Sandwich mapping of schistosomiasis risk in Anhui Province, China

Geospatial Health

Shiqing Zhang¹  Qingwu Jiang¹  Robert Bergquist¹  Henry Lynn²  Rui Li²  Liqian Sun³  Chenglong Xiong³  Congcong Xia³  Qizhi Wang⁴  Zhijie Zhang⁴  Yi Hu⁴  Fenghua Gao⁵ 

[1] Key Laboratory of Public Health Safety, Ministry of Education, Shanghai;Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai;Anhui Institute of Parasitic Diseases, Wuhu;Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai;Ingerod, Brastad;

关键词: Schistosomiasis japonica;    Disease mapping;    Sandwich;    Block Kriging;    China;   

DOI  :  10.4081/gh.2015.324

来源: DOAJ

【 摘 要 】

Schistosomiasis mapping using data obtained from parasitological surveys is frequently used in planning and evaluation of disease control strategies. The available geostatistical approaches are, however, subject to the assumption of stationarity, a stochastic process whose joint probability distribution does not change when shifted in time. As this is impractical for large areas, we introduce here the sandwich method, the basic idea of which is to divide the study area (with its attributes) into homogeneous subareas and estimate the values for the reporting units using spatial stratified sampling. The sandwich method was applied to map the county-level prevalence of schistosomiasis japonica in Anhui Province, China based on parasitological data collected from sample villages and land use data. We first mapped the county-level prevalence using the sandwich method, then compared our findings with block Kriging. The sandwich estimates ranged from 0.17 to 0.21% with a lower level of uncertainty, while the Kriging estimates varied from 0 to 0.97% with a higher level of uncertainty, indicating that the former is more smoothed and stable compared to latter. Aside from various forms of reporting units, the sandwich method has the particular merit of simple model assumption coupled with full utilization of sample data. It performs well when a disease presents stratified heterogeneity over space.

【 授权许可】

Unknown