Sociodemographic and psychosocial risk factors of railway suicide a mixed-methods study combining data of all suicide decedents in the Netherlands with data from a psychosocial autopsy study.

Mixed-methods Prevention Psychosocial autopsy Railway suicide Risk factors

Journal

BMC public health
ISSN: 1471-2458
Titre abrégé: BMC Public Health
Pays: England
ID NLM: 100968562

Informations de publication

Date de publication:
26 Feb 2024
Historique:
received: 25 08 2023
accepted: 15 02 2024
medline: 27 2 2024
pubmed: 27 2 2024
entrez: 26 2 2024
Statut: epublish

Résumé

Railway suicide has profound implications for the victims and their family, and affects train drivers, railway personnel, emergency services and witnesses. To inform a multilevel prevention strategy, more knowledge is required about psychosocial and precipitating risk factors of railway suicide. Data from Statistics Netherlands of all suicides between 2017 and 2021 (n = 9.241) of whom 986 died by railway suicide and interview data from a psychosocial autopsy of railway suicide decedents (n = 39) were integrated. We performed logistic regression analyses to identify sociodemographic predictors of railway suicide compared to other methods of suicide. The Constant Comparative Method was subsequently employed on interview data from the psychosocial autopsy to identify patterns in psychosocial risk factors for railway suicide. The strongest predictors of railway suicide compared to other suicide methods were young age (< 30 years old), native Dutch, a high educational level, living in a multi-person household (especially living with parents or in an institution), living in a rural area and a high annual household income of > 150.000 euros. Several subgroups emerged in the psychosocial autopsy interviews, which specifically reflect populations at risk of railway suicide. These subgroups were [1] young adult males with autism spectrum disorder who strived for more autonomy and an independent life, [2] young adult females with persistent suicidal thoughts and behaviours, [3] middle-aged males with a persistent mood disorder who lived with family and who faced stressors proximal to the suicide in personal and professional settings, [4] male out-of-the-blue suicides and [5] persons with psychotic symptoms and a rapid deterioration. based on our findings we propose and discuss several recommendations to prevent railway suicide. We must continue to invest in a safe railway environment by training personnel and installing barriers. Additionally, we should adopt prevention strategies that align the needs of subgroups at increased risk, including young females who have attempted other methods of suicide and young males with autism spectrum disorder. Future research should determine the cost-effectiveness and feasibility of low-maintenance, automated interventions near crossings and psychiatric facilities.

Sections du résumé

BACKGROUND BACKGROUND
Railway suicide has profound implications for the victims and their family, and affects train drivers, railway personnel, emergency services and witnesses. To inform a multilevel prevention strategy, more knowledge is required about psychosocial and precipitating risk factors of railway suicide.
METHODS METHODS
Data from Statistics Netherlands of all suicides between 2017 and 2021 (n = 9.241) of whom 986 died by railway suicide and interview data from a psychosocial autopsy of railway suicide decedents (n = 39) were integrated. We performed logistic regression analyses to identify sociodemographic predictors of railway suicide compared to other methods of suicide. The Constant Comparative Method was subsequently employed on interview data from the psychosocial autopsy to identify patterns in psychosocial risk factors for railway suicide.
RESULTS RESULTS
The strongest predictors of railway suicide compared to other suicide methods were young age (< 30 years old), native Dutch, a high educational level, living in a multi-person household (especially living with parents or in an institution), living in a rural area and a high annual household income of > 150.000 euros. Several subgroups emerged in the psychosocial autopsy interviews, which specifically reflect populations at risk of railway suicide. These subgroups were [1] young adult males with autism spectrum disorder who strived for more autonomy and an independent life, [2] young adult females with persistent suicidal thoughts and behaviours, [3] middle-aged males with a persistent mood disorder who lived with family and who faced stressors proximal to the suicide in personal and professional settings, [4] male out-of-the-blue suicides and [5] persons with psychotic symptoms and a rapid deterioration.
CONCLUSIONS CONCLUSIONS
based on our findings we propose and discuss several recommendations to prevent railway suicide. We must continue to invest in a safe railway environment by training personnel and installing barriers. Additionally, we should adopt prevention strategies that align the needs of subgroups at increased risk, including young females who have attempted other methods of suicide and young males with autism spectrum disorder. Future research should determine the cost-effectiveness and feasibility of low-maintenance, automated interventions near crossings and psychiatric facilities.

Identifiants

pubmed: 38408949
doi: 10.1186/s12889-024-18120-w
pii: 10.1186/s12889-024-18120-w
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

607

Informations de copyright

© 2024. The Author(s).

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Auteurs

Elias Balt (E)

113 Suicide Prevention, Amsterdam, Netherlands. e.balt@113.nl.

Saskia Mérelle (S)

113 Suicide Prevention, Amsterdam, Netherlands.

Arne Popma (A)

Amsterdam UMC, Amsterdam, Netherlands.

Daan Creemers (D)

GGZ Oost-Brabant, Boekel, Netherlands.

Karlijn Heesen (K)

113 Suicide Prevention, Amsterdam, Netherlands.

Nikki van Eijk (N)

113 Suicide Prevention, Amsterdam, Netherlands.

Isa van den Brand (I)

113 Suicide Prevention, Amsterdam, Netherlands.

Renske Gilissen (R)

113 Suicide Prevention, Amsterdam, Netherlands.

Classifications MeSH