Spatial analysis on human brucellosis incidence in mainland China: 2004-2010.
OBJECTIVE
China has experienced a sharply increasing rate of human brucellosis in recent years. Effective spatial monitoring of human brucellosis incidence is very important for successful implementation of control and prevention programmes. The purpose of this paper is to apply exploratory spatial data analysis (ESDA) methods and the empirical Bayes (EB) smoothing technique to monitor county-level incidence rates for human brucellosis in mainland China from 2004 to 2010 by examining spatial patterns.


METHODS
ESDA methods were used to characterise spatial patterns of EB smoothed incidence rates for human brucellosis based on county-level data obtained from the China Information System for Disease Control and Prevention (CISDCP) in mainland China from 2004 to 2010.


RESULTS
EB smoothed incidence rates for human brucellosis were spatially dependent during 2004-2010. The local Moran test identified significantly high-risk clusters of human brucellosis (all p values <0.01), which persisted during the 7-year study period. High-risk counties were centred in the Inner Mongolia Autonomous Region and other Northern provinces (ie, Hebei, Shanxi, Jilin and Heilongjiang provinces) around the border with the Inner Mongolia Autonomous Region where animal husbandry was highly developed. The number of high-risk counties increased from 25 in 2004 to 54 in 2010.


CONCLUSIONS
ESDA methods and the EB smoothing technique can assist public health officials in identifying high-risk areas. Allocating more resources to high-risk areas is an effective way to reduce human brucellosis incidence.
West China School of Public Health, Sichuan University, Chengdu, People's Republic of China.
Junhui
J
Zhang
Fei
F
Yin
Tao
T
Zhang
Chao
C
Yang
Xingyu
X
Zhang
Zijian
Z
Feng
Xiaosong
X
Li
D001499:Bayes Theorem; D002006:Brucellosis / Q000453:epidemiology*; D002681:China / Q000453:epidemiology; D005260:Female; D006801:Humans; D015994:Incidence; D008297:Male; D018570:Risk Assessment; D062206:Spatial Analysis
Human brucellosis; cluster detection; empirical Bayes smoothing; exploratory spatial data analysis; spatial autocorrelation