Objective: To understand the relationship between visual impairment and risk of all-cause mortality in the elderly aged 65 years and older in 8 longevity areas in China. Methods: The data of the elderly aged 65 years and older in the project in 2012 were obtained from Healthy Aging and Biomarkers Cohort Study, a sub-cohort of the Chinese Longitudinal Healthy Longevity Survey, including physical measurement and survival status, and a follow-up for survival outcomes were conducted in 2014 and 2017 respectively. Cox proportional hazard regression model was used to analyze the influence of visual impairment on mortality. Gender and age specific analysis was conducted. Results: A total of 1 736 elderly adults were included. A total of 943 deaths occurred during the 5-year follow-up period with a 5-year mortality rate of 54.3%. The 5-year mortality rate was 76.7% in the group with visual impairment, and 47.6% in the group without visual impairment (P<0.001). After adjusting for demographic information, life style and some disease factors, the risk of 5-year mortality in the group with visual impairment group was 1.30 times higher than that in the group without visual impairment (HR=1.30, 95%CI: 1.09-1.55). In the females, the risk for mortality in the group with visual impairment was 1.48 times higher than that in the group without visual impairment (HR=1.48, 95%CI:1.20-1.84). However, vision status was not associated with the risk for mortality in males (HR=1.02, 95%CI: 0.72-1.43). The risk for mortality in the group with visual impairment was 1.39 times higher than that in the group without visual impairment in the elderly aged over 90 years (HR=1.39, 95%CI: 1.13-1.70). Vision status was not associated with mortality risk in the elderly aged 65-79 years and 80-89 years (HR=1.37, 95%CI: 0.61-3.07; HR=0.95, 95%CI: 0.61-1.48). Conclusion In the elderly people in China, visual impairment is a risk factor for mortality.

The hazard ratio and median survival time are the routine indicators in survival analysis. We briefly introduced the relationship between hazard ratio and median survival time and the role of proportional hazard assumption. We compared 110 pairs of hazard ratio and median survival time ratio in 58 articles and demonstrated the reasons for the difference by examples. The results showed that the hazard ratio estimated by the Cox regression model is unreasonable and not equivalent to median survival time ratio when the proportional hazard assumption is not met. Therefore, before performing the Cox regression model, the proportional hazard assumption should be tested first. If proportional hazard assumption is met, Cox regression model can be used; if proportional hazard assumption is not met, restricted mean survival times is suggested.

The hazard ratio and median survival time are the routine indicators in survival analysis. We briefly introduced the relationship between hazard ratio and median survival time and the role of proportional hazard assumption. We compared 110 pairs of hazard ratio and median survival time ratio in 58 articles and demonstrated the reasons for the difference by examples. The results showed that the hazard ratio estimated by the Cox regression model is unreasonable and not equivalent to median survival time ratio when the proportional hazard assumption is not met. Therefore,before performing the Cox regression model, the proportional hazard assumption should be tested first. If proportional hazard assumption is met, Cox regression model can be used; if proportional hazard assumption is not met, restricted mean survival times is suggested.

The hazard ratio and median survival time are the routine indicators in survival analysis. We briefly introduced the relationship between hazard ratio and median survival time and the role of proportional hazard assumption. We compared 110 pairs of hazard ratio and median survival time ratio in 58 articles and demonstrated the reasons for the difference by examples. The results showed that the hazard ratio estimated by the Cox regression model is unreasonable and not equivalent to median survival time ratio when the proportional hazard assumption is not met. Therefore,before performing the Cox regression model, the proportional hazard assumption should be tested first. If proportional hazard assumption is met, Cox regression model can be used; if proportional hazard assumption is not met, restricted mean survival times is suggested.

Objective? To explore the effects of applying Information-Motivation-Behavioral skills model (IMB) on the improvement of life quality for patients with chronic heart failure (CHF). Methods? Totally 90 patients with CHF were selected by convenient sampling and divided into two groups based on random number table with 45 cases in each group. The control group received routine nursing care, while the experiment group received the nursing invention guided by the IMB besides the routine nursing care. The Minnesota Living with Heart Failure Questionnaire (MLHFQ) was used to evaluate the CHF patients＇ improvment status of their life quality before and after intervention. Results? Before intervention, there were not statistical differences between the experimental group(65.11±11.31) and control group(66.05±14.72) in terms of their life quality (P＞0.05). After intervention, the score from MLHFQ in the experiment group (50.99±14.56) was lower than the control group (58.57±12.95) with statistical significance (P＜0.05). Conclusions? The application of IMB model can effectively improve life quality of CHF patients.