Nunes Carla
Epidemiology and Statistics Group, National School of Public Health, Lisboa, Portugal.
Int J Health Geogr. 2007 Jul 11;6:30. doi: 10.1186/1476-072X-6-30.
The statistics of disease clustering is one of the most important tools for epidemiologists to detect and monitor public health disease patterns. Nowadays, tuberculosis (TB)--an infectious disease caused by the Mycobacterium tuberculosis--presents different (development in populations and antibiotics resistance) patterns and specialists are very concerned with it and its association to several other diseases and factors. Each year, tuberculosis kills about three million people in the world. In particular, it is responsible for the death of more than one-third of HIV-infected people, who prove particularly susceptible due to a decline in their immune defences. The purpose of this study is to determine if there are spatiotemporal tuberculosis incidence clusters in continental Portugal. The presented case study is based on the notification of new tuberculosis cases (disease incidence), between 2000 and 2004. In methodological terms, the spatial scan statistic, used to identify spatiotemporal clusters, was improved by including two new approaches: definition of window sizes in the cluster scanning processes considering empirical mean spatial semivariograms and an independent and posterior validation of identified clusters (based on geostatistical simulations).
Continental Portugal is organized in 18 districts with 278 sub-districts. For this case study, the number of new notified cases of TB, per sub-district and per year (2000-2004) was available. TB incidence presents clear spatial patterns: a semivariogram consistent with 40% of nugget effect and 60% of spatial contribution, following an exponential model with a range of 143 kilometres. Temporal semivariograms were not conclusive, as only 5 years of data were available. The spatial and temporal persistence of clusters were analyzed considering different models. Significant high incidence rate space-time clusters were identified in three areas of Portugal (between 2000 and 2004) and a purely temporal cluster was identified covering the whole country, during 2002.
In terms of spatiotemporal clustering of tuberculosis disease, the proposed methodology allowed the identification of critical spatiotemporal areas. In Portugal there were 3 critical districts (Porto, Setúbal and Lisbon) with high rates of notified incidences between 2000 and 2004. In methodological terms, semivariogram parameters were successfully applied to define spatiotemporal scan window sizes and shapes (ellipsoidal cylinders), showing very good results and performances in the case study. After defining the clusters, these were authenticated through a validation method, based on geostatistical simulations.
疾病聚集性统计是流行病学家检测和监测公共卫生疾病模式的最重要工具之一。如今，结核病（TB）——一种由结核分枝杆菌引起的传染病——呈现出不同的（人群发展和抗生素耐药性）模式，专家们对其以及它与其他几种疾病和因素的关联非常关注。每年，结核病在全球导致约三百万人死亡。特别是，它导致超过三分之一的艾滋病毒感染者死亡，由于免疫防御能力下降，这些人尤其易感。本研究的目的是确定葡萄牙大陆是否存在时空结核病发病聚集区。所呈现的案例研究基于2000年至2004年期间新结核病病例（疾病发病率）的通报。在方法上，用于识别时空聚集区的空间扫描统计方法通过纳入两种新方法得到了改进：在聚类扫描过程中考虑经验性平均空间半变异函数来定义窗口大小，以及对已识别的聚类进行独立的事后验证（基于地质统计学模拟）。
葡萄牙大陆由18个区和278个分区组成。对于本案例研究，可获取每个分区每年（2000 - 2004年）新通报的结核病病例数。结核病发病率呈现出明显的空间模式：半变异函数符合40%的块金效应和60%的空间贡献，遵循范围为143公里的指数模型。由于仅有5年的数据，时间半变异函数没有得出结论性结果。考虑不同模型分析了聚类的时空持续性。在葡萄牙的三个地区（2000年至2004年期间）识别出了显著的高发病率时空聚集区，并且在2002年识别出了一个覆盖全国的纯时间聚类。
就结核病的时空聚集性而言，所提出的方法能够识别关键的时空区域。在葡萄牙，2000年至2004年期间有3个关键区（波尔图、塞图巴尔和里斯本）通报发病率较高。在方法上，半变异函数参数成功应用于定义时空扫描窗口的大小和形状（椭圆柱体），在案例研究中显示出非常好的数据和性能。在定义聚类之后，通过基于地质统计学模拟的验证方法对这些聚类进行了验证。
