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The cluster-weighted model (CWM) is a member of the family of mixture of regression models and is also known as mixtures of regressions with random covariates. CWMs refer to the framework of model-based clustering and have their natural application when the research interest requires modeling the relationship between a response variable and a set of covariates using a regression-based approach such as a generalized linear model and the sample is suspected to be composed by heterogeneous latent classes. Software for estimating these models is not yet available in Stata. The aim of this article is to introduce the Stata package \code{cwmglm}, which allows fitting CWMs based on the most common generalized linear models with random covariates. Moreover, \code{cwmglm} allows the estimation of parsimonious models of Gaussian distributions, with the parametrization of the variance-covariance matrix based on the eigenvalue decomposition. These features are completely new for Stata users. The \code{cwmglm} package features goodness-of-fit, bootstrapping and model selection tools. We illustrate the use of \code{cwmglm} with real and simulated datasets

In this paper we explore the impact of the 2007 European Union enlargement to Romania and Bulgaria on the mental health of documented immigrants from these countries who arrived in Italy before 2007. Using data from a unique administrative data set for the Italian Lombardy region and by employing a difference-in-differences individual fixed effect estimator, we find that this enlargement caused a significant improvement in the mental health of young male immigrants. To shed light on the mechanisms behind these results, we use data from a unique survey administered in the Lombardy region and show that the enlargement mitigates sources of health concerns and increases income and employment stability through permanent job contracts for young male immigrants. Overall, these findings suggest that enhanced labour market conditions due to enlargement may lead to a subsequent important decrease in psychological distress among immigrants.

In this paper we explore the role of media and language used to comment on economic news in nowcasting and forecasting suicides in England and Wales. This is an interesting question, given the large delay in the release of official statistics on suicides. We use a large data set of over 200,000 news articles published in six major UK newspapers from 2001 to 2015 and carry sentiment analysis of the language used to comment on economic news. We extract daily indicators measuring a set of negative emotions that are often associated with poor mental health and use them to explain and forecast national daily suicide figures. We find that highly negative comments on the economic situation in newspaper articles are predictors of higher suicide numbers, especially when using words conveying stronger emotions of fear and despair. Our results suggest that media language carrying very strong, negative feelings is an early signal of a deterioration in a population's mental health.

The COVID-19 pandemic caused an unprecedented excess mortality. Since 2020, many studies have focussed on the characteristics of COVID-19 patients who did not survive. From the statistical point of view, what seems to dominate is the large heterogeneity of the populations affected by COVID-19 and the extreme difficulty in identifying subpopulations who died affected by a plurality of contemporary characteristics. In this paper, we propose an extremely flexible approach based on a cluster-weighted model, which allows us to identify latent groups of patients sharing similar characteristics at the moment of hospitalisation as well as a similar mortality. We focus on one of the hardest hit areas in Italy and study the heterogeneity in the population of patients affected by COVID-19 using administrative data on hospitalisations in the first wave of the pandemic. Results highlighted that a model-based clustering approach is essential to understand the complexity of the COVID-19 patients treated by hospitals and who die during hospitalisation.

OBJECTIVE: To define macro symptoms of long COVID and to identify predictive factors, with the aim of preventing the development of the long COVID syndrome. DESIGN: A single-centre longitudinal prospective cohort study conducted from May 2020 to October 2022. SETTING: The study was conducted at Luigi Sacco University Hospital in Milan (Italy). In May 2020, we activated the ARCOVID (Ambulatorio Rivalutazione COVID) outpatient service for the follow-up of long COVID. PARTICIPANTS: Hospitalised and non-hospitalised patients previously affected by COVID-19 were either referred by specialists or general practitioners or self-referred. INTERVENTION: During the first visit, a set of questions investigated the presence and the duration of 11 symptoms (palpitations, amnesia, headache, anxiety/panic, insomnia, loss of smell, loss of taste, dyspnoea, asthenia, myalgia and telogen effluvium). The follow-up has continued until the present time, by sending email questionnaires every 3 months to monitor symptoms and health-related quality of life. PRIMARY AND SECONDARY OUTCOME MEASURES: Measurement of synthetic scores (aggregation of symptoms based on occurrence and duration) that may reveal the presence of long COVID in different clinical macro symptoms. To this end, a mixed supervised and empirical strategy was adopted. Moreover, we aimed to identify predictive factors for post-COVID-19 macro symptoms. RESULTS: In the first and second waves of COVID-19, 575 and 793 patients (respectively) were enrolled. Three different post-COVID-19 macro symptoms (neurological, sensorial and physical) were identified. We found significant associations between post-COVID-19 symptoms and (1) the patients' comorbidities, and (2) the medications used during the COVID-19 acute phase. ACE inhibitors (OR=2.039, 95% CI: 1.095 to 3.892), inhaled steroids (OR=4.08, 95% CI: 1.17 to 19.19) and COVID therapies were associated with increased incidence of the neurological macro symptoms. Age (OR=1.02, 95% CI

This paper analyses the future prospects of statistics as a profession and how data science will change it. Indeed, according to Hadley Wickham, Chief Scientist at Rstudio, “a data scientist is a useful statistician”, establishing a strong connection between data science and applied statistics. In this direction, the aim is to look to the future by proposing a structural approach to future scenarios. Some possible definitions of data science are then discussed, considering the relationship with statistics as a scientific discipline. The focus then turns to an assessment of the skills required by the labor market for data scientists and the specific characteristics of this profession. Finally, the phases of a data science project are considered, outlining how these can be exploited by a statistician.

We illustrate a causal latent transition model to evaluate the effects of educational programs administered to pupils in the 6th and 7th grades during their middle school period. The programs are conducted in an Italian region and focus on improving non-cognitive abilities. The interest is in evaluating the effects on the skills acquired in the 8th grade in Italian and Mathematics. The model can be cast in the hidden Markov literature and is formulated as an extension of Rubin's causal model based on potential versions of discrete time-varying latent variables.

BACKGROUND: Media communication during the covid-19 pandemic has been relevant for the population to receive information about the ongoing number of cases, deaths, and social restriction measures. Notably, the effects of the communication methods on young adults during the covid-19 pandemic have not been studied. Therefore the present study aimed to investigate the influence of communication modality about covid-19 on the perception of risk and judgment among young adults. METHODS: A double-blind cross-sectional study was designed. Three hundred four subjects (age range19-25 years old) saw a 4-minute video concerning data communication on the covid-19 pandemic and compiled an online questionnaire about their perceptions. Two videos were randomized, one presenting the covid-19 data negatively (HARD video) while the other showed a positive ongoing resolution of the pandemic (SOFT video). Association tests and nominal logistic regression were used to evaluate differences in responses among the two groups. RESULTS: The two videos lead to different reactions. Participants showed higher disagreement concerning the video content in the "SOFT" group compared to the "HARD" group. The responses of the "SOFT" group were more to be optimistic (OR=2.87, 95% CI 1.311-6.27) than those who had seen the "HARD" video. The sense of helplessness was lower in the "SOFT" compared "HARD" group (OR=3.02, 95% CI 1.311-6.96). The perception of fear was higher for the "HARD" group (OR=2.91, 95% CI 1.21-7-02). DISCUSSION: The modality of data presentation influenced the perception and feelings about the covid-19 pandemic. Likely, pre-existing perception of a pessimistic perspective was present in both groups; thus, the video did not lead to any change in the behavior. CONCLUSIONS: The phobic or counter-phobic reactions shown in the study participants highlighted the importance of the reliability of the information received and how previous feelings may influence the perception of the informati

This chapter aims to provide suggestive evidence on how the Lombardy region dealt with the COVID-19 pandemic in 2020 and discuss future challenges for the Lombardy healthcare system. After an introduction to the wide spread of the virus inside the region, we describe the Lombardy health system so the reader may understand the context in which the virus has taken hold so quickly. The pandemic has heavily stressed the system, mainly because Lombardy experienced an excess of hospital admissions. We have considered the increased mortality rate as a proxy of the proper managing of the COVID-19 pandemic. In addition, we describe the process of treating non-COVID patients, such as those affected by acute myocardial infarction (AMI), stroke and oncological diseases. Despite the pandemic, hospitals have been able to guarantee a high level of performance. A discussion of the future evolution of the healthcare system concludes this chapter.

In order to evaluate the effect of a policy or treatment with pre- and post-treatment outcomes, we propose an approach based on a transition model, which may be applied with multivariate outcomes and accounts for unobserved heterogeneity. This model is based on potential versions of discrete latent variables representing the individual characteristic of interest and may be cast in the hidden (latent) Markov literature for panel data. Therefore, it can be estimated by maximum likelihood in a relatively simple way. The approach extends the difference-in-difference method as it is possible to deal with multivariate outcomes. Moreover, causal effects may be expressed with respect to transition probabilities. The proposal is validated through a simulation study, and it is applied to evaluate educational programs administered to pupils in the sixth and seventh grades during their middle school period. These programs are carried out in an Italian region to improve non-cognitive skills (CSs). We study if they impact also on students’ CSs in Italian and Mathematics in the eighth grade, exploiting the pretreatment test scores available in the fifth grade. The main conclusion is that the educational programs aimed to develop noncognitive abilities help the best students to maintain their higher cognitive abilities over time.

Questo report presenta i risultati del progetto triennale intitolato “Lo sviluppo delle competenze non cognitive negli studenti trentini”, promosso da Dipartimento Istruzione e Cultura della Provincia autonoma di Trento e IPRASE1 in collaborazione con il CRISP2 dell’Università degli Studi di Milano-Bicocca. Il progetto, avviato nel 2017, si proponeva di rispondere alle seguenti domande per il contesto Trentino: cosa sono le competenze non cognitive e quali sono le loro peculiarità? È possibile “svilupparle” durante l’esperienza scolastica? Se sì, come? Come possono essere valutate e certificate tali competenze per contribuire allo sviluppo complessivo degli studenti trentini? E come inserire la valorizzazione di queste particolari competenze all’interno delle politiche scolastiche locali per il futuro? La ricerca si inserisce all’interno dell’ampio dibattito interdisciplinare che si è sviluppato negli ultimi decenni relativamente alla possibilità di considerare altre tipologie di caratteristiche individuali, oltre alle tradizionali di natura “accademica” e cognitiva, per spiegare il successo formativo e, più in generale, l’acquisizione dello status di cittadino adulto, responsabile e attivo partecipante nella vita quotidiana. Un nuovo approccio, che integra la precedente definizione, sottolinea che il capitale umano è legato anche a quegli aspetti che prendono il nome di competenze non cognitive, soft skills, non cognitive skills o character skills: tratti di personalità innati e formati dall’interazione con l’ambiente, che condizionano l’apprendimento e le abilità lavorative e possono cambiare in maniera significativa nel corso dell’esistenza di un individuo. La provincia di Trento è un’area di particolare sviluppo economico, omogenea sotto il profilo sociale e demografico e gli studenti delle sue scuole medie raggiungono i risultati più elevati in Italia nel test internazionale OCSE-PISA e tra i più elevati in assoluto nell’ambito dell’OCSE stesso Il campione di r

Il capitolo presenta i risultati della ricerca Lo sviluppo delle competenze cognitive e non cognitive negli studenti trentini effettuata sugli studenti delle scuole medie della PAT. La ricerca si basa su una prima indagine svolta nel maggio-giugno 2018 su studenti di terza media dell’anno scolastico in corso (2017-2018), volta a valutare l’impatto delle NCS sulle CS e l’efficacia di attività realizzate negli anni scolastici 2015-2018 con lo scopo di migliorare il livello delle NCS. L’indagine è stata svolta su oltre 5.000 giovani, ossia l’insieme degli studenti delle 58 scuole trentine che nel 2017-2018 frequentavano la terza media. Si è poi svolta una seconda indagine basata su due interviste, in dicembre 2018 e nel maggio-giugno 2019, su studenti di terza media dell’anno scolastico 2018-2019 ine- rente l’esito sulle NCS di programmi educativi predisposti ad hoc nell’ambito della ricerca. Dopo una breve ripresa della letteratura che riprende quanto detto nella prima parte del volume e che presenta le ricerche più rilevanti nel panorama internazionale inerenti i legami tra NCS e CS (par. 1), si descrive il progetto di ricerca iniziato nel 2017 per le scuole della PAT (par. 2). Particolare enfasi viene posta sui criteri di costruzione del database, ottenuto mediante matching di dati INVAL- SI, dati amministrativi della PAT e un’indagine diretta sul campione di studenti (par. 3). Il paragrafo 4 descrive la metodologia statistica applicata, mentre nel paragrafo 5 sono riportati risultati e commenti. Infine, le conclusioni evidenziano gli sviluppi ulteriori dei risultati della ricerca, in termini sia di analisi dei sistemi educativi che di policy (par. 6). La descrizione delle modalità con cui, a partire dalle informazioni ottenute dai questionari somministrati durante la ricerca, sono state rilevate e quantificate le NCS considerate nell’analisi e scelti i programmi educativi atti a migliorarle viene riportata nelle appendici., This chapter presents the results of the research The development of cognitive and non-cognitive skills in Trentino students conducted on middle school students in the PAT. The research is based on a first survey carried out in May-June 2018 on eighth-grade students of the current school year (2017-2018), aimed at assessing the impact of NCS on CS and the effectiveness of activities implemented in the 2015-2018 school years with the aim of improving the level of NCS. The survey was conducted on more than 5,000 youth, i.e., the total number of students in 58 schools in Trentino who were in the eighth grade in 2017-2018. A second survey was then conducted based on two interviews, in December 2018 and May-June 2019, of eighth-grade students from the 2018-2019 school year ine- rence the outcome on NCS of educational programs prepared ad hoc as part of the research. After a brief review of the literature that echoes what was said in the first part of the volume and presents the most relevant research in the international landscape pertaining to the links between NCS and CS (para. 1), the research project initiated in 2017 for schools in the PAT is described (para. 2). Particular emphasis is placed on the criteria used to construct of the database, obtained by matching INVAL- SI data, PAT administrative data, and a direct survey of the student sample (par. 3). Paragraph 4 describes the statistical methodology applied, while paragraph 5 reports results and comments. Finally, the conclusions highlight further developments of the research findings, in terms of both educational systems analysis and policy (par. 6). The description of the ways in which, starting with the information obtained from the questionnaires administered during the research, the NCS considered in the analysis were found and quantified, and the educational programs to improve them were chosen, is given in the appendices.

Il volume affronta il tema della conoscenza e dell’ap- prendimento in ambito scolastico e lavorativo considerato come un processo che coinvolge capacità non solo cogni- tive, come ricordare, parlare, comprendere, stabilire nessi, dedurre, valutare, ma che implica anche qualità trasversali, disposizioni della personalità dette character skills, quali l’apertura mentale, la capacità di collaborare, la sicurezza. Il titolo, Viaggio nelle character skills. Persone, relazioni, valori, esprime l’approccio che si è inteso dare al lavoro, che non intende proporre affermazioni di principio, ma vuole inoltrarsi in un percorso esplorativo nel quale confluiscono le riflessioni sviluppate da un gruppo di studiosi di varia formazione e di competenze diverse. I contributi suggeri- scono le molteplici prospettive con cui accostare le character skills per averne piena contezza, proprio come quando in viaggio possiamo guardare il paesaggio naturale e le opere dell’uomo da diversi punti di vista così da farci un’idea più ampia di ciò che sono e significano. Si tratta di un viaggio che vorremmo compiere con il lettore intorno al futuro dell’educazione e della scuola. E non solo., The book addresses the issue of knowledge and learning in the school and work environment, considered as a process that involves not only cognitive skills, such as remembering, speaking, understanding, establishing connections, deducing, evaluating, but also involves transversal qualities, personality dispositions called character skills, such as open-mindedness, the ability to collaborate, confidence. The title, Journey into Character Skills. People, relationships, values, expresses the approach we intend to give to the work, which does not intend to propose statements of principle, but wants to embark on an exploratory journey in which the reflections developed by a group of scholars from various backgrounds and different skills converge. The contributions suggest the multiple perspectives with which to approach character skills in order to have a full understanding of them, just as when we travel we can look at the natural landscape and the works of man from different points of view so as to get a broader idea of what they are and what they mean. This is a journey that we would like to take with the reader around the future of education and schools. And beyond.

The analysis of what human capital (HC) is has a long history and culminates in the acknowledgment that HC and its growth are very important for both cognitive education (cognitive skills (CSs)) and personal life (noncognitive skills (NCSs)) and that CSs and NCSs have a strong reciprocal relationship, as studies by Heckman demonstrated. The present contribution (following Heckman’s approach) analyzed the relationship between CSs and NCSs in a sample of middle school students in the Autonomous Province of Trento. The second goal of the research was to verify whether educational teaching behaviors improved students’ personalities. Aside from the use of administrative data (INVALSI data, 2015 and 2018), one survey was administered in the 2018–2019 schooling year to verify the relationship between NCSs and CSs. Moreover, we sought to determine whether education teaching behavior improved the students’ personalities (1522 students in 25 schools) and whether programs could enhance NCSs. Methodological tools for the analysis involved the generalized least squares approach to answer the first question and a difference-in-differences model for the second. The main results showed that the levels of NCSs affected the ability to learn and improve CSs; a challenging teaching approach, especially if accompanied by programs improving its quality, had positive results. Finally, the research suggested that a wider, national-based survey following students from primary to secondary school would allow for a greater understanding of the dynamics of CSs and NCSs.

We study inter-regional migration flows due to hospital admissions and focus on the impact of migration beaten paths. We estimate a gravity model applied to Italian data for the period 2010–2016. We find that beaten paths have a positive effect on inter-regional patient flows, with estimated elasticity equal to +0.32%. Therefore, family and social ties among people living in the region of origin and destination for hospital admissions may explain the concentration of health migration (HM) in some regions. Moreover, the beaten path effect is stronger for private hospitals. We also find that HM patients are more sensible to hospital quality than those admitted in local hospitals.

Background: Surfactant protein-D (SP-D) is a lung-resident protein that has emerged as a potential biomarker for COVID-19. Previous investigations on acute respiratory distress syndrome patients demonstrated a significant increment of SP-D serum levels in pathological conditions. Since SP-D is not physiologically permeable to alveoli-capillary membrane and poorly expressed by other tissues, this enhancement is likely due to an impairment of the pulmonary barrier caused by prolonged inflammation. Methods: A retrospective study on a relatively large cohort of patients of Hospital Pio XI of Desio was conducted to assess differences of the hematic SP-D concentrations among COVID-19 patients and healthy donors and if SP-D levels resulted a risk factor for disease severity and mortality. Results: The first analysis, using an ANOVA-model, showed a significant difference in the mean of log SP-D levels between COVID-19 patients and healthy donors. Significant variations were also found between dead vs survived patients. Results confirm that SP-D concentrations were significantly higher for both hospitalized COVID-19 and dead patients, with threshold values of 150 and 250 ng/mL, respectively. Further analysis conducted with Logistic Mixed models, highlighted that higher SP-D levels at admission and increasing differences among follow-up and admission values resulted the strongest significant risk factors of mortality (model predictive accuracy, AUC = 0.844). Conclusions: The results indicate that SP-D can be a predictive marker of COVID-19 disease and its outcome. Considering its prognostic value in terms of mortality, the early detection of SP-D levels and its follow-up in hospitalized patients should be considered to direct the therapeutic intervention.

We propose a new approach with which to measure school efficiency by including student Non-Cognitive Skills in the analysis. In classical analysis, efficiency is measured separately from effectiveness. In our framework, we jointly consider efficiency and effectiveness, including both Cognitive Skills and Non-Cognitive Skills. We call our approach “Non-Cognitive Skills Efficiency” and we propose two analyses. The first is called “Static Non-Cognitive Skills Efficiency” and measures the efficiency of transforming Non-Cognitive Skills into Cognitive Skills, by means of a Stochastic Frontier Approach. We verify that some Non-Cognitive Skills have effect on Cognitive Skills and contribute to increase school efficiency. The second is defined “Dynamic Non-Cognitive Skills Efficiency” and measures the efficiency of school educational programmes aimed at improving Non-Cognitive Skills. The statistical method is a Difference-in-Differences model based on a Stochastic Frontier Approach. We find that these educational programmes (treatment) have a positive effect on Non-Cognitive Skills. The survey concerns 8th grade students attending 25 schools in the Provincia Autonoma di Trento, in Italy. The dataset comprises both survey data and the administrative data of local authorities, thus providing a complete set of information at student level on CS and NCS skills, social capital variables, in particular the socioeconomic background of families, and teaching parameters. We measure school efficiency at class level, because this level is less affected by unobserved environmental factors. The results provide new perspectives on education in schools.

Character Skills and New Horizons for Schools · Human Capital is usually measured in terms of Cognitive Skills at the end of schooling, as- sessed in terms of standardized achievements test scores. Recently Human Capital has been also connected to Non Cognitive Skills (also named Character Skill), personality resources linked to motivation in learning, relational capabilities, emotional stability, autonomy in pursuing personal objectives. Many authors showed that Non Cognitive Skills improve the acquisition of Cognitive Skills. We present the results of the research “The development of non cognitive skills in Trentino students”, carried out on lower secondary school students in the Autonomous Province of Trento. The aim is to verify whether Non Cognitive Skills lead to an improvement in Cognitive Skills, as measured by school performance, and whether ap- propriate educational programs increase Non Cognitive Skills, through two analyses, the first related to the 2017-2018 school year, the second to the following year. The results confirm that the presence of more robust skills promotes growth in Cognitive Skills as well; further, that various aspects of Non Cognitive Skills can be improved through specific educational pro- grams conducted in school settings.

We study an airport cost function with a four-random components stochastic frontier model that split the inefficiency in two parts: (1) persistent (long-run) and (2) temporary (short-run). We analyze as determinant of both inefficiency types several exogenous factors such as the airport ownership and LCCs share. We apply the model to the Italian airport system during the period 2010–15. We find that (1) average total cost inefficiency is 14%, and that a it is mainly due to temporary inefficiency, (2) that work load units have the largest cost–output elasticity (+0.31%), (3) that price of airport services has the largest cost-input elasticity (+0.50%) while labor has a +0.41% cost elasticity and capital only a +0.09% cost elasticity. There is also evidence of economies of scale. Moreover we find that LCCs reduce both types of inefficiency and that private airports are more cost efficient than public ones. These findings have interesting managerial (e.g., strictly monitoring service costs, expand LCCs flights) and policy (e.g., provide incentive to reduce temporary inefficiency, push airport privatization) implications.

In order to investigate and correctly measure the monetary benefits obtained when human capital migrates, we have to control for individuals’ characteristics and as many aspects of their “life history” as possible, regardless of the selected statistical approach. Individuals’ backgrounds and experiences will affect their propensity to migrate; as such, only by comparing similar individuals is it possible to estimate the monetary effect of choosing to work abroad after graduation. Socio-economic backgrounds, training choices, learning experiences, and more general life stories all contribute to the propensity of migrating, as well as the possible outcomes. This is a well-known bias problem generated by a self-selection mechanism. We present a method using a statistical adjustment of the self-selection problem applied to a database of life history records. The AlmaLaurea database used in this paper collects thorough data from graduates of Italian universities, including information on graduates’ previous studies and social backgrounds. We analyse this data to measure the impact of the emigration choice in the context of human capital theory as applied to the modern view of a global labour market. We perform a statistical multivariate analysis to study migrants vs. non-migrants, using an innovative data-driven potential outcome approach that corrects for self-selection bias. We continue with a cluster analysis to identify homogeneous groups based on a multiple correspondence analysis and testing the self-selection bias reduction using a global imbalance measure.

A review of certain latent variable models that may be effectively used to build evaluation systems is proposed. The review is focused on the item response theory models, the latent class models and the latent Markov models. Certain latent Markov versions are illustrated in detail from the perspective of evaluation when multilevel or non-experimental longitudinal data are available.

Lo scopo del lavoro è quello di riflettere sul fenomeno della cooperazione, cercando di verificare se il mondo cooperativo ha, ancora oggi, nel contesto della società italiana, una sua ragion d’essere, un suo valore e non solo una storia gloriosa alle spalle.

The aim of this discussion paper is to raise awareness of the conceptual and practical limits of mainstream practices in social measurement and to suggest possible directions for social indicator construction, in view of effectively supporting policies for social sustainability and well-being promotion. We start with a review of the epistemological issues raised by the measurement of social phenomena, investigate the notion of social complexity, and discuss the critical link between it and measurement. We then suggest that social indicators should be primarily designed to build structural syntheses of the data, unfolding the patterns and stylizing the complexity of social phenomena, rather than computed pursuing numerical precision, through hardly interpretable aggregated measures. This calls for tools and algorithms capable of rendering structural information, preserving the essential traits of complexity and overcoming the limitations of classical aggregation procedures. We provide some examples along this line, using real data pertaining to regional well-being in OECD countries.

Background: The first Covid-19 epidemic outbreak has enormously impacted the delivery of clinical healthcare and hospital management practices in most of the hospitals around the world. In this context, it is important to assess whether the clinical management of non-Covid patients has not been compromised. Among non-Covid cases, patients with Acute Myocardial Infarction (AMI) and stroke need non-deferrable emergency care and are the natural candidates to be studied. Preliminary evidence suggests that the time from onset of symptoms to emergency department (ED) presentation has significantly increased in Covid-19 times as well as the 30-day mortality and in-hospital mortality. Methods: We check, in a causal inference framework, the causal effect of the hospital's stress generated by Covid-19 pandemic on in-hospital mortality rates (primary end-point of the study) of AMI and stroke over several time-windows of 15-days around the implementation date of the State of Emergency restrictions for COVID-19 (March, 9th 2020) using two quasi-experimental approaches, regression-discontinuity design (RDD) and difference-in-regression-discontinuity (DRD) designs. Data are drawn from Spedali Civili of Brescia, one of the most hit provinces in Italy by Covid-19 during March and May 2020. Findings: Despite the potential adverse effects on expected mortality due to a longer time to hospitalization and staff extra-burden generated by the first wave of Covid-19, the AMI and stroke mortality rates are overall not statistically different during the first wave of Covid-19 than before the first peak. The obtained results provided by RDD models are robust also when we account for seasonality and unobserved factors with DRD models. Interpretation: The non-statistically significant impact on mortality rates for AMI and stroke patients provides evidence of the hospital ability to manage -with the implementation of a dual track organization- the simultaneous delivery of high-quality cares to b