The development and validation of a dashboard prototype for real-time suicide mortality data
Introduction/AimData 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 detec...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-08-01
|
| Series: | Frontiers in Digital Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2022.909294/full |
| _version_ | 1798037439880626176 |
|---|---|
| author | R. Benson R. Benson C. Brunsdon J. Rigby P. Corcoran M. Ryan E. Cassidy P. Dodd D. Hennebry E. Arensman E. Arensman |
| author_facet | R. Benson R. Benson C. Brunsdon J. Rigby P. Corcoran M. Ryan E. Cassidy P. Dodd D. Hennebry E. Arensman E. Arensman |
| author_sort | R. Benson |
| collection | DOAJ |
| description | Introduction/AimData 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 MethodsIndividual-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.ResultsUsing 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.DiscussionThe 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.ConclusionsThe 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. |
| first_indexed | 2024-04-11T21:26:34Z |
| format | Article |
| id | doaj.art-2ce0ea94438041b2814a8aad0a266751 |
| institution | Directory Open Access Journal |
| issn | 2673-253X |
| language | English |
| last_indexed | 2024-04-11T21:26:34Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Digital Health |
| spelling | doaj.art-2ce0ea94438041b2814a8aad0a2667512022-12-22T04:02:22ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2022-08-01410.3389/fdgth.2022.909294909294The development and validation of a dashboard prototype for real-time suicide mortality dataR. Benson0R. Benson1C. Brunsdon2J. Rigby3P. Corcoran4M. Ryan5E. Cassidy6P. Dodd7D. Hennebry8E. Arensman9E. Arensman10School of Public Health, College of Medicine and Health, University College Cork, Cork, IrelandNational Suicide Research Foundation, WHO Collaborating Centre for Surveillance and Research in Suicide Prevention, Cork, IrelandNational Centre for Geocomputation, National University of Ireland Maynooth, Maynooth, IrelandNational Centre for Geocomputation, National University of Ireland Maynooth, Maynooth, IrelandNational Suicide Research Foundation, WHO Collaborating Centre for Surveillance and Research in Suicide Prevention, Cork, IrelandCork Kerry Community Health Services, Health Service Executive, Cork, IrelandDepartment of Psychiatry and Neurobehavioural Science, University College Cork, Cork, IrelandNational Office for Suicide Prevention, Health Service Executive, Dublin, IrelandCork Kerry Community Health Services, Health Service Executive, Cork, IrelandSchool of Public Health, College of Medicine and Health, University College Cork, Cork, IrelandNational Suicide Research Foundation, WHO Collaborating Centre for Surveillance and Research in Suicide Prevention, Cork, IrelandIntroduction/AimData 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 MethodsIndividual-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.ResultsUsing 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.DiscussionThe 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.ConclusionsThe 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.https://www.frontiersin.org/articles/10.3389/fdgth.2022.909294/fullreal-timesuicidesurveillancedashboarddata visualisationcluster detection |
| spellingShingle | R. Benson R. Benson C. Brunsdon J. Rigby P. Corcoran M. Ryan E. Cassidy P. Dodd D. Hennebry E. Arensman E. Arensman The development and validation of a dashboard prototype for real-time suicide mortality data Frontiers in Digital Health real-time suicide surveillance dashboard data visualisation cluster detection |
| title | The development and validation of a dashboard prototype for real-time suicide mortality data |
| title_full | The development and validation of a dashboard prototype for real-time suicide mortality data |
| title_fullStr | The development and validation of a dashboard prototype for real-time suicide mortality data |
| title_full_unstemmed | The development and validation of a dashboard prototype for real-time suicide mortality data |
| title_short | The development and validation of a dashboard prototype for real-time suicide mortality data |
| title_sort | development and validation of a dashboard prototype for real time suicide mortality data |
| topic | real-time suicide surveillance dashboard data visualisation cluster detection |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2022.909294/full |
| work_keys_str_mv | AT rbenson thedevelopmentandvalidationofadashboardprototypeforrealtimesuicidemortalitydata AT rbenson thedevelopmentandvalidationofadashboardprototypeforrealtimesuicidemortalitydata AT cbrunsdon thedevelopmentandvalidationofadashboardprototypeforrealtimesuicidemortalitydata AT jrigby thedevelopmentandvalidationofadashboardprototypeforrealtimesuicidemortalitydata AT pcorcoran thedevelopmentandvalidationofadashboardprototypeforrealtimesuicidemortalitydata AT mryan thedevelopmentandvalidationofadashboardprototypeforrealtimesuicidemortalitydata AT ecassidy thedevelopmentandvalidationofadashboardprototypeforrealtimesuicidemortalitydata AT pdodd thedevelopmentandvalidationofadashboardprototypeforrealtimesuicidemortalitydata AT dhennebry thedevelopmentandvalidationofadashboardprototypeforrealtimesuicidemortalitydata AT earensman thedevelopmentandvalidationofadashboardprototypeforrealtimesuicidemortalitydata AT earensman thedevelopmentandvalidationofadashboardprototypeforrealtimesuicidemortalitydata AT rbenson developmentandvalidationofadashboardprototypeforrealtimesuicidemortalitydata AT rbenson developmentandvalidationofadashboardprototypeforrealtimesuicidemortalitydata AT cbrunsdon developmentandvalidationofadashboardprototypeforrealtimesuicidemortalitydata AT jrigby developmentandvalidationofadashboardprototypeforrealtimesuicidemortalitydata AT pcorcoran developmentandvalidationofadashboardprototypeforrealtimesuicidemortalitydata AT mryan developmentandvalidationofadashboardprototypeforrealtimesuicidemortalitydata AT ecassidy developmentandvalidationofadashboardprototypeforrealtimesuicidemortalitydata AT pdodd developmentandvalidationofadashboardprototypeforrealtimesuicidemortalitydata AT dhennebry developmentandvalidationofadashboardprototypeforrealtimesuicidemortalitydata AT earensman developmentandvalidationofadashboardprototypeforrealtimesuicidemortalitydata AT earensman developmentandvalidationofadashboardprototypeforrealtimesuicidemortalitydata |