Comparative analysis of pre-Covid19 child immunization rates across 30 European countries and identification of underlying positive societal and system influences.

This study provides a macro-level societal and health system focused analysis of child vaccination rates in 30 European countries, exploring the effect of context on coverage. The importance of demography and health system attributes on health care delivery are recognized in other fields, but generally overlooked in vaccination. The analysis is based on correlating systematic data built up by the Models of Child Health Appraised (MOCHA) Project with data from international sources, so as to exploit a one-off opportunity to set the analysis within an overall integrated study of primary care services for children, and the learning opportunities of the 'natural European laboratory'. The descriptive analysis shows an overall persistent variation of coverage across vaccines with no specific vaccination having a low rate in all the EU and EEA countries. However, contrasting with this, variation between total uptake per vaccine across Europe suggests that the challenge of low rates is related to country contexts of either policy, delivery, or public perceptions. Econometric analysis aiming to explore whether some population, policy and/or health system characteristics may influence vaccination uptake provides important results-GDP per capita and the level of the population's higher education engagement are positively linked with higher vaccination coverage, whereas mandatory vaccination policy is related to lower uptake rates. The health system characteristics that have a significant positive effect are a cohesive management structure; a high nurse/doctor ratio; and use of practical care delivery reinforcements such as the home-based record and the presence of child components of e-health strategies.

Digitally Sustainable Information Systems in Axiomatic Design

Nowadays, information systems are evolving towards increasingly interconnected, smart, and self-adaptive models. This transformation has led to the representation of the systems themselves in terms of natural ecosystems. Similar to the natural environment, the virtual world can be threatened by specific forms of pollution, such as illegitimate access to the system, unwanted changes to data, and loss of information, which affect the only resource it possesses, i.e., data. In order to provide proactive protection of data integrity and confidentiality, in this paper we consider the well-known principles of privacy by design and privacy by default in the design phase of system development. To this end, we propose an approach based on axiomatic design, which allows us to implement these two principles through an appropriate reinterpretation of the information axiom, in terms of privacy impact assessment. We illustrate our approach by a case study, which implements the process of managing patients in home care. However, the proposed method can be applied to processing systems that provide services. The main result achieved is to select the most digitally sustainable design solution, i.e., the one that best prevents the threats mentioned above.

Beyond the FAIRness of COVID-19 Data: What about Quality?

Different datasets have been deployed at national level to share data on COVID-19 already at the beginning of the epidemic spread in early 2020. They distribute daily updated information aggregated at local, gender and age levels. To facilitate the reuse of such data, FAIR principles should be applied to optimally find, access, understand and exchange them, to define intra- and inter-country analyses for different purposes, such as statistical. However, another aspect to be considered when analyzing these datasets is data quality. In this paper we link these two perspectives to analyze to what extent datasets published by national institutions to monitor diffusion of COVID-19 are reusable for scientific purposes, such as tracing the spread of the virus.

Open Data Resources on COVID-19 in Six European Countries: Issues and Opportunities.

Since the beginning of the COVID-19 pandemic in March 2020, national and international authorities started to develop and update datasets to provide data to researchers, journalists and health care providers as well as public opinion. These data became one of the most important sources of information, which are updated daily and analysed by scientists in order to investigate and predict the spread of this epidemic. Despite this positive reaction from both national and international authorities in providing aggregated information on the diffusion of COVID-19, different challenges have been underlined in previously published studies. Different papers have discussed strengths and weaknesses of these types of datasets by focusing on different quality perspectives, which include the statistical methods adopted to analyse them; the lack of standards and models in the adoption of data for their management and distribution; and the analysis of different data quality characteristics. These studies have analysed datasets at the general level or by focusing the attention on specific indicators such as the number of cases or deaths. This paper further investigates issues and opportunities in the diffusion of these datasets under two main perspectives. At the general level, it analyses how data are organized and distributed to scientific and non-scientific communities. Moreover, it further explores the indicators adopted to describe the spread of the COVID-19 epidemic while also highlighting the level of detail used to describe them in terms of gender, age ranges and territorial units. The paper focuses on six European countries: Belgium, France, Germany, Italy, Spain and UK.

When Covid-19 first struck: Analysis of the influence of structural characteristics of countries - technocracy is strengthened by open democracy.

CONTEXT: The Covid-19 pandemic hit the developed world differentially due to accidental factors, and countries had to respond rapidly within existing resources, structures, and processes to manage totally new health challenges. This study aimed to identify which pre-existing structural factors facilitated better outcomes despite different starting points, as understanding of the relative impact of structural aspects should facilitate achieving optimal forward progress. METHODS: Desk study, based on selecting and collecting a range of measures for 48 representative characteristics of 42 countries' demography, society, health system, and policy-making profiles, matched to three pandemic time points. Different analytic approaches were employed including correlation, multiple regression, and cluster analysis in order to seek triangulation. FINDINGS: Population structure (except country size), and volume and nature of health resources, had only minor links to Covid impact. Depth of social inequality, poverty, population age structure, and strength of preventive health measures unexpectedly had no moderating effect. Strongest measured influences were population current enrolment in tertiary education, and country leaders' strength of seeking scientific evidence. The representativeness, and by interpretation the empathy, of government leadership also had positive effects. CONCLUSION: Strength of therapeutic health system, and indeed of preventive health services, surprisingly had little correlation with impact of the pandemic in the first nine months measured in death- or case-rates. However, specific political system features, including proportional representation electoral systems, and absence of a strong single party majority, were consistent features of the most successful national responses, as was being of a small or moderate population size, and with tertiary education facilitated. It can be interpreted that the way a country was lead, and whether leadership sought evidence and shared the reasoning behind resultant policies, had notable effects. This has significant implications within health system development and in promoting the population's health.

The Friendly Health Issue Network to Support Computer-Assisted Education for Clinical Reasoning in Multimorbidity Patients

Clinical reasoning in multimorbidity conditions asks for the ability to anticipate the possible evolutions of the overall health state of a patient and to identify the interactions among the concurrent health issues and their treatments. The HIN (Health Issue Network) approach, as Petri Nets-based formal language, is introduced as capable of providing a novel perspective to facilitate the acquisition of such competencies, graphically representing the network among a set of health issues (HIs) that affect a person throughout their life, and describing how HIs evolve over time. The need to provide a more immediate user-oriented interface has led to the development of f-HIN (friendly HIN), a lighter version based on the same mathematical properties as HIN, from which stems in turn the f-HINe (friendly HIN extracted) model, used to represent networks related to either real patients’ clinical experiences extracted from electronic health records, or from teacher-designed realistic clinical histories. Such models have also been designed to be embedded in a software learning environment that allows drawing a f-HIN diagram, checking for its format correctness, as well as designing clinical exercises for the learners, including their computer-assisted assessment. The present paper aims at introducing and discussing the f-HIN/f-HINe models and their educational use. It also introduces the main features of the software learning environment it was built upon, pointing out its importance to: (i) help medical teachers in designing and representing the context of a learning outcome;and (ii) handle the complex history of a multimorbidity patient, to be conveyed in Case-Based Learning (CBL) exercises.

Spatial Inequity in Access to Intensive Care Unit Beds at Regional Level in Italy.

The high demand of hospitalization in the intensive care units (ICUs) during the first wave of the COVID-19 outbreak brought out the critical issues of the limited capacity of the regional systems to deal with high patient inflows in a short period of time. In this view, a rapid and efficient reallocation of resources is one of the main challenges to be addressed by regional systems to prevent overload and saturation. Aim of this study is to assess the spatial accessibility of ICU beds in the 20 Italian regions to capture the equity distribution of critical care services across the country. This analysis may contribute to gain a deeper understanding of the allocation of health resources. It can provide input for policy makers in view of a possible reorganization of the national system in terms of both its preparedness for emergency period and routine capability.

The efficiency in the ordinary hospital bed management: A comparative analysis in four European countries before the COVID-19 outbreak.

During COVID-19 emergency the majority of health structures in Europe saturated or nearly saturated their availabilities already in the first weeks of the epidemic period especially in some regions of Italy and Spain. The aim of this study is to analyse the efficiency in the management of hospital beds before the COVID-19 outbreak at regional level in France, Germany, Italy and Spain. This analysis can indicate a reference point for future analysis on resource management in emergency periods and help hospital managers, emergency planners as well as policy makers to put in place a rapid and effective response to an emergency situation. The results of this study clearly underline that France and Germany could rely on the robust structural components of the hospital system, compared to Italy and Spain. Presumably, this might have had an impact on the efficacy in the management of the COVID-19 diffusion. In particular, the high availability of beds in the majority of the France regions paired with the low occupancy rate and high turnover interval led these regions to have a high number of available beds. Consider also that this country generally manages complex cases. A similar structural component is present in the German regions where the number of available beds is significantly higher than in the other countries. The impact of the COVID-19 was completely different in Italy and Spain that had to deal with a relevant large number of patients relying on a reduced number of both hospital beds and professionals. A further critical factor compared to France and Germany concerns the dissimilar distribution of cases across regions. Even if in these countries the hospital beds were efficiently managed, the concentration of hospitalized patients and the scarcity of beds have put pressure on the hospital systems.

Analysis of the Different Approaches Adopted in the Italian Regions to Care for Patients Affected by COVID-19.

As the Italian health system is regionally based, COVID-19 emergency actions are based on a general lockdown imposed by national authority and then management at local level by 21 regional authorities. Therefore, the pandemic response plan developed by each region led to different approaches. The aim of this paper is to analyze whether differences in patient management may have influenced the local course of the epidemic. The analysis on the 21 Italian regions considers the strategies adopted in terms of hospitalization, treatment in the ICU and at home. Moreover, an in-depth analysis was carried out on: Lombardia, which adopted a hospitalization approach; Veneto, which tended to confine patients at home; and Emilia Romagna, which adopted a mixed hospitalization-home based approach. The majority of regions implemented a home-based approach, while the hospital approach was followed in three regions (Lombardia, Piemonte, and Lazio), mainly limited to the first period of the outbreak. All regions in the later phases tended to reduce hospitalization, preferring to confine patients at home. This comparison, highlighting the different phases of the pandemic, outlined that the adoption of home-based practices contributed to limiting infection rates among patients and health professionals as well as decreasing the number of deaths.

The efficiency in the ordinary hospital bed management in Italy: An in-depth analysis of intensive care unit in the areas affected by COVID-19 before the outbreak.

Since the end of February 2020 a severe diffusion of COVID-19 has affected Italy and in particular its northern regions, resulting in a high demand of hospitalizations in particular in the intensive care units (ICUs). Hospitals are suffering the high degree of patients to be treated for respiratory diseases and the majority of the health structures, especially in the north of Italy, are or are at risk of saturation. Therefore, the question whether and to what extent the reduction of hospital beds occurred in the past years has biased the management of the emergency has come to the front in the public debate. In our opinion, to start a robust analysis it is necessary to consider the Italian health system capacity prior to the emergency. Therefore, the aim of this study is to analyse the availability of hospital beds across the country as well as to determine their management in terms of complexity and performance of cases treated at regional level. The results of this study underlines that, despite the reduction of beds for the majority of the hospital wards, ICUs availabilities did not change between 2010 and 2017. Moreover, this study confirms that the majority of the Italian regions have a routinely efficient management of their facilities allowing hospitals to treat patients without the risk of having an overabundance of patients and a scarcity of beds. In fact, this analysis shows that, in normal situations, the management of hospital and ICU beds has no critical levels.