Strauss Markus J, Klimek Peter, Sonneck Gernot, Niederkrotenthaler Thomas
Suicide Research Unit, Department of Social and Preventive Medicine , Center for Public Health, Medical University of Vienna , Kinderspitalgasse 15, 1090 Vienna, Austria.
Section for Science of Complex Systems , Medical University of Vienna , Spitalgasse 23, 1090 Vienna, Austria.
R Soc Open Sci. 2017 Mar 8;4(3):160711. doi: 10.1098/rsos.160711. eCollection 2017 Mar.
Railway suicide is a significant public health problem. In addition to the loss of lives, these suicides occur in public space, causing traumatization among train drivers and passengers, and significant public transport delays. Prevention efforts depend upon accurate knowledge of clustering phenomena across the railway network, and spatial risk factors. Factors such as proximity to psychiatric institutions have been discussed to impact on railway suicides, but analytic evaluations are scarce and limited. We identify 15 hotspots on the Austrian railway system while taking case location uncertainties into account. These hotspots represent 0.9% of the total track length (5916 km/3676 miles) that account for up to 17% of all railway suicides (=1130). We model suicide locations on the network using a smoothed inhomogeneous Poisson process and validate it using randomization tests. We find that the density of psychiatric beds is a significant predictor of railway suicide. Further predictors are population density, multitrack structure and-less consistently-spatial socio-economic factors including total suicide rates. We evaluate the model for the identified hotspots and show that the actual influence of these variables differs across individual hotspots. This analysis provides important information for suicide prevention research and practice. We recommend structural separation of railway tracks from nearby psychiatric institutions to prevent railway suicide.
铁路自杀是一个重大的公共卫生问题。除了生命损失外，这些自杀事件发生在公共场所，给火车司机和乘客造成创伤，并导致公共交通严重延误。预防工作取决于对铁路网络中聚集现象和空间风险因素的准确了解。诸如靠近精神病院等因素已被讨论对铁路自杀有影响，但分析评估很少且有限。我们在考虑案例位置不确定性的情况下，确定了奥地利铁路系统上的15个热点地区。这些热点地区占总轨道长度（5916公里/3676英里）的0.9%，却占所有铁路自杀事件的17%（=1130起）。我们使用平滑非齐次泊松过程对网络上的自杀地点进行建模，并使用随机化测试对其进行验证。我们发现精神病床位密度是铁路自杀的一个重要预测因素。其他预测因素包括人口密度、多轨道结构以及空间社会经济因素（包括总自杀率，但一致性较差）。我们对确定的热点地区的模型进行评估，并表明这些变量的实际影响在各个热点地区有所不同。该分析为自杀预防研究和实践提供了重要信息。我们建议将铁路轨道与附近的精神病院进行结构分离，以预防铁路自杀。
