PHYSICAL FRAILTY IN URBAN AFRICAN-AMERICANS

圣路易斯大学(SLU)和社区合作健康服务 (芯片)正在合作开发降低脆弱性的程序，因为 以身体虚弱和步态和平衡不稳定为特征) 居住在3.25平方英里的社区中的非裔美国老年人 圣路易斯市北部地区。本文所述的项目是 实现这一目标的初步步骤。该项目的具体目标 定义(1)老年受试者的脆弱指标的分布 生活在这个社区；(2)与进步有关的因素 脆弱测量；(3)脆弱对自我退化的影响 护理功能发挥作用。脆弱对伤害性跌倒的影响，受限 活动天数、家庭健康服务的使用、住院、长期护理 制度化，死亡率也将被审查。方法包括 随机抽取400名受试者作为研究对象 使用从HCFA的Medicare开发的样本框架的集水区 在第一个奖助金年度和一个 第二组从SLU-CHIP高年级学生中招募400至500名受试者， 在第二年招募的课程。这两组人都将被跟踪 在项目期间每两个月支付一次。测量变量 包括社会人口因素；据报道，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；计时体能表现(手、肩、下肢、 步行)；步态和平衡的稳定性；卫生服务利用； 和死亡率。数据收集的广泛质量控制将是 受雇的。脆弱措施的分布将使用 随机抽取的样本。虚弱进展的风险因素将是 使用样本和多元线性回归(MLR)来确定。MLR打开 将使用多元线性回归来评估脆弱性的影响 或自我照顾功能下降(取决于衡量标准)。

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).