Heterogeneity and event dependence in the analysis of sickness absence

BMC Medical Research Methodology

José Miguel Martínez⁴  Yutaka Yasui³  Constança Alberti⁵  Josefina Jardí⁵  Rafael Manzanera⁵  Fernando G Benavides⁴  George Delclos¹  David Gimeno²  Isabel Torá-Rocamora⁴ 

[1] Southwest Center for Occupational and Environmental Health, Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas School of Public Health, Houston Campus, Houston, TX, USA;Southwest Center for Occupational and Environmental Health, Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas School of Public Health, San Antonio Campus, San Antonio, TX, USA;Department of Public Health Sciences, School of Public Health, University of Alberta, Edmonton, AB, Canada;CIBER in Epidemiology and Public Health, Barcelona, Spain;Institut Català d’Avaluacions Mèdiques i Sanitàries (ICAMS), Departament de Salut, Generalitat de Catalunya, Barcelona, Spain

关键词: Neoplasms;    Mental disorders;    Poisson regression;    Conditional frailty model;    Survival analysis;    Sickness absence;   

Others  :  1091749
DOI  :  10.1186/1471-2288-13-114

 received in 2013-04-18, accepted in 2013-09-11,  发布年份 2013

【 摘 要 】

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.

【 授权许可】

   
2013 Torá-Rocamora et al.; licensee BioMed Central Ltd.

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