The development and validation of a dashboard prototype for real-time suicide mortality data

R Benson; C Brunsdon; J Rigby; P Corcoran; M Ryan; E Cassidy; P Dodd; D Hennebry; E Arensman

doi:10.3389/fdgth.2022.909294

The development and validation of a dashboard prototype for real-time suicide mortality data

Front Digit Health. 2022 Aug 20:4:909294. doi: 10.3389/fdgth.2022.909294. eCollection 2022.

Authors

R Benson^{1

2}, C Brunsdon³, J Rigby³, P Corcoran², M Ryan⁴, E Cassidy⁵, P Dodd⁶, D Hennebry⁴, E Arensman^{1

2}

Affiliations

¹ School of Public Health, College of Medicine and Health, University College Cork, Cork, Ireland.
² National Suicide Research Foundation, WHO Collaborating Centre for Surveillance and Research in Suicide Prevention, Cork, Ireland.
³ National Centre for Geocomputation, National University of Ireland Maynooth, Maynooth, Ireland.
⁴ Cork Kerry Community Health Services, Health Service Executive, Cork, Ireland.
⁵ Department of Psychiatry and Neurobehavioural Science, University College Cork, Cork, Ireland.
⁶ National Office for Suicide Prevention, Health Service Executive, Dublin, Ireland.

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.

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 "rsatscan" and "shiny" packages to conduct the space-time cluster analysis and deliver the mapping and graphic components encompassing the dashboard interface.

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.

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.

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.

Keywords: cluster detection; dashboard; data visualisation; real-time; suicide; surveillance.