@article{ref1,
title="The development and validation of a dashboard prototype for real-time suicide mortality data",
journal="Frontiers in digital health",
year="2022",
author="Benson, R. and Brunsdon, C. and Rigby, J. and Corcoran, P. and Ryan, M. and Cassidy, E. and Dodd, P. and Hennebry, D. and Arensman, E.",
volume="4",
number="",
pages="e909294-e909294",
abstract="INTRODUCTION/AIM: Data visualisation is key to informing data-driven decision-making, yet this is an underexplored area of suicide surveillance. By way of enhancing a real-time suicide surveillance system model, an interactive dashboard prototype has been developed to facilitate emerging cluster detection, risk profiling and trend observation, as well as to establish a formal data sharing connection with key stakeholders via an intuitive interface. <br><br>MATERIALS AND METHODS: Individual-level demographic and circumstantial data on cases of confirmed suicide and open verdicts meeting the criteria for suicide in County Cork 2008-2017 were analysed to validate the model. The retrospective and prospective space-time scan statistics based on a discrete Poisson model were employed via the R software environment using the &quot;rsatscan&quot; and &quot;shiny&quot; packages to conduct the space-time cluster analysis and deliver the mapping and graphic components encompassing the dashboard interface. <br><br>RESULTS: Using the best-fit parameters, the retrospective scan statistic returned several emerging non-significant clusters detected during the 10-year period, while the prospective approach demonstrated the predictive ability of the model. The outputs of the investigations are visually displayed using a geographical map of the identified clusters and a timeline of cluster occurrence. <br><br>DISCUSSION: The challenges of designing and implementing visualizations for suspected suicide data are presented through a discussion of the development of the dashboard prototype and the potential it holds for supporting real-time decision-making. <br><br>CONCLUSIONS: The results demonstrate that integration of a cluster detection approach involving geo-visualisation techniques, space-time scan statistics and predictive modelling would facilitate prospective early detection of emerging clusters, at-risk populations, and locations of concern. The prototype demonstrates real-world applicability as a proactive monitoring tool for timely action in suicide prevention by facilitating informed planning and preparedness to respond to emerging suicide clusters and other concerning trends.<p /> <p>Language: en</p>",
language="en",
issn="2673-253X",
doi="10.3389/fdgth.2022.909294",
url="http://dx.doi.org/10.3389/fdgth.2022.909294"
}