Model-based testing for space-time interaction using point processes: An application to psychiatric hospital admissions in an urban area

TL;DR

This paper introduces a model-based statistical approach using point processes to analyze space-time interactions, applied to psychiatric hospital admissions in Zurich, revealing socio-economic influences but no overall clustering.

Contribution

It develops a novel endemic-epidemic point process model for testing space-time interaction, accounting for population and distance decay effects.

Findings

Socio-economic factors influence admission rates.

No evidence of general space-time clustering.

Model effectively distinguishes background and epidemic components.

Abstract

Spatio-temporal interaction is inherent to cases of infectious diseases and occurrences of earthquakes, whereas the spread of other events, such as cancer or crime, is less evident. Statistical significance tests of space-time clustering usually assess the correlation between the spatial and temporal (transformed) distances of the events. Although appealing through simplicity, these classical tests do not adjust for the underlying population nor can they account for a distance decay of interaction. We propose to use the framework of an endemic-epidemic point process model to jointly estimate a background event rate explained by seasonal and areal characteristics, as well as a superposed epidemic component representing the hypothesis of interest. We illustrate this new model-based test for space-time interaction by analysing psychiatric inpatient admissions in Zurich, Switzerland (2007-2012). Several socio-economic factors were found to be associated with the admission rate, but there was no evidence of general clustering of the cases.