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

cluster detection dashboard data visualisation real-time suicide surveillance

Journal

Frontiers in digital health
ISSN: 2673-253X
Titre abrégé: Front Digit Health
Pays: Switzerland
ID NLM: 101771889

Informations de publication

Date de publication:
2022
Historique:
received: 31 03 2022
accepted: 28 07 2022
entrez: 6 9 2022
pubmed: 7 9 2022
medline: 7 9 2022
Statut: epublish

Résumé

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 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 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. 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. 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.

Identifiants

pubmed: 36065333
doi: 10.3389/fdgth.2022.909294
pmc: PMC9440192
doi:

Types de publication

Journal Article

Langues

eng

Pagination

909294

Informations de copyright

© 2022 Benson, Brunsdon, Rigby, Corcoran, Ryan, Cassidy, Dodd, Hennebry and Arensman.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

R Benson (R)

School of Public Health, College of Medicine and Health, University College Cork, Cork, Ireland.
National Suicide Research Foundation, WHO Collaborating Centre for Surveillance and Research in Suicide Prevention, Cork, Ireland.

C Brunsdon (C)

National Centre for Geocomputation, National University of Ireland Maynooth, Maynooth, Ireland.

J Rigby (J)

National Centre for Geocomputation, National University of Ireland Maynooth, Maynooth, Ireland.

P Corcoran (P)

National Suicide Research Foundation, WHO Collaborating Centre for Surveillance and Research in Suicide Prevention, Cork, Ireland.

M Ryan (M)

Cork Kerry Community Health Services, Health Service Executive, Cork, Ireland.

E Cassidy (E)

Department of Psychiatry and Neurobehavioural Science, University College Cork, Cork, Ireland.

P Dodd (P)

National Office for Suicide Prevention, Health Service Executive, Dublin, Ireland.

D Hennebry (D)

Cork Kerry Community Health Services, Health Service Executive, Cork, Ireland.

E Arensman (E)

School of Public Health, College of Medicine and Health, University College Cork, Cork, Ireland.
National Suicide Research Foundation, WHO Collaborating Centre for Surveillance and Research in Suicide Prevention, Cork, Ireland.

Classifications MeSH