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多州法对慢性心力衰竭患者的影响:行政数据的增值作用。

A multi-state approach to patients affected by chronic heart failure : The value added by administrative data.

机构信息

Department of Accounting, Universitá Commerciale L. Bocconi, Via Guglielmo Rontgen 1, 20136, Milan, Italy.

Modelling and Scientific Computing (MOX) - Department of Mathematics, Politenico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy.

出版信息

Health Care Manag Sci. 2018 Jun;21(2):281-291. doi: 10.1007/s10729-017-9400-z. Epub 2017 May 9.

Abstract

Healthcare administrative databases are becoming more and more important and reliable sources of clinical and epidemiological information. They are able to track several interactions between a patient and the public healthcare system. In the present study, we make use of data extracted from the administrative data warehouse of Regione Lombardia, a region located in the northern part of Italy whose capital is Milan. Data are within a project aiming at providing a description of the epidemiology of Heart Failure (HF) patients at regional level, to profile health service utilization over time, and to investigate variations in patient care according to geographic area, socio-demographic characteristic and other clinical variables. We use multi-state models to estimate the probability of transition from (re)admission to discharge and death adjusting for covariates which are state dependent. To the best of our knowledge, this is the first Italian attempt of investigating which are the effects of pharmacological and outpatient cares covariates on patient's readmissions and death. This allows to better characterise disease progression and possibly identify what are the main determinants of a hospital admission and death in patients with Heart Failure.

摘要

医疗保健管理数据库正成为越来越重要和可靠的临床和流行病学信息来源。它们能够跟踪患者与公共医疗保健系统之间的多次交互。在本研究中,我们利用从意大利北部伦巴第大区行政数据库中提取的数据进行研究。该数据库是一个旨在描述区域水平心力衰竭 (HF) 患者流行病学、随时间推移分析卫生服务利用情况以及根据地理区域、社会人口特征和其他临床变量调查患者护理差异的项目的数据。我们使用多状态模型来估计从(再)入院到出院和死亡的转移概率,并根据与状态相关的协变量进行调整。据我们所知,这是意大利首次尝试调查药物治疗和门诊护理协变量对患者再入院和死亡的影响。这有助于更好地描述疾病进展,并可能确定心力衰竭患者住院和死亡的主要决定因素。

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