Shippee Tetyana P, Henning-Smith Carrie, Kane Robert L, Lewis Teresa
Division of Health Policy and Management, University of Minnesota, Minneapolis.
Minnesota Department of Human Services, Division of Nursing Facility Rates and Policy, St. Paul.
Gerontologist. 2015 Aug;55(4):643-55. doi: 10.1093/geront/gnt148. Epub 2013 Dec 17.
Although there is substantial research on quality of care in nursing homes (NH), less is known about what contributes to quality of life (QOL) for NH residents. This study assesses multiple domains of QOL and examines facility- and resident-level correlates for different domains.
Data come from (a) self-reported resident interviews using a multidimensional measure of QOL; (b) resident clinical data from the Minimum Data Set; and (c) facility-level characteristics from Minnesota Department of Human Services. We used factor analysis to confirm domains of QOL, and then employed cross-sectional hierarchical linear modeling to identify significant resident- and facility-level predictors of each domain.
We examined six unique domains of QOL: environment, personal attention, food, engagement, negative mood, and positive mood. In multilevel models, resident-level characteristics were more reliable correlates of QOL than facility characteristics. Among resident characteristics, gender, age, marital status, activities of daily living, mood disorders, cognitive limitations, and length of stay consistently predicted QOL domains. Among facility characteristics, size, staff hours, quality of care, and percent of residents on Medicaid predicted multiple QOL domains.
Examining separate domains rather than a single summary score makes associations with predictors more accurate. Resident characteristics account for the majority of variability in resident QOL. Helping residents maintain functional abilities, and providing an engaging social environment may be particularly important in improving QOL.
尽管针对养老院护理质量已有大量研究,但对于养老院居民生活质量的影响因素却知之甚少。本研究评估了生活质量的多个领域,并考察了不同领域在机构层面和居民层面的相关因素。
数据来源于:(a)使用多维生活质量量表进行的居民自我报告访谈;(b)最小数据集的居民临床数据;以及(c)明尼苏达州公共服务部的机构层面特征数据。我们使用因子分析来确定生活质量的领域,然后采用横断面分层线性模型来识别每个领域在居民层面和机构层面的显著预测因素。
我们考察了六个独特的生活质量领域:环境、个人关注、食物、参与度、消极情绪和积极情绪。在多层次模型中,居民层面的特征比机构特征更能可靠地反映生活质量。在居民特征中,性别、年龄、婚姻状况、日常生活活动能力、情绪障碍、认知限制和住院时间一直是生活质量领域的预测因素。在机构特征中,规模、员工工作时长、护理质量以及医疗补助居民比例可预测多个生活质量领域。
考察单独的领域而非单一的综合评分能使与预测因素的关联更准确。居民特征在居民生活质量的差异中占主导地位。帮助居民维持功能能力并提供有吸引力的社交环境对于提高生活质量可能尤为重要。