Individual-level space-time analyses of emergency department data using generalized additive modeling.

ABSTRACT:

Background

Although 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.

Methods

GAMs 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 deg

SUBMITTER: Vieira VM 

PROVIDER: S-EPMC3542588 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature