van der Hoek Wim, van de Kassteele Jan, Bom Ben, de Bruin Arnout, Dijkstra Frederika, Schimmer Barbara, Vellema Piet, ter Schegget Ronald, Schneeberger Peter M
Centre for Infectious Disease Control, National Institute for Public Health and the Environment, PO Box 1, 3720 BA Bilthoven, The Netherlands.
Geospat Health. 2012 Nov;7(1):127-34. doi: 10.4081/gh.2012.111.
From 2007 through 2009, The Netherlands faced large outbreaks of human Q fever. Control measures focused primarily on dairy goat farms because these were implicated as the main source of infection for the surrounding population. However, in other countries, outbreaks have mainly been associated with non-dairy sheep and The Netherlands has many more sheep than goats. Therefore, a public discussion arose about the possible role of non-dairy (meat) sheep in the outbreaks. To inform decision makers about the relative importance of different infection sources, we developed accurate and high-resolution incidence maps for detection of Q fever hot spots. In the high incidence area in the south of the country, full postal codes of notified Q fever patients with onset of illness in 2009, were georeferenced. Q fever cases (n = 1,740) were treated as a spatial point process. A 500 x 500 m grid was imposed over the area of interest. The number of cases and the population number were counted in each cell. The number of cases was modelled as an inhomogeneous Poisson process where the underlying incidence was estimated by 2-dimensional P-spline smoothing. Modelling of numbers of Q fever cases based on residential addresses and population size produced smooth incidence maps that clearly showed Q fever hotspots around infected dairy goat farms. No such increased incidence was noted around infected meat sheep farms. We conclude that smooth incidence maps of human notifications give valuable information about the Q fever epidemic and are a promising method to provide decision support for the control of other infectious diseases with an environmental source.
从2007年到2009年,荷兰面临人间Q热的大规模暴发。控制措施主要集中在奶山羊养殖场,因为这些养殖场被认为是周围人群的主要感染源。然而,在其他国家,疫情主要与非奶绵羊有关,而荷兰的绵羊数量比山羊多得多。因此,公众开始讨论非奶(肉用)绵羊在疫情中的可能作用。为了让决策者了解不同感染源的相对重要性,我们绘制了准确且高分辨率的发病率地图,以检测Q热热点地区。在该国南部的高发病率地区,对2009年发病的Q热患者的完整邮政编码进行了地理定位。将Q热病例(n = 1740)视为空间点过程。在感兴趣的区域上覆盖一个500×500米的网格。统计每个单元格中的病例数和人口数。将病例数建模为非齐次泊松过程,其中潜在发病率通过二维P样条平滑估计。基于居住地址和人口规模对Q热病例数进行建模,生成了平滑的发病率地图,清楚地显示了受感染奶山羊养殖场周围的Q热热点地区。在受感染的肉用绵羊养殖场周围未发现此类发病率增加的情况。我们得出结论,人类报告的平滑发病率地图为Q热疫情提供了有价值的信息,并且是为控制其他有环境源的传染病提供决策支持的一种有前景的方法。