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[一项基于年龄、慢性病情况和性别,利用管理数据库进行按人头付费的提议]

[A proposal for capitation payment, based on age, chronicity, and gender, using management databases].

作者信息

Brugos Larumbe A, Lorenzo Vello E, Juanenea Beraza M, Lezáun Larumbe M J, Guillén Grima F, Fernández Martínez de Alegría C

机构信息

Dirección de Atención Primaria, Servicio Navarro de Salud-Osasunbidea, Pamplona.

出版信息

Aten Primaria. 2000 Jan;25(1):11-5. doi: 10.1016/s0212-6567(00)78456-3.

Abstract

OBJECTIVES

To propose a case-mix methodology for primary care, based on chronicity, type and age. To describe the explanatory value of these variables in the variability of the medical case-load.

DESIGN

Observation, descriptive and retrospective study.

SETTING

Primary care. Rochapea Health Centre, Pamplona.

MATERIAL AND METHODS

Computer records of all the consultations between January 1996 and June 1997. Dependent variable: case-load.

INDEPENDENT VARIABLES

age, type, chronic pathologies (diabetes, lipaemia, chronic neurological diseases, COPD-asthma, chronic psychiatric illnesses, cardiopathy, hypertension, alcohol and other drug abuse). The Kruskal-Wallis test was used to compare work-loads by age groups; and multiple linear regression analysis to calculate the predictive power of the independent variables.

RESULTS

Significant differences were observed for age groups. In the multivariate model used for general practitioners, all the variables could be included. They explained 24.2% of the variability in work load (R2). For paediatricians, age and asthma, explaining 23.48%, could also be included.

CONCLUSIONS

Age, type and chronicity are useful variables for predicting case load from administrative data bases. They can be used in adjustments for case load applicable to capitation payment systems.

摘要

目的

基于慢性病、类型和年龄,提出一种针对初级保健的病例组合方法。描述这些变量在医疗工作量变化中的解释价值。

设计

观察性、描述性和回顾性研究。

背景

初级保健。潘普洛纳的罗查佩亚健康中心。

材料与方法

1996年1月至1997年6月期间所有会诊的计算机记录。因变量:工作量。

自变量

年龄、类型、慢性疾病(糖尿病、血脂异常、慢性神经疾病、慢性阻塞性肺疾病 - 哮喘、慢性精神疾病、心脏病、高血压、酒精和其他药物滥用)。采用Kruskal - Wallis检验比较各年龄组的工作量;采用多元线性回归分析计算自变量的预测能力。

结果

各年龄组之间存在显著差异。在用于全科医生的多变量模型中,所有变量均可纳入。它们解释了工作量变化的24.2%(R2)。对于儿科医生,年龄和哮喘可纳入,解释率为23.48%。

结论

年龄、类型和慢性病程度是从行政数据库预测工作量的有用变量。它们可用于适用于按人头付费系统的工作量调整。

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