Cohort profile: the Spanish WORKing life Social Security (WORKss) cohort study

Gómez, María Andrée López; Durán, Xavier; Zaballa, Elena; Sánchez-Niubò, Albert; Delclos, George L.; Benavides, Fernando G.

doi:10.1136/bmjopen-2015-008555

Cited by 19 publications

(18 citation statements)

References 26 publications

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“…The study population was part of the Spanish WORKing life Social Security cohort (WORKss cohort), an annual random representative sample of 4% of affiliates with the Spanish Social Security system. The sampling has taken place at least one day a year starting in 2004, and the information includes employment history register data from 1981 [23]. The sample is updated annually following an algorithm, which selects the same individuals if they continue affiliated with the Spanish Social Security system.…”

Section: Study Populationmentioning

confidence: 99%

Sickness absence trajectories following labour market participation patterns: a cohort study in Catalonia (Spain), 2012–2014

et al. 2020

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Background Previous studies have focused on the relationship between employment pathways and health-related outcomes based on cross-sectional or longitudinal approaches. However, little is known about the cumulative effects of employment status mobility on sickness absence (SA) over time. The aim of the present study was to examine the association between prior labour market participation (LMP) patterns and SA trajectories from a life-course perspective. Methods This cohort study was based on a sample of 11,968 salaried workers living in Catalonia and affiliated with the Spanish Social Security system, who accumulated more than 15 days on SA in at least one quarter during 2012–2014. Individuals were grouped into three different working life stages: early (18–25 years), middle (26–35 years), and late (36–45 years). To identify LMP patterns, we applied sequence analysis and cluster analysis (2002–2011), and we used latent class growth modelling to identify SA trajectories (2012–2014). Finally, we applied multinomial logistic regression models to assess the relationship between LMP patterns and SA trajectories. Results The analyses yielded six LMP patterns: stable employment (value range: 63–81%), increasing employment (5–22%), without long-term coverage (7–8%), decreasing employment (4–10%), fluctuant employment (13–14%), and steeply decreasing employment (7–9%). We also identified four SA trajectories: low stable (83–88%), decreasing (5–9%), increasing (5–11%), and high stable (7–16%). However, the only significant association we identified for LMP patterns and SA trajectories was among young men, for whom an increasing employment pattern was significantly associated with a lower risk for increased days on SA (adjusted odds ratio: 0.21; 95% confidence interval: 0.05–0.96). Conclusions SA trajectories are generally not related to prior 10-year LMP patterns at any stage of working life. To disentangle this relationship, future research might benefit from considering working life transitions with a quality-of-work approach framed with contextual factors closer to the SA course.

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Section: Study Populationmentioning

confidence: 99%

Sickness absence trajectories following labour market participation patterns: a cohort study in Catalonia (Spain), 2012–2014

et al. 2020

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“…The data comes from the Continous Working Life Sample (CWLS; Muestra Continua de Vida Laboral) for the years from 2004 to 2013. The CWLS is a 4% random sample from the Spanish social security register, which covers all individuals who are either making contributions to the social security system or are receiving social security benefits (López Gómez et al, 2016).…”

Section: Datamentioning

confidence: 99%

Expanding the Markov chain tool box: distributions of occupation times and waiting times

Dudel¹

2017

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“…The WORKss cohort is built on an annual random sample of 4% of individuals registered in the Spanish social security system (through contributions or pension) from 2004 to 2013 (22). The dataset provides information about the exact dates (day/month/year) of any contact with social security, allowing the reconstruction of complete labor trajectories from 1981 (the first year that the Spanish social security data is considered to be exhaustive and of high quality) to 2013 (the last available year).…”

Section: Datamentioning

confidence: 99%

“…The aim of this study was to describe the applica-tion of LCGA to identify different WLT using employed working time by year as a repeated measure, taking advantage of the Spanish WORKing life Social Security (WORKss) cohort (22).…”

mentioning

confidence: 99%

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

Serra¹,

Gómez²,

Sánchez-Niubò³

et al. 2016

Scand J Work Environ Health

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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.

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Cohort profile: the Spanish WORKing life Social Security (WORKss) cohort study

Cited by 19 publications

References 26 publications

Sickness absence trajectories following labour market participation patterns: a cohort study in Catalonia (Spain), 2012–2014

Sickness absence trajectories following labour market participation patterns: a cohort study in Catalonia (Spain), 2012–2014

Expanding the Markov chain tool box: distributions of occupation times and waiting times

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

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