Bayesian hierarchical model for the prediction of football results

The problem of modelling football data has become increasingly popular in the last few years and many different models have been proposed with the aim of estimating the characteristics that bring a team to lose or win a game, or to predict the score of a particular match. We propose a Bayesian hierarchical model to fulfil both these aims and test its predictive strength based on data about the Italian Serie A 1991-1992 championship. To overcome the issue of overshrinkage produced by the Bayesian hierarchical model, we specify a more complex mixture model that results in a better fit to the observed data. We test its performance using an example of the Italian Serie A 2007-2008 championship

Parent and self-report health-related quality of life measures in young patients with Tourette syndrome

Tourette syndrome is a neurodevelopmental disorder characterized by tics and comorbid behavioral problems. This study compared child- and parent-reported quality of life and everyday functioning. We assessed 75 children with Tourette syndrome, of which 42 (56%) had comorbid conditions (obsessive-compulsive disorder = 25; attention-deficit hyperactivity disorder = 6; both comorbidities = 4). All patients completed psychometric instruments, including the Gilles de la Tourette Syndrome-Quality of Life Scale for Children and Adolescents (child report) and the Child Tourette's Syndrome Impairment Scale (parent report). Data were compared for patients with pure Tourette syndrome, Tourette syndrome + obsessive-compulsive disorder, Tourette syndrome + attention-deficit hyperactivity disorder, and Tourette syndrome + both comorbidities. There were no group differences in quality of life. However, there were differences for total, school, and home activities impairment scores. Children and parents may not share similar views about the impact of Tourette syndrome on functioning. The measurement of health-related quality of life in Tourette syndrome is more complex in children than adults

Detecting early signals of COVID-19 outbreaks in 2020 in small areas by monitoring healthcare utilisation databases: first lessons learned from the Italian Alert_CoV project

During the COVID-19 pandemic, large-scale diagnostic testing and contact tracing have proven insufficient to promptly monitor the spread of infections.AimTo develop and retrospectively evaluate a system identifying aberrations in the use of selected healthcare services to timely detect COVID-19 outbreaks in small areas. Methods: Data were retrieved from the healthcare utilisation (HCU) databases of the Lombardy Region, Italy. We identified eight services suggesting a respiratory infection (syndromic proxies). Count time series reporting the weekly occurrence of each proxy from 2015 to 2020 were generated considering small administrative areas (i.e. census units of Cremona and Mantua provinces). The ability to uncover aberrations during 2020 was tested for two algorithms: the improved Farrington algorithm and the generalised likelihood ratio-based procedure for negative binomial counts. To evaluate these algorithms' performance in detecting outbreaks earlier than the standard surveillance, confirmed outbreaks, defined according to the weekly number of confirmed COVID-19 cases, were used as reference. Performances were assessed separately for the first and second semester of the year. Proxies positively impacting performance were identified. Results: We estimated that 70% of outbreaks could be detected early using the proposed approach, with a corresponding false positive rate of ca 20%. Performance did not substantially differ either between algorithms or semesters. The best proxies included emergency calls for respiratory or infectious disease causes and emergency room visits. Conclusion: Implementing HCU-based monitoring systems in small areas deserves further investigations as it could facilitate the containment of COVID-19 and other unknown infectious diseases in the future

Drinking Water Salinity and Raised Blood Pressure: Evidence from a Cohort Study in Coastal Bangladesh.

BACKGROUND: Millions of coastal inhabitants in Southeast Asia have been experiencing increasing sodium concentrations in their drinking-water sources, likely partially due to climate change. High (dietary) sodium intake has convincingly been proven to increase risk of hypertension; it remains unknown, however, whether consumption of sodium in drinking water could have similar effects on health. OBJECTIVES: We present the results of a cohort study in which we assessed the effects of drinking-water sodium (DWS) on blood pressure (BP) in coastal populations in Bangladesh. METHODS: DWS, BP, and information on personal, lifestyle, and environmental factors were collected from 581 participants. We used generalized linear latent and mixed methods to model the effects of DWS on BP and assessed the associations between changes in DWS and BP when participants experienced changing sodium levels in water, switched from "conventional" ponds or tube wells to alternatives [managed aquifer recharge (MAR) and rainwater harvesting] that aimed to reduce sodium levels, or experienced a combination of these changes. RESULTS: DWS concentrations were highly associated with BP after adjustments for confounding factors. Furthermore, for each 100 mg/L reduction in sodium in drinking water, systolic/diastolic BP was lower on average by 0.95/0.57 mmHg, and odds of hypertension were lower by 14%. However, MAR did not consistently lower sodium levels. CONCLUSIONS: DWS is an important source of daily sodium intake in salinity-affected areas and is a risk factor for hypertension. Considering the likely increasing trend in coastal salinity, prompt action is required. Because MAR showed variable effects, alternative technologies for providing reliable, safe, low-sodium fresh water should be developed alongside improvements in MAR and evaluated in "real-life" salinity-affected settings. https://doi.org/10.1289/EHP659

Financial crisis and income-related inequalities in the universal provision of a public service: the case of healthcare in Spain

Background The objective of this paper is to analyse whether the recent recession has altered health care utilisation patterns of different income groups in Spain. Methods Based on information concerning individuals ‘income and health care use, along with health need indicators and demographic characteristics (provided by the Spanish National Health Surveys from 2006/07 and 2011/12), econometric models are estimated in two parts (mixed logistic regressions and truncated negative binominal regressions) for each of the public health services studied (family doctor appointments, appointments with specialists, hospitalisations, emergencies and prescription drug use). Results The results show that the principle of universal access to public health provision does not in fact prevent a financial crisis from affecting certain income groups more than others in their utilisation of public health services. Conclusions Specifically, in relative terms the recession has been more detrimental to low-income groups in the cases of specialist appointments and hospitalisations, whereas it has worked to their advantage in the cases of emergency services and family doctor appointments

The legacy of Corrado Gini in population studies

This volume contains 12 papers that range over many different research subjects, taking in many of the population questions that, directly or indirectly, absorbed Corrado Gini as demographer and social scientist over several decades. They vary from the analysis of the living conditions and behaviours of the growing foreign population (measurements and methods of analysis, socio-economic conditions and health, ethnic residential segregation, sex-ratio at birth), to studies on the homogamy of couples; from population theories (with reference to the cyclical theory of populations) to the modelling approach to estimating mortality in adult ages or estimating time transfers, by age and sex, related to informal child care and adult care; from historical studies that take up themes dear to Gini (such as the estimates of Italian military deaths in WWI), to the application of Gini’s classical measurements to studying significant phenomena today (transition to adulthood and leaving the parental home, health care, disabled persons and social integration). The subjects and measurements that appear here are not intended to exhaust the broad spectrum of Gini’s research work in the demographic and social field (nor could they), but they can make up a part of the intersection between his vast legacy and some interesting topics in current research, some of which were not even imaginable in the mid twentieth century. Looking at the many contributions that celebrated Gini in Treviso and thinking about his legacy, it seems possible to identify at least two typologies of approach, to be found in this issue of the journal, too. On the one hand, there are contributions that aim to retrieve and discuss themes, methodologies and measurements dealt with or used by Gini so as to evaluate their present relevance and importance in the current scholarly debate. On the other, there are contributions that deal with topics that are far from Gini’s work, as they study very recent phenomena, but actually, among other things, make use of methods and indicators devised by Gini that are now so much part of the common currency of methodology, so they don’t require explicit reference to their Author

Rapporto sulla popolazione. Le molte facce della presenza straniera in Italia

Al di là delle emergenze recenti, l’immigrazione straniera è un fenomeno le cui origini risalgono a circa quaranta anni fa: proprio i demografi italiani furono tra i primi a segnalarne l’importanza, analizzandone cause, caratteristiche e conseguenze. Questo Rapporto permette di seguire la pluridecennale evoluzione dell’immigrazione e della presenza straniera in Italia, con attenzione alle specificità dei diversi contesti territoriali. Una ricca e affidabile documentazione statistica consente di illustrare le origini e le caratteristiche degli stranieri, i loro comportamenti demografici, l’inserimento nel mercato del lavoro e le condizioni di integrazione. Tra le questioni affrontate si segnalano quelle, rilevantissime, dei profughi, della cittadinanza e delle seconde generazioni

Discovering collectively informative descriptors from high-throughput experiments

<p>Abstract</p> <p>Background</p> <p>Improvements in high-throughput technology and its increasing use have led to the generation of many highly complex datasets that often address similar biological questions. Combining information from these studies can increase the reliability and generalizability of results and also yield new insights that guide future research.</p> <p>Results</p> <p>This paper describes a novel algorithm called BLANKET for symmetric analysis of two experiments that assess informativeness of descriptors. The experiments are required to be related only in that their descriptor sets intersect substantially and their definitions of case and control are consistent. From resulting lists of n descriptors ranked by informativeness, BLANKET determines <b>shortlists </b>of descriptors from each experiment, generally of different lengths p and q. For any pair of shortlists, four numbers are evident: the number of descriptors appearing in both shortlists, in exactly one shortlist, or in neither shortlist. From the associated contingency table, BLANKET computes Right Fisher Exact Test (RFET) values used as scores over a plane of possible pairs of shortlist lengths <abbrgrp><abbr bid="B1">1</abbr><abbr bid="B2">2</abbr></abbrgrp>. BLANKET then chooses a pair or pairs with RFET score less than a threshold; the threshold depends upon n and shortlist length limits and represents a quality of intersection achieved by less than 5% of random lists.</p> <p>Conclusions</p> <p>Researchers seek within a universe of descriptors some minimal subset that collectively and efficiently predicts experimental outcomes. Ideally, any smaller subset should be insufficient for reliable prediction and any larger subset should have little additional accuracy. As a method, BLANKET is easy to conceptualize and presents only moderate computational complexity. Many existing databases could be mined using BLANKET to suggest optimal sets of predictive descriptors.</p

A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic

The potential utility of wastewater-based epidemiology as an early warning tool has been explored widely across the globe during the current COVID-19 pandemic. Methods to detect the presence of SARS-CoV-2 RNA in wastewater were developed early in the pandemic, and extensive work has been conducted to evaluate the relationship between viral concentration and COVID-19 case numbers at the catchment areas of sewage treatment works (STWs) over time. However, no attempt has been made to develop a model that predicts wastewater concentration at fine spatio-temporal resolutions covering an entire country, a necessary step towards using wastewater monitoring for the early detection of local outbreaks. We consider weekly averages of flow-normalised viral concentration, reported as the number of SARS-CoV-2N1 gene copies per litre (gc/L) of wastewater available at 303 STWs over the period between 1 June 2021 and 30 March 2022. We specify a spatially continuous statistical model that quantifies the relationship between weekly viral concentration and a collection of covariates covering socio-demographics, land cover and virus associated genomic characteristics at STW catchment areas while accounting for spatial and temporal correlation. We evaluate the model’s predictive performance at the catchment level through 10-fold cross-validation. We predict the weekly viral concentration at the population-weighted centroid of the 32,844 lower super output areas (LSOAs) in England, then aggregate these LSOA predictions to the Lower Tier Local Authority level (LTLA), a geography that is more relevant to public health policy-making. We also use the model outputs to quantify the probability of local changes of direction (increases or decreases) in viral concentration over short periods (e.g. two consecutive weeks). The proposed statistical framework can predict SARS-CoV-2 viral concentration in wastewater at high spatio-temporal resolution across England. Additionally, the probabilistic quantification of local changes can be used as an early warning tool for public health surveillance