当前位置: X-MOL 学术Econometrics › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Hospital Emergency Room Savings via Health Line S24 in Portugal
Econometrics ( IF 1.1 ) Pub Date : 2021-02-20 , DOI: 10.3390/econometrics9010008
Paula Simões , Sérgio Gomes , Isabel Natário

Hospital emergency departments are often overused by patients that do not really need urgent care. These admissions are one of the major factors contributing to hospital costs, which should not be allowed to compromise the response and effectiveness of the National Health Services (SNS). The aim of this study is to perform a detailed spatial health econometrics analysis of the non-urgent emergency situations (classified by Manchester triage) by area, linking them with the efficient use of the national health line, the Saude24 line (S24 line). This is evaluated through the S24 savings calls, using a savings index and its spatial effectiveness in solving the non-urgent emergency situations. A savings call is a call by a user whose initial intention was to go to an urgency department, but who. after calling the S24 line. changed his/her mind. Given the spatial nature of the data, and resorting to INLA in a Bayesian paradigm, the number of non-urgent cases in the Portuguese urgency hospital departments is modeled in an autoregressive way. The spatial structure is accounted for by a set of random effects. The model additionally includes regular covariates and a spatially lagged covariate savings index, related with the S24 savings calls. Therefore, the response in a given area depends not only on the (weighted) values of the response in its neighborhood and of the considered covariates, but also on the (weighted) values of the covariate savings index measured in each neighbor, by means of a Bayesian Poisson spatial Durbin model.

中文翻译:

通过葡萄牙的S24健康线节省医院急诊室费用

医院急诊科经常被确实不需要紧急护理的患者过度使用。这些入院是导致医院费用增加的主要因素之一,不应允许这些因素影响国家卫生服务(SNS)的响应和有效性。这项研究的目的是按地区对非紧急情况(按曼彻斯特分类法分类)进行详细的空间健康计量经济学分析,并将其与有效利用国家卫生线Saude24线(S24线)联系起来。这是通过S24储蓄电话进行评估的,其中使用了储蓄指数及其在解决非紧急紧急情况时的空间有效性。储蓄电话是用户的电话,其初衷是去紧急部门,但他却去了。拨打S24线路后。改变了主意 考虑到数据的空间性质,并且采用贝叶斯范式进行的INLA,葡萄牙紧急医院部门的非紧急病例数以自回归模型建模。空间结构是由一组随机效应引起的。该模型还包括与S24储蓄电话相关的常规协变量和空间滞后的协变量储蓄指数。因此,给定区域中的响应不仅取决于其邻域中响应的(加权)值以及所考虑的协变量,而且还取决于每个邻居通过以下方法测得的协变量储蓄指数的(加权)值:贝叶斯泊松空间杜宾模型。葡萄牙紧急医院部门的非紧急病例数以自回归模型建模。空间结构是由一组随机效应引起的。该模型还包括与S24储蓄电话相关的常规协变量和空间滞后协变量储蓄指数。因此,给定区域中的响应不仅取决于其邻域中响应的(加权)值以及所考虑的协变量,而且还取决于每个邻居通过以下方法测得的协变量储蓄指数的(加权)值:贝叶斯泊松空间杜宾模型。葡萄牙紧急医院部门的非紧急病例数以自回归模型建模。空间结构是由一组随机效应引起的。该模型还包括与S24储蓄电话相关的常规协变量和空间滞后协变量储蓄指数。因此,给定区域中的响应不仅取决于其邻域中响应的(加权)值以及所考虑的协变量,而且还取决于每个邻居通过以下方法测得的协变量储蓄指数的(加权)值:贝叶斯泊松空间杜宾模型。
更新日期:2021-02-20
down
wechat
bug