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.
ASJC Scopus subject areas
- Health Information Management
- Statistics and Probability