Spatial epidemiological study on malaria epidemics in Hainan province
10.3321/j.issn:0254-6450.2008.06.016
- VernacularTitle:海南省疟疾流行空间分布的环境影响因素初步分析
- Author:
Liang WEN
1
;
Run-He SHI
;
Li-Qun FANG
;
De-Zhong XU
;
Cheng-Yi LI
;
Yong WANG
;
Zheng-Quan YUAN
;
Hui ZHANG
Author Information
1. 军事医学科学院疾病预防控制所
- Keywords:
Malaria;
Spatial epidemiology;
Land use type;
Land surface temperature;
Negative binomial regression analysis
- From:
Chinese Journal of Epidemiology
2008;29(6):581-585
- CountryChina
- Language:Chinese
-
Abstract:
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. 399 × LST). Conclusion 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.
