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Background: The direct modeling of water networks is not a common practice inmodern epidemiology. While space often serves as a proxy, it can beproblematic. There are multiple ways to directly model water networks, butthese methods are not straightforward and can be difficult to implement. Thisstudy suggests a simple approach for modeling water networks and diseases, andapplies this method to a dataset of self-reported gastrointestinal conditionsfrom a questionnaire-based population health survey in central Norway.  Method: Our approach is based on a standard conditional autoregressive (CAR)model. An inverse matrix was constructed, with nodes weighted based on thedistance to neighboring nodes within the networks. This matrix was then fittedas a generic model. To illustrate its possible use, we utilized data taken froma questionnaire-based population health survey, the HUNT Study, to measureself-reported gastrointestinal complaints. For hypothesis testing, we used thedeviance information criterion (DIC) and included variables in a stepwisemanner.  Results: The full model converged after six hours. We found no relationbetween the water networks and the health conditions of people whose residencesconnected to different parts of the network in the geographical area studied.  Conclusion: All water network models are simplifications of the realnetworks. Nevertheless, we suggest a valid approach for distinguishing betweenthe general spatial effect and the water network using a generic model.

Modeling water supply networks and gastrointestinal disorder symptoms with CAR models