particular is effective when it is deemed important to distinguish nearby clusters from each other. We suggest that these adaptations of adjacency-constrained spatial scans are particularly well suited to chronic disease and injury surveillance. |
format | Text |
id | pubmed-1939838 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-19398382007-08-04 Adaptations for finding irregularly shaped disease clusters Yiannakoulias, Nikolaos Rosychuk, Rhonda J Hodgson, John Int J Health Geogr Methodology BACKGROUND: Recent adaptations of the spatial scan approach to detecting disease clusters have addressed the problem of finding clusters that occur in non-compact and non-circular shapes – such as along roads or river networks. Some of these approaches may have difficulty defining cluster boundaries precisely, and tend to over-fit data with very irregular (and implausible) clusters shapes. RESULTS & DISCUSSION: We describe two simple adaptations to these approaches that can be used to improve the effectiveness of irregular disease cluster detection. The first adaptation penalizes very irregular cluster shapes based on a measure of connectivity (non-connectivity penalty). The second adaptation prevents searches from combining smaller clusters into large super-clusters (depth limit). We conduct experiments with simulated data in order to observe the performance of these adaptations on a number of synthetic cluster shapes. CONCLUSION: Our results suggest that the combination of these two adaptations may increase the ability of a cluster detection method to find irregular shapes without affecting its ability to find more regular (i.e., compact) shapes. The depth limit in particular is effective when it is deemed important to distinguish nearby clusters from each other. We suggest that these adaptations of adjacency-constrained spatial scans are particularly well suited to chronic disease and injury surveillance. BioMed Central 2007-07-05 /pmc/articles/PMC1939838/ /pubmed/17615077 http://dx.doi.org/10.1186/1476-072X-6-28 Text en Copyright © 2007 Yiannakoulias et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Yiannakoulias, Nikolaos Rosychuk, Rhonda J Hodgson, John Adaptations for finding irregularly shaped disease clusters |
title | Adaptations for finding irregularly shaped disease clusters |
title_full | Adaptations for finding irregularly shaped disease clusters |
title_fullStr | Adaptations for finding irregularly shaped disease clusters |
title_full_unstemmed | Adaptations for finding irregularly shaped disease clusters |
title_short | Adaptations for finding irregularly shaped disease clusters |
title_sort | adaptations for finding irregularly shaped disease clusters |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1939838/ https://www.ncbi.nlm.nih.gov/pubmed/17615077 http://dx.doi.org/10.1186/1476-072X-6-28 |
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