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The statistical analysis of annual growth of Posidonia oceanica is traditionally carried out through Gaussian linear models applied to untransformed, or log-transformed, data. In this paper, we claim that there are good reasons for re-considering this established practice, since real data on annual growth often violate the assumptions of Gaussian linear models, and show that the class of Generalized Linear Models (GLMs) represents a useful alternative for handling such violations. By analyzing Sicily PosiData-1, a real dataset on P. oceanica growth data gathered in the period 2000-2002 along the coasts of Sicily, we find that in the majority of cases Normality is rejected and the effect of age on growth is nonlinear. A GLM with Gamma distribution and identity or log link appears to be a satisfactory choice in most cases. Furthermore, when back-dating techniques are employed, each plant provides a longitudinal set of dependent data, and a proper statistical analysis should take such dependence into account. We show that the class of Generalized Linear Mixed Models (GLMM), an extension of GLM's, provides an effective way to analyze longitudinal P. oceanica growth data. Again, by using examples taken from Sicily PosiData-1, we show that misleading results can be obtained if dependence is ignored and that other techniques, like sub-sampling, are not a good option for overcoming the so-called 'pseudo-replications' problem. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Presents a log-linear modelling approach for analyzing matched data from retrospective case-control studies. Basic concepts concerning the representation of matched pairs data in a square concordance table; Demonstration of the relationship between hypotheses on epidemiological parameters and hypotheses on expected frequencies in the case-by-control concordance table.

Background: The independent and interrelated long-term effects of the exposome such as air pollution, greenness, and ambient temperature on lung function are not well understood, yet relevant in the light of climate change., Methods: Pre-bronchodilation FEV1 from five mature birth cohorts (N = 4724) and three adult cohorts (N = 6052) from five European countries were used to assess cross-sectional associations with air pollution, greenness, and ambient temperature, assigned to their residential address. All two-way interactions and square terms were a priori included in building the final elastic net regression model. Elastic net regression results were put into the context of different environmental scenarios such as improvement of air quality, improvement of greenness, climate change, or their combinations., Results: Elastic net regression of FEV1 z-scores identified non-zero coefficients for many interaction terms, indicating the importance of joint effects of exposure to air pollution, greenness, and temperature. The non-zero coefficients were bigger and more stable in adults than in children. Upon exploring lung function benefits for different environmental scenarios, an improvement of FEV1 was expected in the scenario of improving air quality or greenness. In contrast, negative changes in FEV1 z-scores were expected in the scenario of climate change, characterized by daily temperature increase in summer and decrease in winter. The beneficial FEV1 effects of improving air pollution or greenness were attenuated in the presence of climate change., Conclusion: Complex exposome profiles of long-term exposure to air pollution, greenness, and temperature showed associations with FEV1 in European adults, and to less extent in children and adolescents. Climate change seems to have a negative impact on lung function and modifies the association of air pollution and greenspace with lung function., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Maintaining physical activity throughout life is crucial for overall health and wellbeing. Yet the age-related decline in average physical activity, a natural phenomenon also observed in animals, poses a challenge. This study aimed to investigate whether participation in organised sports supported by the Swiss Youth+Sports (Y+S) programme is associated with sustaining or enhancing physical activity among children and adolescents during 5 years of follow-up., Methods: The longitudinal study was nested in the population-based SOPHYA (Swiss children's Objectively measured PHYsical Activity) cohort. Participants aged 6-16 years at SOPHYA1 (2014) with complete accelerometer data from baseline and follow-up assessment (SOPHYA2, 2019) were included. The primary exposure was participation in organised sport during the follow-up period, calculated by linkage with the Y+S database as the number of days with at least one activity in Y+S-offered programmes for ages 5 to 20 years. The primary outcome was the categorisation of participants into physical activity "improvers" or "worseners". Improvers in the respective physical activity categories - total activity counts per minute (CPM), minutes in moderate-to-vigorous activity (MVPA), minutes in light activity (LPA) and minutes in sedentary behaviour (SB) - increased or maintained their active physical activity during the 5 follow-up years. Information on confounders and effect modifiers (sex, age, body mass index (BMI), language region, household income, education) was obtained by self-report at baseline. Logistic regressions examined the relationship between organised sport participation and the probability of being a physical activity improver in each physical activity intensity category separately. Covariates for the final models were selected through a stepwise procedure based on the Bayesian information criterion from a maximal model containing all covariates as well as all two-way interactions with organised sport and between them. All models were a priori adjusted for technical variables (season of measurement; wear time; duration of follow-up)., Results: The analysis included 432 participants. There was a strong CPM, MVPA and LPA decline from 2014 to 2019, but an increase in SB. Nevertheless, the prevalence of improvers was 22.5% for CPM, 9.5% for MVPA, 26.9% for LPA and 9.7% for SB. Engagement in organised sport between 2014 and 2019 was positively associated with CPM, MVPA and SB, but not with LPA improver status. For 30 additional days of participation in organised sport over the five years of the study, the odds of being an improver vs being a worsener increased by 4.0% for CPM (95% CI: 0.13-7.69), 6.2% for MVPA (95% CI: 0.82-11.54) and 6.0% for SB (95% CI:-1.49-13.97)., Conclusion: The results provide supporting evidence that organised sport in the context of the Swiss Y+S programme may empower the young to maintain an active lifestyle and even offset the age-related decline in physical activity.

Previous mental health trajectory studies were mostly limited to the months before access to vaccination. They are not informing on whether public mental health has adapted to the pandemic. The aim of this analysis was to 1) investigate trajectories of monthly reported depressive symptoms from July 2020 to December 2021 in Switzerland, 2) compare average growth trajectories across regions with different stringency phases, and 3) explore the relative impact of self-reported worries related to health, economic and social domains as well as socio-economic indicators on growth trajectories. As part of the population-based Corona Immunitas program of regional, but harmonized, adult cohorts studying the pandemic course and impact, participants repeatedly reported online to the DASS-21 instrument on depressive symptomatology. Trajectories of depressive symptoms were estimated using a latent growth model, specified as a generalised linear mixed model. The time effect was modelled parametrically through a polynomial allowing to estimate trajectories for participants' missing time points. In all regions level and shape of the trajectories mirrored those of the KOF Stringency-Plus Index, which quantifies regional Covid-19 policy stringency. The higher level of average depression in trajectories of those expressing specific worries was most noticeable for the social domain. Younger age, female gender, and low household income went along with higher mean depression score trajectories throughout follow-up. Interventions to promote long-term resilience are an important part of pandemic preparedness, given the observed lack of an adaptation in mental health response to the pandemic even after the availability of vaccines in this high-income context., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022., Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1-4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models' predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models' forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models' past predictive performance., Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models' forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models' forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models' forecasts of deaths (N=763 predictions from 20 models). Across a 1-4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models., Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks., Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z)., Competing Interests: KS, HG, RG, HJ, RN, BP, FS, JD, DW, SA, AU, GG, ER, NR, DS, YW, NW, LW, JT, GO, TS, DT, LP, EK, JM, MB, NL, JM, JS, JW, JF, BL, IR, JB, RW, IV, VT, MS, MZ, CP, BR, NB, SM, LC, GF, IM, DO, PA, AM, VE, AK, DK, LP, BD, ML, SS, JD, SM, VP, GR, BB, GA, NP, GZ, WB, SH, TH, DS, MG, JA, DM, SA, EA, DL, CP, JB, AR, TZ, AK, JZ, FP, FD, MC, GL, PG, BT, FB, GJ, MM, AF, AS, PM, AA, BH, BL, MM, PP, SV, RB, FD, AG, KG, MG, BK, AM, KN, JN, MR, FR, MS, ES, JZ, JK, BP, KH, YK, MS, BP, MB, MF, RI, TK, TO, JB, SF No competing interests declared, PB, HG, SS, BS Affiliated with IEM, Inc. The author has no financial interests to declare

The amount and poor quality of available data and the need of appropriate modeling of the main epidemic indicators require specific skills. In this context, the statistician plays a key role in the process that leads to policy decisions, starting with monitoring changes and evaluating risks. The "what" and the "why" of these changes represent fundamental research questions to provide timely and effective tools to manage the evolution of the epidemic. Answers to such questions need appropriate statistical models and visualization tools. Here, we give an overview of the role played by Statgroup-19, an independent Italian research group born in March 2020. The group includes seven statisticians from different Italian universities, each with different backgrounds but with a shared interest in data analysis, statistical modeling, and biostatistics. Since the beginning of the COVID-19 pandemic the group has interacted with authorities and journalists to support policy decisions and inform the general public about the evolution of the epidemic. This collaboration led to several scientific papers and an accrued visibility across various media, all made possible by the continuous interaction across the group members that shared their unique expertise., (© 2022 John Wiley & Sons Ltd.)

Background: Subjective well-being is an important target in the COVID-19 pandemic. Residential greenness may help cope with stress and hence influence subjective well-being during this mentally and physically challenging time., Methods: We analysed the association between residential greenness and life satisfaction in 9,444 adults in the COVCO-Basel cohort. We assessed if the association is modified by age, sex, household income, financial worries, canton of residence, or month of study entry. In addition, we assessed if the association is attributed to specific types of greenspace or accessibility to greenspace., Results: The association between residential greenness and life satisfaction varied by age groups, household income, and financial worries. Residential greenness was positively associated with life satisfaction in those with high household income and the least financially worried, and negatively associated with life satisfaction in the youngest age group (18-29 years) and the most financially worried. Living closer to a forest, but not to a park or an agricultural area, was associated with lower life satisfaction in the youngest age group., Conclusions: Residential greenness effects on life satisfaction vary according to sociodemographic characteristics. Living in a greener area does not benefit all dwellers in Basel and its region equally, with the most apparent benefit for those with high household income and without financial concerns.

We introduce an extended generalised logistic growth model for discrete outcomes, in which spatial and temporal dependence are dealt with the specification of a network structure within an Auto-Regressive approach. A major challenge concerns the specification of the network structure, crucial to consistently estimate the canonical parameters of the generalised logistic curve, e.g. peak time and height. We compared a network based on geographic proximity and one built on historical data of transport exchanges between regions. Parameters are estimated under the Bayesian framework, using Stan probabilistic programming language. The proposed approach is motivated by the analysis of both the first and the second wave of COVID-19 in Italy, i.e. from February 2020 to July 2020 and from July 2020 to December 2020, respectively. We analyse data at the regional level and, interestingly enough, prove that substantial spatial and temporal dependence occurred in both waves, although strong restrictive measures were implemented during the first wave. Accurate predictions are obtained, improving those of the model where independence across regions is assumed., (© 2021 Elsevier B.V. All rights reserved.)

Competing Interests: Conflict of interest We declare that we have no conflict of interest.

We compare the expected all-cause mortality with the observed one for different age classes during the pandemic in Lombardy, which was the epicenter of the epidemic in Italy. The first case in Italy was found in Lombardy in early 2020, and the first wave was mainly centered in Lombardy. The other three waves, in Autumn 2020, March 2021 and Summer 2021 are also characterized by a high number of cases in absolute terms. A generalized linear mixed model is introduced to model weekly mortality from 2011 to 2019, taking into account seasonal patterns and year-specific trends. Based on the 2019 year-specific conditional best linear unbiased predictions, a significant excess of mortality is estimated in 2020, leading to approximately 35000 more deaths than expected, mainly arising during the first wave. In 2021, instead, the excess mortality is not significantly different from zero, for the 85+ and 15-64 age classes, and significant reductions with respect to the 2020 estimated excess mortality are estimated for other age classes., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Background: Residential greenness has been associated with health benefits, but its biological mechanism is largely unknown. Investigation of greenness-related DNA methylation profiles can contribute to mechanistic understanding of the health benefits of residential greenness., Objective: To identify DNA methylation profiles associated with greenness in the immediate surroundings of the residence., Methods: We analyzed genome-wide DNA methylation in 1938 blood samples (982 participants) from the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults (SAPALDIA). We estimated residential greenness based on normalized difference vegetation index at 30 × 30 m cell (green30) and 500 m buffer (green500) around the residential address. We conducted epigenome-wide association study (EWAS) to identify differentially methylated CpGs and regions, and enrichment tests by comparing to the CpGs that previous EWAS identified as associated with allergy, physical activity, and allostatic load-relevant biomarkers., Results: We identified no genome-wide significant CpGs, but 163 and 56 differentially methylated regions for green30 and green500, respectively. Green30-related DNA methylation profiles showed enrichments in allergy, physical activity, and allostatic load, while green500-related methylation was enriched in allergy and allostatic load., Conclusions: Residential greenness may have health impacts through allergic sensitization, stress coping, or behavioral changes. Exposure to more proximal greenness may be more health-relevant., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real-time monitoring and short-term forecasting of the main epidemiological indicators within the first outbreak of COVID-19 in Italy. Accurate short-term predictions, including the potential effect of exogenous or external variables are provided. This ensures to accurately predict important characteristics of the epidemic (e.g., peak time and height), allowing for a better allocation of health resources over time. Parameter estimation is carried out in a maximum likelihood framework. All computational details required to reproduce the approach and replicate the results are provided., (© 2021 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

The availability of intensive care beds during the COVID-19 epidemic is crucial to guarantee the best possible treatment to severely affected patients. In this work we show a simple strategy for short-term prediction of COVID-19 intensive care unit (ICU) beds, that has proved very effective during the Italian outbreak in February to May 2020. Our approach is based on an optimal ensemble of two simple methods: a generalized linear mixed regression model, which pools information over different areas, and an area-specific nonstationary integer autoregressive methodology. Optimal weights are estimated using a leave-last-out rationale. The approach has been set up and validated during the first epidemic wave in Italy. A report of its performance for predicting ICU occupancy at regional level is included., (© 2020 Wiley-VCH GmbH.)

A high body mass (BMI) index has repeatedly been associated with non-atopic asthma, but the biological mechanism linking obesity to asthma is still poorly understood. We aimed to test the hypothesis that inflammation and/or innate immunity plays a role in the obesity-asthma link. DNA methylome was measured in blood samples of 61 non-atopic participants with asthma and 146 non-atopic participants without asthma (non-smokers for at least 10 years) taking part in the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults (SAPALDIA) study. Modification by DNA methylation of the association of BMI or BMI change over 10 years with adult-onset asthma was examined at each CpG site and differentially methylated region. Pathway enrichment tests were conducted for genes in a priori curated inflammatory pathways and the NLRP3-IL1B-IL17 axis. The latter was chosen on the basis of previous work in mice. Inflammatory pathways including glucocorticoid/PPAR signaling ( p = 0.0023), MAPK signaling ( p = 0.013), NF-κB signaling ( p = 0.031), and PI3K/AKT signaling ( p = 0.031) were enriched for the effect modification of BMI, while NLRP3-IL1B-IL17 axis was enriched for the effect modification of BMI change over 10 years ( p = 0.046). DNA methylation measured in peripheral blood is consistent with inflammation as a link between BMI and adult-onset asthma and with the NLRP3-IL1B-IL17 axis as a link between BMI change over 10 years and adult-onset asthma in non-atopic participants.

Background: Epidemiologic evidence indicates common risk factors, including air pollution exposure, for respiratory and cardiovascular diseases, suggesting the involvement of common altered molecular pathways., Objectives: The goal was to find intermediate metabolites or metabolic pathways that could be associated with both air pollutants and health outcomes ("meeting-in-the-middle"), thus shedding light on mechanisms and reinforcing causality., Methods: We applied a statistical approach named 'meet-in-the-middle' to untargeted metabolomics in two independent case-control studies nested in cohorts on adult-onset asthma (AOA) and cardio-cerebrovascular diseases (CCVD). We compared the results to identify both common and disease-specific altered metabolic pathways., Results: A novel finding was a strong association of AOA with ultrafine particles (UFP; odds ratio 1.80 [1.26, 2.55] per increase by 5000 particles/cm 3 ). Further, we have identified several metabolic pathways that potentially mediate the effect of air pollution on health outcomes. Among those, perturbation of Linoleate metabolism pathway was associated with air pollution exposure, AOA and CCVD., Conclusions: Our results suggest common pathway perturbations may occur as a consequence of chronic exposure to air pollution leading to increased risk for both AOA and CCVD., (Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Although evidence for the heterogeneity of asthma accumulated, consensus for definitions of asthma phenotypes is still lacking. Obesity may have heterogeneous effects on various asthma phenotypes. We aimed to distinguish asthma phenotypes by latent class analysis and to investigate their associations with different obesity parameters in adults using a population-based Swiss cohort (SAPALDIA). We applied latent class analysis to 959 self-reported asthmatics using information on disease activity, atopy, and age of onset. Associations with obesity were examined by multinomial logistic regression, after adjustments for age, sex, smoking status, educational level, and study centre. Body mass index, percent body fat, waist hip ratio, waist height ratio, and waist circumference were used as obesity measure. Four asthma classes were identified, including persistent multiple symptom-presenting asthma (n = 122), symptom-presenting asthma (n = 290), symptom-free atopic asthma (n = 294), and symptom-free non-atopic asthma (n = 253). Obesity was positively associated with symptom-presenting asthma classes but not with symptom-free ones. Percent body fat showed the strongest association with the persistent multiple symptom-presenting asthma. We observed heterogeneity of associations with obesity across asthma classes, indicating different asthma aetiologies., (Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.)

This paper describes an analysis of the quality of hospital discharge data stored in an Italian regional registry. Although limited in scope, this analysis sheds some light on the level of quality that users can expect in official health data and describes some factors which can influence such quality. Attention focuses on the accuracy with which official administrators code Diseases of Circulatory System (DCS) in a specified health district, using the discharge diagnosis formulated on the case-notes. Investigation of the data, a random sample of 993 medical records, shows a disturbing level of inaccuracy in the assignment of the primary diagnosis to the (DCS) group and sub-groups, especially when compared with other international experiences. Two factors, namely the type of hospital where official administrators routinely collect the data and the age of the in-patients, are found to significantly influence such inaccuracy. Explanations of these findings are suggested and proposals for the improved accuracy of hospital discharge data are presented.