2021
DOI: 10.1016/j.ssmph.2021.100737
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Lifetime employment, tobacco use, and alcohol consumption trajectories and cardiovascular diseases in old age

Abstract: Despite the great advances of life course epidemiology studies during the last decade in understanding the general health effects of employment trajectories, research has yet to evaluate the effects of employment trajectories along with other major risk factors, such as tobacco and alcohol consumption, on cardiovascular diseases (CVDs)—the main cause of deaths worldwide. This is highly relevant, since health advantages in one domain (e.g., being a permanent formal full-time worker) may offset health disadvanta… Show more

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Cited by 7 publications
(3 citation statements)
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“…This study contributes to understanding little explored cardiovascular risk factors, which might optimize cardiovascular prevention strategies throughout life. These findings contribute to greater understanding of the role of unexplored cardiovascular risk factors such as parenthood transition timing, and therefore might be used to optimizing lifelong cardiovascular prevention strategies [ 36 ].…”
Section: Discussionmentioning
confidence: 99%
“…This study contributes to understanding little explored cardiovascular risk factors, which might optimize cardiovascular prevention strategies throughout life. These findings contribute to greater understanding of the role of unexplored cardiovascular risk factors such as parenthood transition timing, and therefore might be used to optimizing lifelong cardiovascular prevention strategies [ 36 ].…”
Section: Discussionmentioning
confidence: 99%
“…We used MCSA to estimate simultaneous employment and health trajectories separately for each age group in Germany, Sweden, the US, and Italy. MCSA, an extension of sequence analysis ( Gauthier et al, 2010 ; Madero-Cabib et al, 2021 ), is a longitudinal statistical method that examines how similar or different every pair of individual sequences is in two or more domains (in our case employment and health domains), the sequential order in which individuals experience status changes, and the timing of these status changes. A pairwise distance matrix summarizes the ‘distance’ between the individual sequences – that is, the number of modifications needed to make one sequence be exactly like another.…”
Section: Methodsmentioning
confidence: 99%
“…We used individual-level longitudinal data and sequence analysis to create clusters of individuals with similar sequences ( Macindoe & Abbott, 2004 ). Furthermore, with multichannel sequence analysis (MCSA; Gauthier et al, 2010 ; Madero-Cabib et al, 2021 ), we explored older workers’ simultaneous sequences of both employment and health. We relied on a harmonized pooled-country dataset from large representative panel surveys, HRS, and the Survey of Health, Ageing and Retirement in Europe (SHARE) and provide novel empirical analyses.…”
Section: Introductionmentioning
confidence: 99%