Mining epidemiological time series: an approach based on dynamic regression

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Abstract
In epidemiology, time-series regression models are specially suitable for evaluating short-term effects of time-varying exposures to pollution. To summarize findings from different studies on different cities, the techniques of designed meta-analyses have been employed. In this context, city-specific findings are summarized by an ‘effect size’ measured on a common scale. Such effects are then pooled together on a second hierarchy of analysis. The objective of this article is to exploit exploratory analysis of city-specific time series. In fact, when dealing with many sources of data, that is, many cities, an exploratory analysis becomes almost unaffordable. Our idea is to explore the time series by fitting complete dynamic regression models. These models are easier to fit than models usually employed and allow implementation of very fast automated model selection algorithms. The idea is to highlight the common features across cities through this analysis, which might then be used to design the meta-analysis. The proposal is illustrated by analysing data on the relationship between daily nonaccidental deaths and air pollution in the 20 US largest cities.