The Hospital Emigration to Another Region in the Light of the Environmental, Social and Governance Model in Italy During the Period 2004-2021 (original) (raw)

Beds in Health Facilities in the Italian Regions: A Socio-Economic Approach

SSRN Electronic Journal

In this article, we consider the determinants of the beds in healthcare facilities-BEDS in the Italian regions between 2004 and 2022. We use the ISTAT-BES database. We use different econometric techniques i.e.: Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled Ordinary Least Squares-OLS, Weighted Least Squares-WLS, and Dynamic Panel at 1 Stage. The results show that the level of BEDS is positively associated, among others, to "General Doctors with a Number of Clients over the Threshold" and "Life Satisfaction", and negatively associated among others, to "Trust in Parties" and "Positive Judgment on Future Prospects". Furthermore, we apply a clusterization with the k-Means algorithm optimized with the Silhouette Coefficient and we find the presence of two clusters in terms of BEDS. Finally, we make a confrontation among eight machine-learning algorithms and we find that the best predictor is the ANN-Artificial Neural Network.

The Renunciation of Healthcare Services in the Italian Regions in the ESG Context

Social Science Research Network, 2024

In the following article, we estimate the Renunciation of Healthcare Services-RHS in Italian regions in the context of the Environmental, Social and Governance-ESG model during the period 2004-2022. The data were acquired from the ISTAT-BES dataset. The data were analyzed using the following econometric techniques: Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled Ordinary Least Squares-OLS, Weighted Least Square-WLS,. Results show that RHS tends to growth with the E-Component, is negatively associated to the S-Component, and positively associate with the G-Component within the ESG model. Furthermore, a clusterization with the unsupervised k-Means algorithm is presented and the results are discussed with a confrontation between optimal and suboptimal k values optimized with the Silhouette Coefficient. Finally, a confrontation among eight different machine-learning algorithms is performed to predict the future value of RHS. Outcomes show that the Simple Regression Tree is the best predictive algorithm and that the level of RHS is predicted to growth on average of 4.4% for the Italian regions. Results are critically discussed.

Does regional belonging explain the similarities in the expenditure determinants of Italian healthcare deliveries? An approach based on Artificial Neural Networks

The investigation of the determinants of public health expenditure is the focus of a vivid debate among health economists whereas the actual crisis of the welfare systems calls for the adoption of innovative tools to inform rational decisions, in the light of stringent budget constraints. The purpose of this paper is to show the potentialities of Artificial Neural Networks (ANNs) in investigating whether healthcare providers belonging to the same jurisdiction show similarities in their health care expenditure determinants. Similarities are reproduced in terms of fuzzy dependencies between health budgetary data of the healthcare providers belonging to five Italian regions. The analysis carried out sees the application of Auto Contractive Maps (AutoCM) model. The methodology is effective in illustrating regional patterns of expenditure and similarities across Local Health Units (Aziende Sanitarie Locali—ASLs). The results give interesting insights on the presence of notable regional models for health expenditure.

The Socio-Economic Determinants of the Number of Physicians in Italian Regions

SSRN Electronic Journal

In the following article, we analyse the determinants of the number of physicians in the context of ISTAT BES-Benessere Equo Sostenibile data among twenty Italian regions in the period 2004-2022. We apply Panel Data with Random Effects, Panel Data with Fixed Effects, and Pooled OLS-Ordinary Least Squares. We found that the number of Physicians among Italian regions is positively associated, among others, to "Trust in the Police and Firefighters", "Net Income Inequality", and negatively associated, among others, to "Research and Development Intensity" and "Soil waterproofing by artificial cover". Furthermore, we apply the k-Means algorithm optimized with the Silhouette Coefficient and we find the presence of two clusters. Finally, we confront eight different machinelearning algorithms to predict the future value of physicians and we find that the PNN-Probabilistic Neural Network is the best predictive algorithm.

The ESG Determinants of Mental Health Index Across Italian Regions A Machine Learning Approach

Research Square (Research Square), 2024

The following article analyses the relationship between the mental health index and the variables of the Environment, Social and Governance-ESG model in the Italian regions between 2004 and 2023. First of all, a static analysis is proposed aimed at identifying trends relating to mental health in the Italian regions with indication of the regional gaps. Subsequently, a clustering with k-Means algorithm is proposed. Below is a comparison of 11 machine learning algorithms for predicting the performance of the mental health index. Finally, the article offers some economic policy suggestions. The results are critically discussed in light of the scientific literature.

Does regional belonging explain the similarities in the expenditure determinants of Italian healthcare deliveries?

Economic Analysis and Policy, 2017

The investigation of the determinants of public health expenditure is the focus of a vivid debate among health economists whereas the actual crisis of the welfare systems calls for the adoption of innovative tools to inform rational decisions, in the light of stringent budget constraints. The purpose of this paper is to show the potentialities of Artificial Neural Networks (ANNs) in investigating whether healthcare providers belonging to the same jurisdiction show similarities in their health care expenditure determinants. Similarities are reproduced in terms of fuzzy dependencies between health budgetary data of the healthcare providers belonging to five Italian regions. The analysis carried out sees the application of Auto Contractive Maps (AutoCM) model. The methodology is effective in illustrating regional patterns of expenditure and similarities across Local Health Units (Aziende Sanitarie Locali-ASLs). The results give interesting insights on the presence of notable regional models for health expenditure.

Assessment and prediction of short term hospital admissions: the case of Athens, Greece

Atmospheric Environment, 2008

The contribution of air pollution on hospital admissions due to respiratory and heart diseases is a major issue in the health-environmental perspective. In the present study, an attempt was made to run down the relationships between air pollution levels and meteorological indexes, and corresponding hospital admissions in Athens, Greece. The available data referred to a period of eight years (1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000) including the daily number of hospital admissions due to respiratory and heart diseases, hourly mean concentrations of CO, NO 2 , SO 2 , O 3 and particulates in several monitoring stations, as well as, meteorological data (temperature, relative humidity, wind speed/direction). The relations among the above data were studied through widely used statistical techniques (multivariate stepwise analyses) and Artificial Neural Networks (ANNs). Both techniques revealed that elevated particulate concentrations are the dominant parameter related to hospital admissions (an increase of 10 mg m À3 leads to an increase of 10.2% in the number of admissions), followed by O 3 and the rest of the pollutants (CO, NO 2 and SO 2 ). Meteorological parameters also play a decisive role in the formation of air pollutant levels affecting public health. Consequently, increased/decreased daily hospital admissions are related to specific types of meteorological conditions that favor/do not favor the accumulation of pollutants in an urban complex. In general, the role of meteorological factors seems to be underestimated by stepwise analyses, while ANNs attribute to them a more important role. Comparison of the two models revealed that ANN adaptation in complicate environmental issues presents improved modeling results compared to a regression technique. Furthermore, the ANN technique provides a reliable model for the prediction of the daily hospital admissions based on air quality data and meteorological indices, undoubtedly useful for regulatory purposes.

Clustering of European Countries in terms of Healthcare Indicators

International Journal of Computational and Experimental Science and Engineering, 2019

Health is always considered as one of the most important issues related to human being. Due to this importance, governments should primarily provide the best healthcare services to their citizens. Some indicators can show the quality of healthcare services in the country. However, one country can have a higher value of one indicator and can have a lower value of another. Thus, countries can be categorized in terms of quality of healthcare services. Clustering is a useful tool for comparing countries and defining the similar countries in terms of healthcare services. In this study, 28 European Union (EU) countries were evaluated on 14 health factors and the number of clusters was determined by the generally accepted rule of thumb. To cluster countries, k-means clustering method is run in WEKA software for two cluster numbers and four different initial solution approaches. The resulting clusters were evaluated according to the Spearman rank correlation coefficient using the order of the GDP per capita values of the countries in each cluster. It seems using four clusters with Canopy initial solution approach is the most appropriate way of clustering.

The geography of hospital admission in a National Health Service with patient choice: Evidence from Italy

Health Econometrics and Data Group Working Papers, 2008

It is evaluated that, each year, 35% out of the 10 million hospital admissions in Italy take place outside the LHAs of residence. In our paper we try to give an explanation of this phenomenon making reference to the social gravity model of spatial interaction. We estimate gravity equations using a Poisson pseudo maximum likelihood method, as proposed by Santos-Silva and Tenreyro (2006). Our results suggest that the gravity model is a good framework for explaining the patient mobility phenomenon for most of the examined diagnostic groups. Our evidence suggests that the ability to contain the imports of hospital services increases with the size of the pool of enrolees. Moreover we find that the ability to export hospital services, as proxied by the ratio of export-to-internal demand, is U-shaped. Therefore our evidence suggests that there are scale effect played by the size of the pool of enrolees.

Determinants of Health System Performance in Europe: A Study Based on Clustering Analysis

Acibadem Universitesi Saglik Bilimleri Dergisi, 2021

Health system performance is influenced by many factors such as behavioral and educational factors other than health system indicators. The aim of this study was to evaluate the effects of behavioral risk factors and educational factors on the health system performance in European Union member and candidate countries. Clustering analysis method was employed in the study. Firstly, clustering analysis was performed using health indicators and then, indicators related to behavioral risk factors and education were included in the analysis and it was investigated whether indicators related to behavioral risk factors and education affected the clusters formed by using health indicators. 4 clusters were formed in the clustering analysis using the health indicators, 5 clusters were formed in the clustering analysis with the addition of indicators related to behavioral risk factors to the health indicators and finally 5 clusters were formed in the clustering analysis with the addition of ind...