Developing an early warning system of suicide using Google Trends and media reporting
Developing an early warning system of suicide using Google Trends and media reporting
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利用谷歌趋势和媒体报道开发自杀预警系统
DOI：
10.1016/j.jad.2019.05.030
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发表时间：
2019-08-01
影响因子：
6.6
通讯作者：
Yip, Paul S. F.
中科院分区：
文献类型：
--
作者：
Chai Yi;Luo Hao;Yip, Paul S. F.
背景：传统的自杀监测系统通常存在六个月到两年的严重时滞。本研究旨在利用谷歌趋势和与自杀相关的媒体报道，开发香港可能爆发自杀事件的预警系统。方法：从香港政府统计处和死因裁判法庭获得2011年至2015年3,534起自杀事件的数据。利用谷歌趋势的数据和从媒体报道自杀新闻中提取的特征，我们拟合了泊松回归模型来预测按性别和年龄定义的六个亚组每周的自杀数量并估计自杀强度。我们采用基于累积和 (CUSUM) 控制图的方法来识别自杀爆发。结果：所提出的模型能够以相当低的归一化均方根误差预测自杀数量，范围从年轻女性的 15.6% 到老年女性的 24.16%。所提出的模型很好地捕捉到了年轻男性和女性的自杀强度曲线，但没有捕捉到其他群体的自杀强度曲线。基于 CUSUM 的方法的灵敏度、精确度和 F1 分数对于年轻女性分别为 50%、100% 和 67%，对于年轻男性分别为 93%、54% 和 68%。 局限性：本研究仅侧重于预测当前一周的自杀人数，而不是未来几周的自杀人数。该模型不包括社交媒体、社会经济和气候数据。结论：我们的结果表明，谷歌趋势搜索词和媒体报道数据可能是预测香港可能爆发的自杀事件的宝贵数据来源。拟议的系统可以支持有效且有针对性的干预措施。
Background: Conventional surveillance systems for suicides typically suffer from a substantial time lag of six months to two years. This study aims to develop an early warning system of possible suicide outbreaks in Hong Kong using Google Trends and suicide-related media reporting.Methods: Data on 3,534 suicides from 2011 to 2015 were obtained from Hong Kong Census and Statistics Department, and the Coroner's Court. Using data from Google Trends and features extracted from media reporting on suicide news, we fitted Poisson regression models to predict the number and estimate the intensity of suicides on a weekly basis, for six subgroups, defined by gender and age. We adopted the cumulative sum (CUSUM) control chart-based method to identify outbreaks of suicide.Results: The proposed model was able to predict the number of suicides with reasonably low normalized root mean squared errors, ranging from 15.6% for young females to 24.16% for old females. The suicide intensity curves were well captured by the proposed models for young males and females, but not for other groups. The Sensitivity, Precision and F1 Score of the CUSUM-based method were 50%, 100% and 67% for young females, and 93%, 54% and 68% for young males.Limitations: This study focused only on predicting the number of suicides in the current week, not in the future weeks. The model did not include social media, socioeconomic and climate data.Conclusions: Our results indicate that Google Trends search terms and media reporting data may be valuable data sources for predicting possible outbreak of suicides in Hong Kong. The proposed system could support effective and targeted interventions.