A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemic

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Lieslehto , J , Rantanen , N , Oksanen , L-M A H , Oksanen , S A , Kivimäki , A , Paju , S , Pietiäinen , M , Lahdentausta , L , Pussinen , P , Anttila , V-J , Lehtonen , L , Lallukka , T , Geneid , A & Sanmark , E 2022 , ' A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemic ' , Scientific Reports , vol. 12 , no. 1 , 8055 . https://doi.org/10.1038/s41598-022-12107-6

Title: A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemic
Author: Lieslehto, Johannes; Rantanen, Noora; Oksanen, Lotta-Maria A. H.; Oksanen, Sampo A.; Kivimäki, Anne; Paju, Susanna; Pietiäinen, Milla; Lahdentausta, Laura; Pussinen, Pirkko; Anttila, Veli-Jukka; Lehtonen, Lasse; Lallukka, Tea; Geneid, Ahmed; Sanmark, Enni
Contributor organization: University of Helsinki
Department of Ophthalmology and Otorhinolaryngology
Faculty of Medicine
HUS Head and Neck Center
Clinicum
Department of Oral and Maxillofacial Diseases
HUS Inflammation Center
HUSLAB
HUS Diagnostic Center
Department of Public Health
Helsinki Inequality Initiative (INEQ)
Faculty Common Matters (Faculty of Medicine)
Korva-, nenä- ja kurkkutautien klinikka
Date: 2022-05-16
Language: eng
Number of pages: 9
Belongs to series: Scientific Reports
ISSN: 2045-2322
DOI: https://doi.org/10.1038/s41598-022-12107-6
URI: http://hdl.handle.net/10138/345932
Abstract: During the COVID-19 pandemic, healthcare workers (HCWs) have faced unprecedented workloads and personal health risks leading to mental disorders and surges in sickness absence. Previous work has shown that interindividual differences in psychological resilience might explain why only some individuals are vulnerable to these consequences. However, no prognostic tools to predict individual HCW resilience during the pandemic have been developed. We deployed machine learning (ML) to predict psychological resilience during the pandemic. The models were trained in HCWs of the largest Finnish hospital, Helsinki University Hospital (HUS, N = 487), with a six-month follow-up, and prognostic generalizability was evaluated in two independent HCW validation samples (Social and Health Services in Kymenlaakso: Kymsote, N = 77 and the City of Helsinki, N = 322) with similar follow-ups never used for training the models. Using the most predictive items to predict future psychological resilience resulted in a balanced accuracy (BAC) of 72.7-74.3% in the HUS sample. Similar performances (BAC = 67-77%) were observed in the two independent validation samples. The models' predictions translated to a high probability of sickness absence during the pandemic. Our results provide the first evidence that ML techniques could be harnessed for the early detection of COVID-19-related distress among HCWs, thereby providing an avenue for potential targeted interventions.
Subject: MENTAL-HEALTH
BURNOUT
STRESS
3142 Public health care science, environmental and occupational health
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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