衰弱测量（GFI）和病例复杂性测量（IM-E-SA）对老年人群医疗成本的预测有效性。
Predictive validity of a frailty measure (GFI) and a case complexity measure (IM-E-SA) on healthcare costs in an elderly population.
机构信息
University of Groningen, University Medical Centre Groningen, Department of Epidemiology, Unit HTA, The Netherlands.
University of Groningen, University Medical Centre Groningen, Department of Epidemiology, Unit Medical Statistics, The Netherlands.
出版信息
J Psychosom Res. 2015 Nov;79(5):404-11. doi: 10.1016/j.jpsychores.2015.09.015. Epub 2015 Oct 5.
OBJECTIVES
Measures of frailty (Groningen Frailty Indicator, GFI) and case complexity (INTERMED for the Elderly, IM-E-SA) may assist healthcare professionals to allocate healthcare resources. Both instruments have been evaluated with good psychometric properties. Limited evidence has been published about their predictive validity. Thus, our aim is to evaluate the predictive validity of both instruments on healthcare costs.
METHODS
Multivariate linear regression models were developed to estimate associations between the predictors frailty (GFI) and/or case complexity (IM-E-SA) and the healthcare costs (in € log transformed) in the following year. All models were adjusted for demographics and the presence of morbidity.
RESULTS
In the multivariate regression analyses the continuous scores of the GFI and IM-E-SA remained significant predictors for total healthcare costs. Adjusted βs for GFI and IM-E-SA were respectively 0.14 (95% CI 0.10-0.18) and 0.06 (95% CI 0.04-0.07). The corresponding explained variance (R(2)) for both models was 0.40. Frailty remained a significant predictor of long-term care costs (adjusted β 0.13 [95% CI 0.09-0.16]), while case complexity was a significant predictor of curative care costs (adjusted β 0.03 [95% CI 0.02-0.05]).
CONCLUSIONS
The GFI and IM-E-SA both accurately predict total healthcare costs in the following year.
目的
衰弱（格罗宁根衰弱指标，GFI）和病例复杂性（老年人 INTERMED，IM-E-SA）的测量可以帮助医疗保健专业人员分配医疗保健资源。这两种工具都具有良好的心理测量学特性。关于它们的预测有效性的证据有限。因此，我们的目的是评估这两种工具对医疗保健成本的预测有效性。
方法
建立了多元线性回归模型，以估计预测因子衰弱（GFI）和/或病例复杂性（IM-E-SA）与下一年的医疗保健成本（以€对数转换）之间的关联。所有模型均根据人口统计学和发病情况进行了调整。
结果
在多元回归分析中，GFI 和 IM-E-SA 的连续评分仍然是总医疗保健成本的重要预测指标。GFI 和 IM-E-SA 的调整β值分别为 0.14（95%CI 0.10-0.18）和 0.06（95%CI 0.04-0.07）。两个模型的解释方差（R²）分别为 0.40。衰弱仍然是长期护理成本的重要预测指标（调整β 0.13 [95%CI 0.09-0.16]），而病例复杂性是治疗性护理成本的重要预测指标（调整β 0.03 [95%CI 0.02-0.05]）。
结论
GFI 和 IM-E-SA 都能准确预测下一年的总医疗保健成本。
Zotero 插件