National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, People's Republic of China.
PLoS Negl Trop Dis. 2008 Jun 11;2(6):e250. doi: 10.1371/journal.pntd.0000250.
Spatial modeling is increasingly utilized to elucidate relationships between demographic, environmental, and socioeconomic factors, and infectious disease prevalence data. However, there is a paucity of studies focusing on spatio-temporal modeling that take into account the uncertainty of diagnostic techniques.
METHODOLOGY/PRINCIPAL FINDINGS: We obtained Schistosoma japonicum prevalence data, based on a standardized indirect hemagglutination assay (IHA), from annual reports from 114 schistosome-endemic villages in Dangtu County, southeastern part of the People's Republic of China, for the period 1995 to 2004. Environmental data were extracted from satellite images. Socioeconomic data were available from village registries. We used Bayesian spatio-temporal models, accounting for the sensitivity and specificity of the IHA test via an equation derived from the law of total probability, to relate the observed with the 'true' prevalence. The risk of S. japonicum was positively associated with the mean land surface temperature, and negatively correlated with the mean normalized difference vegetation index and distance to the nearest water body. There was no significant association between S. japonicum and socioeconomic status of the villages surveyed. The spatial correlation structures of the observed S. japonicum seroprevalence and the estimated infection prevalence differed from one year to another. Variance estimates based on a model adjusted for the diagnostic error were larger than unadjusted models. The generated prediction map for 2005 showed that most of the former and current infections occur in close proximity to the Yangtze River.
CONCLUSION/SIGNIFICANCE: Bayesian spatial-temporal modeling incorporating diagnostic uncertainty is a suitable approach for risk mapping S. japonicum prevalence data. The Yangtze River and its tributaries govern schistosomiasis transmission in Dangtu County, but spatial correlation needs to be taken into consideration when making risk prediction at small scales.
空间建模越来越多地被用于阐明人口、环境和社会经济因素与传染病流行数据之间的关系。然而，很少有研究关注时空建模，这些研究考虑到了诊断技术的不确定性。
方法/主要发现：我们获得了来自中华人民共和国东南部当涂县 114 个血吸虫病流行村的日本血吸虫病流行数据，这些数据基于标准化间接血凝试验（IHA），时间跨度为 1995 年至 2004 年。环境数据从卫星图像中提取。社会经济数据可从村庄登记册中获得。我们使用贝叶斯时空模型，通过从全概率定律推导出的方程来考虑 IHA 测试的灵敏度和特异性，将观察到的与“真实”流行率相关联。日本血吸虫病的风险与平均地表温度呈正相关，与平均归一化差异植被指数和与最近水体的距离呈负相关。日本血吸虫病与所调查村庄的社会经济地位之间没有显著关联。观察到的日本血吸虫病血清流行率和估计的感染流行率的空间相关结构随年份而变化。基于调整诊断误差的模型的方差估计值大于未调整模型的方差估计值。生成的 2005 年预测图显示，大部分以前和现在的感染都发生在长江及其支流附近。
结论/意义：纳入诊断不确定性的贝叶斯时空建模是一种适合于风险映射日本血吸虫病流行数据的方法。长江及其支流控制了当涂县的血吸虫病传播，但在进行小尺度风险预测时需要考虑空间相关性。
