Next Article in Journal
Temperature Anomalies, Long Memory, and Aggregation
Next Article in Special Issue
Estimating Endogenous Treatment Effects Using Latent Factor Models with and without Instrumental Variables
Previous Article in Journal
Erratum: Hoover, K.D. 2020. The Discovery of Long-Run Causal Order: A Preliminary Investigation. Econometrics 8: 31
Previous Article in Special Issue
Long-Lasting Economic Effects of Pandemics:Evidence on Growth and Unemployment
Article

Hospital Emergency Room Savings via Health Line S24 in Portugal

by 1,2,*,†, 3 and 1,4,†
1
Centro de Matemática e Aplicações (CMA), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
2
Centro de Investigação, Desenvolvimento e Inovação da Academia Militar (CINAMIL), 1169-203 Lisboa, Portugal
3
Direção Geral de Saúde, 1049-005 Lisboa, Portugal
4
Departamento de Matemática, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 4 September 2020 / Revised: 30 January 2021 / Accepted: 3 February 2021 / Published: 20 February 2021
(This article belongs to the Special Issue Health Econometrics)
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. View Full-Text
Keywords: spatial econometrics; bayesian analysis; autoregressive models; spatio-temporal correlation; poisson; health line; hospital emergency spatial econometrics; bayesian analysis; autoregressive models; spatio-temporal correlation; poisson; health line; hospital emergency
Show Figures

Figure 1

MDPI and ACS Style

Simões, P.; Gomes, S.; Natário, I. Hospital Emergency Room Savings via Health Line S24 in Portugal. Econometrics 2021, 9, 8. https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010008

AMA Style

Simões P, Gomes S, Natário I. Hospital Emergency Room Savings via Health Line S24 in Portugal. Econometrics. 2021; 9(1):8. https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010008

Chicago/Turabian Style

Simões, Paula, Sérgio Gomes, and Isabel Natário. 2021. "Hospital Emergency Room Savings via Health Line S24 in Portugal" Econometrics 9, no. 1: 8. https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010008

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop