The development and validation of a dashboard prototype for real-time suicide mortality data - Benson, R.; Brunsdon, C.; Rigby, J.; Corcoran, P.; Ryan, M.; Cassidy, E.; Dodd, P.; Hennebry, D.; Arensman, E. (Frontiers in Digital Health, 2022)
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.