# Hospital Emergency Room Savings via Health Line S24 in Portugal

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## Abstract

**:**

## 1. Introduction

## 2. Methods

#### 2.1. A Bayesian Poisson Spatial Durbin Model

## 3. Data

#### 3.1. The Non-Urgent Emergency Situations

#### 3.2. The Savings Index via Health Line Saúde24

## 4. Results—Modeling Real Urgency Cases in Hospitals

**schooling**and the proportion of

**active population**; the most significant spatially lagged covariate was the

**lag savings index**. Parameter estimates for the selected model are summarized in Table 1. Fitted models were compared by means of their predictive accuracy, using the Deviance Information Criterion (DIC) measure and the Watanabe-Akaike Information Criterion (WAIC) measure (Gelman et al. 2014; Spiegelhalter et al. 2002).

**schooling**per municipality and the proportion of

**active population**residents, per municipality; the

**spatial component**is quite relevant, which is confirmed by the high value of the estimate of the spatial autocorrelation parameter, significant estimated value of

**$\rho $**of $0.69$; the

**spatially lagged**covariate is significant and contributes to a better performance of the selected model, as the model only including regular covariates and spatial random effects did worse.

## 5. Discussion

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

MDPI | Multidisciplinary Digital Publishing Institute |

S24 | Portuguese national health line |

DGS | Portuguese Directorate-General Health |

SAR | Simultaneous Autoregressive |

CAR | Conditional autoregressive |

TCR | Triage, Counselling and Routing |

INLA | Integrated Nested Laplace Approximations |

DIC | Deviance information criteria |

WAIC | Watanabe-Akaike information criteria |

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**Figure 1.**Number of non-urgent episodes, locations of the hospitals and resident population, by district, for the 2016 year (

**left**–

**right**).

**Table 1.**Parameter estimates (mean, 2.5% and 97.5% quantiles) for the Spatial Durbin Poisson model, for 2016.

Variable | ID | Coefficients | $(2.5\%,95\%)$ |
---|---|---|---|

Average number of years of schooling | ${x}_{1}$ | $0.63$ | $(0.50\phantom{\rule{0.166667em}{0ex}};\phantom{\rule{0.166667em}{0ex}}0.76)$ |

Proportion of Active population residents | ${x}_{2}$ | $-0.24$ | $(-2.80\phantom{\rule{0.166667em}{0ex}};\phantom{\rule{0.166667em}{0ex}}-2.33)$ |

Rurality index | ${x}_{3}$ | $0.15$ | $(-0.36\phantom{\rule{0.166667em}{0ex}};\phantom{\rule{0.166667em}{0ex}}0.65)$ |

Lag savings index | ${x}_{4}$ | $-0.015$ | $(-0.02\phantom{\rule{0.166667em}{0ex}};\phantom{\rule{0.166667em}{0ex}}-0.01)$ |

Intercept | $1.72$ | $(-0.34\phantom{\rule{0.166667em}{0ex}};\phantom{\rule{0.166667em}{0ex}}3.81)$ | |

${\sigma}^{2}$ | $0.48$ | $(0.40\phantom{\rule{0.166667em}{0ex}};\phantom{\rule{0.166667em}{0ex}}0.57)$ | |

$\rho $ | $0.69$ | $(0.63\phantom{\rule{0.166667em}{0ex}};\phantom{\rule{0.166667em}{0ex}}0.75)$ |

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**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://doi.org/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://doi.org/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://doi.org/10.3390/econometrics9010008