Version 1
: Received: 30 December 2023 / Approved: 2 January 2024 / Online: 3 January 2024 (02:03:57 CET)
How to cite:
Resta, O.; Resta, E.; Costantiello, A.; Leogrande, A. Elderly People Treated in Integrated Home Care in Italian Regions: A Metric Approach. Preprints2024, 2024010017. https://doi.org/10.20944/preprints202401.0017.v1
Resta, O.; Resta, E.; Costantiello, A.; Leogrande, A. Elderly People Treated in Integrated Home Care in Italian Regions: A Metric Approach. Preprints 2024, 2024010017. https://doi.org/10.20944/preprints202401.0017.v1
Resta, O.; Resta, E.; Costantiello, A.; Leogrande, A. Elderly People Treated in Integrated Home Care in Italian Regions: A Metric Approach. Preprints2024, 2024010017. https://doi.org/10.20944/preprints202401.0017.v1
APA Style
Resta, O., Resta, E., Costantiello, A., & Leogrande, A. (2024). Elderly People Treated in Integrated Home Care in Italian Regions: A Metric Approach. Preprints. https://doi.org/10.20944/preprints202401.0017.v1
Chicago/Turabian Style
Resta, O., Alberto Costantiello and Angelo Leogrande. 2024 "Elderly People Treated in Integrated Home Care in Italian Regions: A Metric Approach" Preprints. https://doi.org/10.20944/preprints202401.0017.v1
Abstract
In this article, we analyse the ESG determinants of the “Elderly People Treated in Integrated Home Care”-EPIHC in the Italian regions between 2004 and 2022. We used data from the ISTAT-BES database. We used different econometric techniques i.e.: Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled Ordinary Least Squares-OLS and Weighted Least Squares-WLS. The results show that the EPIHC is positively associated with “Nurses, midwives, and Soil sealing by artificial cover" and negatively associated with "Museum heritage density and relevance" and "Trust in law enforcement agencies and firefighters fire". Furthermore, we have applied a k-Means algorithm with the Silhouette Coefficient and we find the presence of two clusters. Finally, we propose a confrontation among eight different machine-learning algorithms and we find that Linear Regression is the best predictive algorithm.
Keywords
Analysis of Health Care Markets; Health Behaviors; Health Insurance; Public and Private; Health and Inequality; Health and Economic Development; Government Policy; Regulation; Public Health
Subject
Business, Economics and Management, Economics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
