[Spatial epidemiological study on malaria epidemics in Hainan province].
OBJECTIVE
To better understand the characteristics of spatial distribution of malaria epidemics in Hainan province and to explore the relationship between malaria epidemics and environmental factors, as well to develop prediction model on malaria epidemics.


METHODS
Data on Malaria and meteorological factors were collected in all 19 counties in Hainan province from May to Oct., 2000, and the proportion of land use types of these counties in this period were extracted from digital map of land use in Hainan province. Land surface temperatures (LST) were extracted from MODIS images and elevations of these counties were extracted from DEM of Hainan province. The coefficients of correlation of malaria incidences and these environmental factors were then calculated with SPSS 13.0, and negative binomial regression analysis were done using SAS 9.0.


RESULTS
The incidence of malaria showed (1) positive correlations to elevation, proportion of forest land area and grassland area; (2) negative correlations to the proportion of cultivated area, urban and rural residents and to industrial enterprise area, LST; (3) no correlations to meteorological factors, proportion of water area, and unemployed land area. The prediction model of malaria which came from negative binomial regression analysis was: I (monthly, unit: 1/1,000,000) = exp (-1.672-0.399xLST).


CONCLUSIONS
Spatial distribution of malaria epidemics was associated with some environmental factors, and prediction model of malaria epidemic could be developed with indexes which extracted from satellite remote sensing images.
Wen
Liang
L
PLA Institute of Center for Disease Control and Prevention, Beijing 100166, China.
Shi
Run-He
RH
Fang
Li-Qun
LQ
Xu
De-Zhong
DZ
Li
Cheng-Yi
CY
Wang
Yong
Y
Yuan
Zheng-Quan
ZQ
Zhang
Hui
H
D002681:China / Q000453:epidemiology; D004777:Environment; D016021:Epidemiologic Studies; D005843:Geography; D006801:Humans; D015994:Incidence; D008288:Malaria / Q000453:epidemiology*; D012621:Seasons; D013696:Temperature