Health status transitions in community-living elderly with complex care needs: a latent class approach.
Health status transitions in community-living elderly with complex care needs: a latent class approach.
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DOI：
10.1186/1471-2318-9-6
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发表时间：
2009-02-03
期刊：
影响因子：
4.1
通讯作者：
Ankri, Joel
中科院分区：
文献类型：
--
作者：
Lafortune, Louise;Beland, Francois;Bergman, Howard;Ankri, Joel
对于有复杂护理需要的老年人，有必要考虑健康各方面表现形式的可变性和相互依赖性，以了解健康状况的动态。我们的目标是检验这样一个假设，即潜在分类可以捕捉住在社区的体弱老年人群体的这种异质性。基于以人为本的方法，分类对应于具有可比健康问题星座的具有实质性意义的个人群体。利用SIPA项目（一个针对体弱老年人的综合护理系统）收集的数据（n = 1164），我们进行了潜在类别分析，以根据17个普遍健康问题指标（慢性病、抑郁症、认知、功能和感觉限制、健康状况）确定健康状况的同质类别（即健康概况）。然后，我们进行了潜在转变分析，分别在连续12个月和10个月的两个时间段内研究了个人资料成员的变化。我们将死亡率和随访损失的竞争风险建模为吸收状态，以避免损耗偏差。我们确定了四种健康概况，它们区分了健康的身体和认知维度，并捕捉了残疾维度的严重程度。随着时间的推移，这些特征是稳定的，对死亡率和对后续损耗的损失是稳健的。转变概率的差异和性别差异模式表明，这些特征对健康状况变化的敏感性，揭示了身体和认知领域与残疾进展的差异关系。我们的方法可能在组织和政策层面被证明是有用的，因为许多问题需要将个人分类到实际有意义的群体中。在处理损耗偏差时，我们的分析策略可以为老龄化纵向研究的规划提供关键信息。综合起来，这些发现通过使健康的多维和动态性质在计算上易于处理，解决了老年病学的一个核心挑战。
For older persons with complex care needs, accounting for the variability and interdependency in how health dimensions manifest themselves is necessary to understand the dynamic of health status. Our objective is to test the hypothesis that a latent classification can capture this heterogeneity in a population of frail elderly persons living in the community. Based on a person-centered approach, the classification corresponds to substantively meaningful groups of individuals who present with a comparable constellation of health problems. Using data collected for the SIPA project, a system of integrated care for frail older people (n = 1164), we performed latent class analyses to identify homogenous categories of health status (i.e. health profiles) based on 17 indicators of prevalent health problems (chronic conditions; depression; cognition; functional and sensory limitations; instrumental, mobility and personal care disability) Then, we conducted latent transition analyses to study change in profile membership over 2 consecutive periods of 12 and 10 months, respectively. We modeled competing risks for mortality and lost to follow-up as absorbing states to avoid attrition biases. We identified four health profiles that distinguish the physical and cognitive dimensions of health and capture severity along the disability dimension. The profiles are stable over time and robust to mortality and lost to follow-up attrition. The differentiated and gender-specific patterns of transition probabilities demonstrate the profiles' sensitivity to change in health status and unmasked the differential relationship of physical and cognitive domains with progression in disability. Our approach may prove useful at organization and policy levels where many issues call for classification of individuals into pragmatically meaningful groups. In dealing with attrition biases, our analytical strategy could provide critical information for the planning of longitudinal studies of aging. Combined, these findings address a central challenge in geriatrics by making the multidimensional and dynamic nature of health computationally tractable.