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. 2023 Mar 1:11:1010264.
doi: 10.3389/fpubh.2023.1010264. eCollection 2023.

A Bayesian network analysis of psychosocial risk and protective factors for suicidal ideation

Jaime Delgadillo  1 , Sanja Budimir  2   3 , Michael Barkham  1 , Elke Humer  2 , Christoph Pieh  2 , Thomas Probst  2
Affiliations

A Bayesian network analysis of psychosocial risk and protective factors for suicidal ideation

Jaime Delgadillo et al. Front Public Health. .

Abstract

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 (n = 1,005 Austria; n = 1,006 UK), sociodemographic features and a multi-domain battery of health, wellbeing and quality of life (QOL) measures were completed. Predictive accuracy was examined using the area under the curve (AUC) within-sample (country) and out-of-sample.

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.

Keywords: Bayesian network analysis; COVID-19; depression; risk factors; suicide.

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Conflict of interest statement

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.

Figures

Figure 1
Figure 1
Schematic representation of the cross-country, cross-validation design used to identify suicidal ideation risk factors that are country-specific and those that are common across samples. Model training used a tree-augmented naïve Bayes algorithm, applying 10-fold internal cross-validation for variable selection. Classification accuracy was assessed within-sample and out-of-sample using the Area Under the Curve (AUC).
Figure 2
Figure 2
Bayesian network model for the Austrian sample, with variable importance indices for each variable. The red upward arrows denote risk factors for suicidal ideation, and the green downward arrows denote protective factors. The model also shows two-way interactions between variables.
Figure 3
Figure 3
Bayesian network model for the British sample, with variable importance indices for each variable. The red upward arrows denote risk factors for suicidal ideation, and the green downward arrows denote protective factors. The model also shows two-way interactions between variables.
See this image and copyright information in PMC

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