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

Hu, Wenbiao, Mengersen, Kerrie and Tong, Shilu (2010) Risk factor analysis and spatiotemporal CART model of cryptosporidiosis in Queensland, Australia. BMC Infectious Diseases, 10 311-1-311-13. doi:10.1186/1471-2334-10-311


Author Hu, Wenbiao
Mengersen, Kerrie
Tong, Shilu
Title Risk factor analysis and spatiotemporal CART model of cryptosporidiosis in Queensland, Australia
Journal name BMC Infectious Diseases   Check publisher's open access policy
ISSN 1471-2334
Publication date 2010-10
Sub-type Article (original research)
DOI 10.1186/1471-2334-10-311
Open Access Status DOI
Volume 10
Start page 311-1
End page 311-13
Total pages 13
Place of publication London, United Kingdom
Publisher BioMed Central
Collection year 2011
Language eng
Formatted abstract
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 32oC, 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.
Keyword Ecological data
Seasonality
Cryptosporidiosis disease
Prediction model
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article # 311

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2011 Collection
School of Public Health Publications
 
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Created: Sun, 12 Dec 2010, 00:11:48 EST