Abstract
BackgroundAlthough daily emergency department (ED) data is a source of information that often includes residence, its potential for space-time analyses at the individual level has not been fully explored. We propose that ED data collected for surveillance purposes can also be used to inform spatial and temporal patterns of disease using generalized additive models (GAMs). This paper describes the methods for adapting GAMs so they can be applied to ED data.MethodsGAMs are an effective approach for modeling spatial and temporal distributions of point-wise data, producing smoothed surfaces of continuous risk while adjusting for confounders. In addition to disease mapping, the method allows for global and pointwise hypothesis testing and selection of statistically optimum degree of smoothing using standard statistical software. We applied a two-dimensional GAM for location to ED data of overlapping calendar time using a locally-weighted regression smoother. To illustrate our methods, we investigated the association between participants’ address and the risk of gastrointestinal illness in Cape Cod, Massachusetts over time.ResultsThe GAM space-time analyses simultaneously smooth in units of distance and time by using the optimum degree of smoothing to create data frames of overlapping time periods and then spatially analyzing each data frame. When resulting maps are viewed in series, each data frame contributes a movie frame, allowing us to visualize changes in magnitude, geographic size, and location of elevated risk smoothed over space and time. In our example data, we observed an underlying geographic pattern of gastrointestinal illness with risks consistently higher in the eastern part of our study area over time and intermittent variations of increased risk during brief periods.ConclusionsSpatial-temporal analysis of emergency department data with GAMs can be used to map underlying disease risk at the individual-level and view changes in geographic patterns of disease over time while accounting for multiple confounders. Despite the advantages of GAMs, analyses should be considered exploratory in nature. It is possible that even with a conservative cutoff for statistical significance, results of hypothesis testing may be due to chance. This paper illustrates that GAMs can be adapted to measure geographic trends in public health over time using ED data.
Highlights
Daily emergency department (ED) data is a source of information that often includes residence, its potential for space-time analyses at the individual level has not been fully explored
Our approach using generalized additive models (GAMs) implemented with standard software offers public health researchers and practitioners an opportunity for exploratory secondary analyses of existing ED data, but other methods may be more appropriate for real-time surveillance of patterns at a smaller scale [7,8,9,10,11,12,13,14]
We applied our methods to a subset of ED data collected over a 5 year period (1826 days) that included 7,111 cases of gastrointestinal illness and 37,310 patients with respiratory symptoms who served as controls
Summary
Daily emergency department (ED) data is a source of information that often includes residence, its potential for space-time analyses at the individual level has not been fully explored. We propose that ED data collected for surveillance purposes can be used to inform spatial and temporal patterns of disease using generalized additive models (GAMs). We describe methods for applying generalized additive models (GAMs) to retrospectively analyze spatial-temporal illness patterns using existing emergency department (ED) data. GAMs allow for smoothing of data while adjusting for known covariates, providing a useful framework for individual-level analyses [1,2,3,4]. Our approach using GAMs implemented with standard software offers public health researchers and practitioners an opportunity for exploratory secondary analyses of existing ED data, but other methods may be more appropriate for real-time surveillance of patterns at a smaller scale [7,8,9,10,11,12,13,14]
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