估计国家和州级的自杀死亡人数,使用一种新的在线症状搜索数据源数据源
Steven A Sumner1, Alen Alic1, Royal K Law1
1National Center for Injury Prevention and Control, U.S. Centers for Disease Control and Prevention, Atlanta, GA, USA.
Journal of affective disorders
|September 13, 2023
概括
谷歌症状搜索数据集 (SSD) 的数据准确地预测了国家自杀死亡率趋势. 这种新的方法为了解自杀率提供了对延迟的官方统计数据的实时替代方案.
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:
- 美国官方的自杀死亡率数据非常重要,但可能会有很大的延迟.
- 谷歌症状搜索数据集 (SSD),利用在线搜索行为,提供了一个新的人口级数据源.
- 预测自杀死亡率趋势的SSD的实用性以前没有得到评估.
研究的目的:
- 评估谷歌症状搜索数据集 (SSD) 以预测国家和州级自杀死亡率趋势.
- 将SSD与基线模型的心理健康相关症状模型的预测性能进行比较.
- 评估2020年自杀数量的SSD衍生预测的准确性.
主要方法:
- 从SSD中确定了五个心理健康变量 (自杀念头,自我伤害,抑郁,严重抑郁症,疼痛).
- 利用这些症状的每日搜索趋势,用线性回归模型估计2020年国家和州的自杀人数.
- 将"心理健康模型"的表现与自回归集成移动平均线 (ARIMA) 基线和"所有症状"模型进行了比较.
主要成果:
- "心理健康模型"实现了全国自杀死亡预测误差 -3.86%,明显超过ARIMA基线 (7.17%) 和"所有症状" (28.49%) 模型.
- 在州一级,70% (N=35) 的州使用"心理健康模型"预测误差低于10%.
- 在州级的预测准确性通常对人口较大和自杀死亡人数较高的州更高.
结论:
- 谷歌SSD提供了一个有价值的新的实时数据源,用于准确地每月预测国家自杀死亡率趋势.
- 需要进一步的研究来完善州级预测,特别是对于自杀率较低的州.
- 这些发现表明在线搜索数据作为自杀监控的补充工具的潜力.
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