Abstract
BackgroundPublic health surveillance is often concerned with the analysis of health outcomes over small areas. Funnel plots have been proposed as a useful tool for assessing and visualizing surveillance data, but their full utility has not been appreciated (for example, in the incorporation and interpretation of risk factors).MethodsWe 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.ResultsSmall 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.ConclusionsFunnel 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.
Highlights
Public health surveillance is often concerned with the analysis of health outcomes over small areas
In this paper we demonstrate the use of funnel plots for model development using motor vehicle mortality data in Alberta, Canada
Maps are based on the Alberta Regional Health Authorities (RHA), reflecting boundary changes introduced in December 2003 and in force until 2009
Summary
Public health surveillance is often concerned with the analysis of health outcomes over small areas. League plots display observed rates (with confidence intervals) ordered by those rates. Choropleth maps of rates apply differential color schemes to chosen categorizations (often quintiles) of observed rates and color each area on a map according to the category of its observed rate. These are easy to misinterpret because the map reflects geographic area rather than population density and because the same data may result in maps with very different appearances, since the choice of category is arbitrary. Cartogram versions [7] attempt to redraw areas in proportion to populations but are often difficult to reconcile to geographies and still suffer from the arbitrary category problem
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