Original article

Scand J Work Environ Health 2017;43(1):42-49  Price: EUR 15.00 Add to Cart


Application of latent growth modeling to identify different working life trajectories: the case of the Spanish WORKss cohort

by Serra L, López Gómez MA, Sanchez-Niubo A, Delclos GL, Benavides FG

Objective The aim of this study was to describe the application of latent class growth analysis (LCGA) to identify different working life trajectories (WLT) using employed working time by year as a repeated measure.

Methods Trajectories are estimated using LCGA, which considers all individuals within a trajectory to be homogeneous. The methodology was applied to a subsample of the Spanish WORKing life Social Security (WORKss) cohort, limited to persons born 1956–1965 (N=247 475). The number of days worked per year is used as a repeated measure across 32 time points (1981–2013).

Results According to the model-fit results and further guided by expert knowledge, a four WTL model was selected as the optimal approach: WLT1 or “high labor force participation” (N=99 591; 40.2%); WLT2 or “decreased labor force participation” (N= 22 846; 9.2%); WLT3 or “increased labor force participation” (N=59 213; 23.9%); and WLT4 or “low labor force participation” (N=65 827; 26.6%). WLT1 consisted mainly of men with more years of work experience (>19 years) while WLT4 was mainly composed by women with <9 years. The other two trajectories had opposite trends and no sex differences. The occupational category variable had little influence in the trajectories.

Conclusions Longitudinal data that are regularly collected by administrative systems can benefit from LCGA approaches to identify different trajectory patterns that may be associated with an outcome of interest. In occupational epidemiology, this study represents a step forward by using this modeling approach to identify different WLT.

This article refers to the following texts of the Journal: 2011;37(6):455-463  2012;38(5):456-466  2012;38(5):409-417  2012;38(6):485-488  2013;39(2):121-124  2016;42(4):346-353