School of Public Health, University of California Berkeley, Berkeley, California, USA.
California Pacific Medical Center Research Institute, San Francisco, California, USA.
J Gerontol A Biol Sci Med Sci. 2024 Nov 1;79(11). doi: 10.1093/gerona/glae217.
Very little is known about specific trajectories or patterns of falls over time. Using the well-characterized cohort of the Osteoporotic Fractures in Men Study (MrOS), we classified individuals by fall trajectories across age and identified predictors of group assignment based on characteristics at baseline.
Using an analysis sample of 5 976 MrOS participants and 15 years of follow-up data on incident falls, we used group-based trajectory models (PROC TRAJ in SAS) to identify trajectories of change. We assessed the association of baseline characteristics with group assignment using 1-way analysis of variance and chi-square tests. Multivariable logistic regression was used to analyze the outcome of the high-risk fall trajectory groups compared to the low-risk groups.
Changes in rates of falls were relatively constant or increasing with 5 distinct groups identified. Mean posterior probabilities for all 5 trajectories were similar and consistently above 0.8 indicating a reasonable model fit. Among the 5 fall trajectory groups, 2 were deemed high risk, those with steeply increasing fall risk and persistently high fall risk. Factors associated with fall risk included body mass index, use of central nervous agents, prior history of diabetes and Parkinson's disease, back pain, grip strength, and physical and mental health scores.
Two distinct groups of high fall risk individuals were identified among 5 trajectory groups, those with steeply increasing fall risk and persistently high fall risk. Statistically significant characteristics for group assignment suggest that future fall risk of older men may be predictable at baseline.
关于随时间推移的特定跌倒轨迹或模式,我们知之甚少。本研究使用特征明确的男性骨质疏松性骨折研究(MrOS)队列,根据年龄分类个体的跌倒轨迹,并根据基线特征确定分组预测因素。
使用 5976 名 MrOS 参与者的分析样本和 15 年的跌倒事件随访数据,我们使用基于群组的轨迹模型(PROC TRAJ in SAS)来确定变化轨迹。我们使用单因素方差分析和卡方检验评估基线特征与分组的关系。使用多变量逻辑回归分析与低风险组相比,高风险跌倒轨迹组的结果。
5 个不同的组确定了跌倒率的变化相对稳定或增加。所有 5 个轨迹的平均后验概率相似,且始终高于 0.8,表明模型拟合合理。在 5 个跌倒轨迹组中,有 2 个被认为是高风险组,即跌倒风险急剧增加和持续高跌倒风险的组。与跌倒风险相关的因素包括体重指数、中枢神经系统药物的使用、既往糖尿病和帕金森病病史、背痛、握力、身体和心理健康评分。
在 5 个轨迹组中,确定了 2 个明显的高跌倒风险个体组,即跌倒风险急剧增加和持续高跌倒风险的组。用于分组的统计学显著特征表明,未来老年男性的跌倒风险可能在基线时可预测。