Using funnel plots in public health surveillance

We investigate a way to simultaneously focus funnel plot analyses on direct policy implications while visually incorporating model fit and the effects of risk factors. Health survey data representing modifiable and nonmodifiable risk factors are used in an analysis of 2007 small area motor vehicle mortality rates in Alberta, Canada.Small area variations in motor vehicle mortality in Alberta were well explained by the suite of modifiable and nonmodifiable risk factors. Funnel plots of raw rates and of risk adjusted rates lead to different conclusions; the analysis process highlights opportunities for intervention as risk factors are incorporated into the model. Maps based on funnel plot methods identify areas worthy of further investigation.Funnel plots provide a useful tool to explore small area data and to routinely incorporate covariate relationships in surveillance analyses. The exploratory process has at each step a direct and useful policy-related result. Dealing thoughtfully with statistical overdispersion is a cornerstone to fully understanding funnel plots.According to a widely cited definition proposed by the CDC, "Public Health Surveillance is the on-going, systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice, closely integrated with the timely dissemination to those who need to know" [1]. The results of analyses conducted on data collected within a surveillance system can be used to inform public health policy and planning, to monitor the health status of a population, and to stimulate research. A functional surveillance system will provide information about the number of health events of specified types that occur within specified populations on an ongoing basis and can therefore be used to derive disease and health event rates over time in different areas (or subpopulations of other types).One routine surveillance activity may be to monitor rates of disease