Identifying most important predictors for suicidal thoughts and behaviours among healthcare workers active during the Spain COVID-19 pandemic: a machine-learning approach
Abstract 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 o...
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Cambridge University Press
2025-01-01
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| Series: | Epidemiology and Psychiatric Sciences |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2045796025000198/type/journal_article |
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| author | Itxaso Alayo Oriol Pujol Jordi Alonso Montse Ferrer Franco Amigo Ana Portillo-Van Diest Enric Aragonès Andrés Aragon Peña Ángel Asúnsolo Del Barco Mireia Campos Meritxell Espuga Ana González-Pinto Josep Maria Haro Nieves López-Fresneña Alma D. Martínez de Salázar Juan D. Molina Rafael M. Ortí-Lucas Mara Parellada José Maria Pelayo-Terán Maria João Forjaz Aurora Pérez-Zapata José Ignacio Pijoan Nieves Plana Elena Polentinos-Castro Maria Teresa Puig Cristina Rius Ferran Sanz Cònsol Serra Iratxe Urreta-Barallobre Ronny Bruffaerts Eduard Vieta Víctor Pérez-Solá Philippe Mortier Gemma Vilagut |
| author_facet | Itxaso Alayo Oriol Pujol Jordi Alonso Montse Ferrer Franco Amigo Ana Portillo-Van Diest Enric Aragonès Andrés Aragon Peña Ángel Asúnsolo Del Barco Mireia Campos Meritxell Espuga Ana González-Pinto Josep Maria Haro Nieves López-Fresneña Alma D. Martínez de Salázar Juan D. Molina Rafael M. Ortí-Lucas Mara Parellada José Maria Pelayo-Terán Maria João Forjaz Aurora Pérez-Zapata José Ignacio Pijoan Nieves Plana Elena Polentinos-Castro Maria Teresa Puig Cristina Rius Ferran Sanz Cònsol Serra Iratxe Urreta-Barallobre Ronny Bruffaerts Eduard Vieta Víctor Pérez-Solá Philippe Mortier Gemma Vilagut |
| author_sort | Itxaso Alayo |
| collection | DOAJ |
| description | Abstract
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
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| format | Article |
| id | doaj-art-d48febbf056d487f8d570c2c79d0dc96 |
| institution | DOAJ |
| issn | 2045-7960 2045-7979 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Epidemiology and Psychiatric Sciences |
| spelling | doaj-art-d48febbf056d487f8d570c2c79d0dc962025-08-20T02:56:03ZengCambridge University PressEpidemiology and Psychiatric Sciences2045-79602045-79792025-01-013410.1017/S2045796025000198Identifying most important predictors for suicidal thoughts and behaviours among healthcare workers active during the Spain COVID-19 pandemic: a machine-learning approachItxaso Alayo0https://orcid.org/0000-0002-7333-3450Oriol Pujol1Jordi Alonso2https://orcid.org/0000-0001-8627-9636Montse Ferrer3Franco Amigo4Ana Portillo-Van Diest5https://orcid.org/0000-0001-7199-8339Enric Aragonès6Andrés Aragon Peña7Ángel Asúnsolo Del Barco8Mireia Campos9Meritxell Espuga10Ana González-Pinto11Josep Maria Haro12Nieves López-Fresneña13Alma D. Martínez de Salázar14Juan D. Molina15Rafael M. Ortí-Lucas16Mara Parellada17José Maria Pelayo-Terán18Maria João Forjaz19Aurora Pérez-Zapata20José Ignacio Pijoan21Nieves Plana22Elena Polentinos-Castro23Maria Teresa Puig24Cristina Rius25Ferran Sanz26Cònsol Serra27Iratxe Urreta-Barallobre28Ronny Bruffaerts29Eduard Vieta30https://orcid.org/0000-0002-0548-0053Víctor Pérez-Solá31Philippe Mortier32https://orcid.org/0000-0003-2113-6241Gemma Vilagut33Hospital del Mar Research Institute, Barcelona, Spain Biosistemak Institute for Health Systems Research, Bilbao, Bizkaia, Spain Red de Investigación en Cronicidad, Atención Primaria y Promoción de la Salud RICAPPS-(RICORS), Instituto de Salud Carlos III (ISCIII), Madrid, Spain Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra, Barcelona, SpainDepartament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, SpainHospital del Mar Research Institute, Barcelona, Spain Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra, Barcelona, Spain CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, SpainHospital del Mar Research Institute, Barcelona, Spain Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra, Barcelona, Spain CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, SpainHospital del Mar Research Institute, Barcelona, Spain CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, SpainHospital del Mar Research Institute, Barcelona, Spain Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra, Barcelona, Spain CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, SpainInstitut d’Investigació en Atenció Primària IDIAP Jordi Gol, Barcelona, Spain Atenció Primària Camp de Tarragona, Institut Català de la Salut, Tarragona, SpainEpidemiology Unit, Regional Ministry of Health, Community of Madrid, Madrid, Spain Fundación Investigación e Innovación Biosanitaria de AP, Comunidad de Madrid, Madrid, SpainDepartment of Surgery, Medical and Social Sciences, Faculty of Medicine and Health Sciences, University of Alcala, Alcalá de Henares, Spain Ramón y Cajal Institute of Sanitary Research (IRYCIS), Madrid, Spain Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, The City University of New York, New York, NY, USAService of Prevention of Labor Risks, Medical Emergencies System, Generalitat de Catalunya, Barcelona, SpainOccupational Health Service, Hospital Universitari Vall d’Hebron, Barcelona, SpainBIOARABA, Hospital Universitario Araba-Santiago, UPV/EHU, Vitoria-Gasteiz, Spain CIBER Salud Mental (CIBERSAM), Madrid, SpainCIBER Salud Mental (CIBERSAM), Madrid, Spain Parc Sanitari Sant Joan de Déu, Institut de Recerca Sant Joan de Deu (IRSJD), Sant Boi de Llobregat, Barcelona, SpainHospital General Universitario Gregorio Marañón, Madrid, SpainUGC Salud Mental, Hospital Universitario Torrecárdenas, Almería, SpainCIBER Salud Mental (CIBERSAM), Madrid, Spain Villaverde Mental Health Center, Clinical Management Area of Psychiatry and Mental Health, Psychiatric Service, Hospital Universitario 12 de Octubre, Madrid, Spain Research Institute Hospital 12 de Octubre (i+12), Madrid, Spain Facultad de Medicina, Universidad Francisco de Vitoria, Madrid, SpainServicio de Medicina Preventiva y Calidad Asistencial, Hospital Clínic Universitari de Valencia, Valencia, SpainCIBER Salud Mental (CIBERSAM), Madrid, Spain Hospital General Universitario Gregorio Marañón, Madrid, SpainCIBER Salud Mental (CIBERSAM), Madrid, Spain Servicio de Psiquiatría y Salud Mental, Hospital el Bierzo, Gerencia de Asistencia Sanitaria del Bierzo (GASBI). Gerencia Regional de Salud de Castilla y Leon (SACYL), Ponferrada, León, Spain Area de Medicina Preventiva y Salud Pública, Departamento de Ciencias Biomédicas, Universidad de León, León, SpainRed de Investigación en Cronicidad, Atención Primaria y Promoción de la Salud RICAPPS-(RICORS), Instituto de Salud Carlos III (ISCIII), Madrid, Spain National Center of Epidemiology, Instituto de Salud Carlos III (ISCIII), Madrid, SpainHospital Universitario Príncipe de Asturias, Servicio de Prevención de Riesgos Laborales, SpainRed de Investigación en Cronicidad, Atención Primaria y Promoción de la Salud RICAPPS-(RICORS), Instituto de Salud Carlos III (ISCIII), Madrid, Spain Clinical Epidemiology Unit-Hospital Universitario Cruces/ OSI EEC, Biobizkaia Health Research Institute, Barakaldo, SpainRed de Investigación en Cronicidad, Atención Primaria y Promoción de la Salud RICAPPS-(RICORS), Instituto de Salud Carlos III (ISCIII), Madrid, Spain Ramón y Cajal University Hospital, IRYCIS, Department of Surgery, Medical and Social Sciences, Faculty of Medicine and Health Sciences, University of Alcalá, Alcala de Henares, MAD, SpainService of Prevention of Labor Risks, Medical Emergencies System, Generalitat de Catalunya, Barcelona, Spain Research Unit, Primary Care Management, Madrid Health Service, Madrid, Spain Department of Medical Specialities and Public Health, King Juan Carlos University, Madrid, SpainUniversitat Autònoma de Barcelona (UAB), Barcelona, Spain Department of Epidemiology and Public Health, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain Biomedical Research Institute Sant Pau (IIB Sant Pau), Barcelona, Spain CIBER Enfermedades Cardiovasculares (CIBERCV), Madrid, SpainCIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain Agència de Salut Pública de Barcelona, Barcelona, SpainDepartment of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra, Barcelona, Spain Research Progamme on Biomedical Informatics (GRIB), Hospital del Mar Research Institute, Barcelona, Spain Instituto Nacional de Bioinformatica – ELIXIR-ES, Barcelona, SpainCIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain CiSAL-Centro de Investigación en Salud Laboral, Hospital del Mar Research Institute/University Pompeu Fabra, Barcelona, Spain Occupational Health Service, Hospital del Mar, Barcelona, SpainCIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain Osakidetza Basque Health Service, Donostialdea Integrated Health Organisation, Donostia University Hospital, Clinical Epidemiology Unit, San Sebastián, Spain Biodonostia Health Research Institute, Clinical Epidemiology, San Sebastián, SpainCenter for Public Health Psychiatry, Universitair Psychiatrisch Centrum, KU Leuven, Leuven, BelgiumCIBER Salud Mental (CIBERSAM), Madrid, Spain Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, Barcelona, SpainCIBER Salud Mental (CIBERSAM), Madrid, Spain Universitat Autònoma de Barcelona (UAB), Barcelona, Spain Institute of Neuropsychiatry and Addiction (INAD), Parc de Salut Mar, Barcelona, SpainHospital del Mar Research Institute, Barcelona, Spain CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, SpainHospital del Mar Research Institute, Barcelona, Spain CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, SpainAbstract 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 https://www.cambridge.org/core/product/identifier/S2045796025000198/type/journal_articleattempted suicideinterpretabilitymachine learningmental healthsuicidal ideation |
| spellingShingle | Itxaso Alayo Oriol Pujol Jordi Alonso Montse Ferrer Franco Amigo Ana Portillo-Van Diest Enric Aragonès Andrés Aragon Peña Ángel Asúnsolo Del Barco Mireia Campos Meritxell Espuga Ana González-Pinto Josep Maria Haro Nieves López-Fresneña Alma D. Martínez de Salázar Juan D. Molina Rafael M. Ortí-Lucas Mara Parellada José Maria Pelayo-Terán Maria João Forjaz Aurora Pérez-Zapata José Ignacio Pijoan Nieves Plana Elena Polentinos-Castro Maria Teresa Puig Cristina Rius Ferran Sanz Cònsol Serra Iratxe Urreta-Barallobre Ronny Bruffaerts Eduard Vieta Víctor Pérez-Solá Philippe Mortier Gemma Vilagut Identifying most important predictors for suicidal thoughts and behaviours among healthcare workers active during the Spain COVID-19 pandemic: a machine-learning approach Epidemiology and Psychiatric Sciences attempted suicide interpretability machine learning mental health suicidal ideation |
| title | 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_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_sort | identifying most important predictors for suicidal thoughts and behaviours among healthcare workers active during the spain covid 19 pandemic a machine learning approach |
| topic | attempted suicide interpretability machine learning mental health suicidal ideation |
| url | https://www.cambridge.org/core/product/identifier/S2045796025000198/type/journal_article |
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