PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
未经评估：数据可视化是通知数据驱动决策的关键，然而，这是一个未充分探索的自杀监测领域。通过增强实时自杀监测系统模型，已经开发了一个交互式仪表板原型，以促进新兴的集群检测，风险分析和趋势观察，以及通过直观的界面与关键利益相关者建立正式的数据共享连接。
UNASSIGNED：分析了2008-2017年科克郡确认自杀和公开判决符合自杀标准的个人人口统计和间接数据，以验证该模型。通过R软件环境，使用“rsatscan”和“shiny”软件包，采用了基于离散泊松模型的回顾性和前瞻性时空扫描统计数据，以进行时空聚类分析，并提供包含仪表板界面的映射和图形组件。
UNASSIGNED：使用最佳拟合参数，回顾性扫描统计数据返回了在10年期间检测到的几个新兴的非显著集群，而前瞻性方法证明了模型的预测能力。使用所识别的集群的地理地图和集群发生的时间线,在视觉上显示调查的输出。
UNASSIGNED：通过讨论仪表板原型的开发及其支持实时决策的潜力，提出了为可疑自杀数据设计和实现可视化的挑战。
UNASSIGNED：结果表明，涉及地理可视化技术的集群检测方法的集成，时空扫描统计和预测建模将有助于对新兴集群的前瞻性早期检测，高危人群,和关注的地点。该原型展示了作为主动监测工具的现实适用性，通过促进知情的计划和准备应对新出现的自杀集群和其他有关趋势，及时采取行动预防自杀。
UNASSIGNED：分析了2008-2017年科克郡确认自杀和公开判决符合自杀标准的个人人口统计和间接数据，以验证该模型。通过R软件环境，使用“rsatscan”和“shiny”软件包，采用了基于离散泊松模型的回顾性和前瞻性时空扫描统计数据，以进行时空聚类分析，并提供包含仪表板界面的映射和图形组件。
UNASSIGNED：使用最佳拟合参数，回顾性扫描统计数据返回了在10年期间检测到的几个新兴的非显著集群，而前瞻性方法证明了模型的预测能力。使用所识别的集群的地理地图和集群发生的时间线,在视觉上显示调查的输出。
UNASSIGNED：通过讨论仪表板原型的开发及其支持实时决策的潜力，提出了为可疑自杀数据设计和实现可视化的挑战。
UNASSIGNED：结果表明，涉及地理可视化技术的集群检测方法的集成，时空扫描统计和预测建模将有助于对新兴集群的前瞻性早期检测，高危人群,和关注的地点。该原型展示了作为主动监测工具的现实适用性，通过促进知情的计划和准备应对新出现的自杀集群和其他有关趋势，及时采取行动预防自杀。
