Environmental Risk Factors for Railway Suicide: A Nationwide Analysis of England’s Mainline Network (2019–2021)
Year: 2025
Type: article
Study design: Observational Study
Abstract: OBJECTIVE: Suicide is the leading cause of death on the railway networks, yet little is known about the geography and socio-environmental determinants of such deaths in England. METHOD: We analyzed official records of all suspected suicides recorded on England's mainline railway from April 2019 to March 2021 (N = 436), alongside area-level data (e.g., deprivation and urbanicity) and railway characteristics (e.g., station size). We used SaTScan's Poisson discrete scan statistic to detect spatial-temporal clusters and compared suicides at station and non-station locations, including stations with no recorded suicides over the study period. RESULTS: Most railway suicides-particularly those occurring at stations-took place in urban areas, typically at locations with a very low incidence of previous suicides. A large majority (84.5%) lived within 2 miles of a station and died close to home (median 2.1 miles) or their last known location (1.5 miles). Suicides occurred most often at stations (43.3%), followed by trackside locations (37.6%), foot/level crossing (15.1%) or other rail areas (3.9%). These were often not the nearest station to the deceased (59.6%), but were generally smaller and more likely to be served by fast/non-stopping trains. Across the network, stations without suicides were far less likely to be served by such trains (66% vs. 94.3%; OR = 6.62, 95% CI 3.33-13.18). CONCLUSIONS: Urbanicity and the availability of non-stopping train services are key predictors of railway suicide. Restricting access to high-speed trains-especially at smaller urban stations and at non-station locations-could be an effective element of broader strategies and local partnership approaches to prevent suicide.
Source: Archives of Suicide Research
Language: English
Subfield: Clinical Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4416996265