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Individual-level space-time analyses of emergency department data using generalized additive modeling
Verónica M Vieira1, Janice M Weinberg, Thomas F Webster
1Program in Public Health, University of California, Irvine, CA 92697, USA. vmv@bu.edu
BMC Public Health
|August 24, 2012
Summary
Emergency department data can reveal spatial-temporal disease patterns using generalized additive models (GAMs). This method visualizes geographic disease risk over time, offering insights for public health surveillance.
Area of Science:
- Epidemiology
- Biostatistics
- Geographic Information Systems (GIS)
Background:
- Daily emergency department (ED) data contains residence information, valuable for spatial-temporal analyses.
- The potential of ED data for individual-level space-time disease pattern exploration remains underexplored.
- Generalized additive models (GAMs) offer a robust framework for analyzing such data.
Purpose of the Study:
- To adapt generalized additive models (GAMs) for analyzing emergency department (ED) data.
- To explore the utility of ED data for individual-level space-time disease pattern analysis.
- To demonstrate the application of GAMs for public health surveillance using ED data.
Main Methods:
- Applied a two-dimensional GAM for location to ED data with overlapping calendar time.
- Utilized a locally-weighted regression smoother for space-time smoothing.
- Investigated the association between participant address and gastrointestinal illness risk in Cape Cod, MA.
Main Results:
- GAM space-time analyses simultaneously smooth distance and time, creating data frames of overlapping periods.
- Visualized changes in risk magnitude, geographic size, and location over time through sequential data frames.
- Observed a consistent higher risk of gastrointestinal illness in the eastern study area, with intermittent localized increases.
Conclusions:
- Spatial-temporal analysis of ED data using GAMs effectively maps individual-level disease risk and geographic trends over time.
- The method accounts for multiple confounders, providing smoothed surfaces of continuous risk.
- While valuable, GAM analyses of ED data should be considered exploratory due to potential chance findings.
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