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Prediction of Labor Absenteeism due to Incapacity: A Generalized Linear Model Approach

10.24050/reia.v22i44.1890
Published 2025-07-19
Articles

Abstract

Workplace absenteeism poses a critical challenge for human talent management, with significant economic implications, negative impacts on organizational reputation, and risks to business competitiveness. This article examines the factors influencing absenteeism, focusing on the prediction of two key aspects: the expected number of absence days for an employee over the next three months and the probability of registering at least one absence day during the same period. To achieve this, count and binary response models framed within generalized linear models are employed, yielding highly accurate predictions of the occurrence of work incapacities, at an individual level, in a Colombian financial institution. In addition, from both modeling perspectives, it was found that the factors that have the greatest influence on the occurrence of work incapacities are the month of the year, sex, type of contract, work area, and the history of the number of months in which the employee had already presented incapacity.

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Authors

  • Laura Margarita Guerrero Guerra

    Colombia

  • Nicolás Alberto Moreno Reyes

    Colombia

  • Juan Daniel Molina Muñoz

    Colombia

References

Ali Shah, S.A.; Uddin, I; Aziz, F; Ahmad, S; Al-Khasawneh, M.A.; Sharaf, M. (2020). An Enhanced Deep Neural Network for Predicting Workplace Absenteeism, Hindawi Complexity, 5843932. https://doi.org/10.1155/2020/5843932

Araujo, Vanessa S.; Rezende, Thiago S.; Guimaraes, Augusto J.; Silva Araujo, Vinicius J.; de Campos Souza, Paulo, V. (2019). A hybrid approach of intelligent systems to help predict absenteeism at work in companies. SN Applied Sciences, 1(6), 536. https://link.springer.com/article/10.1007/s42452-019-0536-y

AWS, (2025). ¿Qué es la regresión logística?. https://aws.amazon.com/es/what-is/logistic-regression/

Berg, T.; Elders, L.; Zwart, B.; Burdorf, A. (2008). The effects of work-related and individual factors on the work ability index: A systematic review. Occupational and Environmental Medicine, 66(4), 211–20. https://oem.bmj.com/content/66/4/211

Borda, M.C.; Rolón, E.; Díaz-Piraquive, F.N. y González, J. (2017). Ausentismo Laboral: Impacto en la Productividad y Estrategias de Control desde los Programas de Salud Empresarial. Universidad del Rosario. https://doi.org/10.48713/10336_13583

Coussement, K., & Van den Poel, D. (2008). Churn prediction in subscription

services: An application of support vector machines while comparing two parameter selection techniques. Expert systems with applications, 34(1), 313-327. https://doi.org/10.1016/j.eswa.2006.09.038

Daza, F.M.; Perez Bilbao, J. (1997). NTP 443: Factores psicosociales: metodología de evaluación. https://www.insst.es/documents/94886/326962/ntp_443.pdf/35f6978d-1338-43c3-ace4-e81dd39c11f0

Fischer, JE; Genser, B; Nauroth, P; Litaker, D; Mauss, D. (2020). Estimating the potential reduction in future sickness absence from optimizing group-level psychosocial work characteristics: a prospective, multicenter cohort study in German industrial settings. Journal of Occupational Medicine and Toxicology, 15(1), 33. https://doi.org/10.1186/s12995-020-00284-x

Gil Bellosta, Carlos J. (2018). Modelos con Inflación de Ceros y Separación Perfecta. https://www.datanalytics.com/2018/04/11/modelos-con-inflacion-de-ceros-y-separacion-perfecta/

Gomes, J.M.; Lopes, F.M. (2022). Interpretability with Relevance Aggregation in Neural Networks for Absenteeism Prediction. 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). https://ieeexplore.ieee.org/document/9926870

IBM, (2025). Regresión Logística. https://www.ibm.com/docs/es/spss-statistics/saas?topic=regression-logistic

IBM, (2025). Modelos Lineales Generalizados. https://www.ibm.com/docs/es/spss-statistics/saas?topic=statistics-generalized-linear-models

International Labour Organization. (2020). Skilled Workers Matter. The business case for addressing absenteeism and turnover in Myanmar’s garment sector. https://www.ilo.org/publications/skilled-workers-matter-business-case-addressing-absenteeism-and-turnover-0

Instituto Colombiano de Normas Técnicas y Certificación (ICONTEC). (1996). Norma Técnica Colombiana NTC 3793. Salud Ocupacional. Clasificación, Registro y Estadísticas de Ausetismo Laboral, 1-2. https://www.studocu.com/co/document/uninpahu-institucion-universitaria-sede-principal/administracion/pdfslide-dfs/28685112

Isaac P., Jaime; Rivera, Gerson. (2021). Modelos Lineales Generalizados con R. Chapter 8: Regresión de Poisson. https://bookdown.org/jaimeisaacp/bookglm/regresi%C3%B3n-de-poisson.html

Kocakulah, M.C.; Kelley, A.; Mitchell, K.M.; Ruggieri, M.P. (2016). Absenteeism Problems And Costs: Causes, Effects And Cures. International Business & Economics Research Journal (IBER), 15(3). https://clutejournals.com/index.php/IBER/article/view/9673

Lawrance, N; Petrides, G; Guerry, M.A. (2021). Predicting employee absenteeism for cost effective interventions. Decision Support Systems, 147, 113539. https://doi.org/10.1016/j.dss.2021.113539

Management Solutions, (2023). Explainable artificial intelligence (XAI): Desafíos en la interpretabilidad de los modelos. https://www.managementsolutions.com/sites/default/files/minisite/static/22959b0f-b3da-47c8-9d5c-80ec3216552b/iax/pdf/explainable-artificial-intelligence-sp.pdf

Martiniano, A.; Ferreira, R. P.; Sassi, R. J.; Affonso, C. (2012). Application of a neuro fuzzy network in prediction of absenteeism at work. 7th Iberian Conference on Information Systems and Technologies (CISTI). https://ieeexplore.ieee.org/document/6263151

Montano, IH; Marques, G; Alonso, SG; Lopez-Coronado, M; Diez, ID. (2020). Predicting Absenteeism and Temporary Disability Using Machine Learning: A Systematic Review and Analysis. Journal of Medical Systems, 44(9), 162. http://dx.doi.org/10.1007/s10916-020-01626-2

Navarro, C; Bass, C. (2006). The cost of employee absenteeism. Compensation & Benefits Review, 38(6), 26–30. https://doi.org/10.1177/0886368706295343

Qaisar, S. (2020). Predicting Absenteeism at Work Using Machine Learning Algorithms. MJPS, 7(1). https://www.researchgate.net/publication/350955612

Raja, H.; Gupta, R. (2019). The impact of employee absenteeism on organizational productivity with special reference to service sector. International Journal of Research in Humanities, Arts and Literature, 4(7), 581–594. https://oaji.net/articles/2019/488-1558087237.pdf

Salonsalmi, A; Rahkonen, O; Lahelma, E; Pietiläinen, O; Lallukka, T. (2023). Associations between low parental education, childhood adversities and sickness absence in midlife public sector employees. Scandinavian Journal of Public Health, 51(6), 953 – 962.

https://journals.sagepub.com/doi/10.1177/14034948221087996

Sanchez, D.C. (2015). Ausentismo laboral: una visión desde la gestión de la seguridad y la salud en el trabajo. Revista Salud Bosque, 5(1), 182. http://revistas.unbosque.edu.co/index.php/RSB/article/view/182

Svärd, A; Lallukka, T; Oakman, J; Roos, E; Ervasti, J; Salmela, J. (2024). The Joint Contributions of Overweight/Obesity and Physical Mental Working Conditions to Short and Long Sickness Absence among Young and Midlife Finnish Employees: A Register-Linked Follow-Up Study. Obesity Facts, 17(1), 37 – 46.

https://doi.org/10.1159/000534525

Vafeiadis, T; Diamantaras K. I.; Sarigiannidis, G; Chatzisavvas, K. Ch. (2015). A comparison of machine learning techniques for customer churn prediction. Simulation Modelling Practice and Theory, 55, 1-9. https://doi.org/10.1016/j.simpat.2015.03.003

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