van Lier Lisanne I, Bosmans Judith E, van der Roest Henriëtte G, Heymans Martijn W, Garms-Homolová Vjenka, Declercq Anja, V Jónsson Pálmi, van Hout Hein Pj
Department of General Practice and Medicine of Older People, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, and Department on Aging, Netherlands Institute of Mental Health and Addiction (Trimbos Institute), Utrecht, Utrecht, The Netherlands.
Department of Health Sciences, Faculty of Science, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Health Serv Insights. 2020 Dec 23;13:1178632920980462. doi: 10.1177/1178632920980462. eCollection 2020.
This study aims to develop and validate a prediction model of societal costs during a period of 6-months in older community care-recipients across multiple European countries. Participants were older community care-recipients from 5 European countries. The outcome measure was mean 6-months total societal costs of resource utilisation (healthcare and informal care). Potential predictors included sociodemographic characteristics, functional limitations, clinical conditions, and diseases/disorders. The model was developed by performing Linear Mixed Models with a random intercept for the effect of country and validated by an internal-external validation procedure. Living alone, caregiver distress, (I)ADL impairment, required level of care support, health instability, presence of pain, behavioural problems, urinary incontinence and multimorbidity significantly predicted societal costs during 6 months. The model explained 32% of the variation within societal costs and showed good calibration in Iceland, Finland and Germany. Minor model adaptations improved model performance in The Netherland and Italy. The results can provide a valuable orientation for policymakers to better understand cost development among older community care-recipients. Despite substantial differences of countries' care systems, a validated cross-national set of key predictors could be identified.
本研究旨在开发并验证一个针对多个欧洲国家老年社区护理接受者6个月期间社会成本的预测模型。参与者为来自5个欧洲国家的老年社区护理接受者。结局指标是资源利用(医疗保健和非正式护理)的6个月平均社会总成本。潜在预测因素包括社会人口学特征、功能受限、临床状况以及疾病/病症。该模型通过对国家效应进行随机截距的线性混合模型来开发,并通过内部-外部验证程序进行验证。独居、照顾者困扰、日常生活活动能力受损、所需护理支持水平、健康不稳定、疼痛、行为问题、尿失禁和多种疾病显著预测了6个月期间的社会成本。该模型解释了社会成本中32%的变异,并在冰岛、芬兰和德国显示出良好的校准。对模型进行微小调整可改善在荷兰和意大利的模型性能。研究结果可为政策制定者更好地理解老年社区护理接受者的成本变化提供有价值的指导。尽管各国护理体系存在显著差异,但仍可确定一套经过验证的跨国关键预测因素。