A Bayesian network analysis of psychosocial risk and protective factors for suicidal ideation.
Bayesian network analysis
COVID-19
depression
risk factors
suicide
Journal
Frontiers in public health
ISSN: 2296-2565
Titre abrégé: Front Public Health
Pays: Switzerland
ID NLM: 101616579
Informations de publication
Date de publication:
2023
2023
Historique:
received:
02
08
2022
accepted:
06
02
2023
entrez:
20
3
2023
pubmed:
21
3
2023
medline:
22
3
2023
Statut:
epublish
Résumé
The aim of this study was to investigate and model the interactions between a range of risk and protective factors for suicidal ideation using general population data collected during the critical phase of the COVID-19 pandemic. Bayesian network analyses were applied to cross-sectional data collected 1 month after the COVID-19 lockdown measures were implemented in Austria and the United Kingdom. In nationally representative samples ( The AUC of the Bayesian network models were ≥ 0.84 within-sample and ≥0.79 out-of-sample, explaining close to 50% of variability in suicidal ideation. In total, 15 interrelated risk and protective factors were identified. Seven of these factors were replicated in both countries: depressive symptoms, loneliness, anxiety symptoms, self-efficacy, resilience, QOL physical health, and QOL living environment. Bayesian network models had high predictive accuracy. Several psychosocial risk and protective factors have complex interrelationships that influence suicidal ideation. It is possible to predict suicidal risk with high accuracy using this information.
Sections du résumé
Background
The aim of this study was to investigate and model the interactions between a range of risk and protective factors for suicidal ideation using general population data collected during the critical phase of the COVID-19 pandemic.
Methods
Bayesian network analyses were applied to cross-sectional data collected 1 month after the COVID-19 lockdown measures were implemented in Austria and the United Kingdom. In nationally representative samples (
Results
The AUC of the Bayesian network models were ≥ 0.84 within-sample and ≥0.79 out-of-sample, explaining close to 50% of variability in suicidal ideation. In total, 15 interrelated risk and protective factors were identified. Seven of these factors were replicated in both countries: depressive symptoms, loneliness, anxiety symptoms, self-efficacy, resilience, QOL physical health, and QOL living environment.
Conclusions
Bayesian network models had high predictive accuracy. Several psychosocial risk and protective factors have complex interrelationships that influence suicidal ideation. It is possible to predict suicidal risk with high accuracy using this information.
Identifiants
pubmed: 36935710
doi: 10.3389/fpubh.2023.1010264
pmc: PMC10014716
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1010264Informations de copyright
Copyright © 2023 Delgadillo, Budimir, Barkham, Humer, Pieh and Probst.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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