通过罗德岛的预测建模来评估用户参与度,使用交互式映射仪表板来进行过量预防
Alexandra Skinner1, Daniel B Neill, Bennett Allen
1Author Affiliations: Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island (Ms Skinner, Mr Krieger, Ms Gray, Ms Pratty, Dr Macmadu, Dr Goedel, Dr Marshall); Department of Computer Science, New York University Courant Institute of Mathematical Sciences, New York, New York (Dr Neill); Robert F. Wagner Graduate School of Public Service, New York University, New York, New York (Dr Neill); Center for Urban Science and Progress, New York University Tandon School of Engineering, New York, New York (Dr Neill); Department of Population Health, New York University Grossman School of Medicine, New York, New York (Drs Allen and Cerdá); Department of Emergency Medicine, University of California Los Angeles David Geffen School of Medicine, Los Angeles, California (Dr Samuels); and Division of Epidemiology, University of California Berkeley School of Public Health, Berkeley, California (Dr Ahern).
对过量使用风险的预测建模有助于社区组织分配减少危害的资源. 提供者模式 (PROVIDENT)
科学领域:
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
- 流行病学 流行病学
背景情况:
- 预测模型有助于识别过量死亡的高风险社区.
- 机器学习模型,如罗德岛的PROVIDENT,在人口普查区组 (CBG) 层面上预测过量风险.
- 这种预测能力支持社区组织对减少伤害的资源分配.
研究的目的:
- 评估通过PROVIDENT模型识别的CBG是否在在线仪表板上获得了用户参与度的增加.
- 评估预测性过量预测和资源规划仪表板对社区组织的有用性.
主要方法:
- 使用修改后的Poisson回归来估计患病率,调整以混CBG级别特征.
- 在2021年11月至2024年7月期间,分析了罗德岛 (N=809) 的CBG水平数据.
- 曝光是CBG是否被PROVIDENT模型优先考虑并显示在交互式仪表板上.
主要成果:
- 仪表板用户与CBG优先进行互动的可能性是仪表板上显示的1.0至2.4倍.
- 根据先前的预测,参与度,过量计数和人口因素进行了调整.
- 这表明模型优先级和用户与仪表板的交互之间存在显著的关联.
结论:
- 交互式绘图工具与预测建模相结合,可以有效地支持减少危害的组织.
- 这些工具有助于将资源分配给被确定为未来过量死亡高风险的社区.
- 普罗维登模型的仪表板显示了增强社区公共卫生干预的潜力.
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