Association between all-cause mortality and trajectories across quality and duration of sleep and cognitive function: based on Group-Based Multivariate Trajectory modeling

BACKGROUND

Sleep duration and quality are associated with cognition, but the interaction of the 3 indicators and their association with all-cause mortality is unclear.

METHODS

We used data from the Chinese Longitudinal Healthy Longevity Survey from 2005-2018 to identify latent trajectories of sleep duration, sleep quality, and cognitive function. Secondly, the multinomial logistic model was adopted to determine predictors of trajectory groups. Finally, the Cox regression model was used to examine the association between these trajectory groups and all-cause mortality.

RESULTS

A total of 5046 adults (49% women) with an average age of 76.34 were included in the study. The median follow-up period was 11.11 years, during which 1784 (35%) participants died. We identified 4 latent groups among older adults: 'Good-performance' (51%), 'Decreasing' (26%), 'Oversleep & cognitive impairment' (12%), and 'Sleep-deprived' (11%). Individuals in the 'Decreasing' had a 51% increased risk of all-cause mortality (HR = 1.51, 95% CI: 1.25 - 1.81, p < .001). Individuals in the 'Oversleep & cognitive impairment' had a 170% increased risk of all-cause mortality (HR = 2.7 95% CI: 2.13 - 3.43, p < .001). Women had a higher risk of all-cause mortality regardless of trajectory group (47-143% men VS. 74-365% women). Both urban and rural areas have a similarly increased risk of all-cause mortality (48-179%).

CONCLUSIONS

Our study reveals the latent trajectories across sleep duration, sleep quality, and cognitive function in older Chinese and further explores their association with death. These findings provide a rational basis for cognitive interventions and reduce all-cause mortality.

[Study of incubation period of infection with 2019-nCoV Omicron variant BA.5.1.3]

Objective: To study the incubation period of the infection with 2019-nCoV Omicron variant BA.5.1.3. Methods: Based on the epidemiological survey data of 315 COVID-19 cases and the characteristics of interval censored data structure, log-normal distribution and Gamma distribution were used to estimate the incubation. Bayes estimation was performed for the parameters of each distribution function using discrete time Markov chain Monte Carlo algorithm. Results: The mean age of the 315 COVID-19 cases was (42.01±16.54) years, and men accounted for 30.16%. A total of 156 cases with mean age of (41.65±16.32) years reported the times when symptoms occurred. The log-normal distribution and Gamma distribution indicated that the M (Q₁, Q₃) of the incubation period from exposure to symptom onset was 2.53 (1.86, 3.44) days and 2.64 (1.91, 3.52) days, respectively, and the M (Q₁, Q₃) of the incubation period from exposure to the first positive nucleic acid detection was 2.45 (1.76, 3.40) days and 2.57 (1.81, 3.52) days, respectively. Conclusions: The incubation period by Bayes estimation based on log-normal distribution and Gamma distribution, respectively, was similar to each other, and the best distribution of incubation period was Gamma distribution, the difference between the incubation period from exposure to the first positive nucleic acid detection and the incubation period from exposure to symptom onset was small. The median of incubation period of infection caused by Omicron variant BA.5.1.3 was shorter than those of previous Omicron variants.