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Statistical Analysis of Absenteeism in a University Hospital Center between 2007 and 2019

Objectives: To estimate the evolution of compressible absenteeism in a hospital center and identify the professional and sociodemographic factors that influence absenteeism. Method: All hospital center employees have been included over a period of twelve consecutive years (2007 to 2019). Compressibl...

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Autores principales: Millot, Charlène, Pereira, Bruno, Miallaret, Sophie, Clinchamps, Maëlys, Vialatte, Luc, Guillin, Arnaud, Bailly, Yan, Ugbolue, Ukadike Chris, Navel, Valentin, Baker, Julien Steven, Bouillon-Minois, Jean-Baptiste, Dutheil, Frédéric
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9565198/
https://www.ncbi.nlm.nih.gov/pubmed/36232261
http://dx.doi.org/10.3390/ijerph191912966
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author Millot, Charlène
Pereira, Bruno
Miallaret, Sophie
Clinchamps, Maëlys
Vialatte, Luc
Guillin, Arnaud
Bailly, Yan
Ugbolue, Ukadike Chris
Navel, Valentin
Baker, Julien Steven
Bouillon-Minois, Jean-Baptiste
Dutheil, Frédéric
author_facet Millot, Charlène
Pereira, Bruno
Miallaret, Sophie
Clinchamps, Maëlys
Vialatte, Luc
Guillin, Arnaud
Bailly, Yan
Ugbolue, Ukadike Chris
Navel, Valentin
Baker, Julien Steven
Bouillon-Minois, Jean-Baptiste
Dutheil, Frédéric
author_sort Millot, Charlène
collection PubMed
description Objectives: To estimate the evolution of compressible absenteeism in a hospital center and identify the professional and sociodemographic factors that influence absenteeism. Method: All hospital center employees have been included over a period of twelve consecutive years (2007 to 2019). Compressible absences and occupational and sociodemographic factors were analyzed using Occupational Health data. Since the distribution of the data did not follow a normal distribution, the number of days of absence was presented as a median (interquartile range (IQR): 1st quartile–3rd quartile), and comparisons were made using non-parametric tests followed by a negative binomial model with zero inflation (ZINB). Results: A total of 16,413 employees were included, for a total of 2,828,599 days of absence, of which 2,081,553 were compressible absences (73.6% of total absences). Overall, 42% of employees have at least one absence per year. Absent employees had a median of 15 (IQR 5–53) days of absence per year, with an increase of a factor of 1.9 (CI95 1.8–2.1) between 2007 and 2019 (p < 0.001). Paramedical staff were most at risk of absence (p < 0.001 vs. all other occupational categories). Between 2007 and 2019, the number of days of absence was multiplied by 2.4 (CI95 1.8–3.1) for administrative staff, 2.1 (CI95 1.9–2.3) for tenured, 1.7 (CI95 1.5–2.0) for those living more than 12 km from the workplace, 1.8 (CI95 1.6–2.0) among women, 2.1 (CI95 1.8–2.6) among those over 50 years of age, 2.4 (CI95 1.8–3.0) among “separated” workers, and 2.0 (CI95 1.8–2.2) among those with at least one child. Conclusions: Paramedical personnel are most at risk of absenteeism. Meanwhile, absenteeism is increasing steadily, and overall, the increase is major for administrative staff. The profile of an employee at risk of absenteeism is a titular employee, living at distance from work, probably female, over 50 years old, separated, and with children. Identifying professionals at risk of absenteeism is essential to propose adapted and personalized preventive measures.
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spelling pubmed-95651982022-10-15 Statistical Analysis of Absenteeism in a University Hospital Center between 2007 and 2019 Millot, Charlène Pereira, Bruno Miallaret, Sophie Clinchamps, Maëlys Vialatte, Luc Guillin, Arnaud Bailly, Yan Ugbolue, Ukadike Chris Navel, Valentin Baker, Julien Steven Bouillon-Minois, Jean-Baptiste Dutheil, Frédéric Int J Environ Res Public Health Article Objectives: To estimate the evolution of compressible absenteeism in a hospital center and identify the professional and sociodemographic factors that influence absenteeism. Method: All hospital center employees have been included over a period of twelve consecutive years (2007 to 2019). Compressible absences and occupational and sociodemographic factors were analyzed using Occupational Health data. Since the distribution of the data did not follow a normal distribution, the number of days of absence was presented as a median (interquartile range (IQR): 1st quartile–3rd quartile), and comparisons were made using non-parametric tests followed by a negative binomial model with zero inflation (ZINB). Results: A total of 16,413 employees were included, for a total of 2,828,599 days of absence, of which 2,081,553 were compressible absences (73.6% of total absences). Overall, 42% of employees have at least one absence per year. Absent employees had a median of 15 (IQR 5–53) days of absence per year, with an increase of a factor of 1.9 (CI95 1.8–2.1) between 2007 and 2019 (p < 0.001). Paramedical staff were most at risk of absence (p < 0.001 vs. all other occupational categories). Between 2007 and 2019, the number of days of absence was multiplied by 2.4 (CI95 1.8–3.1) for administrative staff, 2.1 (CI95 1.9–2.3) for tenured, 1.7 (CI95 1.5–2.0) for those living more than 12 km from the workplace, 1.8 (CI95 1.6–2.0) among women, 2.1 (CI95 1.8–2.6) among those over 50 years of age, 2.4 (CI95 1.8–3.0) among “separated” workers, and 2.0 (CI95 1.8–2.2) among those with at least one child. Conclusions: Paramedical personnel are most at risk of absenteeism. Meanwhile, absenteeism is increasing steadily, and overall, the increase is major for administrative staff. The profile of an employee at risk of absenteeism is a titular employee, living at distance from work, probably female, over 50 years old, separated, and with children. Identifying professionals at risk of absenteeism is essential to propose adapted and personalized preventive measures. MDPI 2022-10-10 /pmc/articles/PMC9565198/ /pubmed/36232261 http://dx.doi.org/10.3390/ijerph191912966 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Millot, Charlène
Pereira, Bruno
Miallaret, Sophie
Clinchamps, Maëlys
Vialatte, Luc
Guillin, Arnaud
Bailly, Yan
Ugbolue, Ukadike Chris
Navel, Valentin
Baker, Julien Steven
Bouillon-Minois, Jean-Baptiste
Dutheil, Frédéric
Statistical Analysis of Absenteeism in a University Hospital Center between 2007 and 2019
title Statistical Analysis of Absenteeism in a University Hospital Center between 2007 and 2019
title_full Statistical Analysis of Absenteeism in a University Hospital Center between 2007 and 2019
title_fullStr Statistical Analysis of Absenteeism in a University Hospital Center between 2007 and 2019
title_full_unstemmed Statistical Analysis of Absenteeism in a University Hospital Center between 2007 and 2019
title_short Statistical Analysis of Absenteeism in a University Hospital Center between 2007 and 2019
title_sort statistical analysis of absenteeism in a university hospital center between 2007 and 2019
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9565198/
https://www.ncbi.nlm.nih.gov/pubmed/36232261
http://dx.doi.org/10.3390/ijerph191912966
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