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Using Real-Time Coronial Data to Detect Spatiotemporal Suicide Clusters.

Leo Roberts¹, Angela Clapperton¹, Jeremy Dwyer²

¹Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia.

|August 13, 2024

Summary

This summary is machine-generated.

This study demonstrates that real-time suicide registers can effectively identify potential suicide clusters using spatiotemporal scanning. This approach offers a practical method for ongoing public health surveillance of suicide patterns.

Keywords:

SaTScan real-time registers scan statistic suicide clusters surveillance

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Area of Science:

Epidemiology
Public Health
Data Science

Background:

Real-time suicide registers are increasingly available for public health surveillance.
Utilizing these registers for detecting suicide clusters is an emerging field.
Current methods for cluster detection are limited in real-time application.

Purpose of the Study:

To assess the feasibility of using real-time suicide register data for detecting spatiotemporal suicide clusters.
To develop and test a methodology for rapid identification of potential suicide clusters.
To explore the application of scanning statistics on dynamic suicide data.

Main Methods:

Employed the Victorian Suicide Register and SaTScan's spatiotemporal scan statistic.
Simulated monthly cluster searches using rolling 2-year data windows (Jan 2015 - June 2022).
Analyzed data at multiple geographic granularities and for different age groups (all-ages, under-25).

Main Results:

Successfully demonstrated rapid identification of potential suicide clusters.
Validated a practical approach combining real-time suicide data with scanning algorithms.
Developed novel model outputs visualizing suicide cluster timelines.

Conclusions:

Real-time suicide register data can be effectively used to scan for space-time suicide clusters.
The methodology supports monthly updates and ongoing surveillance.
This approach provides a valuable tool for proactive public health interventions.