PHYSICAL FRAILTY IN URBAN AFRICAN AMERICANS

圣路易斯大学（SLU）和社区卫生合作服务 （CHIPS）正在合作开发计划，以减少脆弱性， 通过身体虚弱和步态和平衡不稳定来识别）， 居住在3.25平方英里的社区居住的非洲裔美国老年人 圣路易斯市北部地区。 本文所述的项目是 为实现这一目标迈出的第一步。 项目的具体目标 定义（1）老年受试者中虚弱指标的分布 生活在这个社区;（2）与进展相关的因素， 脆弱的措施;和（3）脆弱的影响恶化的自我 护理功能。 虚弱对伤害性福尔斯跌落的影响，受限 活动日、使用家庭保健服务、住院、长期护理 机构化和死亡率也将受到审查。 方法涉及 随机选择400名受试者， 使用HCFA的Medicare开发的样本框架， 在第一个资助年度招募的资格登记档案和 第二组400至500名受试者招募自SLU-CHIPS老年人， 第二年招募的项目。两组都将被跟踪 在项目期间每两个月。 测量变量 包括社会人口因素;报告的身体、心理和 社会功能，包括日常生活活动;社会支持; 社会经济地位;医学诊断;药物治疗;认知障碍; 抑郁症;定时的身体表现（手，肩，下肢， 和行走）;步态和平衡稳定性;卫生服务利用; and mortality.将对数据收集进行广泛的质量控制， 就业。虚弱指标的分布将使用 随机抽样。 虚弱进展的风险因素将是 使用样品和多元线性回归（MLR）测定。 MLR开启 将使用多元线性回归来评估脆弱性的影响 或自我护理功能下降（取决于测量）。

St. Louis University (SLU) and Community Health-in-Partnership Services (CHIPS) are collaborating to develop programs to reduce frailty as identified by physical weakness and instability of gait and balance) in community-dwelling African-American seniors living in a 3.25 square mile area of north St. Louis city. The project described herein is one of the initial steps toward meeting this goal. The specific aims of the project are to define (1) the distribution of frailty measures in elderly subjects living in this community; (2) factors associated with progression in frailty measures; and (3) the effect of frailty on deterioration in self care functioning. The effect of frailty on injurious falls, restricted activity days, use of home health services, hospitalization, long term care institutionalization, and mortality will also be examined. Methods involve the acquisition of one group of 400 subjects selected at random from the catchment area using a sample frame developed from HCFA's Medicare Eligibility Enrollment File recruited during the first grant year and a second group of 400 to 500 subjects recruited from SLU-CHIPS seniors, programs recruited during the second year. Both groups will be followed every two months for the duration of the project. Measured variables include sociodemographic factors; reported physical, psychological, and social functioning, including activities of daily living; social support; socioeconomic status; medical diagnoses; medications; cognitive impairment; depression; timed physical performance (hand, shoulder, lower extremity, and walking); stability of gait and balance; health services utilization; and mortality. Extensive quality control of data collection will be employed. Distribution of frailty measures will be estimated using the randomly selected sample. Risk factors for frailty progression will be determined using both samples and multiple linear regression (MLR). MLR on multiple linear regression will be used to evaluate the impact of frailty or decline in self care functioning (depending on the measure).