Held Leonhard, Graziano Giusi, Frank Christina, Rue Håvard
Department of Statistics, University of Munich, Ludwigstr. 33, 80539 Munich, Germany.
Stat Methods Med Res. 2006 Oct;15(5):465-80. doi: 10.1177/0962280206071642.
A major obstacle in the spatial analysis of infectious disease surveillance data is the problem of under-reporting. This article investigates the possibility of inferring reporting rates through joint statistical modelling of several infectious diseases with different aetiologies. Once variation in under-reporting can be estimated, geographic risk patterns for infections associated with specific food vehicles may be discerned. We adopt the shared component model, proposed by Knorr-Held and Best for two chronic diseases and further extended by (Held L, Natario I, Fenton S, Rue H, Becker N. Towards joint disease mapping. Statistical Methods in Medical Research 2005b; 14: 61-82) for more than two chronic diseases to the infectious disease setting. Our goal is to estimate a shared component, common to all diseases, which may be interpreted as representing the spatial variation in reporting rates. Additional components are introduced to describe the real spatial variation of the different diseases. Of course, this interpretation is only allowed under specific assumptions, in particular, the geographical variation in under-reporting should be similar for the diseases considered. In addition, it is vital that the data do not contain large local outbreaks, so adjustment based on a time series method recently proposed by (Held L, Höhle M, Hofmann M. A statistical framework for the analysis of multivariate infectious disease surveillance data. Statistical Modelling 2005a; 5: 187-99) is made at a preliminary stage. We will illustrate our approach through the analysis of gastrointestinal diseases notification data obtained from the German infectious disease surveillance system, administered by the Robert Koch Institute in Berlin.
传染病监测数据空间分析中的一个主要障碍是漏报问题。本文研究了通过对几种病因不同的传染病进行联合统计建模来推断报告率的可能性。一旦能够估计出漏报的差异，就可以识别与特定食物载体相关的感染的地理风险模式。我们采用了由克诺尔 - 黑尔德和贝斯特提出的用于两种慢性病的共享成分模型，并由（黑尔德L、纳塔里奥I、芬顿S、吕厄H、贝克尔N。迈向联合疾病制图。医学研究中的统计方法2005b；14：61 - 82）将其扩展到两种以上慢性病的传染病情况。我们的目标是估计所有疾病共有的一个共享成分，它可以被解释为代表报告率的空间变化。引入其他成分来描述不同疾病的实际空间变化。当然，只有在特定假设下才能进行这种解释，特别是，所考虑疾病的漏报地理差异应该相似。此外，至关重要的是数据不包含大规模的局部疫情爆发，因此在初步阶段要基于（黑尔德L、赫勒M、霍夫曼M。多元传染病监测数据分析的统计框架。统计建模2005a；5：187 - 99）最近提出的时间序列方法进行调整。我们将通过分析从柏林罗伯特·科赫研究所管理的德国传染病监测系统获得的胃肠道疾病通报数据来说明我们的方法。
