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Identification of high-risk regions for schistosomiasis in the Guichi region of China: an adaptive kernel density estimation-based approach

Published online by Cambridge University Press:  07 March 2013

ZHI-JIE ZHANG ,
TILMAN M. DAVIES ,
JIE GAO ,
ZENGLIANG WANG  and
QING-WU JIANG
ZHI-JIE ZHANG*
Affiliation:
Department of Epidemiology, School of Public Health, Fudan University, Shanghai 200032, People's Republic of China Key Laboratory of Public Health Safety, Ministry of Education, Shanghai 200032, People's Republic of China Laboratory for Spatial Analysis and Modelling, School of Public Health, Fudan University, Shanghai 200032, People's Republic of China
TILMAN M. DAVIES
Affiliation:
Department of Statistics, Institute of Fundamental Sciences, Massey University, Private Bag 11222, Palmerston North, New Zealand
JIE GAO
Affiliation:
Department of Epidemiology, School of Public Health, Fudan University, Shanghai 200032, People's Republic of China Key Laboratory of Public Health Safety, Ministry of Education, Shanghai 200032, People's Republic of China Laboratory for Spatial Analysis and Modelling, School of Public Health, Fudan University, Shanghai 200032, People's Republic of China
ZENGLIANG WANG
Affiliation:
Department of Epidemiology, School of Public Health, Fudan University, Shanghai 200032, People's Republic of China Key Laboratory of Public Health Safety, Ministry of Education, Shanghai 200032, People's Republic of China Laboratory for Spatial Analysis and Modelling, School of Public Health, Fudan University, Shanghai 200032, People's Republic of China
QING-WU JIANG
Affiliation:
Department of Epidemiology, School of Public Health, Fudan University, Shanghai 200032, People's Republic of China Key Laboratory of Public Health Safety, Ministry of Education, Shanghai 200032, People's Republic of China
*
*Corresponding author: Department of Epidemiology, School of Public Health, Fudan University, Shanghai 200032, People's Republic of China. Tel: +86 21 54237410. Fax: +86 21 54237410. E-mail: epistat@gmail.com
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Summary

Identification of high-risk regions of schistosomiasis is important for rational resource allocation and effective control strategies. We conducted the first study to apply the newly developed method of adaptive kernel density estimation (KDE)-based spatial relative risk function (sRRF) to detect the high-risk regions of schistosomiasis in the Guichi region of China and compared it with the fixed KDE-based sRRF. We found that the adaptive KDE-based sRRF had a better ability to depict the heterogeneity of risk regions, but was more sensitive to altering the user-defined smoothing parameters. Specifically, the impact of bandwidths on the estimated risk value and risk significance (P value) was higher for the adaptive KDE-based sRRF, but lower on the estimated risk variation standard error (s.e.) compared with the fixed KDE-based sRRF. Based on this application the adaptive and fixed KDE-based sRRF have their respective advantages and disadvantages and the joint application of the two approaches can warrant the best possible identification of high-risk subregions of diseases.

Keywords

schistosomiasiskernel density estimationspatial analysisspatial epidemiology
Type
Research Article
Information
Parasitology , Volume 140 , Issue 7 , June 2013 , pp. 868 - 875
Copyright
Copyright © Cambridge University Press 2013 

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