Comparison of importance of 11 common influencing factors for fall and fall-induced injury in the elderly
Objective To compare the importance of 11 common influencing factors for fall and fall-induced injury reported previously in the elderly.Methods The data were collected from the follow-up of the China Health and Retirement Longitudinal Study(CHARLS)between 2011 and 2018.Binary logistic regression model and negative binomial regression model were used to test the significance of correlations between 11 factors and the incidence of fall and fall-induced injury during this period.The absolute value of the(β)was used to evaluate importance of 11 influencing factors.Results This study included 9 279,6 153,4 142,4 148,and 3 583 old persons.The cumulative incidence rates of fall in the 2nd,3rd,4th,5th,and 7th years were 19.4％(95％CI:18.6％-20.2％),22.1％(95％CI:21.0％-23.1％),31.9％(95％CI:30.4％-33.3％),35.1％(95％CI:33.6％-36.5％),and 43.2％(95％CI:41.6％-44.8％),respectively.The cumulative incidence rates of fall-induced injury were 8.4％(95％CI:7.8％-8.9％),9.4％(95％CI:8.7％-10.1％),15.1％(95％CI:14.0％-16.2％),16.2％(95％CI:15.1％-17.3％),and 22.0％(95％CI:20.6％-23.3％).The results of multivariate logistic regression and negative binomial regression analyses showed that in the 11 factors,only gender,history of fall,and depressive symptoms were identified as common risk factors for fall and fall-induced injury in the elderly in all the follow up visits(all P＜0.05);the history of fall had the highest absolute value of(β)in all models,while gender ranked second except for the 5-year fall-induced injury model.Conclusions Of the 11 influencing factors for fall and fall-induced injury reported by previous literature,only gender,history of falls,and depressive symptoms were identified as common risk factors for fall and fall-induced injury in the eldely in the 2nd,3 rd,4th,5th,and 7th years follow-up visits.History of fall and gender were important influencing factors for fall and fall-induced injury in the elderly.
FallFall-induced injuryThe elderlyInfluencing factorCohort study
NETL
NSTL
万方数据
