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Heterogeneity and event dependence in the analysis of sickness absence

BACKGROUND: Sickness absence (SA) is an important social, economic and public health issue. Identifying and understanding the determinants, whether biological, regulatory or, health services-related, of variability in SA duration is essential for better management of SA. The conditional frailty mode...

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Autores principales: Torá-Rocamora, Isabel, Gimeno, David, Delclos, George, Benavides, Fernando G, Manzanera, Rafael, Jardí, Josefina, Alberti, Constança, Yasui, Yutaka, Martínez, José Miguel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3852331/
https://www.ncbi.nlm.nih.gov/pubmed/24040880
http://dx.doi.org/10.1186/1471-2288-13-114
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author Torá-Rocamora, Isabel
Gimeno, David
Delclos, George
Benavides, Fernando G
Manzanera, Rafael
Jardí, Josefina
Alberti, Constança
Yasui, Yutaka
Martínez, José Miguel
author_facet Torá-Rocamora, Isabel
Gimeno, David
Delclos, George
Benavides, Fernando G
Manzanera, Rafael
Jardí, Josefina
Alberti, Constança
Yasui, Yutaka
Martínez, José Miguel
author_sort Torá-Rocamora, Isabel
collection PubMed
description BACKGROUND: Sickness absence (SA) is an important social, economic and public health issue. Identifying and understanding the determinants, whether biological, regulatory or, health services-related, of variability in SA duration is essential for better management of SA. The conditional frailty model (CFM) is useful when repeated SA events occur within the same individual, as it allows simultaneous analysis of event dependence and heterogeneity due to unknown, unmeasured, or unmeasurable factors. However, its use may encounter computational limitations when applied to very large data sets, as may frequently occur in the analysis of SA duration. METHODS: To overcome the computational issue, we propose a Poisson-based conditional frailty model (CFPM) for repeated SA events that accounts for both event dependence and heterogeneity. To demonstrate the usefulness of the model proposed in the SA duration context, we used data from all non-work-related SA episodes that occurred in Catalonia (Spain) in 2007, initiated by either a diagnosis of neoplasm or mental and behavioral disorders. RESULTS: As expected, the CFPM results were very similar to those of the CFM for both diagnosis groups. The CPU time for the CFPM was substantially shorter than the CFM. CONCLUSIONS: The CFPM is an suitable alternative to the CFM in survival analysis with recurrent events, especially with large databases.
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spelling pubmed-38523312013-12-19 Heterogeneity and event dependence in the analysis of sickness absence Torá-Rocamora, Isabel Gimeno, David Delclos, George Benavides, Fernando G Manzanera, Rafael Jardí, Josefina Alberti, Constança Yasui, Yutaka Martínez, José Miguel BMC Med Res Methodol Research Article BACKGROUND: Sickness absence (SA) is an important social, economic and public health issue. Identifying and understanding the determinants, whether biological, regulatory or, health services-related, of variability in SA duration is essential for better management of SA. The conditional frailty model (CFM) is useful when repeated SA events occur within the same individual, as it allows simultaneous analysis of event dependence and heterogeneity due to unknown, unmeasured, or unmeasurable factors. However, its use may encounter computational limitations when applied to very large data sets, as may frequently occur in the analysis of SA duration. METHODS: To overcome the computational issue, we propose a Poisson-based conditional frailty model (CFPM) for repeated SA events that accounts for both event dependence and heterogeneity. To demonstrate the usefulness of the model proposed in the SA duration context, we used data from all non-work-related SA episodes that occurred in Catalonia (Spain) in 2007, initiated by either a diagnosis of neoplasm or mental and behavioral disorders. RESULTS: As expected, the CFPM results were very similar to those of the CFM for both diagnosis groups. The CPU time for the CFPM was substantially shorter than the CFM. CONCLUSIONS: The CFPM is an suitable alternative to the CFM in survival analysis with recurrent events, especially with large databases. BioMed Central 2013-09-16 /pmc/articles/PMC3852331/ /pubmed/24040880 http://dx.doi.org/10.1186/1471-2288-13-114 Text en Copyright © 2013 Torá-Rocamora et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Torá-Rocamora, Isabel
Gimeno, David
Delclos, George
Benavides, Fernando G
Manzanera, Rafael
Jardí, Josefina
Alberti, Constança
Yasui, Yutaka
Martínez, José Miguel
Heterogeneity and event dependence in the analysis of sickness absence
title Heterogeneity and event dependence in the analysis of sickness absence
title_full Heterogeneity and event dependence in the analysis of sickness absence
title_fullStr Heterogeneity and event dependence in the analysis of sickness absence
title_full_unstemmed Heterogeneity and event dependence in the analysis of sickness absence
title_short Heterogeneity and event dependence in the analysis of sickness absence
title_sort heterogeneity and event dependence in the analysis of sickness absence
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3852331/
https://www.ncbi.nlm.nih.gov/pubmed/24040880
http://dx.doi.org/10.1186/1471-2288-13-114
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