Italian National Statistical Institute Research Articles

Consumption of Tap Water and Sociodemographic-Associated Characteristics: A Nationwide Cross-Sectional Study.

Safe water is a global public health concern amid increasing scarcity and pollution. Bottled water production and consumption contribute to these problems. This study examines tap water consumption in Italy, assessing associated sociodemographic factors and related health outcomes such as obesity and self-perceived health status. Data from the Italian National Statistics Institute's "Aspects of daily life" survey (N = 45,597) were analyzed. Covariates included education, age, gender, economic status, region, concerns about waste and climate change, consumption of carbonated drinks excluding water, alcohol consumption, consumption of vegetables, consumption of snacks, body mass index, and self-perceived health status. Bivariate analyses and mixed-effect logistic regression models explored the associations. People who drink tap water made up 19,674, with a higher prevalence in people aged 45 to 59 old, people with a graduate/post-graduate degree diploma, with optimal economic resources, people concerned about waste production and climate change, and those coming from the north-east regions of Italy. Underweight people showed a higher prevalence of TW consumption as well as those who less than occasionally drank carbonated drinks, drank alcohol, consumed vegetables more than once a day and snacks less than once a week, dairy products more than once a day, sweet less than once a week, cured meat less than once a week, and chicken meat less than once a week, those with no consumption of sheep meat, consumption of beef meat less than once a week and consumption of pork meat less than once a week, and those with a satisfactory level of perceived health status. Regressions showed that all other age classes are less likely to drink tap water than people younger than 20 years old. The category with "inadequate" economic resources is more likely to consume tap water. Low educational classes show a low likelihood of consuming tap water as well as islands. A concern about waste production and climate change is associated with an increased likelihood of consuming tap water. Tap water consumption was negatively associated with obesity but not with a satisfactory self-perceived health status. Insights from this study can inform public health strategies.

How does accounting save lives? An econometric analysis exploring the effectiveness of the Crispi-Pagliani Health Reform (1888) in Italy

This study compares the healthcare management systems for cholera epidemics in Italy before and after the Crispi-Pagliani Health Reform of 1888. Specifically, we compare the monitoring, accounting, and reporting systems of deaths and infections during the 1884 and 1893 epidemics. Based on an econometric analysis of data from the Italian National Statistical Institute, we demonstrate that the post-reform system was more effective in reducing deaths by optimising medical personnel allocation in areas with higher epidemic intensity. From a Foucauldian perspective, we provide insights into the role of accounting in supporting noso-politics, bio-politics, and bio-power. Further, this study advances our understanding of reporting systems and the dynamics that governments trigger as part of strategies for preventing or containing contagion during epidemics. Finally, this study supports the use of econometrics in accounting history.

Socioeconomic differences in dietary habits in Italy before and during COVID-19 pandemic: secondary analysis of a nationwide cross-sectional study

BackgroundSeveral socioeconomic conditions may influence subjects’ adherence to healthy eating habits. Food consumption may be influenced by external stress during crisis periods; however, the effects of these events on food habits are difficult to predict. Also, a pandemic crisis like the recent COVID-19 pandemic may have influenced dietary habits and food consumption. The objective of this study was to compare the dietary habits of Italian people before the COVID-19 pandemic with those belonging to the year 2020 in a nationwide population sample.Materials and methodsInformation on dietary habits has been obtained from the multi-purpose survey on families’ “Aspects of daily life”, carried out in Italy by the Italian National Statistics Institute (ISTAT). We analyzed data coming from 2016 and 2020 editions of the survey (43,000 subjects each year). We used population attributable fraction (PAF) adjusted for age, defined as the proportional reduction in unhealthy diet that would occur if all participants had had a higher education, assuming higher educated individuals as more socially advantaged. Prevalence association for each dietary exposition has been calculated through logistic regression.ResultsLooking at aggregated data from 2016 and 2020 both men and women showed a high prevalence of unhealthy dietary habits. Regarding men, excessive consumption of eggs, pork meat, and bovine meat was characterized by a PAF attributable to socioeconomic conditions to an extent greater than 30%. Women showed the same trend. Focusing on different years of investigation, in 2020, during the COVID-19 pandemic, men and women increased their consumption of eggs, cooked fats, snacks, and sweets, and reduced consumption of fruits and vegetables. Additionally, women increased the assumption of dietary products and meat. Both sexes registered an increase in overweight and obese subjects in 2020.ConclusionsTo our knowledge, this study was the first in our country to use a yearly, nationwide sample to analyze dietary habits by examining specific types of various foods on a nationwide scale and establishing a correlation between these habits and the COVID-19 pandemic. Our results showed unbalanced dietary habits of the Italian population with an excess of consumption of several foods like eggs, cooked fats snacks, and sweets with low consumption of fruits and vegetables. Socioeconomic differences influence food choices but in a complex way since they seemed to affect some wrong dietary habits but not others, especially regarding fruits and vegetables assumption where differences were less evident among social classes. Outside stressors like a crisis period such as the COVID-19 pandemic seem to have an important role in both men and women regarding the assumption of so-called “junk food”.

The contributing factors to suicide in Italian prisons: An 11‐year analysis (2010–2020)

AbstractBackgroundSuicide is a leading cause of death globally, with approximately 800,000 deaths annually and accounting for 1.5% of all deaths. Risk factors are multifaceted, encompassing individual factors (such as genetics, family history and mental illnesses) and environmental factors (such as economic conditions, social support and life events). In prisons, suicide rates are markedly higher than in the general population, particularly in Italy, where the prisoner suicide rate is approximately 20 times that of the non‐incarcerated population. There is, however, little research on suicide in Italian prisons.AimsTo analyse the characteristics of all people who died by suicide in Italian prisons between 2010 and 2020.MethodsWe carried out a records‐based cohort study analysing official data from the Italian Ministry of Justice on prison suicides between 2010 and 2020. The data were cross‐referenced and, when required, supplemented with information from Ristretti Orizzonti, a journal specialising in health and living conditions in prisons, as well as from the website of ISTAT (Italian National Statistical Institute), newspapers, radio broadcasts and news agencies.ResultsFactors associated with an increased risk of suicide in prisons were nighttime periods, the months of June, July and October, a relatively brief duration of detention (<6 months), having been convicted of murder, male gender, being about 40 years old, having access to hanging materials and being interned (i.e. subjected to the execution of custodial security measures) or awaiting trial. Prison overcrowding was not a risk factor for suicide.ConclusionOur findings hold substantial implications for suicide prevention in Italian prisons as they suggest both characteristics of individuals and characteristics of the institutions that could be taken as risk indicators. This knowledge can inform the development of targeted interventions to manage both individual and environmental factors better, leading to improved prison conditions and reduced suicide rates. Furthermore, our research establishes a foundation for more systematic and in‐depth investigations that could further improve suicide prevention strategies in Italian prisons, ultimately influencing policy changes in both practice and research, including perhaps establishing a national database on every completed suicide in prisons.

Statistical indicators based on mobile phone and street maps data for risk management in small urban areas

AbstractThe use of new sources of big data collected at a high-frequency rate in conjunction with administrative data is critical to developing indicators of the exposure to risks of small urban areas. Correctly accounting for the crowding of people and for their movements is crucial to mitigate the effect of natural disasters, while guaranteeing the quality of life in a “smart city” approach. We use two different types of mobile phone data to estimate people crowding and traffic intensity. We analyze the temporal dynamics of crowding and traffic using a Model-Based Functional Cluster Analysis, and their spatial dynamics using the T-mode Principal Component Analysis. Then, we propose five indicators useful for risk management in small urban areas: two composite indicators based on cutting-edge mobile phone dynamic data and three indicators based on open-source street map static data. A case study for the flood-prone area of the Mandolossa (the western outskirts of the city of Brescia, Italy) is presented. We present a multi-dimensional description of the territory based on the proposed indicators at the level of small areas defined by the Italian National Statistical Institute as “Sezioni di Censimento” and “Aree di Censimento”.

Small banks and innovative entrepreneurial culture in Italy: A historical perspective

Based on provincial (NUTS 3) data from a comprehensive survey conducted in 1927 by the Italian National Statistical Institute (ISTAT), this paper investigates the effect of the historical presence of small financial institutions on current start-up rates. It finds evidence that their presence positively affects current start-up rates in high and medium-high technology manufacturing sectors. In contrast, it does not find any significant effect in low and medium-low technology manufacturing sectors. These results suggest that the local presence of small financial institutions may have been essential in promoting the development of entrepreneurship in those sectors that are intrinsically more opaque and riskier.

Informal childcare arrangements: a comparison between Italians and migrants

As migrants settle in their destination country, for those who reunited the family or after childbirth childcare becomes a priority. Most studies on migrants’ childcare arrangements have focused on parental use of formal childcare rather than on different informal childcare solutions by analysing only families with preschool-age children. Italy poses an interesting case study because its welfare system is characterised by a familistic model of care, based on solidarity between generations. In familistic countries, migrants’ childcare solutions are more constrained. In this study, we analysed differences in informal childcare needs and arrangements for children younger than 14 between Italians and migrants from different countries of origin. We merged two surveys conducted by the Italian National Statistics Institute in 2011–2012: ‘Social Condition and Integration of Foreign Citizens’, a sample of households with at least one migrant with foreign citizenship, and ‘Multiscopo—Aspects of Daily Life’, a sample of households in Italy. We found that household composition and parents’ employment status play an important role in shaping informal childcare arrangements. Overall, migrants are less likely to use informal childcare, especially grandparents, than Italians but when they do, they rely more on other relatives and non-relatives than Italians. Moreover, differences emerge across migrant subgroups. This study is the first in Italy to contribute to an understanding of the role of migrant status in determining parents’ childcare arrangements for children up to 13 years.

Short and long-term survival in patients over ninety years-old undergoing pacemaker implantation

Abstract Funding Acknowledgements Type of funding sources: None. Background According to the Italian National Statistical Institute, the 12-month probability of survival in the 90-94 years-old general population is 26%. In very old patients (pts) the benefit of pacemaker (PM) implantation in terms of quality and duration of life is unclear and could be considered futile. Purpose To analyze clinical characteristics, outcome and factors associated with survival in pts who had turned 90 at the time of the first PM implant. Methods All the PM implants performed in our Centre from 1/1/2019 to 1/31/2020 in pts ≥90 year-old at the time of the procedure were analyzed. Clinical parameters, device characteristics and follow-up data until 31/3/2022 were extrapolated from the SuitEstensa Ebit reporting system; the exitus was verified by analyzing data from the Regional Health System. Results During the study interval, among the 554 pts undergoing PM implantation in our Center, 69 (12%) were ≥90 years-old at the date of implantation (mean age 92±2 years, 46% male; complete/advanced AV block in 76%). Twenty-six (38%) pts had history of atrial fibrillation and 19 (28%) ischemic heart disease. A cardiological co-morbidity (excluding AF) was present in 23 pts (33%). Oncological, pneumological and neurological comorbidities were present in 12 (18%), 19 (28%) and 32 (46%) respectively. Renal impairment was present in 25 pts (36%). In 47 pts (68%) at least 2 co-morbidities were present. After implantation (VVIR in 36, DDDR in 25 and VDD in 8 patients) a pneumothorax developed in 2 pts and lead dislodgment in 1. Remote monitoring was activated in 57 pts (83%). Within 31/3/2022 (median follow-up 17 months, IQR: 13-24) 33 pts died (37%, in median 12 months after implant, IQR: 3-16), with 12-month survival probability of 75.4% (95% CI: 65.9-86.2). According to the Regional Health System database, no pts died for cardiac reason or device malfunction. Three pts died because of COVID-19 pneumonia. Renal dysfunction (Hazard Ratio-HR 2.64, p=0.006), the presence of 2 or more co-morbidities (HR 6.19; p= 0.003), diabetes (HR 2.53; p=0.009), ejection fraction (HR 0.88 for a difference of 5%; p=0.035), walking impairment (HR 3.64, p<0.001), the presence of oncological (HR 2.62; p=0.019), pneumological (HR 2.05; p=0.049), neurological (HR 4.57, p<0.001) and cardiovascular (HR: 2.20; p=0.015) comorbidities were associated with a higher risk of death at univariate analysis. Presence of oncological and neurological comorbidities were significant at multivariable analysis. Moreover, truncating the outcome at 6 months, renal dysfunction, anticoagulant therapy and ischemic disease were associated with short-term mortality. Conclusions At our Center, pts over the age of ninety undergo PM implantation mainly for advanced AVB. The good survival does not seem to justify particularly conservative attitudes, in the presence of clear indications for the implantation of the PM.

Multi-source statistics on employment status in Italy, a machine learning approach

In recent decades, National Statistical Institutes have started to produce official statistics by exploiting multiple sources of information (multi-source statistics) rather than a single source, usually a statistical survey. In this context, one of the research projects addressed by the Italian National Statistical Institute (Istat) concerned methods for producing estimates on employment in Italy using survey data and administrative sources. The former are drawn from the Labour Force survey conducted by Istat, the latter from several administrative sources that Istat regularly acquires from external bodies. We use machine learning methods to predict the individual employment status. This approach is based on the application of decision tree and random forest techniques, that are frequently used to classify large amounts of data. We show how to construct a “new” response variable denoting agreement of the data sources: this approach is shown to maximise the information we may derive by machine learning approach in some problematic cases. The methods have been applied using the R software.

Climate stressors modulate interannual olive yield at province level in Italy: A composite index approach to support crop management

AbstractAlthough a large part of Italy is characterized by a Mediterranean climate intrinsically highly suitable for olive cultivation, farmers may experience variable interannual yields with associated agronomic and management costs. A detailed overview of major climate stressors and their ongoing impacts on olive yield variability at a broad spatial–temporal scale across Italy could improve the understanding of how interannual olive yields are modulated by seasonal local climate and would enhance the development of actionable services to alert stakeholders of potential climate risks. We analysed aggregated olive yield data from the Italian National Statistics Institute (ISTAT) at the provincial level during 2006–2020, and several climatic variables from the Reanalysis v5 (ERA5) of the European Centre for Medium‐Range Weather Forecasts (ECMWF) as a basis to (i) explore olive yield trends and inter‐annual variations over the whole country; (ii) identify major seasonal climate stressors likely responsible for the largest variations in yield and (iii) develop a composite index that summarizes the risk of having exceptionally low yields due to the co‐occurrence of multiple stressors. To this end, we defined two extreme classes of yield, namely, exceptionally low and high yields (LY and HY, respectively) and explored the climatic variables aggregated on a bimonthly time scale that influenced yield outcomes. Our analysis showed that exceptional low yields have been erratically increasing since 2014 with temperature‐related variables having the highest impact, especially a relatively warm winter and cool early autumn. These period‐specific variables were major components of the resulting composite index predicting the likelihood of LY ranging from 28% to 49% due to increasing stress effects. Possible explanations of our findings are discussed, including the proliferation of the olive fly. We suggest the composite risk approach could lay the groundwork for an integrated meteorological seasonal forecasting system that provides timely insights on factors affecting within‐season olive yield development.

An occupational health risk index: definition, description and mapping at municipality level.

to develop an occupational health risk index that can help to introduce the occupational risk component into epidemiological studies and assess the level of occupational risk in Italian municipalities useful for supporting prevention measures. defi nition of a municipal index as a combination of occupational and territorial indicators derived from national registers of occupational data and environmental archives. the index composition is based on data on work injuries, occupational diseases and workers exposed to carcinogens in the years 2015-2019 available at the municipal level, as well as data on municipalities hosting sites of national interest for environmental remediation (SIN) and those in which big industrial facilities (GIE), registered as pollutant emitters, are located. standardized rates of occupational injuries and occupational diseases occurred in Italian municipalities were calculated from data collected by the Italian National Institute for Insurance against Accidents at Work (Inail) in relation to the working-age population (15-69 and 15+ for injuries and occupational diseases, respectively), estimated by the Italian National Statistical Institute (Istat) in 2018. In addition, data from the National Information System on Occupational Exposure to carcinogens (SIREP) were included, from which raw rates were calculated at municipal level. Finally, two other indicators were included, describing the presence/absence of a SIN and the number of GIEs industrial facilities in each municipality. The index of occupational health risk (INDORS) is calculated by summing standardised values of the above fi ve indicators as a continuous variable and it was also classifi ed by quintiles of population as a categorical variable. The association between cause-specific mortality and INDORS levels was evaluated using data on mortality occurring in 2015. during the observation period 2,011,457, 131,353 and 140,183 events were recorded for injuries, occupational diseases and workers exposed to carcinogens, mainly among male workers. A municipal map of INDORS levels (1-5) shows a strong South-North gradient, in line with the Italian industrial geographical context. The contributions of the SIN and GIE indicators are higher in the hot spot municipalities located in the Southern regions and islands. Among the municipalities analysed 1,099 were classifi ed in the lowest risk level, 1,331 in the low-medium level, 1,619 in the medium level, 2,621 in the medium-high level and 1,284 in the highest risk level. The index shows a direct correlation with accidental mortality and an inverse correlation with all-cause and malignant neoplasm mortality. the proposed index can be useful to introduce the occupational risk dimension in ecological studies and results as a flexible tool to rank Italian municipalities in terms of occupational risk.

Hyperparameter Black-Box Optimization to Improve the Automatic Classification of Support Tickets

User requests to a customer service, also known as tickets, are essentially short texts in natural language. They should be grouped by topic to be answered efficiently. The effectiveness increases if this semantic categorization becomes automatic. We pursue this goal by using text mining to extract the features from the tickets, and classification to perform the categorization. This is however a difficult multi-class problem, and the classification algorithm needs a suitable hyperparameter configuration to produce a practically useful categorization. As recently highlighted by several researchers, the selection of these hyperparameters is often the crucial aspect. Therefore, we propose to view the hyperparameter choice as a higher-level optimization problem where the hyperparameters are the decision variables and the objective is the predictive performance of the classifier. However, an explicit analytical model of this problem cannot be defined. Therefore, we propose to solve it as a black-box model by means of derivative-free optimization techniques. We conduct experiments on a relevant application: the categorization of the requests received by the Contact Center of the Italian National Statistics Institute (Istat). Results show that the proposed approach is able to effectively categorize the requests, and that its performance is increased by the proposed hyperparameter optimization.

Natural language processing in official statistics: The social mood on economy index experience

In recent years, Eurostat promoted Experimental Statistics, i.e., statistics that “have not reached full maturity in terms of harmonization, coverage or methodology”. These statistics are based on new data sources, mainly Big Data, able to improve timeliness and provide new complementary measures to Official Statistics estimates. However, the use of Big Data implies new quality challenges, and a well-defined Big Data quality framework is not available. The Social Mood on Economy Index (SMEI) is among the first experimental statistics on Big Data published by the Italian National Statistics Institute (Istat) since 2018. SMEI is a daily index computed from the Italian Twitter’s public stream aimed at representing the evolution of the feelings on economics topics. The longevity of SMEI makes it a perfect candidate to investigate Big Data related quality issues. Its intrinsic multivariate approach hinders the interpretation of the index. Are we able to track its quality characteristics? What are specific uses of the SME index? The paper will report current discussion and solutions implemented at Istat, in particular it focuses on SMEI’s revision due to COVID-19 pandemic. The present work is aiming at contributing to the ESS debate on setting up quality standards for processing Big Data-based statistical products.

Internet as a Meeting Place for Spouses: Online Dating, Homogamy and Assortative Mating in Contemporary Italy

In this article places where spouses first met in Italy are studied. The focus is on online settings in the most recent marriage cohorts (2000-2016). The aim is to investigate trends over time of using the Internet as a meeting place and to explore whether Internet dating can affect assortative mating and homogamy rules. Information on first marriages is analysed to study highly committed and long-lasting relationships between partners. Data used for the analyses come from the nationally representative survey "Family, Social Subjects and Life Cycle" carried out by the Italian National Statistical Institute (Istat) in 2016 on a sample of about 32,000 individuals made available in 2020. The results allow a preliminary quantification of the phenomenon and document an increase in meetings occurring online in Italy (from 0.4 per cent in the marriage cohort 2000-2009 to 2.5 per cent in the marriage cohort 2010-2016). Furthermore, data support the idea that online contexts show homogamy paths not different from those that characterize offline dating venues. Meeting a partner online does not seem to imply heterogamy.

Trends in the Use of Home LTC Services in Large, Medium and Small Municipalities in Italy: Lessons for the Post-COVID-19 Reappraisal.

Italian Long-Term Care is considered largely inadequate, and the recent COVID-19 pandemic has dramatically exposed its limitations. Public Home Care Services in particular were revealed as under-financed and unable to cover the potential demand for care from the older population. But does the type of municipality and its geographic location play a role in creating or mitigating unmet demand? This is the first study addressing this research question in Italy. Our hypothesis is that older people’s care preferences and care possibilities may vary between small, medium and metropolitan areas, as will the organisation, funding and availability of services, and the combination will influence (unmet) demand for public home care services. In this paper, using nationally representative survey data collected by the Italian National Statistical Institute in 2003 and 2016, we investigate changes and differences in the use of public and private home care services among people aged 75 or older in Italy by size of the municipality. Our results reveal inequalities in service use between Northern and Southern areas of the country and in particular between metropolitan areas, medium and small municipalities. Such differences reinforce post-pandemic calls for new investment and changes in the design of the Italian Long-Term Care system.

Forecasting Model Based on Lifestyle Risk and Health Factors to Predict COVID-19 Severity.

The COVID-19 pandemic has now spread worldwide, becoming a real global health emergency. The main goal of this work is to present a framework for studying the impact of COVID-19 on Italian territory during the first year of the pandemic. Our study was based on different kinds of health features and lifestyle risk factors and exploited the capabilities of machine learning techniques. Furthermore, we verified through our model how these factors influenced the severity of the pandemics. Using publicly available datasets provided by the Italian Civil Protection, Italian Ministry of Health and Italian National Statistical Institute, we cross-validated the regression performance of a Random Forest model over 21 Italian regions. The robustness of the predictions was assessed by comparison with two other state-of-the-art regression tools. Our results showed that the proposed models reached a good agreement with data. We found that the features strongly associated with the severity of COVID-19 in Italy are the people aged over 65 flu vaccinated () together with individual lifestyle behaviors. These findings could shed more light on the clinical and physiological aspects of the disease.

Medium term survival in patients over ninety years-old undergoing pacemaker implantation

Abstract Background According to the Italian National Statistical Institute, the 12-month probability of survival in the general population between 90 and 94 years-old is 26%. Pacemaker (PM) implantation is often an urgent and necessary intervention, but in these patients the benefit in terms of quality and duration of life is unclear. Purpose To analyze characteristics, outcome and factors associated with survival in patients who had turned 90 at the time of PM implant. Methods All the PM implants performed in patients ≥90 from 1/1/2019 to 12/31/2020 were analyzed. Clinical parameters, device characteristics and follow-up data were extrapolated from the SuitEstensa Ebit reporting system; the exitus was verified by analyzing data from the Regional Health System. Results During the study interval, among the 554 patients undergoing PM implantation in our Center, 69 (12%) were ≥90 years-old (mean age 92±2 years, 46% male; complete/advanced AV block in 76%). Twenty-six (38%) patients had history of atrial fibrillation and 19 (28%) ischemic heart disease. A cardiological co-morbidity (excluding AF) was present in 23 patients (33%). Oncological, pneumological and neurological comorbidities were present in 12 (18%), 19 (28%) and 32 (46%) respectively. Renal impairment was present in 25 patients (36%). In 47 patients (68%) there were at least 2 co-morbidities. After implantation (single-chamber in 36, dual-chamber in 25 and VDD single-lead dual-chamber in 8 patients) complications occurred in 3 patients (2 pneumothorax and 1 lead dislodgment). Remote monitoring was activated in 57 patients (83%). Within August 31st 2021 (mean follow-up 288±193 days) 24 patients died (35%, 219±241 days after implant). Five patients (19% of patients implanted in 2019) died within 12 months. No patients died for device malfunction. Three patients died because of COVID-19 pneumonia. Renal dysfunction (Hazard Ratio-HR 8.05, p=0.002) and the presence of 2 or more co-morbidities (HR 6.03; p=0.015) were associated with a higher risk of death at univariate analysis; other significant variables were diabetes (HR 2.34; p=0.038), left ventricular ejection fraction (LVEF) (HR 0.70 for 5% variation; p=0.005), walking impairment (HR 2.99, p=0.006), the presence of oncological (HR 2.21; p=0.003), pneumological (HR 2.55; p=0.024) and neurological (HR 1.90, p=0.007) comorbidities. At multivariable analysis the only significant parameter associated with survival was LVEF (0.76 for 5% difference; p=0.043) Conclusions At our Center, patients ≥90 years-old undergo PM implantation mainly for advanced AVB. The good survival in the medium term, even better than expected in the general population, does not justify a too conservative attitude especially, but exclusively, in patients with less comorbidities. Funding Acknowledgement Type of funding sources: None.

Total Process Error: An Approach for Assessing and Monitoring the Quality of Multisource Processes

Abstract Most National Statistical Institutes are progressively moving from traditional production models to new strategies based on the combined use of different sources of information, which can be both primary and secondary. In this article, we propose a framework for assessing the quality of multisource processes, such as statistical registers. The final aim is to develop a tool supporting decisions about the process design and its monitoring, and to provide quality measures of the whole production. The starting point is the adaptation of the life-cycle paradigm, that results in a three-phases framework described in recent literature. An evolution of this model is proposed, focusing on the first two phases of the life-cycle, to better represent the source integration/combination phase, that can vary accordingly to the features of different types of processes. The proposed enhancement would improve the existing quality framework to support the evaluation of different multisource processes. An application of the proposed framework to two Istat (Italian national statistical institute) registers in the economic area taken as case studies is presented. These experiences show the potentials of such tool in supporting National Statistical Institutes in assessing multisource statistical production processes.

[Impact of the COVID-19 epidemic on total and cause-specific mortality in Rome (Italy) in 2020].

to estimate the impact of the COVID-19 epidemic on total and cause-specific mortality in people residing and dead in the Municipality of Rome (Italy) in 2020, and to describe the causes of death of subjects with SARS-CoV-2 infection confirmed by molecular test. descriptive analysis of total and cause-specific mortality in 2020 in Rome and comparison with a reference period (2015-2018 for total mortality and 2018 for cause-specific mortality); descriptive analysis of cause-specific mortality in the cohort of SARS-CoV-2 infected subjects. 27,471 deaths registered in the Lazio mortality-cause Registry, relating to people residing and died in the municipality of Rome in 2020, 2,374 of which died from COVID-19.MAIN OUCOME MEASURES: all-cause mortality by month, gender, age group and place of death, cause-specific mortality (ICD-10 codes). in the municipality of Rome in 2020, an excess of mortality from all causes equal to +10% was observed, with a greater increase in the months of October-December (+27%, +56%, and +26%, respectively) in people aged 50+, with the greatest contribution from the oldest age groups (80+) who died in the nursing homes or at home. Lower mortality was observed in the age groups 0-29 years (-30%) and 40-49 years (-13%). In 2020, COVID-19 represents the fourth cause of death in Rome after malignant tumours, diseases of the circulatory system, and respiratory diseases. Excess mortality was observed from stroke and pneumonia (both in men and women), from respiratory diseases (in men), from diabetes, mental disorders, dementia and Parkinson's disease (in women). On the contrary, mortality is lower for all cancers, for diseases of the blood and haematopoietic organs and for the causes of the circulatory system. The follow-up analysis of SARS-CoV-2 positive subjects residing in Rome shows that a share of deaths (about 20%) reports other causes of death such as cardiovascular diseases, malignant tumours, and diseases of the respiratory system on the certificate collected by the Italian National Statistics Institute. the 2020 mortality study highlighted excesses for acute and chronic pathologies, indicative of possible delays in the diagnosis or treatment of conditions indirectly caused by the pandemic, but also a share of misclassification of the cause of death that is recognized as COVID-19 death.

Historical roots of innovative entrepreneurial culture: the case of Italian regions

ABSTRACT This paper investigates persistence in entrepreneurship across Italian provinces over the period 1927–2017. We distinguish between a ‘general’ dimension (sociopsychological) and a ‘specific’ dimension (learning) of the role model effect. Based on data obtained from a comprehensive survey conducted in 1927 by the Italian National Statistical Institute, we find different drivers of persistence across sectors: the only driver of persistence in high-technology sectors is the share of entrepreneurs using motive power (‘specific’ role model effect), while the share of industrial entrepreneurs (‘general’ role model effect) is an important driver of persistence in low-technology manufacturing and service sectors.