[The gender disparity and relevant factors of frailty in the elderly of communities in Beijing based on Fairlie decomposition analysis].

Objective: To analyze the gender disparity and relevant factors of frailty in the elderly of communities in Beijing. Methods: From November 2015 to January 2016, 1 557 participants aged 60 and older in four communities of Dongcheng district in Beijing were recruited by cluster sampling. The information of demographic characteristics, social support, economic status, health status, prevalence situation, cognitive function, emotion and comprehensive assessment of the elderly were collected by a self-made questionnaire. The frailty index (FI) model was used to evaluate the frailty of the elderly. Multivariate nonconditional logistic regression model and Fairlie decomposition method were applied to analyze the relevant factors and their contribution rate to the difference between males and females. Results: The age of subjects was (74.5±8.5) years old, ranging from 60-102 years old, among which 641 were males, accounting for 41.2%. The M (Q1, Q3) of FI was 0.09 (0.06, 0.14), among which the value in males was 0.08 (0.05, 0.13), lower than females [0.10 (0.06, 0.15)] (P<0.001).The frail proportion in female was 14.9% (137/916), higher than that of male [8.4% (54/641)] (P<0.001). Multivariate nonconditional logistic regression model analysis demonstrated that common relevant factors associated with frailty in older women and men include: age ≥80 years old, marital status as not married (unmarried, separated, divorced, or widowed), living alone increased the risk of frailty; participating in group activities ≥3 times/week and exercising regularly decreased the risk of frailty (all P<0.05). Fairlie decomposition method showed that the contribution rate of life style, family support, marital status and social support were 32.21%, 15.26%, 8.23% and 4.34%, respectively (all P<0.05). Conclusions: The frailty degree and frailty proportion of elderly women in communities in Beijing were higher than those of men of the same age. The frailty gender difference was related to lifestyle, family support, marital status and social support.

[Relationship between frailty status and risk of death in the elderly based on frailty index analysis].

Objective: To analyze the relationship between frailty status and the risk of death in the elderly based on the frailty index (FI). Methods: Data from a prospective cohort study conducted between 2005 and 2015 in elderly people of an urban community in Beijing were analyzed. The variables related to health and frailty status based on the 2005 baseline survey and death as outcome variables collected in 2015 were used. A FI model was used to evaluate the correlation between FI and mortality in the elderly people in different age groups was analyzed. Cox regression was applied to evaluate the influence of FI on the risk of death, and Kaplan-Meier curves was used to show the survival rate of different frailty levels in the elderly adults. Results: Of the 1 301 elderly people included in the analysis, 403 died during 2005-2015, with the 10-year mortality rate of 31.0%(403/1 301). The mortality rate of the elderly increased with the increase of FI, but, with the increase of FI value, the rate of mortality increased slowly. The limit value of FI causing death was around 0.70, indicating any new health problem might cause death at this value. Cox regression analysis showed that higher FI was associated with higher risk for death (HR=1.143, 95%CI: 1.034-1.248, P=0.000), and FI was more significantly associated with death than age (HR=1.143 vs. HR=1.048, t=5.827, P=0.000). With the increase of age, the effect of frailty on the risk of death decreased (HR=1.179 to HR=1.120). Kaplan-Meier curves showed that the survival rate of the elderly in all age groups decreased with the increase of frailty (Log-rank=317.812, 354.203, 247.258, all P=0.000). The survival time between different frailty levels in the elderly were significantly different, except for the elderly adults aged ≥80 years with severe frailty level (0.4≤FI<0.5, FI≥0.5, P=0.368). Conclusions: Compared with other evaluation tools of frailty, FI model can better reflect the frailty status of the elderly in communities in Beijing and has a high sensitivity in predicting adverse outcomes such as mortality. In the intervention of frailty in the elderly, focusing on relatively young elderly might be more effective in reducing the adverse outcomes caused by frailty.