The impact of disease changes and mental health illness on readapted return to work after repeated sick leaves among Brazilian public university employees

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dc.contributor.authorDias, Adriano [UNESP]
dc.contributor.authorNunes, Hélio Rubens de Carvalho [UNESP]
dc.contributor.authorRuiz-Frutos, Carlos
dc.contributor.authorGómez-Salgado, Juan
dc.contributor.authorSpröesser Alonso, Melissa [UNESP]
dc.contributor.authorBernardes, João Marcos [UNESP]
dc.contributor.authorGarcía-Iglesias, Juan Jesús
dc.contributor.authorLacalle-Remigio, Juan Ramón
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversity of Huelva
dc.contributor.institutionUniversidad Espíritu Santo
dc.contributor.institutionUniversity of Sevilla
dc.date.accessioned2023-07-29T13:39:08Z
dc.date.available2023-07-29T13:39:08Z
dc.date.issued2023-01-09
dc.description.abstractIntroduction: Health affects work absenteeism and productivity of workers, making it a relevant marker of an individual's professional development. Objectives: The aims of this article were to investigate whether changes in the main cause of the sick leaves and the presence of mental health illnesses are associated with return to work with readaptation. Materials and methods: A historical cohort study was carried out with non-work-related illnesses suffered by statutory workers of university campuses in a medium-sized city in the state of São Paulo, Brazil. Two exposures were measured: (a) changes, throughout medical examinations, in the International Classification of Diseases (ICD-10) chapter regarding the main condition for the sick leave; and (b) having at least one episode of sick leave due to mental illness, with or without change in the ICD-10 chapter over the follow-up period. The outcome was defined as return to work with adapted conditions. The causal model was established a priori and tested using a multiple logistic regression (MLR) model considering the effects of several confounding factors, and then compared with the same estimators obtained using Targeted Machine Learning. Results: Among workers in adapted conditions, 64% were health professionals, 34% had had changes in the ICD-10 chapter throughout the series of sick leaves, and 62% had diagnoses of mental health issues. In addition, they worked for less time at the university and were absent for longer periods. Having had a change in the illness condition reduced the chance of returning to work in another function by more than 30%, whereas having had at least one absence because of a cause related to mental and behavioral disorders more than doubled the chance of not returning to work in the same activity as before. Conclusion: These results were independent of the analysis technique used, which allows concluding that there were no advantages in the use of targeted maximum likelihood estimation (TMLE), given its difficulties in access, use, and assumptions.en
dc.description.affiliationDepartment of Public Health Botucatu Medical School São Paulo State University (UNESP)
dc.description.affiliationPublic/Collective Health Graduate Program Botucatu Medical School São Paulo State University (UNESP)
dc.description.affiliationGraduate Program in Nursing Academic Master's and Doctoral Programs Botucatu Medical School São Paulo State University (UNESP)
dc.description.affiliationDepartment of Sociology Social Work and Public Health Faculty of Labour Sciences University of Huelva
dc.description.affiliationSafety and Health Postgraduate Programme Universidad Espíritu Santo
dc.description.affiliationDepartment of Preventive Medicine and Public Health Faculty of Medicine University of Sevilla
dc.description.affiliationUnespDepartment of Public Health Botucatu Medical School São Paulo State University (UNESP)
dc.description.affiliationUnespPublic/Collective Health Graduate Program Botucatu Medical School São Paulo State University (UNESP)
dc.description.affiliationUnespGraduate Program in Nursing Academic Master's and Doctoral Programs Botucatu Medical School São Paulo State University (UNESP)
dc.identifierhttp://dx.doi.org/10.3389/fpubh.2022.1026053
dc.identifier.citationFrontiers in Public Health, v. 10.
dc.identifier.doi10.3389/fpubh.2022.1026053
dc.identifier.issn2296-2565
dc.identifier.scopus2-s2.0-85146859361
dc.identifier.urihttp://hdl.handle.net/11449/248268
dc.language.isoeng
dc.relation.ispartofFrontiers in Public Health
dc.sourceScopus
dc.subjectabsenteeism
dc.subjectlogistic regression
dc.subjectreadaptation
dc.subjectreturn to work
dc.subjectTargeted Machine Learning
dc.titleThe impact of disease changes and mental health illness on readapted return to work after repeated sick leaves among Brazilian public university employeesen
dc.typeArtigo
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Medicina, Botucatupt
unesp.departmentEnfermagem - FMBpt

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Artigos - Saúde Pública - FMB
Botucatu - FMB - Faculdade de Medicina