A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemic
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 i...
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-05-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-12107-6 |
| _version_ | 1811341074331860992 |
|---|---|
| author | Johannes Lieslehto Noora Rantanen Lotta-Maria A. H. Oksanen Sampo A. Oksanen Anne Kivimäki Susanna Paju Milla Pietiäinen Laura Lahdentausta Pirkko Pussinen Veli-Jukka Anttila Lasse Lehtonen Tea Lallukka Ahmed Geneid Enni Sanmark |
| author_facet | Johannes Lieslehto Noora Rantanen Lotta-Maria A. H. Oksanen Sampo A. Oksanen Anne Kivimäki Susanna Paju Milla Pietiäinen Laura Lahdentausta Pirkko Pussinen Veli-Jukka Anttila Lasse Lehtonen Tea Lallukka Ahmed Geneid Enni Sanmark |
| author_sort | Johannes Lieslehto |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T18:51:22Z |
| format | Article |
| id | doaj.art-e4b935113f704df095a27c9ca7adf343 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-13T18:51:22Z |
| publishDate | 2022-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-e4b935113f704df095a27c9ca7adf3432022-12-22T02:34:25ZengNature PortfolioScientific Reports2045-23222022-05-011211910.1038/s41598-022-12107-6A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemicJohannes Lieslehto0Noora Rantanen1Lotta-Maria A. H. Oksanen2Sampo A. Oksanen3Anne Kivimäki4Susanna Paju5Milla Pietiäinen6Laura Lahdentausta7Pirkko Pussinen8Veli-Jukka Anttila9Lasse Lehtonen10Tea Lallukka11Ahmed Geneid12Enni Sanmark13Niuvanniemi Hospital, University of Eastern FinlandFaculty of Medicine, University of HelsinkiFaculty of Medicine, University of HelsinkiNordic Healthcare GroupDepartment of Oral and Maxillofacial Diseases, University of Helsinki and Helsinki University HospitalDepartment of Oral and Maxillofacial Diseases, University of Helsinki and Helsinki University HospitalDepartment of Oral and Maxillofacial Diseases, University of Helsinki and Helsinki University HospitalDepartment of Oral and Maxillofacial Diseases, University of Helsinki and Helsinki University HospitalDepartment of Oral and Maxillofacial Diseases, University of Helsinki and Helsinki University HospitalFaculty of Medicine, University of HelsinkiFaculty of Medicine, University of HelsinkiDepartment of Public Health, University of HelsinkiFaculty of Medicine, University of HelsinkiFaculty of Medicine, University of HelsinkiAbstract 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.https://doi.org/10.1038/s41598-022-12107-6 |
| spellingShingle | Johannes Lieslehto Noora Rantanen Lotta-Maria A. H. Oksanen Sampo A. Oksanen Anne Kivimäki Susanna Paju Milla Pietiäinen Laura Lahdentausta Pirkko Pussinen Veli-Jukka Anttila Lasse Lehtonen Tea Lallukka Ahmed Geneid Enni Sanmark A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemic Scientific Reports |
| title | A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemic |
| title_full | A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemic |
| title_fullStr | A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemic |
| title_full_unstemmed | A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemic |
| title_short | A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemic |
| title_sort | machine learning approach to predict resilience and sickness absence in the healthcare workforce during the covid 19 pandemic |
| url | https://doi.org/10.1038/s41598-022-12107-6 |
| work_keys_str_mv | AT johanneslieslehto amachinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT noorarantanen amachinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT lottamariaahoksanen amachinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT sampoaoksanen amachinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT annekivimaki amachinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT susannapaju amachinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT millapietiainen amachinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT lauralahdentausta amachinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT pirkkopussinen amachinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT velijukkaanttila amachinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT lasselehtonen amachinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT tealallukka amachinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT ahmedgeneid amachinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT ennisanmark amachinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT johanneslieslehto machinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT noorarantanen machinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT lottamariaahoksanen machinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT sampoaoksanen machinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT annekivimaki machinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT susannapaju machinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT millapietiainen machinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT lauralahdentausta machinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT pirkkopussinen machinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT velijukkaanttila machinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT lasselehtonen machinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT tealallukka machinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT ahmedgeneid machinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic AT ennisanmark machinelearningapproachtopredictresilienceandsicknessabsenceinthehealthcareworkforceduringthecovid19pandemic |