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Genetic and environmental contributions to individual differences in sustainable working life-A Swedish twin cohort
Annina Ropponen1,2, Jurgita Narusyte1, Mo Wang1
1Division of Insurance Medicine, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Abstract:
Although genetics is known to have a role in sickness absences (SA), disability pensions (DP) and in their mutual associations, the empirical knowledge is scarce on not having these interruptions, i.e., sustainable working life. Hence, we aimed to investigate how genetic and environmental factors affect individual variation in sustainable working life in short-term (two consecutive years) and in long-term (22 years of follow-up) using the classical twin modeling based on different genetic relatedness of mono- and dizygotic twins. The final sample (n = 51 071) included Swedish same-sex twins with known zygosity born between 1930 and 1990 (53% women) with complete national register data of employment, SA, DP, unemployment, old-age pension, emigration, and death. For the short-term sustainable working life, genetic factors explained 36% (95% confidence intervals (CI) 31-41%), environmental factors shared by co-twins such as family background 8% (95% CI 5-14%) and environmental factors unique to each twin individual 56% (95% CI 56-56%) on the individual differences. For the long-term sustainable working life, the largest proportions on individual differences were explained by environmental factors shared by co-twins (46%, 95% CI 44-48%) and unique to each twin individual (37% 95% CI 36-38%) whereas a small proportion was explained by genetic factors (18%, 95%CI 14-22%). To conclude, short-term sustainable working life was explained to a large extent by unique environment and to lesser extent by genetic factors whereas long-term (22 years) sustainable working life had both moderate unique and common environmental effect, and to lower extent genetic effects contributing to individual differences. These findings suggest that sustainable working life have different short- and long-term predictors.
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