Preface to the papers on ‘health econometrics’

2016 ◽  
Vol 179 (4) ◽  
pp. 883-884 ◽  
Author(s):  
A. Chevalier ◽  
F. Moscone ◽  
J. Mullahy
Keyword(s):  
Health Econometrics

scholarly journals Hospital Emergency Room Savings via Health Line S24 in Portugal

Econometrics ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 8
Author(s):  
Paula Simões ◽  
Sérgio Gomes ◽  
Isabel Natário
Keyword(s):  
National Health ◽  
Hospital Costs ◽  
Urgent Care ◽  
Hospital Emergency ◽  
Health Econometrics ◽  
Emergency Situations ◽  
Hospital Emergency Room ◽  
Major Factors ◽  
Hospital Emergency Departments ◽  
Weighted Values

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.

Health Econometrics

2018 ◽  
pp. 5691-5696
Author(s):  
Andrew M. Jones
Keyword(s):  
Health Econometrics

Health Econometrics

2012 ◽  
pp. 1-7 ◽  
Author(s):  
Andrew M. Jones
Keyword(s):  
Health Econometrics

Special issue on health econometrics: Editors' introduction

2010 ◽  
Vol 27 (4) ◽  
pp. 789-790 ◽  
Author(s):  
Francesco Moscone ◽  
Andrew M. Jones ◽  
John Mullahy
Keyword(s):  
Special Issue ◽  
Health Econometrics

Something old, something new, something borrowed, something blue: a framework for the marriage of health econometrics and cost-effectiveness analysis

2002 ◽  
Vol 11 (5) ◽  
pp. 415-430 ◽  
Author(s):  
Jeffrey S. Hoch ◽  
Andrew H. Briggs ◽  
Andrew R. Willan
Keyword(s):  
Cost Effectiveness ◽  
Cost Effectiveness Analysis ◽  
Health Econometrics ◽  
Effectiveness Analysis

Health Econometrics

2018 ◽  
Keyword(s):  
Health Econometrics

Special issue on health econometrics: editors’ introduction

2012 ◽  
Vol 42 (2) ◽  
pp. 365-368 ◽  
Author(s):  
Badi H. Baltagi ◽  
Andrew M. Jones ◽  
Francesco Moscone ◽  
John Mullahy
Keyword(s):  
Special Issue ◽  
Health Econometrics

Introductory applied health econometrics

2020 ◽  
pp. 271-280
Author(s):  
Barbara McPake ◽  
Charles Normand ◽  
Samantha Smith ◽  
Anne Nolan
Keyword(s):  
Health Econometrics

Much ado about two: reconsidering retransformation and the two-part model in health econometrics

1998 ◽  
Vol 17 (3) ◽  
pp. 247-281 ◽  
Author(s):  
John Mullahy
Keyword(s):  
Health Econometrics ◽  
Part Model

Health Econometrics: Overview

2014 ◽  
pp. 355-356
Author(s):  
A. Basu ◽  
J. Mullahy
Keyword(s):  
Health Econometrics
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