The Joint Effect of Sleep Duration and Disturbed Sleep on Cause-Specific Mortality: Results from the Whitehall II Cohort Study

Background: Both sleep duration and sleep quality are related to future health, but their combined effects on mortality are unsettled. We aimed to examine the individual and joint effects of sleep duration and sleep disturbances on cause-specific mortality in a large prospective cohort study. Methods: We included 9,098 men and women free of pre-existing disease from the Whitehall II study, UK. Sleep measures were self-reported at baseline (1985-1988). Participants were followed until 2010 in a nationwide death register for total and cause-specific (cardiovascular disease, cancer and other) mortality. Results: There were 804 deaths over a mean 22 year follow-up period. In men, short sleep (≤6 hrs/night) and disturbed sleep were not independently associated with CVD mortality, but there was an indication of higher risk among men who experienced both (HR = 1.57; 95% CI: 0.96-2.58). In women, short sleep and disturbed sleep were independently associated with CVD mortality, and women with both short and disturbed sleep experienced a much higher risk of CVD mortality (3.19; 1.52-6.72) compared to those who slept 7-8 hours with no sleep disturbances; equivalent to approximately 90 additional deaths per 100,000 person years. Sleep was not associated with death due to cancer or other causes. Conclusion: Both short sleep and disturbed sleep are independent risk factors for CVD mortality in women and future studies on sleep may benefit from assessing disturbed sleep in addition to sleep duration in order to capture health-relevant features of inadequate sleep. © 2014 Rod et al

Intergenerational educational trajectories and premature mortality from chronic diseases: A registry population-based study.

The tracking of educational gradients in mortality across generations could create a long shadow of social inequality, but it remains understudied. We aimed to assess whether intergenerational educational trajectories shape inequalities in early premature mortality from chronic diseases. The study included 544 743 participants of the Swiss National Cohort, a registry population-based study. Individuals were born 1971-1980 and aged 10-19 at the start of the study (1990). Mortality follow-up was until 2018. Educational trajectories were High-High (reference), High-Low, Low-High, Low-Low, corresponding to the sequence of parental-individual attained education. Examined deaths were related to cardiovascular diseases (CVD), cancers, and substance use. Sex-specific inequalities in mortality were quantified via standardized cumulative risk differences/ratios between age 20 and 45. We triangulated findings with a negative outcome control. For women, inequalities were negligible. For men, while inequalities in cancers deaths were negligible, inequalities in CVD mortality were associated to low individual education regardless of parental education. Excess CVD deaths for Low-High were negligible while High-Low provided 234 (95% confidence intervals: 100 to 391) and Low-Low 185 (115 to 251) additional CVD deaths per 100 000 men compared to High-High. That corresponded to risk ratios of 2.7 (1.6 to 4.5) and 2.3 (1.6 to 3.4), respectively. Gradients in substance use mortality were observed only when education changed across parent-offspring. Excess substance use deaths for Low-Low were negligible while High-Low provided 225 (88 to 341) additional and Low-High 80 (23 to 151) fewer substance use deaths per 100 000 men compared to High-High. That corresponded to risk ratios of 1.8 (1.3 to 2.5) and 0.7 (0.5 to 0.9), respectively. Inequalities in premature mortality were driven by individual education and by parental education for some chronic diseases. This could justify the development of intergenerational prevention strategies

Proposing network analysis for early life adversity:An application on life event data

Commonly used methods for modelling early life adversity (e.g., sum-scores, latent class or trajectory approaches, single-adversity approaches, and factor-analytical approaches) have not been able to capture the complex nature of early life adversity. We propose network analysis as an alternative way of modelling early life adversity (ELA). Our aim was to construct a network of fourteen adverse events (AEs) that occurred before the age of 16 in the TRacking Adolescents Individual Lives Survey (TRAILS, N = 1029). To show how network analysis can provide insight into why AEs are associated, we compared findings from the resulting network model to findings from tetrachoric correlation analyses. The resulting network of ELA comprised direct relationships between AEs and more complex, indirect relationships. A total of fifteen edges emerged in the network of AEs (out of 91 possible edges). The correlation coefficients suggested that many AEs were associated. The network model of ELA indicated, however, that several associations were attributable to interactions with other AEs. For example, the zero-order correlation between parental addiction and familial conflicts (0.24) could be explained by interactions with parental divorce. Our application of network analysis shows that using network analysis for modelling the ELA construct allows capturing the constructs' complex nature. Future studies should focus on gaining more insight into the most optimal model estimation and selection procedures, as well as sample size requirements. Network analysis provides researchers with a valuable tool that allows them as well as policy-makers and professionals to gain insight into potential mechanisms through which adversities are associated with each other, and conjunctively, with life course outcomes of interest