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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.
  • Benson R; School of Public Health, College of Medicine and Health, University College Cork, Cork, Ireland.
  • Brunsdon C; National Suicide Research Foundation, WHO Collaborating Centre for Surveillance and Research in Suicide Prevention, Cork, Ireland.
  • Rigby J; National Centre for Geocomputation, National University of Ireland Maynooth, Maynooth, Ireland.
  • Corcoran P; National Centre for Geocomputation, National University of Ireland Maynooth, Maynooth, Ireland.
  • Ryan M; National Suicide Research Foundation, WHO Collaborating Centre for Surveillance and Research in Suicide Prevention, Cork, Ireland.
  • Cassidy E; Cork Kerry Community Health Services, Health Service Executive, Cork, Ireland.
  • Dodd P; Department of Psychiatry and Neurobehavioural Science, University College Cork, Cork, Ireland.
  • Hennebry D; National Office for Suicide Prevention, Health Service Executive, Dublin, Ireland.
  • Arensman E; Cork Kerry Community Health Services, Health Service Executive, Cork, Ireland.
Front Digit Health ; 4: 909294, 2022.
Article in English | MEDLINE | ID: covidwho-20233144
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

Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study / Prognostic study Language: English Journal: Front Digit Health Year: 2022 Document Type: Article Affiliation country: Fdgth.2022.909294

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study / Prognostic study Language: English Journal: Front Digit Health Year: 2022 Document Type: Article Affiliation country: Fdgth.2022.909294