Identifying most important predictors for suicidal thoughts and behaviours among healthcare workers active during the Spain COVID-19 pandemic: a machine-learning approach

Aims Studies conducted during the COVID-19 pandemic found high occurrence of suicidal thoughts and behaviours (STBs) among healthcare workers (HCWs). The current study aimed to (1) develop a machine learning-based prediction model for future STBs using data from a large prospective cohort of Spanish...

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Autores: Alayo I, Pujol O, Alonso J, Ferrer M, Amigo F, Portillo-Van Diest A, Aragonès E, Aragon Peña A, Asúnsolo Del Barco Á, Campos M, Espuga M, González-Pinto A, Haro JM, López-Fresneña N, Martínez de Salázar AD, Molina JD, Ortí-Lucas RM, Parellada M, Pelayo-Terán JM, Forjaz MJ, Pérez-Zapata A, Pijoan JI, Plana N, Polentinos-Castro E, Puig MT, Rius C, Sanz F, Serra C, Urreta-Barallobre I, Bruffaerts R, Vieta E, Pérez-Solá V, Mortier P, Vilagut G
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Recursos:Fundació Sant Joan de Déu
Repositorio:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
OAI Identifier:oai:fsjd.fundanetsuite.com:p28604
Acesso em linha:https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=28604
Access Level:acceso abierto
Palavra-chave:attempted suicide
interpretability
machine learning
mental health
suicidal ideation
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network_name_str España
repository_id_str
spelling Identifying most important predictors for suicidal thoughts and behaviours among healthcare workers active during the Spain COVID-19 pandemic: a machine-learning approachAlayo IPujol OAlonso JFerrer MAmigo FPortillo-Van Diest AAragonès EAragon Peña AAsúnsolo Del Barco ÁCampos MEspuga MGonzález-Pinto AHaro JMLópez-Fresneña NMartínez de Salázar ADMolina JDOrtí-Lucas RMParellada MPelayo-Terán JMForjaz MJPérez-Zapata APijoan JIPlana NPolentinos-Castro EPuig MTRius CSanz FSerra CUrreta-Barallobre IBruffaerts RVieta EPérez-Solá VMortier PVilagut Gattempted suicideinterpretabilitymachine learningmental healthsuicidal ideationAims Studies conducted during the COVID-19 pandemic found high occurrence of suicidal thoughts and behaviours (STBs) among healthcare workers (HCWs). The current study aimed to (1) develop a machine learning-based prediction model for future STBs using data from a large prospective cohort of Spanish HCWs and (2) identify the most important variables in terms of contribution to the model's predictive accuracy.Methods This is a prospective, multicentre cohort study of Spanish HCWs active during the COVID-19 pandemic. A total of 8,996 HCWs participated in the web-based baseline survey (May-July 2020) and 4,809 in the 4-month follow-up survey. A total of 219 predictor variables were derived from the baseline survey. The outcome variable was any STB at the 4-month follow-up. Variable selection was done using an L1 regularized linear Support Vector Classifier (SVC). A random forest model with 5-fold cross-validation was developed, in which the Synthetic Minority Oversampling Technique (SMOTE) and undersampling of the majority class balancing techniques were tested. The model was evaluated by the area under the Receiver Operating Characteristic (AUROC) curve and the area under the precision-recall curve. Shapley's additive explanatory values (SHAP values) were used to evaluate the overall contribution of each variable to the prediction of future STBs. Results were obtained separately by gender.Results The prevalence of STBs in HCWs at the 4-month follow-up was 7.9% (women = 7.8%, men = 8.2%). Thirty-four variables were selected by the L1 regularized linear SVC. The best results were obtained without data balancing techniques: AUROC = 0.87 (0.86 for women and 0.87 for men) and area under the precision-recall curve = 0.50 (0.55 for women and 0.45 for men). Based on SHAP values, the most important baseline predictors for any STB at the 4-month follow-up were the presence of passive suicidal ideation, the number of days in the past 30 days with passive or active suicidal ideation, the number of days in the past 30 days with binge eating episodes, the number of panic attacks (women only) and the frequency of intrusive thoughts (men only).Conclusions Machine learning-based prediction models for STBs in HCWs during the COVID-19 pandemic trained on web-based survey data present high discrimination and classification capacity. Future clinical implementations of this model could enable the early detection of HCWs at the highest risk for developing adverse mental health outcomes.Study registration NCT04556565CAMBRIDGE UNIV PRESS2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=28604Epidemiology and Psychiatric SciencesISSN: 20457960ISSNe: 20457979reponame:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déuinstname:Fundació Sant Joan de DéuInglésinfo:eu-repo/semantics/openAccessoai:fsjd.fundanetsuite.com:p286042026-05-27T12:37:41Z
dc.title.none.fl_str_mv Identifying most important predictors for suicidal thoughts and behaviours among healthcare workers active during the Spain COVID-19 pandemic: a machine-learning approach
title Identifying most important predictors for suicidal thoughts and behaviours among healthcare workers active during the Spain COVID-19 pandemic: a machine-learning approach
spellingShingle Identifying most important predictors for suicidal thoughts and behaviours among healthcare workers active during the Spain COVID-19 pandemic: a machine-learning approach
Alayo I
attempted suicide
interpretability
machine learning
mental health
suicidal ideation
title_short Identifying most important predictors for suicidal thoughts and behaviours among healthcare workers active during the Spain COVID-19 pandemic: a machine-learning approach
title_full Identifying most important predictors for suicidal thoughts and behaviours among healthcare workers active during the Spain COVID-19 pandemic: a machine-learning approach
title_fullStr Identifying most important predictors for suicidal thoughts and behaviours among healthcare workers active during the Spain COVID-19 pandemic: a machine-learning approach
title_full_unstemmed Identifying most important predictors for suicidal thoughts and behaviours among healthcare workers active during the Spain COVID-19 pandemic: a machine-learning approach
title_sort Identifying most important predictors for suicidal thoughts and behaviours among healthcare workers active during the Spain COVID-19 pandemic: a machine-learning approach
dc.creator.none.fl_str_mv Alayo I
Pujol O
Alonso J
Ferrer M
Amigo F
Portillo-Van Diest A
Aragonès E
Aragon Peña A
Asúnsolo Del Barco Á
Campos M
Espuga M
González-Pinto A
Haro JM
López-Fresneña N
Martínez de Salázar AD
Molina JD
Ortí-Lucas RM
Parellada M
Pelayo-Terán JM
Forjaz MJ
Pérez-Zapata A
Pijoan JI
Plana N
Polentinos-Castro E
Puig MT
Rius C
Sanz F
Serra C
Urreta-Barallobre I
Bruffaerts R
Vieta E
Pérez-Solá V
Mortier P
Vilagut G
author Alayo I
author_facet Alayo I
Pujol O
Alonso J
Ferrer M
Amigo F
Portillo-Van Diest A
Aragonès E
Aragon Peña A
Asúnsolo Del Barco Á
Campos M
Espuga M
González-Pinto A
Haro JM
López-Fresneña N
Martínez de Salázar AD
Molina JD
Ortí-Lucas RM
Parellada M
Pelayo-Terán JM
Forjaz MJ
Pérez-Zapata A
Pijoan JI
Plana N
Polentinos-Castro E
Puig MT
Rius C
Sanz F
Serra C
Urreta-Barallobre I
Bruffaerts R
Vieta E
Pérez-Solá V
Mortier P
Vilagut G
author_role author
author2 Pujol O
Alonso J
Ferrer M
Amigo F
Portillo-Van Diest A
Aragonès E
Aragon Peña A
Asúnsolo Del Barco Á
Campos M
Espuga M
González-Pinto A
Haro JM
López-Fresneña N
Martínez de Salázar AD
Molina JD
Ortí-Lucas RM
Parellada M
Pelayo-Terán JM
Forjaz MJ
Pérez-Zapata A
Pijoan JI
Plana N
Polentinos-Castro E
Puig MT
Rius C
Sanz F
Serra C
Urreta-Barallobre I
Bruffaerts R
Vieta E
Pérez-Solá V
Mortier P
Vilagut G
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv attempted suicide
interpretability
machine learning
mental health
suicidal ideation
topic attempted suicide
interpretability
machine learning
mental health
suicidal ideation
description Aims Studies conducted during the COVID-19 pandemic found high occurrence of suicidal thoughts and behaviours (STBs) among healthcare workers (HCWs). The current study aimed to (1) develop a machine learning-based prediction model for future STBs using data from a large prospective cohort of Spanish HCWs and (2) identify the most important variables in terms of contribution to the model's predictive accuracy.Methods This is a prospective, multicentre cohort study of Spanish HCWs active during the COVID-19 pandemic. A total of 8,996 HCWs participated in the web-based baseline survey (May-July 2020) and 4,809 in the 4-month follow-up survey. A total of 219 predictor variables were derived from the baseline survey. The outcome variable was any STB at the 4-month follow-up. Variable selection was done using an L1 regularized linear Support Vector Classifier (SVC). A random forest model with 5-fold cross-validation was developed, in which the Synthetic Minority Oversampling Technique (SMOTE) and undersampling of the majority class balancing techniques were tested. The model was evaluated by the area under the Receiver Operating Characteristic (AUROC) curve and the area under the precision-recall curve. Shapley's additive explanatory values (SHAP values) were used to evaluate the overall contribution of each variable to the prediction of future STBs. Results were obtained separately by gender.Results The prevalence of STBs in HCWs at the 4-month follow-up was 7.9% (women = 7.8%, men = 8.2%). Thirty-four variables were selected by the L1 regularized linear SVC. The best results were obtained without data balancing techniques: AUROC = 0.87 (0.86 for women and 0.87 for men) and area under the precision-recall curve = 0.50 (0.55 for women and 0.45 for men). Based on SHAP values, the most important baseline predictors for any STB at the 4-month follow-up were the presence of passive suicidal ideation, the number of days in the past 30 days with passive or active suicidal ideation, the number of days in the past 30 days with binge eating episodes, the number of panic attacks (women only) and the frequency of intrusive thoughts (men only).Conclusions Machine learning-based prediction models for STBs in HCWs during the COVID-19 pandemic trained on web-based survey data present high discrimination and classification capacity. Future clinical implementations of this model could enable the early detection of HCWs at the highest risk for developing adverse mental health outcomes.Study registration NCT04556565
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=28604
url https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=28604
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv CAMBRIDGE UNIV PRESS
publisher.none.fl_str_mv CAMBRIDGE UNIV PRESS
dc.source.none.fl_str_mv Epidemiology and Psychiatric Sciences
ISSN: 20457960
ISSNe: 20457979
reponame:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
instname:Fundació Sant Joan de Déu
instname_str Fundació Sant Joan de Déu
reponame_str r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
collection r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812455

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