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Risk factor analysis and spatiotemporal CART model of cryptosporidiosis in Queensland, Australia

BACKGROUND: It remains unclear whether it is possible to develop a spatiotemporal epidemic prediction model for cryptosporidiosis disease. This paper examined the impact of social economic and weather factors on cryptosporidiosis and explored the possibility of developing such a model using social e...

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Autores principales: Hu, Wenbiao, Mengersen, Kerrie, Tong, Shilu
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2987305/
https://www.ncbi.nlm.nih.gov/pubmed/21029426
http://dx.doi.org/10.1186/1471-2334-10-311
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author Hu, Wenbiao
Mengersen, Kerrie
Tong, Shilu
author_facet Hu, Wenbiao
Mengersen, Kerrie
Tong, Shilu
author_sort Hu, Wenbiao
collection PubMed
description BACKGROUND: It remains unclear whether it is possible to develop a spatiotemporal epidemic prediction model for cryptosporidiosis disease. This paper examined the impact of social economic and weather factors on cryptosporidiosis and explored the possibility of developing such a model using social economic and weather data in Queensland, Australia. METHODS: Data on weather variables, notified cryptosporidiosis cases and social economic factors in Queensland were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Three-stage spatiotemporal classification and regression tree (CART) models were developed to examine the association between social economic and weather factors and monthly incidence of cryptosporidiosis in Queensland, Australia. The spatiotemporal CART model was used for predicting the outbreak of cryptosporidiosis in Queensland, Australia. RESULTS: The results of the classification tree model (with incidence rates defined as binary presence/absence) showed that there was an 87% chance of an occurrence of cryptosporidiosis in a local government area (LGA) if the socio-economic index for the area (SEIFA) exceeded 1021, while the results of regression tree model (based on non-zero incidence rates) show when SEIFA was between 892 and 945, and temperature exceeded 32°C, the relative risk (RR) of cryptosporidiosis was 3.9 (mean morbidity: 390.6/100,000, standard deviation (SD): 310.5), compared to monthly average incidence of cryptosporidiosis. When SEIFA was less than 892 the RR of cryptosporidiosis was 4.3 (mean morbidity: 426.8/100,000, SD: 319.2). A prediction map for the cryptosporidiosis outbreak was made according to the outputs of spatiotemporal CART models. CONCLUSIONS: The results of this study suggest that spatiotemporal CART models based on social economic and weather variables can be used for predicting the outbreak of cryptosporidiosis in Queensland, Australia.
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spelling pubmed-29873052010-11-22 Risk factor analysis and spatiotemporal CART model of cryptosporidiosis in Queensland, Australia Hu, Wenbiao Mengersen, Kerrie Tong, Shilu BMC Infect Dis Research Article BACKGROUND: It remains unclear whether it is possible to develop a spatiotemporal epidemic prediction model for cryptosporidiosis disease. This paper examined the impact of social economic and weather factors on cryptosporidiosis and explored the possibility of developing such a model using social economic and weather data in Queensland, Australia. METHODS: Data on weather variables, notified cryptosporidiosis cases and social economic factors in Queensland were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Three-stage spatiotemporal classification and regression tree (CART) models were developed to examine the association between social economic and weather factors and monthly incidence of cryptosporidiosis in Queensland, Australia. The spatiotemporal CART model was used for predicting the outbreak of cryptosporidiosis in Queensland, Australia. RESULTS: The results of the classification tree model (with incidence rates defined as binary presence/absence) showed that there was an 87% chance of an occurrence of cryptosporidiosis in a local government area (LGA) if the socio-economic index for the area (SEIFA) exceeded 1021, while the results of regression tree model (based on non-zero incidence rates) show when SEIFA was between 892 and 945, and temperature exceeded 32°C, the relative risk (RR) of cryptosporidiosis was 3.9 (mean morbidity: 390.6/100,000, standard deviation (SD): 310.5), compared to monthly average incidence of cryptosporidiosis. When SEIFA was less than 892 the RR of cryptosporidiosis was 4.3 (mean morbidity: 426.8/100,000, SD: 319.2). A prediction map for the cryptosporidiosis outbreak was made according to the outputs of spatiotemporal CART models. CONCLUSIONS: The results of this study suggest that spatiotemporal CART models based on social economic and weather variables can be used for predicting the outbreak of cryptosporidiosis in Queensland, Australia. BioMed Central 2010-10-28 /pmc/articles/PMC2987305/ /pubmed/21029426 http://dx.doi.org/10.1186/1471-2334-10-311 Text en Copyright ©2010 Hu 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 Research Article
Hu, Wenbiao
Mengersen, Kerrie
Tong, Shilu
Risk factor analysis and spatiotemporal CART model of cryptosporidiosis in Queensland, Australia
title Risk factor analysis and spatiotemporal CART model of cryptosporidiosis in Queensland, Australia
title_full Risk factor analysis and spatiotemporal CART model of cryptosporidiosis in Queensland, Australia
title_fullStr Risk factor analysis and spatiotemporal CART model of cryptosporidiosis in Queensland, Australia
title_full_unstemmed Risk factor analysis and spatiotemporal CART model of cryptosporidiosis in Queensland, Australia
title_short Risk factor analysis and spatiotemporal CART model of cryptosporidiosis in Queensland, Australia
title_sort risk factor analysis and spatiotemporal cart model of cryptosporidiosis in queensland, australia
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2987305/
https://www.ncbi.nlm.nih.gov/pubmed/21029426
http://dx.doi.org/10.1186/1471-2334-10-311
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