Citation: Palazzo, L.; Ievoli, R. Detecting Regional Differences in Italian Health Services during Five COVID-19 Waves. Stats 2023, 6, 506–518. https://doi.org/10.3390/ stats6020032 Academic Editor: Eddy Kwessi Received: 19 January 2023 Revised: 11 April 2023 Accepted: 13 April 2023 Published: 15 April 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Article Detecting Regional Differences in Italian Health Services during Five COVID-19 Waves Lucio Palazzo 1, * ,† and Riccardo Ievoli 2, * ,† 1 Department of Political Sciences, University of Naples Federico II, 80143 Naples, Italy 2 Department of Chemical, Pharmaceutical and Agricultural Sciences, University of Ferrara, 44121 Ferrara, Italy * Correspondence: lucio.palazzo@unina.it (L.P.); riccardo.ievoli@unife.it (R.I.) † These authors contributed equally to this work. Abstract: During the waves of the COVID-19 pandemic, both national and/or territorial healthcare systems have been severely stressed in many countries. The availability (and complexity) of data requires proper comparisons for understanding differences in the performance of health services. With this aim, we propose a methodological approach to compare the performance of the Italian healthcare system at the territorial level, i.e., considering NUTS 2 regions. Our approach consists of three steps: the choice of a distance measure between available time series, the application of weighted multidimensional scaling (wMDS) based on this distance, and, finally, a cluster analysis on the MDS coordinates. We separately consider daily time series regarding the deceased, intensive care units, and ordinary hospitalizations of patients affected by COVID-19. The proposed procedure identifies four clusters apart from two outlier regions. Changes between the waves at a regional level emerge from the main results, allowing the pressure on territorial health services to be mapped between 2020 and 2022. Keywords: regional healthcare; time series; multidimensional scaling; cluster analysis; trimmed k-means 1. Introduction The current COVID-19 pandemic has witnessed the importance of managing the pressure on territorial healthcare systems and foreseeing potential issues to mitigate the sources of crises [1]. The evaluation of similarities and differences between territorial health services [2] is relevant for decision makers and should guide the governance of countries [3] through the so-called “waves”. This type of analysis becomes even more crucial in countries where the national healthcare system is regionally based, which is the case of Italy (or, for instance, other European countries such as Spain), among others. Nowadays, Italy is one of the countries in Europe which is most affected by the pandemic, where the pressure on regional health services (RHS) has been producing dramatic effects also in the economic [4] and the social [5] spheres. Furthermore, regional COVID-19-related health indicators are extremely relevant for monitoring of the pandemic’s territorial-wide spread [6], which is essential to impose (or relax) restrictions in accordance with the level of health risk. The aim of this work is to investigate and quantify the main imbalances that occurred in the RHS, observing the hospital admission dynamics of patients with confirmed COVID- 19 disease. To this end, daily time series regarding deceased patients (DEC), people treated in intensive care (IC) units, and individuals hospitalized with symptoms in other hospital wards (HO) are used to evaluate and compare the reaction to healthcare pressure in 21 geographical areas (NUTS 2 Italian regions), considering five waves [7,8] of the pandemic in a time period that starts from February 2020 to March 2022. Apart from economic and social imbalances among the regions, some similarities and differences between the performance in the RHS should be attributable to the local governance model adopted and to its level of efficiency and effectiveness [9]. Stats 2023, 6, 506–518. https://doi.org/10.3390/stats6020032 https://www.mdpi.com/journal/stats