|
Citation
|
Benson R, Brunsdon C, Rigby J, Corcoran P, Ryan M, Cassidy E, Dodd P, Hennebry D, Arensman E. Front. Digit. Health 2022; 4: 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.
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.
Language: en |