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To develop the FRIDA (Functional Risk Index for Dependence in Ambulation) score, a nomogram to predict individual risk of dependence in ambulation at discharge (DAD) from post-acute rehabilitation and validate its performance temporally and spatially.We analyzed the database of a multicenter prospective observational quality cohort study conducted from January 2012 to March 2016, including data from 8796 consecutive inpatients who underwent rehabilitation after stroke, hip fracture, lower limb joint replacement, debility, and other neurologic, orthopedic, or miscellaneous conditions.A total of 3026 patients (34.4%) were discharged with DAD. In the training set of 5162 patients (58.7%), Lasso-regression selected advanced age, premorbid disability, and eight indicators of medical and functional adverse syndromes at baseline to establish the FRIDA score. At the temporal validation obtained on a set of 3234 patients (41.3%), meta-analysis showed that the FRIDA score had good homogeneous discrimination (I2= 0.0%) (synthetic AUC 0.84, 95% CI= 0.83-0.86) combined with accurate calibration (synthetic Log O/E ratio 0.02, 95% CI -0.16-0.19). These performances remained stable at spatial validation obtained on 3626 patients from 9 facilities, with higher heterogeneity. Decision curve analyses showed that a FRIDA score-supported strategy far outperformed the usual "treat all" approach in each impairment categories.The FRIDA score is a new clinically useful tool to predict individual risk for dependence in ambulation at rehabilitation discharge across many different disabilities.

Background: Sporadic Creutzfeldt-Jakob disease (CJD) is a rare neurodegenerative disease caused by prions that is randomly distributed in all countries, with an overall yearly mortality rate of about 1–2 cases per million people. On a few occasions, however, sporadic CJD occurred with higher than expected rates, but further investigations failed to recognize any convincing causal link. In Italy, cluster analyses of sporadic CJD cases have not been performed previously. Objective: To investigate the geographical distribution of sporadic CJD using municipality geographical data of Apulia with the aim of detecting spatial clusters of disease. Patients and Methods: Patients included in this study were diagnosed as probable or definite sporadic CJD and were residents of the Apulia Region (Italy). Bayesian hierarchical models with spatially structured and unstructured random components were used to describe the spatial pattern of the disease and to assess the extent of heterogeneity among municipalities. The Kulldorff-Nagarwalla scan test and the flexible spatial scan statistic were used for detecting spatial clusters. Results: Smoothed Bayesian relative risks above the null value were observed in a few adjacent municipalities in the north and middle areas of Apulia. However, both the circular scanning method and the flexible spatial scan statistic identified only a single cluster in the central part of the region. Conclusion: Geographical analyses and tests for spatial randomness identified a restricted area with an unusually high number of sporadic CJD cases in the Apulia region of Italy. Environmental and genetic risk factors other than mutations in the prion protein gene however, need to be investigated.

Never as with the present pandemics, numbers and the attendant activities of measuring and modelling have taken centre-stage. Yet these numbers, often delivered by academicians and media alike with extraordinary precision, rely on a rich repertoire of assumptions, including forms of bias, that can significantly skew both the numbers per se and the trust we repose in them. We discuss the issue in relation to a particular case relative to the numbers on excess mortality during the first wave of the Covid-19 pandemic in Italy. We conclude with some considerations about the use of science at the science policy interface in situations where facts are uncertain, stakes high, values in dispute and decision urgent.

This is the first paper on the project ‘Aria di Ricerca in Valle del Serchio’ launched in January2019 and based on a participatory approach. This investigation is part of a European project about epidemiology and citizen science named‘CitieS-Health’ and it is motivated by the resident population’s concern about the possiblehealth effects of environmental pollution. In fact, the Serchio Valley is an area located in TuscanyRegion (Central Italy) characterized both by historical, cultural, and naturalistic beautiesand by potentially polluting large industrial plants. The project is based on a strong public engagement in each phase of the epidemiological investigation. In this article, we report the phase of the investigation which follows the definition of the researchobjectives, in which citizens and local institutions consider all the possible results ofthe analysis and their implications in terms of public health action plans. This public phase is relevant in order to collectively recognize the limitations of the epidemiologicalstudy due to uncertainties and assure transparency to the decision-making processes.

Randomization procedure in randomized controlled trials (RCTs) permits an unbiased estimation of causal effects. However, in clinical practice, differential compliance between arms may cause a strong violation of randomization balance and biased treatment effect among those who comply. We evaluated the effect of the consolidation phase on disease-free survival of patients with multiple myeloma in an RCT designed for another purpose, adjusting for potential selection bias due to different compliance to previous treatment phases. We computed two propensity scores (PS) to model two different selection processes: the first to undergo autologous stem cell transplantation, the second to begin consolidation therapy. Combined stabilized inverse probability treatment weights were then introduced in the Cox model to estimate the causal effect of consolidation therapy miming an ad hoc RCT protocol. We found that the effect of consolidation therapy was restricted to the first 18 months of the phase (HR: 0.40, robust 95 % CI: 0.17-0.96), after which it disappeared. PS-based methods could be a complementary approach within an RCT context to evaluate the effect of the last phase of a complex therapeutic strategy, adjusting for potential selection bias caused by different compliance to the previous phases of the therapeutic scheme, in order to simulate an ad hoc randomization procedure. ClinicalTrials.gov: NCT01134484 May 28, 2010 (retrospectively registered) EudraCT: 2005-003723-39 December 17, 2008 (retrospectively registered)

Background Respond to alarms for possible cancer cluster is a public health problem, but the management of these alarms is difficult and sometime conflictual. Methods We reviewed the guidelines on the management of disease clusters developed in various countries and the approaches used in previous disease cluster episodes in Tuscany. Statistical approaches for cluster detection were also reviewed. We performed a clustering analysis (spatio or spatio-temporal when appropriate) on childhood leukemia using data from Tuscany Cancer Registry (RTT). We used spatial hierarchical Bayesian model on aggregate data at municipality level. We performed test for general clustering and cluster detection on individual data. Results More than 100 tests for clustering analysis has been identified in the literature. Bayesian analysis on aggregate data did not show areas at higher risk with the exception of the city of Florence for childhood cancer among males. We did not found clusters for leukemia. In previous studies on disease clusters in Tuscany, most investigations have been started from community concerns and in the majority of situations a multidisciplinary approach was used. In some case an increase of incidence rate was observed, but rarely specific cluster cancer tests were used. Conclusions Hierarchical Bayesian models to aggregate data provided useful to identify long range geographical patterns while clustering analysis on individual data is a useful tool for small scale patterns. Both represent important tools for epidemiological surveillance studies particularly on childhood cancer. The best test for all situations doesn't exist, but the choice is determined by the type of question being asked from the data, by different situations and by different approaches. The Tuscany cancer clusters survey and the review of the guidelines on the management of clusters developed in different countries, give us the opportunity to formulate some suggestions for the health agencies. Key messages Respond to alarms for cluster of cancer and suggest recommendations for epidemiological and statistical standardized approaches is a public health issue. Tuscany cancer clusters survey and the review of the guidelines on the management of clusters developed in different countries, give the opportunity to formulate some suggestions for health agencies.

Following publication of the original article [1], the typesetters have missed to add the below listed study group author names in XML in author group section. The study group authors have been added to the author group and are presented correctly in this correction article. A minor change has been made to the electronic supplementary files by removing the yellow highlights and included in this correction. Study Group Authors: Andrea Festa1 Gaia Claudia Viviana Viola1 Ilaria Zerbini1 Cristina Fatigoni2 Sara Levorato2 Silvano Monarca2 Tania Salvatori2 Samuele Vannini2 Gabriele Donzelli3 Francesco Bagordo4 Mattia De Giorgi4 Marcello Guido4 Alessandra Panico4 Francesca Serio4 Maria Rosaria Tumolo4 Silvia Bonetta5 Marta Gea5 Giorgio Gilli5 Cristina Pignata5 Tiziana Schilirò5 Valeria Romanazzi5 Camilla Furia6 Beatrice Bruni7 Beatrice Casini7 Study Group Author Affiliations: 1Department of Medical and Surgical Specialties, Radiological Science and Public Health, University of Brescia, 11 Viale Europa, 25123 Brescia, Italy 2Department of Pharmaceutical Sciences, Unit of Public Health, University of Perugia, Via del Giochetto, 06122 Perugia, Italy 3Department of Biology, University of Pisa, 35/39 Via S. Zeno, 56127 Pisa, Italy 4Department of Biological and Environmental Science and Technology, University of Salento, 165 Via Monteroni, 73100 Lecce, Italy 5Department of Public Health and Pediatrics, University of Torino, 94 Piazza Polonia, 10126 Turin, Italy 6Brescia Municipality, 1 Piazza Repubblica, 25100 Brescia, Italy 7Department of Translational Research, N.T.M.S., University of Pisa, 35/39 Via S. Zeno, 56127 Pisa, Italy Author details 1 Department of Medical and Surgical Specialties, Radiological Science and Public Health, University of Brescia, 11 Viale Europa, 25123 Brescia, Italy. 2 Department of Pharmaceutical Sciences, Unit of Public Health, University of Perugia, Via del Giochetto, 06122 Perugia, Italy. 3 Department of Biology, University of Pisa, 35/39 Via S. Zeno, 56127 Pisa, Italy. 4 Department of Biological and Environmental Science and Technology, University of Salento, 165 Via Monteroni, 73100 Lecce, Italy. 5 Department of Public Health and Pediatrics, University of Torino, 94 Piazza Polonia, 10126 Turin, Italy. 6 Multi-sector and Technological Service Centre - CSMT Gestione S.c.a.r.l, 45 Via Branze, 25123 Brescia, Italy. 7 Brescia Municipality, 1 Piazza Repubblica, 25100 Brescia, Italy. 8 Department of Statistic, Computer Science, Applications, University of Florence, 59 Viale Morgagni, 50134 Florence, Italy.

Purpose The Italian Network of Longitudinal Metropolitan Studies (IN-LiMeS) is a system of integrated data on health outcomes, demographic and socioeconomic information, and represents a powerful tool to study health inequalities. Participants IN-LiMeS is a multicentre and multipurpose pool of metropolitan population cohorts enrolled in nine Italian cities: Turin, Venice, Reggio Emilia, Modena, Bologna, Florence, Leghorn, Prato and Rome. Data come from record linkage of municipal population registries, the 2001 population census, mortality registers and hospital discharge archives. Depending on the source of enrolment, cohorts can be closed or open. The census-based closed cohort design includes subjects resident in any of the nine cities at the 2001 census day; 4 466 655 individuals were enrolled in 2001 in the nine closed cohorts. The open cohort design includes subjects resident in 2001 or subsequently registered by birth or immigration until the latest available follow-up (currently 31 December 2013). The open cohort design is available for Turin, Venice, Reggio Emilia, Modena, Bologna, Prato and Rome. Detailed socioeconomic data are available for subjects enrolled in the census-based cohorts; information on demographic characteristics, education and citizenship is available from population registries. Findings to date The first IN-LiMeS application was the study of differentials in mortality between immigrants and Italians. Either using a closed cohort design (nine cities) or an open one (Turin and Reggio Emilia), individuals from high migration pressure countries generally showed a lower mortality risk. However, a certain heterogeneity between the nine cities was noted, especially among men, and an excess mortality risk was reported for some macroareas of origin and specific causes of death. Future plans We are currently working on the linkage of the 2011 population census data, the expansion of geographical coverage and the implementation of the open design in all the participating cohorts.

Background When communicating risks to the general population, the format of the epidemiological results may affect individual reactions. In environmental epidemiology, no study has compared the use of different statistical formats in communicating results to the population. The aim of this paper is to investigate whether the degree of concern expressed by residents of a high environmental risk site, regarding epidemiological results on cancer mortality in the area where they live, is influenced by the statistical indicator used in communication. Methods A sample of residents in the high environmental risk area of Livorno (Italy) was randomized to respond to different questionnaires, in which the same epidemiological results were expressed by two alternative risk indexes: percent excess risk and time needed to harm, defined as the number of days that one has to wait for, on average, to observe 1 death in excess in respect to the baseline. Participants were asked to express their concern on a quantitative scale or to rank different diseases according to their impressions. The statistical analysis was performed using an Inverse Probability of Treatment Weighting approach based on propensity score, in order to account for sample stratification and adjust for unbalance between groups occurring despite randomization. Results The probability of high concern levels was larger under time needed to harm than under percent excess, with a difference between proportions of 6.7% (95% Confidence Interval, 0.6,12.8%). Mortality from sexual glands cancer was ranked as more worrisome and mortality from thyroid gland cancer as less worrisome under time needed to harm than under percent excess. No rank change was found for lung cancer. Larger differences between the two indicators arose in subjects with higher education or better numerical skills. Conclusions Communicating epidemiological results to the population is not a neutral task. The degree of concern and judgments when comparing results on different diseases may depend on the risk indicators used. Translating scientific results into lay language should not exempt from careful evaluation of the impact of this translation on lay people. Electronic supplementary material The online version of this article (10.1186/s12889-019-7003-y) contains supplementary material, which is available to authorized users.

Infections with parasitic helminths (nematodes and trematodes) represent a significant economic and welfare burden to the global ruminant livestock industry. The increasing prevalence of anthelmintic resistance means that current control programmes are costly and unsustainable in the long term. Recent changes in the epidemiology, seasonality and geographic distribution of helminth infections have been attributed to climate change. However, other changes in environment (e.g., land use) and in livestock farming, such as intensification and altered management practices, will also have an impact on helminth infections. Sustainable control of helminth infections in a changing world requires detailed knowledge of these interactions. In particular, there is a need to devise new, sustainable strategies for the effective control of ruminant helminthoses in the face of global change. In this paper, we consider the impact of helminth infections in grazing ruminants, taking a European perspective, and identify scientific and applied priorities to mitigate these impacts. These include the development and deployment of efficient, high- throughput diagnostic tests to support targeted intervention, modelling of geographic and seasonal trends in infection, more thorough economic data and analysis of the impact of helminth infections and greater translation and involvement of end-users in devising and disseminating best practices. Complex changes in helminth epidemiology will require innovative solutions. By developing and using new technologies and models, the use of anthelmintics can be optimised to limit the development and spread of drug resistance and to reduce the overall economic impact of helminth infections. This will be essential to the continued productivity and profitability of livestock farming in Europe and its contribution to regional and global food security. View Full-Text

Despite a rapid increase in immigration from low-income countries, studies on immigrants’ mortality in Italy are scarce. We aimed to describe differences in all and cause-specific mortality among immigrants and Italians residing in Turin and Reggio Emilia (Northern Italy), two cities participating in the Italian Network of Longitudinal Metropolitan Studies (IN-LiMeS). We used individual data from the municipal population registers linked to the cause of death registers. All people aged 1–64 years residing between 2001 and 2010 were enrolled (open cohort) and followed up until 2013. The mortality of citizens from high migratory pressure countries (as a whole, and for each macro-area group) was compared with that of Italians; differences were estimated by Poisson regression adjusted by age and calendar year mortality rate ratios (MRRs), and by age-standardized mortality ratios for the analysis of cause-specific mortality. Compared with Italians, immigrants had lower overall mortality (MRR for men: 0.82, 95 % CI: 0.75–0.90; for women: 0.71, 95 % CI: 0.63–0.81). Sub-Saharan Africans experienced a significant higher mortality than Italians (MRR for men 1.29, 95 % CI: 1.03–1.61; for women: 1.70, 95 % CI: 1.22–2.36). Higher mortality for immigrants compared to Italians was observed for infectious diseases, congenital anomalies, some site-specific tumours and homicide mortality. Our study showed heterogeneity in mortality across the macro-areas of origin, and in particular Sub-Saharan Africans seemed to be a vulnerable population. The extension to other cohorts of IN-LiMeS will allow the health status of immigrants and vulnerable groups to be studied and monitored in more depth.

Observational prospective population-based incidence study. The main objective of this study was to assess the incidence of traumatic spinal cord injuries (TSCIs) and incidence rates, in order to provide estimates by age, gender, characteristics and cause. This study was conducted at acute-care spinal cord injury (SCI) hospitals and SCI centers from 11 Italian regions, between 1 October 2013 and 30 September 2014. Data of all consecutive patients with acute TSCI who met the inclusion criteria were obtained through case reporting by clinicians. The data were collected into a web database. Incidence rates and incidence rate ratios were calculated and stratified by age, gender, cause, level and completeness. From 50% of the entire population of Italy, 445 new cases of TSCI were included. The crude incidence rate of TSCI was 14.7 cases per million per year (95% CI: 13.4–16.4); the overall male to female ratio was 4:1 and the mean age was 54. Complete information was available in 85% of the sample and revealed tetraplegia in 58% and incomplete lesion in 67% of cases. The leading cause of TSCI was falls (40.9%) followed by road traffic accidents (33.5%). The leading cause was falls for patients over 55 and road traffic accidents for patients under 55. The changing trend of TSCI epidemiology concerns the increase in the average age of TSCI people and the increase of both cervical and incomplete lesions. The etiology shows the primacy of falls over road traffic accidents and suggests the need for a change in prevention policies.

Participatory approaches to environmental research and decision-making require that all social stakeholders are involved from the onset of the debate. In such a setting, communication among different expertise is crucial, but language and technicalities may represent a barrier. In the clinical setting, decisions regarding treatment preferences may be influenced by the summary statistics used, but, according to the literature, no study has compared different statistical indicators for risk communication in environmental epidemiology. In this paper, we report on the qualitative results of the cognitive interviews conducted for assessing two questionnaires devoted to investigating risk perception when selected epidemiological results are communicated, by using different statistical indicators of health impact and uncertainty. The initial questionnaires were tested on 15 people residing in the high environmental risk area of Livorno (Italy). Cognitive interviewing led to substantial revision of the initial drafts. Moreover, it highlighted the difficulty of communicating statistical uncertainty and the need to account for the complex interaction between mathematical skills, affective factors and individual a priori knowledge on environmental risk perception.

Model-based geostatistics and Bayesian approaches are useful in the context of veterinary epidemiology when point data have been collected by appropriate study design. We take advantage of an example of Epidemiological Surveillance on urban settings where a two-stage sampling design with first stage transects is applied to study the risk of dog parasite infection in the city of Naples, 2004-2005. We specified Bayesian Gaussian spatial exponential models and Bayesian kriging were performed to predict the continuous risk surface of parasite infection on the study region. We compared the results with those obtained by the application of hierarchical Bayesian models on areal data (proportion of positive specimens by transect). The models results were consistent with each other and the Bayesian geostatistical approach proved to be more accurate in identifying areas at risk of zoonotic parasitic diseases. In general, larger risk areas were identified at the city border where wild dogs mixed with domestic dogs and human or urban barriers were less present.

BACKGROUND: Although studies have documented increased mortality during heat waves, little information is available on the subgroups most susceptible to these effects. We evaluated the effects of summertime high temperature on daily mortality among population subgroups defined by demographic characteristics, socioeconomic status, and episodes of hospitalization for various conditions during the preceding 2 years. METHODS: We studied a total of 205,019 residents of 4 Italian cities (Bologna, Milan, Rome, and Turin) age 35 or older who died during 1997-2003. The case-crossover design was applied to evaluate the association between mean apparent temperature (same and previous day) and all-cause mortality. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) of dying at 30 degrees C (apparent temperature) relative to 20 degrees C were estimated accounting for time, population changes, and air pollution. RESULTS: We found an overall OR of 1.34 (CI = 1.27-1.42) at 30 degrees C relative to 20 degrees C. The odds ratio increased with age and was higher among women (OR = 1.45; 1.37-1.52) and among widows and widowers (1.50; 1.33-1.69). Low area-based income modestly increased the effect. Among the preexisting medical conditions investigated, effect modification was detected for previous psychiatric disorders (1.69; 1.39-2.07), depression (1.72; 1.24-2.39), heart conduction disorders (1.77; 1.38-2.27), and circulatory disorders of the brain (1.47; 1.34-1.62). Temperature-related mortality was higher among people residing in nursing homes, and a large effect was also detected for hospitalized subjects. CONCLUSIONS: Subsets of the population that are particularly vulnerable to high summer temperatures include the elderly, women, widows and widowers, those with selected medical conditions, and those staying in nursing homes and healthcare facilities. Language: en