Environmental Change Institute, National University of Ireland, Galway, Ireland.
Int J Health Geogr. 2009 Nov 24;8:64. doi: 10.1186/1476-072X-8-64.
Cryptosporidiosis is increasingly recognised as a cause of gastrointestinal infection in Ireland and has been implicated in several outbreaks. This study aimed to investigate the spatial and temporal distribution of human cryptosporidiosis in the west of Ireland in order to identify high risk seasons and areas and to compare Classically Calculated (CC) and Empirical Bayesian (EB) incidence rates. Two spatial scales of analysis were used with a view to identifying the best one in assessing geographical patterns of infection. Global Moran's I and Local Moran's I tests of autocorrelation were used to test for evidence of global and local spatial clustering.
There were statistically significant seasonal patterns of cryptosporidiosis with peaks in spring and an increasing temporal trend. Significant (p < 0.05) global spatial clustering was observed in CC rates at the Electoral Division (ED) level but not in EB rates at the same level. Despite variations in disease, ED level was found to provide the most accurate account of distribution of cryptosporidiosis in the West of Ireland but required spatial EB smoothing of cases. There were a number of areas identified with significant local clustering of cryptosporidiosis rates.
This study identified spatial and temporal patterns in cryptosporidiosis distribution. The study also showed benefit in performing spatial analyses at more than one spatial scale to assess geographical patterns in disease distribution and that smoothing of disease rates for mapping in small areas enhances visualisation of spatial patterns. These findings are relevant in guiding policy decisions on disease control strategies.
隐孢子虫病日益被认为是爱尔兰胃肠道感染的一个原因，并且与几起暴发有关。本研究旨在调查爱尔兰西部人类隐孢子虫病的时空分布，以确定高风险季节和地区，并比较经典计算（CC）和经验贝叶斯（EB）发病率。使用了两种空间尺度分析，以期确定评估感染地理模式的最佳方法。全局 Moran's I 和局部 Moran's I 自相关检验用于检验全局和局部空间聚类的证据。
隐孢子虫病存在明显的季节性模式，春季高峰，时间趋势增加。在选举分区（ED）水平的 CC 率中观察到统计学上显著的全局空间聚类（p < 0.05），但在同一水平的 EB 率中没有观察到。尽管疾病存在差异，但发现 ED 水平能够最准确地说明爱尔兰西部隐孢子虫病的分布情况，但需要对病例进行空间 EB 平滑处理。确定了一些具有显著局部隐孢子虫病率聚类的地区。
本研究确定了隐孢子虫病分布的时空模式。该研究还表明，在评估疾病分布的地理模式时，在多个空间尺度上进行空间分析是有益的，并且在小区域中对疾病率进行平滑处理可以增强空间模式的可视化。这些发现与指导疾病控制策略的政策决策相关。
