Ratnapradipa Kendra L, Wang Jing, Berg-Weger Marla, Schootman Mario
Center for Injury Research and Policy, The Research Institute at Nationwide Children's Hospital, Columbus, Ohio.
Department of Graduate Nursing, College of Nursing & Health Innovation, University of Texas at Arlington.
Innov Aging. 2018 Oct 31;2(3):igy030. doi: 10.1093/geroni/igy030. eCollection 2018 Sep.
Predictors and consequences of driving cessation in older adults have been studied extensively. This study sought to establish the extent to which former drivers resume driving and identify associated factors.
Descriptive analysis of the 2011-2015 National Health and Aging Trends Study data (Round 1: = 6,680; Round 5: = 3,409) characterized the extent of driving resumption through 2015 by baseline driving status (driver, former driver, never driver). Weighted multivariate logistic regression and multilevel longitudinal models examined predictors of driving resumption.
Among drivers who stopped driving during the study, 17%-28% resumed driving. Age, vehicle ownership, stroke, hospitalization, memory, and perceived transportation barriers were associated with resumption in regression analysis. In multilevel analysis stratified by baseline driving status, poor word recall (OR = 0.62; 95% CI = 0.40, 0.95) and use of public transportation (OR = 9.74; 95% CI = 1.54, 61.77) were significantly associated with driving resumption for baseline drivers, while use of taxi (OR < 0.001; 95% CI = <0.001, 0.02) was negatively associated with resumption for baseline former drivers.
This study highlights several factors associated with driving resumption. Uncertainty about the underlying causes for resumption remains, so results should be interpreted with caution. However, predictive factors may help to identify individuals in need of additional mobility transition counseling. Ongoing transportation assessment may be warranted among former drivers.
老年人停止驾驶的预测因素及后果已得到广泛研究。本研究旨在确定曾经停止驾驶的老年人恢复驾驶的程度,并识别相关因素。
对2011 - 2015年国家健康与老龄化趋势研究数据进行描述性分析(第1轮:n = 6680;第5轮:n = 3409),根据基线驾驶状态(驾驶员、曾经的驾驶员、从未驾驶过的人)描述截至2015年恢复驾驶的程度。加权多变量逻辑回归和多水平纵向模型用于检验恢复驾驶的预测因素。
在研究期间停止驾驶的驾驶员中,17% - 28%恢复了驾驶。在回归分析中,年龄、拥有车辆情况、中风、住院、记忆力和感知到的交通障碍与恢复驾驶有关。在按基线驾驶状态分层的多水平分析中,对于基线为驾驶员的人群,单词回忆能力差(OR = 0.62;95%CI = 0.40, 0.95)和使用公共交通(OR = 9.74;95%CI = 1.54, 61.77)与恢复驾驶显著相关,而对于基线为曾经驾驶员的人群,使用出租车(OR < 0.001;95%CI = <0.001, 0.02)与恢复驾驶呈负相关。
本研究突出了几个与恢复驾驶相关的因素。恢复驾驶的潜在原因仍存在不确定性,因此对结果的解释应谨慎。然而,预测因素可能有助于识别需要额外出行转换咨询的个体。对于曾经的驾驶员,可能需要持续进行交通出行评估。