%0 Journal Article
%T The development and validation of a dashboard prototype for real-time suicide mortality data.
%A Benson R
%A Brunsdon C
%A Rigby J
%A Corcoran P
%A Ryan M
%A Cassidy E
%A Dodd P
%A Hennebry D
%A Arensman E
%A Benson R
%A Brunsdon C
%A Rigby J
%A Corcoran P
%A Ryan M
%A Cassidy E
%A Dodd P
%A Hennebry D
%A Arensman E
%J Front Digit Health
%V 4
%N 0
%D 2022
%M 36065333
暂无%R 10.3389/fdgth.2022.909294
%X <B>UNASSIGNED: </B>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><B>UNASSIGNED: </B>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 "rsatscan" and "shiny" packages to conduct the space-time cluster analysis and deliver the mapping and graphic components encompassing the dashboard interface.<BR><B>UNASSIGNED: </B>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><B>UNASSIGNED: </B>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><B>UNASSIGNED: </B>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.