Epidemiology, Biostatistics, and Public Health
EBPH aims to be a cross-discipline, international forum for scientific integration and evidence-based policymaking
Examining Youth Protections against Gambling in Ireland: A Tiger Team (Red Team) Examination of the National Lottery
Background: Youth gambling in Ireland is of growing concern. Objectives: Thus, this study evaluated youth gambling protections in the Irish National Lottery’s online age verification system. Methods: A Tiger Team (Red Team) methodology was used. This involved using falsified National Lottery accounts (n = 56). Results: Over half (55.4%) of attempts were successfully verified, with successful verification significantly varying by gender. Conclusions: These findings highlight vulnerabilities in the National Lottery’s online age-verification system and underscore the urgent need for more robust safeguards to prevent underage access.
Copyright (c) 2025 Mia Daly, Anne Campbell, Frank Houghton https://creativecommons.org/licenses/by-sa/4.0
An Examination of Public Concerns Relating to Combined Text and Graphic Alcohol Warning Labels: An all-Ireland Cross-Sectional Study
Alcohol remains a serious threat to the health and wellbeing of people across the island of Ireland. Ireland introduced legislation to introduce mandatory alcohol warning labels (AWLs) in 2026, although implementation is deferred until 2028. The cross-sectional survey research sought to explore support for AWLs across the island. The remainder of the survey then explored concerns people had with more graphic AWLs, as well as exploring what additional warnings or information people would like to see displayed in AWLs. Responses were gathered from 475 staff members and students at two universities, one located in Ireland and the other in Northern Ireland. A high level of support for the proposed Irish AWLs was noted (77.9%), with support for more graphic cigarette-style AWLs lower at 60.2%. The potential development of graphic AWLs evoked a considerable range of concerns thematically arranged as 1) Children; 2) Desensitisation; 3) Risks Over-played; 4) No Impact; 5) Unpleasant Viewing; 6) Addiction; 7) Trauma Trigger; 8) Stigmatisation; 9) Diverse Warnings. Although overall there is support for AWLs amongst the convenience sample, there remains confusion about the need and intent of AWLs. Further research is therefore recommended.
Copyright (c) 2025 Frank Houghton, Jennifer Moran Stritch, Gillian Shorter, Anne Campbell https://creativecommons.org/licenses/by-sa/4.0
Effectiveness and Safety of Control-IQ Technology in Preschool and School-Aged Children with Type 1 Diabetes: A Real-World Multicenter Study
Introduction Achieving and maintaining optimal glycemic control from the onset of type 1 diabetes (T1D) is crucial in pediatric care, especially in early childhood when the developing brain is highly vulnerable to both hypo- and hyperglycemia [1-3]. Hyperglycemia during early childhood increases the risk of long-term vascular complications, while severe hypoglycemia may impair neurocognitive development, causes family anxiety, and complicates social integration [4-5]. Although automated insulin delivery (AID) systems have demonstrated efficacy in controlled trials, real-world evidence in children under six years of age, particularly involving off-label use, remains limited. The Control-IQ (CIQ) algorithm, integrated into the Tandem t:slim X2 insulin pump, has shown benefits in adolescents and school-aged children [6-10]. However, few studies have evaluated its long-term use in children under six in routine clinical practice. Objective This study aimed to compare the real-world effectiveness and safety of the CIQ system in two pediatric age groups—children aged 0.5–5 years and children aged 6–10 years—over an 18-month follow-up period. We evaluated effectiveness in terms of glycemic control (% of time in glucose range 70–180 mg/dL [TIR], % of time in glucose range 70–140 mg/dL [TITR], and HbA1c) and safety in term of adverse events (diabetic ketoacidosis [DKA], hyperglycemia and severe hypoglycemia). Methods This prospective, multicenter observational study used retrospective data from 32 Italian pediatric diabetes centers. Eligible participants had T1D diagnosed ≥ 6 months, , were < 11 years old at CIQ initiation, and had continuous glucose monitoring (CGM) data available via Glooko® or Clarity® software at least every 6 months during the 18-month follow-up. Children with non-T1D or aged >10 years at CIQ start were excluded. Participants were stratified by age at CIQ initiation (0.5–5 and 6–10 years). At CIQ initiation (baseline) sex, presence of celiac disease or thyroiditis and parents’ age, nationality and education, were collected. HbA1c, BMI z-score, CGM-derived data (TIR, TITR, % of time spent in glucose ranges: <54 mg/dL, 54-<70 mg/dL, 180-<250 mg/dL, >250 mg/dL, Glucose Monitoring Indicators and coefficient of variation of glucose), Glycemia, Standard Deviation of Glycemia [SD] and DKA episodes were assessed at baseline, 6, 12, and 18 months. Descriptive statistics were used for baseline comparisons. Chi-square or t tests evaluated group differences. Trend over time points in TIR, TITR, and HbA1c were analysed using mixed-effects models for repeated measures, adjusted by age group, sex, time from diagnosis to CIQ initiation, DKA at onset and parents’ socio-economic characteristics (at least one non-Italian parent, parents’ education). A sequential difference contrast was used to model time; interaction between time and age groups was evaluated. Only children with complete data on the outcomes at all four time points were included in these models. Safety outcomes included the proportions of DKA and severe hypoglycaemias occurring during 18-month follow-up. Results Of the 334 children enrolled, 253 (106 aged 0.5–5; 147 aged 6–10) had complete data on the outcomes and were included in longitudinal analyses. At T1D diagnosis, a higher prevalence of thyroiditis in the older group was found, and no significant sociodemographic differences. At CIQ initiation, younger children had a significantly shorter time from diagnosis to CIQ initiation (1.36 vs 2.61 years, p<0.001), higher HbA1c (8.3%% vs 7.7%, p=0.020) and higher glycaemic variability (SD 63.3 mg/dL vs 58.3 mg/dL, p = 0.023) while TIR, TITR, and the other CGM-derived data were comparable. Longitudinal analysis (Figure 1) showed significant improvement in both groups 6 months after CIQ initiation: TIR increased by 6.62% (95% CI: 4.89–8.36) and TITR by 5.63% (95% CI: 3.61–7.66), corresponding to over 80 additional minutes/day spent in target ranges. These improvements were sustained at 12 and 18 months. HbA1c decreased by an average of 0.82% (95% CI: –1.01 to –0.62) in the first 6 months, remaining stable thereafter. No significant interaction between time and age groups was observed, indicating similar trends in both cohorts. Having at least one non-Italian parent was significantly associated with lower TIR (-5.82%, 95% CI: -10.33 to -1.31) and higher HbA1c levels (0.31%, 95% CI: 0.01 to 0.63). A high parental education level (university or higher vs. up to lower secondary education) was associated with higher TIR (8.61%, 95% CI: 3.03–14.18) and lower HbA1c levels (−0.42%, 95% CI: −0.78 to −0.06). Age at CIQ initiation, time from diagnosis to CIQ initiation, DKA at diagnosis, and sex were not significant predictor. Regarding safety, no severe hypoglycaemia episodes were reported in the younger group, and only one occurred in the older group after 12 months. A single DKA episode was recorded in a child under six. Moreover, CGM-derived data indicated that time spent in hypoglycaemia (<54 and 54–69 mg/dL) remained consistently below clinically relevant thresholds (<1% and <3%, respectively). Conclusion In this large real-world cohort of young children with T1D, the CIQ system demonstrated consistent and sustained improvements in glycaemic outcomes over 18 months, with minimal adverse events. Significant gains in TIR, TITR, and HbA1c were observed in both age groups, particularly in the first 6 months after CIQ initiation. These benefits were maintained long-term, regardless of initial glycaemic status and presence of DKA at diagnosis. The system proved safe even in children under six, supporting its current use in off-label settings with appropriate clinical oversight. Our findings reinforce the value of early AID adoption to optimize long-term metabolic outcomes in pediatric T1D.
Congress Abstract - Section 2: Epidemiologia Clinica
Diabetes Study Group of the Italian Society for Pediatric Endocrinology and Diabetology
Copyright (c) 2025 Andrea Faragalli, Roberto Franceschi, Marco Marigliano, Valentino Cherubini, Rosaria Gesuita, Diabetes Study Group of the Italian Society for Pediatric Endocrinology and Diabetology https://creativecommons.org/licenses/by-sa/4.0
Long-Term Mental Health Consequences Of Road Traffic Crashes: A Scoping Review
Introduction The considerable number of road crashes resulting in death, injury and disability makes road safety a crucial issue worldwide [1], [2]. Preventing road deaths, addressing severe injuries in road traffic and their long-term effects, both mental and physical, remains a critical aspect of achieving a resilient and future-proof transportation system. Aims To summarize findings from longitudinal studies on mental health, psychological outcomes, and mental health-related quality of life (HRQoL) following road traffic crashes (RTCs) in order to suggest implications to improve recovery and rehabilitation. Methods Long-term consequences (LTCs), physical and mental health, quality of life, and road traffic crash were the keywords used to search the PubMed, Scopus, and Web of Science electronic databases. The initial search yielded 420 records, reduced to 390 after the removal of duplicates. Following the PRISMA 2020 guidelines, 50 observational studies published between 2010 and 2024 were identified as relevant, and one was found through citation searching. At the full-text reading 17 papers were excluded. As a result, 34 articles were included in the review. These studies investigated the long-term physical and psychological consequences of road crashes across all categories of adult road users. Seven papers showed a specific focus on mental health outcomes, including post-traumatic stress syndrome, pain-related psychological distress, depressive symptoms, mental HRQoL, and cognitive impairment. Results The reviewed studies consistently show that RTCs can lead to substantial long-term mental health consequences. A considerable number of individuals experience post-traumatic stress symptoms, with higher rates observed among those with more severe injuries. Persistent pain and residual physical or psychological symptoms are common even several months after the crash, often impairing daily functions and delaying return to work. Depression and anxiety symptoms are associated with slower recovery, and negative expectations about recovery strongly predict persistent pain and emotional distress over time. Mental HRQoL, measured by means of standardized instruments, generally improves within the first year after the crash, but often remains below pre-injury levels, especially among those with longer hospital stays or more severe injuries. Sociodemographic and clinical factors such as female sex, middle age, higher injury severity, and low physical or mental HRQoL scores are significantly associated with poorer mental health outcomes. In contrast, higher education levels, better physical functioning, and protective behaviors (such as the use of safety equipment) are associated with improved mental recovery and reintegration into daily life and work. Overall, these findings highlight the need for targeted mental health screening and supportive interventions in the post-injury care of RTC survivors. Characteristics of the included studies are summarized in Table 1. Conclusions This review emphasizes the importance of early psychological screening, targeted intervention, and policy efforts to mitigate the mental health burden among RTC survivors.
Congress Abstract - Section 1: Epidemiologia Generale
Copyright (c) 2025 Manuela Anelli , Ottavia Eleonora Ferraro , Mattia Celebrin , Anna Morandi, Cristina Montomoli https://creativecommons.org/licenses/by-sa/4.0
Prevention of cervical cancer in a rural primary care centre in Eswatini
Background: Cervical cancer is a priority public health concern in the Kingdom of Eswatini, primarily due to a high incidence rate of high-risk human papillomavirus (HR-HPV) infection [1,2]. Socio-cultural, economic, and policy-related barriers often limit women’s access to essential screening and preventive services, resulting in delayed diagnoses and high mortality rates [3]. Test invasiveness and sampling modalities may further reduce women's participation in existing screening programs implemented mainly through Visual Inspection with Acetic Acid (VIA), [4, 5, 6, 7]. Since May 2023, the 4-valent (4v, HPV-6, 11, 16, 18) HPV vaccine has been offered to girls aged 9–14, with an estimated 79% of the target population vaccinated by May 2025 [8,9,10]. However, data on the prevalence and genotype distribution of HPV in Eswatini are limited, and prevention strategies do not rely on local epidemiological data that are currently missing. Objectives: In line with WHO recommendations for HPV-DNA testing as a primary screening method, this project aims to identify and address implementation challenges by implementing a “screen, triage, and treat” prevention programme featuring non-invasive urine-based HR-HPV testing at St. Philip’s Clinic, a rural primary care centre in the Lubombo region of Eswatini. Specific objectives are to assess feasibility, acceptability, effectiveness, and cost-effectiveness of urine-based HR-HPV testing with the aim to enhance access among adolescent girls and women (AGW). Furthermore, it aims to evaluate the distribution of HPV genotypes in this area of Eswatini. Methods: A 12-month cross-sectional pilot study (February 2023–February 2024) was conducted at St. Philip’s Clinic, Eswatini. Women aged 12-49 presenting for any reason were asked to provide a 30 mL urine sample. A 10 mL aliquot was centrifuged to concentrate viral particles and eliminate debris, and the Xpert® HPV test was performed locally to detect 14 high-risk HPV (HR-HPV) genotypes. Women aged 21-49 were additionally offered cervical brush testing for cytological analysis to be performed at Mbabane Central Pathology Laboratory. Screening-positive women underwent VIA to establish the need for local treatment or hospital referral according to the national screening programme. Concentrated urine samples were also dried on filter paper (dried urine spots, DUS) and sent to Italy for comprehensive HPV genotyping using in-house PCR, sequencing, and Ampliquality HPV-Type Express assay. Results: The study enrolled 510 AGWs (median age 29). 37% (190/510) were HIV-positive. First-time screenings accounted for 45% of women (228/510). The Xpert® HPV test provided valid results for 473 participants (93%), detecting HR-HPV in 42% (199/473). Women aged 21–25 had the highest HR-HPV prevalence (55%, 56/101). HIV-positive participants had a 1.8-fold increased risk of HR-HPV infection compared with the HIV-negative (95%CI 1.23–2.64). Cervical brush samples were collected for 220 women consecutively recruited from the start of the project and high-grade lesions were identified in 46 women, with 7 cases of CC, including 2 in women under 30. Overall, 333 DUS were successfully genotyped in Italy. HPV35 was the most frequently identified genotype (24%, 80/333), followed by HPV16 (18%, 61/333), and the predominant genotype among high-grade lesions (33%, 15/46) and the sole oncogenic genotype in 15% of these cases, including one CC. Among women identified with high-grade lesions, 37% (17/46) tested positive for tetravalent vaccine HR-types (HPV-16, 18), 65% (30/46) for nonavalent-vaccine HR-types (9v, HPV-16, 18, 31, 33, 45, 52, 58). Adding HPV35 to the nonavalent vaccine formulation potentially increases coverage to 80% (37/46) (100% considering only CC). Preliminary findings suggest high acceptability and good overall performance of the screening algorithm. However, several challenges emerged that may affect feasibility, including a proportion of invalid HPV test results or inconclusive outcomes in VIA assessment, and difficulties in ensuring referral compliance for women requiring further management. Conclusion: This project provides fundamental evidence to enhance cervical cancer prevention efforts in Eswatini in two critical areas. The first is the support to the validity of the non-invasive urine-based screening methods. The second is the detection of a high prevalence of HPV 35 genotype and the consequent need to revisit formulation of available vaccines. Urine-based HR-HPV rapid testing proved feasible, acceptable and well-received. The study enabled the identification of critical underperforming steps and the development of corresponding solutions. A high frequency of invalid Xpert results was addressed by re-training laboratory personnel in the proper preparation of the sample. The failure of VIA to recognise presence of cervical lesions as compared with the effectiveness of the rapid molecular testing in detecting HR genotypes raises concerns about the future usefulness of VIA. The slow recruitment rate at the start could be addressed via engagement of community workers and leaders. Our findings also revealed that HPV35 is highly prevalent in high-grade lesions in Eswatini, accounting for 15% as the sole oncogenic genotype in 15% of these cases, including one CC. Current HPV vaccines do not include coverage of HPV35: such coverage could extend protection to a high proportion of AGW and further research is warranted to assess the type-specific CC burden in Sub-Saharan Africa.
Copyright (c) 2025 Motsa Tengetile, Msibi Velaphi, Mzamo Sikhondze , Alex Nhleko Bongani , Nicole Rose Niemann , Clara Fappani , Maria Gori , Daniela Colzani, Simone Villa , Paola Angelone , Sante Leandro Baldi , Enrica Valentini , Maphalala Gugu, Gcinile Buthelezi , Camilla Torriani, Assefa Mulubirhan Alemayohu, Maria Cristina Monti , Antonella Amendola , Elisabetta Tanzi , Mario Raviglione https://creativecommons.org/licenses/by-sa/4.0
Anthropometric Indicators Of Central Obesity In The Identification Of General And Cancer Related Risk Mortality: Findings From The Epic Italian Cohort
Introduction Obesity is a growing risk factor among adults, and its accurate identification remains a public health priority. While body mass index (BMI) is widely used, it does not account for fat distribution [1]. Combining BMI with central obesity indicators – such as waist circumference (WC), waist-to-hip ratio (WHR), and waist-to-height ratio (WtHR) – has been shown to improve mortality risk prediction [2,3]. Nevertheless, these indicators have limitations, such as the absence of standardized measurement procedures [4]. Accordingly, the potential predictive role of non-traditional anthropometric indicators – such as the weight-adjusted waist index (WWI), the conicity index (C-index), and relative fat mass (RFM) – has been highlighted [5-7]. However, their clinical interpretation may be limited by the complexity of the required calculations. Aims To develop a novel, clinically applicable and easily interpretable anthropometric method for the classification of central obesity, and to assess its predictive value for all-cause and cancer mortality in comparison with both traditional (BMI, WC, WHR, WtHR) and non-traditional (WWI, C-index, RFM) anthropometric indicators. Methods This study included 45698 adult men and women from the Italian EPIC cohort (1993–1998). Demographic, lifestyle, and medical history data were collected enrollment using standardized questionnaires. Anthropometric measurements (height, weight, waist and hip circumferences) were used to calculate indicators. To calculate Delta Waist (ΔW) a linear regression was performed on WC as a function of height, weight, sex, age, sex/height and sex/age interaction. The model was weighted based on health risk categories defined by NICE [8]: participants with no increased risk were assigned a weighting factor of 1, those with increased risk a weighting factor of 0.5, and those with high or very high risk a weighting factor of 0. Predicted waist circumference was derived from this model by standardizing age to 35 years and reclassifying high/very high risk individuals into the nearest increased risk group stratified by waist circumference, assigning them the highest value of BMI. On this basis, weighting factor was recalculated. ΔW was calculated as the difference between observed and predicted WC. Pearson correlation coefficients were calculated between ΔW, the other non-traditional and traditional indicators. Cox proportional hazards models, with age as the primary time variable, were used to assess the association between ΔW, or other anthropometric variables, and the risk of all-cause and cancer mortality. All models were stratified by study center and adjusted for years of education, smoking status, physical activity level, diabetes, hypertension, and menopausal status for women (model 1). All models were also adjusted for BMI (model 2). All analyses were performed separately for men and women. Hazard ratios (HRs) and 95% confidence intervals were estimated. Model performance was assessed using the Bayesian Information Criterion (BIC), with lower values indicating better fit. Statistical significance was set at p < 0.05. Results During a median follow-up of 15 years, all-cause and cancer deaths were 1002 and 548 in men and 1473 and 888 in women, respectively. ΔW showed a lower correlation with BMI (men: r=0.64; women: r=0.71) than WC (men: r=0.87; women: r=0.91), WtHR (men: r=0.84; women: r=0.90) and WHR (men: r=0.69; women: r=0.75). A strong positive correlation was observed between RFM and BMI (men: r=0.84; women: r=0.85), while correlations between WWI, C-index and BMI were lower than r=0.5 in both sexes. WWI, C-index and RFM were positively correlated with WC, WtHR, WHR in both sexes. After adjustment for covariates, the estimated HRs of all-cause and cancer mortality according to BMI, WC, WtHR, WHR, ΔW, WWI, C-index and RFM are shown in Table 1. In both sexes ΔW was associated with an increased risk of both all-cause mortality (men: HR 1.02 (95% CI 1.01-1.03); women: HR 1.02 (95% CI 1.01-1.02)) and cancer mortality (men: HR 1.02 (95% CI 1.01-1.03); women: HR 1.01 (95% CI 1.003-1.02)) (model 1). Adjustment for BMI did not affect the association between ΔW and all-cause mortality in both sexes and cancer mortality in men, but modified the association between ΔW and cancer mortality in women (model 2). The other indicators were associated with an increased risk of all-cause mortality in men and women and with cancer mortality in men, while in women the association with cancer mortality was observed for all indicators except WHR, WWI and C-index (model 1). After adjusting for BMI no association was found between RFM and all-cause mortality in both sexes, nor between WHR and all-cause mortality in women. Furthermore, in women after adjusting for BMI, WC, WtHR, and RFM were no longer associated with cancer mortality (model 2). In the BIC analysis, the model with ΔW showed the lowest value in both men and women for all-cause mortality (men: BIC=14650; women: BIC=24329). Lower BIC values ​​for cancer mortality were found for C-index in men (BIC=8096) and for WHR in women (BIC=15009). Conclusions ΔW represents a new anthropometric indicator of central obesity, easy to use and understand. In the Italian EPIC cohort it was associated with all-cause mortality risk in both sexes, with better results than other anthropometric indicators already validated and standardized.
Copyright (c) 2025 Stefania Arena , Vittorio Simeon , Sabina Sieri, Claudia Agnoli, Fulvio Ricceri , Lorenzo Milani , Benedetta Bendinelli , Melania Assedi , Rosario Tumino , Salvatore Panico , Fabrizio Pasanisi , Carla Pollastro, Simona Signoriello, Mario Fordellone , Paolo Chiodini https://creativecommons.org/licenses/by-sa/4.0
Dietary Patterns, Metabolomic Profiles, And Metabolic Outcomes By Menopausal Status
Introduction: Menopause is a critical period marked by significant metabolic and hormonal changes that impact cardiometabolic disease risk.[1] Diet plays a crucial role in lipid metabolism and visceral adiposity, key components of cardiometabolic health.[2] Metabolomics, the analysis of small molecules in biological samples, offers a tool for capturing both dietary metabolites and systemic adaptations to habitual diet.[3] In addition to provide a more objective assessment of dietary intake it can reveal different profiles between subgroups of individuals, such as women at different reproductive stages.[4] However, the underlying mechanisms by which diet affects metabolism after menopause are not yet well understood, limiting the development of targeted dietary strategies to mitigate health risks in female populations.[5] Objective: This investigation aimed to examine the role of menopausal status in the association between dietary patterns, metabolomic profiles associated with the dietary patterns, and lipid profile. To achieve this objective, two specific aims were pursued: To determine the metabolomic profile associated with different dietary patterns according to menopausal status To explore how diet influences the lipid profile and visceral fat by menopausal status, considering potential differences in the metabolome. Methods: We conducted a cross-sectional analysis on 1,179 women participating in the Cooperative Health Research In South Tyrol (CHRIS) study,[6] stratifying by menopausal status, defined based on an algorithm incorporating self-reported information and age. We excluded women who were pregnant, non-fasting, or in the menopause transition phase at the study visit. To evaluate the dietary patterns, we derived three well-established healthy dietary indexes: Planetary Health Diet Index (PHDI), Plant-based Diet Index (PDI), and Alternative Healthy Eating Index (AHEI). Additionally, we included the Dietary Inflammatory Index (DII) to specifically investigate the role of inflammation. The outcomes were visceral fat (%), total cholesterol (mg/dL), HDL cholesterol (mg/dL), LDL cholesterol (mg/dL), and log-transformed triglycerides (log-mmol/L) concentrations. We used elastic net regression to identify metabolites associated with each dietary index. The dataset was divided into training (70%) and testing (30%) sets. In the training set, we performed multiple iterations of 10-fold cross-validation with 100 different seeds. Optimal lambda values were selected based on the consensus mode of the number of selected features across iterations. For each dietary index, we estimated a metabolite score, calculated as the weighted sum of selected metabolite concentrations. The trained models were subsequently applied and validated in the testing set to compute final dietary metabolite scores. Associations between dietary indexes, metabolite scores, and outcomes were assessed using linear regression models. All models were adjusted for age, physical activity, smoking, self-reported diabetes and hypertension, education, following a special diet, total energy intake, and self-reported medications for lipid control. Additionally for pre-menopausal women, we adjusted for contraception use, while for post-menopausal women we adjusted for the usage of hormone replacement therapy. Results: In this sample of women, the pre-menopause group (n=762) had a median age of 35 years (interquartile range, IQR: 25-44 years) and a median BMI of 23 kg/m2 (IQR: 21-27 kg/m2). The post-menopause group (n=417) had a median age of 62 years (IQR: 56-69 years) and a median BMI of 26 kg/m2 (IQR: 23-30 kg/m2). The selection of metabolites revealed group-specific profiles, with a modest overlap between the pre-menopause and post-menopause groups, except for the DII, which showed distinct metabolomic profiles in the two groups. Glycerophospholipids were consistently selected across almost all dietary patterns and in both groups.(Figure) When examining associations between dietary indexes and outcomes,(Figure) the PDI and the AHEI were associated with lower visceral fat and LDL cholesterol among pre-menopausal women. In the post-menopausal group, the PHDI was positively associated with LDL cholesterol, while AHEI remained negatively associated with visceral fat. When examining associations between metabolite scores and outcomes,(Figure) in the pre-menopausal group, all metabolite scores were associated with lower visceral fat. The PDI-metabolite score was additionally associated with lower total cholesterol. The PHDI-metabolite score was associated with lower HDL cholesterol and higher triglyceride levels. The DII-metabolic score was associated with higher levels of total, LDL, and HDL cholesterol, and with lower triglycerides. The AHEI-metabolite score was also associated with higher triglyceride levels. In post-menopausal women, the DII-metabolite score was negatively associated with total, LDL, and HDL cholesterol. The PHDI-metabolite score was associated with lower total and HDL cholesterol. The AHEI-metabolite score was positively associated with HDL cholesterol. Conclusion: The metabolomic profiles associated with dietary patterns varied by menopausal status. We observed a general consistency when investigating associations between dietary indexes, their corresponding metabolomic profiles, and lipid marker concentrations. However, the associations with metabolite scores were notably stronger than those with dietary index, suggesting that while dietary patterns do influence metabolic outcomes, the specific metabolomic profiles might provide a more precise and robust measure of these associations.
Copyright (c) 2025 Giulia Barbieri , Naixin Zhang, Marta Trius-Soler, Cristian Pattaro, Marta Guasch-Ferré, Essi Marjatta Hantikainen https://creativecommons.org/licenses/by-sa/4.0
Health Literacy, Diet, and Lifestyles for the Promotion of Health and Empowerment of University Students and Staff: The “ALFADIET” Study
Introduction Appropriate nutrition and healthy lifestyles are key elements in the prevention of non-communicable diseases and in promoting individual well-being. In this context, health and digital literacy are essential tools to foster people’s empowerment in managing their own health. Universities, as environments with a high concentration of young people and professionals, play a strategic role in health promotion. They have the opportunity to optimize the health literacy of their students and empower them to make informed decisions for themselves and their living environments [1]. Health Literacy, meaning the ability of individuals to meet the complex demands of health in a modern society, has been associated with better health decision-making and is considered a key social determinant of healthier behaviors. Moreover, in the field of nutrition, this competency affects food selection and preparation processes and, more broadly, eating habits, thus contributing to improved health and well-being of individuals [2]. For this reason, it is important to investigate Health Literacy as well as eHealth (defined as the use of information technologies in support of health and related fields) and Digital Health Literacy, all of which are increasingly important in Public Health. Objectives The ALFADIET study aims to analyze eating habits, adherence to the Mediterranean Diet, lifestyles, and levels of Health and Digital Health Literacy among students, faculty, and administrative staff of the University of Catania and the University of Palermo. Methods The population of the “ALFADIET” study will include students, from various degree programs and academic years, as well as teaching and non-teaching staff at the University of Catania and the University of Palermo. The study is based on an innovative real-time data collection methodology using the mobile Ecological Momentary Assessment (mEMA), aimed at capturing participants' daily behaviors. A web-based survey will be designed to collect basic demographic data and additional information. The study will use an updated, specially designed version of the HEALTHY-UNICT web app, customized and developed at the University of Catania [3]. This application will allow for the administration of validated questionnaires assessing dietary habits, behavioral factors, and health literacy. The information collected will include anthropometric variables, lifestyles (including smoking and physical activity), emotions, and demographic characteristics. The survey will be conducted on a representative sample of students, teaching and non-teaching staff, through the use of an innovative mEMA application, specially developed by an interdisciplinary team to meet the specific objectives of the study. Results The preliminary findings derive from a pilot study conducted on a small sample of 27 participants from the University of Catania, including students (74.1%), faculty members (18.5%), and administrative/technical staff (7.4%). The majority were female (81.5%), with a median age of 24 years. Among students, 70% were enrolled on schedule with their academic plan, and 50% lived away from their university location. A total of 92.6% of participants reported no significant medical conditions, and 88.9% did not report any food intolerances. Regarding lifestyle habits, 81.5% reported using multivitamin and multimineral supplements, 77.8% were non-smokers, and 88.9% preferred white meat (e.g., chicken, turkey, rabbit) over red meat. Most participants (63%) slept between 7 and 9 hours per night. Psychophysical issues impacting daily activities were reported as moderately difficult by 37% of respondents, very difficult by 14.8%, and extremely difficult by one participant, while 44.4% reported no difficulties. As for digital health literacy, 55.6% stated they were very capable of finding health-related information online, 51.9% felt very capable of knowing where to look for it, and 44.4% felt very capable of evaluating the quality of such information. Additionally, 48.1% reported being very capable of distinguishing between high- and low-quality health information. However, only 29.6% felt very confident using that information to make health-related decisions. Conclusions This pilot study provides an initial overview of dietary habits, lifestyle behaviors, and levels of health and digital health literacy among the university population, though based on a small sample. The data collected will help identify factors associated with greater adherence to the Mediterranean Diet and higher levels of health and digital health literacy, supporting the identification of target groups for future health promotion interventions. Moreover, the study serves to test the effectiveness of innovative digital tools for real-time behavioral data collection. The future evidence will serve as a foundation for developing personalized, sustainable, and replicable strategies aimed at improving health and fostering empowerment within the academic community.
Copyright (c) 2025 Martina Barchitta , Andrea Maugeri , Claudia La Mastra , Noemi Cortese , Marco Enea , Laura Maniscalco , Domenica Matranga , Antonella Agodi https://creativecommons.org/licenses/by-sa/4.0
Validity Of Socioeconomic Inequality Indices over Time in Public Health Research: A Case Study on Ivsm and Maternal Data
Introduction In recent years, studies on pregnancy outcomes and reproductive health indicators have increasingly considered the impact of socioeconomic deprivation. Area-based indices such as the Caranci Deprivation Index [1] or Synthetic Index of Socioeconomic Disadvantage (IVSM) from ISTAT [2] are commonly used as proxies to evaluate variations in fertility rates, voluntary and spontaneous abortion, and other maternal-child health outcomes. Moreover, these indices are widely used to examine how socioeconomic disadvantage influences adverse maternal and reproductive health outcomes, as well as accessibility of healthcare services [3-5]. However, both the Caranci Index and IVSM are constructed using data from the 2011 national census. Applying these outdated indicators to more recent healthcare databases risks to introduce bias and misrepresenting the current socioeconomic reality of municipalities. Objectives This study aims to determine a municipality-level socioeconomic score that aligns as closely as possible with the IVSM but based on individual-level health data from birth certificates (CEDAP) collected in the years 2010–2012 that are near the year of IVSM determination. The primary objectives are to estimate a predictive model of the IVSM based on aggregated CEDAP data (2010–2012), to validate the model internally within the same time period, and to test the temporal stability of the derived score by applying it to subsequent years and assessing its consistency with the IVSM. Methods Municipality-level frequencies were calculated for selected sociodemographic variables present in the CEDAP records, including maternal and paternal citizenship, marital status, educational attainment, occupational status, maternal age class, and parity. The IVSM value, available for each municipality from ISTAT (2011), was used as the dependent variable in a General Linear Model (GLM), where independent variables were the proportions of each sociodemographic category in the reference period 2010–2012. Coefficients estimated from the GLM were then used to compute a composite score (Socieconomic Maternal Score, SMScore) for each municipality by applying the same formula to the frequencies in subsequent time periods (e.g., 2013–2015, 2016–2018). To evaluate model performance the data of 2010–2012 were splitted into a training and validation sets. In later time periods, the SMScore was compared to the original IVSM using Pearson’s correlation coefficient and Mean Squared Error (MSE). Furthermore, both the IVSM and SMScore were categorized into quartiles to simulate typical use in epidemiological studies. Linearly weighted Cohen’s Kappa was calculated to assess agreement between the quartile-based classifications. This analysis rests on the assumption that, although a municipality’s continuous deprivation score may fluctuate, shifts between quartiles over time are less frequent and may therefore offer a more robust indicator of socioeconomic positioning in longitudinal analyses. Results The initial validation of the SMScore against the 2011 IVSM showed a positive correlation (Pearson’s r = 0.64, p < 0.0001) and a relatively low mean squared error (MSE = 3.27), confirming the validity of the constructed scoring model. When applied retrospectively across five consecutive three-year periods (T1: 2010–2012 through T5: 2018–2020), the correlation between the calculated score and the original IVSM index progressively decreased. In the earlier periods, the relationship remained statistically significant (T1: r = 0.64, MSE = 1.87; T2: r = 0.60, MSE = 1.93). However, the strength of association weakened in later periods, with T3 showing a low correlation (r = 0.24, MSE = 4.19), and T4 a slightly stronger but still moderate low correlation (r = 0.34, MSE = 3.19). By T5, the correlation dropped to non-significant levels (r = 0.10, p = 0.0841) with a marked increase in prediction error (MSE = 6.44). The heatmap visualization (Fig.1) comparing the quartile classification of municipalities based on IVSM 2011 and the recalculated SMScore across the five periods provides further insight. In the earliest triennia (T1 and T2), the majority of municipalities remained within the same quartile or shifted only marginally, indicating good agreement between classifications. This concordance deteriorated progressively in T3 and T4, with more municipalities diverging from their original IVSM quartile. In T5, the misclassification pattern became more evident, with substantial deviation between the IVSM and SMScore classifications. This trend is quantitatively supported by the linearly weighted Cohen’s Kappa, which declined from 0.83 [0.80–0.87] in T1 to 0.67 [0.62–0.72] in T5, confirming the decreasing agreement over time. Despite changes in the continuous score, quartile stability appeared more robust in the early triennia. Conclusions The findings suggest that the use of area-level socioeconomic indices such as the IVSM should be temporally bounded, as their capacity to reflect current population-level vulnerability deteriorates over time. While the index performed well in the early years following its development, its predictive and classificatory coherence weakened in later periods. This divergence may stem from genuine socioeconomic transformations within municipalities or from changes in the demographic and social profiles of the women giving birth, such as age at delivery or parity. The observed mismatch highlights the importance of regularly updating deprivation indices or developing dynamic proxies that can adapt to shifting population characteristics. Integrating information from different health service databases, which contain additional relevant variables, may refine the scoring approach and enable more robust comparisons with existing socioeconomic indices. This integration can improve area-level deprivation measures and support their effective use in public health monitoring and planning.
Copyright (c) 2025 Nicola Bartolomeo , Letizia Lorusso , Paolo Trerotoli https://creativecommons.org/licenses/by-sa/4.0
Scrolling and Self: How Self-Esteem and Physical Activity Shape the Effects of Intagram Use on Eating Behavior in Universitary Students
Introduction The use of Social Networking Sites (SNS) has been continuously increasing in recent years [1], contributing to the rise of significant behavioral and psychological issues, especially when SNS are used as a coping mechanism for stress and loneliness among young people. One of the most widely used SNS today is Instagram, and several studies indicate that excessive use of this platform may be associated with decreased cognitive functioning in young adults, as well as behavioral and sleep disorders [2]. In addiction, the Instagram platform's focus on food imagery, fitness content, and body aesthetics often perpetuates unattainable beauty standards, potentially triggering disordered eating patterns or body dissatisfaction [3,4]. Moreover, the study by Romero-Rodríguez et al. [5], found that while problematic smartphone use negatively influences self-esteem, the intensity of Instagram use itself was not a direct predictor of self-esteem among university students. This suggests a nuanced relationship between SNS engagement and self-perception, warranting further exploration. Aims This study aimed to explore the relationships between problematic social media use (PSMU), self-esteem, physical activity (PA), and eating behaviors, using a moderated-mediation model. In this study, we tested a moderated-mediation model of how explained variables may be interrelated. The model’s starting point is the well-established link between PSMU and self-esteem, and PSMU and eating behaviour, with the hypothesis that higher PSMU is associated with higher levels of problematic affects and behaviors. We then hypothesized that participants who have higher self-esteem will show better eating habits despite higher levels of PSMU (mediation analysis). Finally, we supposed that physical activity and sedentary levels could moderate the relationship between PSMU and self-esteem (moderation analysis), with a protective role of PA. Methods A cross-sectional study was conducted with 1237 university students (74.4% females, mean age = 22.7 ± 5.5 years). Participants completed an online survey assessing Instagram use, enrolled in 13 different academic programs (e.g., languages, art, biology, pharmacy, sport science). The students completed an online questionnaire composed of four sections about: physical activity (International Physical Activity Questionnaire, IPAQ) [6]; Instagram usage (Social Media Use Questionnaire, SMUQ) [7]; self-esteem (Rosenberg Self-Esteem Scale) [8]; eating behaviors (Dutch Eating Behavior Questionnaire, DEBQ) [9]. A chi-square test was used to test the gender differences in the distribution of participants in the three PA categories (low, moderate, high). Non-parametric Spearman’s correlations were then computed between Instagram use (minutes per day, reasons for use), SMUQ subscales (Withdrawal and Compulsion), PA (MET/min/week), sitting time (hours/day), self-esteem and eating behaviors (emotional eating, restrained eating, external eating). A multiple linear regression was then performed to test the effect of Instagram time (minutes per day) and motives of use on the two SMUQ subscales (Withdrawal and Compulsion). Finally, a moderated-mediation analysis [10] was conducted to examine the relationships between PSMU and Eating behaviors, considering PA and sitting time, and self-esteem, as potential moderators and mediator, respectively. Results In our sample, females reported higher levels of PSMU and more problematic eating behaviors than males, who in turn exhibited significantly higher levels of physical activity (p < 0.001). Regarding Instagram use, females reported significantly higher daily usage (p < 0.001), number of followers (p < 0.01), following (p < 0.01), and higher scores for all reasons for Instagram use, except for self-promotion, where responses were similar to males. Two multiple linear regression analyses (one for each subscale of SMUQ) were used to investigate which of the motives for use or usage time were more influential on the problematic use. Considering Withdrawal as the dependent variable, the overall regression was statistically significant (R2adj = 0.222, F(6,1165) = 56.71, p < 0.001). All the independent variables were significant predictors of Withdrawal, with the exception of the use for Documenting. The correlation analysis showed that the PSMU (both Withdrawal and Compulsion) was significantly positively correlated to the daily usage time, and to all the three typologies of eating behaviors (restrained, emotional, and external eating). PSMU was positively correlated with restrained, emotional, and external eating behaviors and negatively correlated with PA and self-esteem. Self-esteem mediated the relationship between PSMU and eating behaviors, with higher self-esteem associated with healthier eating patterns. Sitting time moderated the relationship between PSMU and self-esteem, reinforcing the negative effects of excessive Instagram use. Gender differences were observed, with females reporting higher levels of PSMU, problematic eating behaviors and lower PA levels compared to males. This study highlights the complex interplay between PSMU, self-esteem, PA, and eating behaviors in university students. Conclusion These findings highlight the importance of addressing PSMU’s impact on self-esteem and eating behaviors in young adults. PA emerged as a beneficial factor for self-esteem, but its inability to moderate the PSMU–self-esteem relationship suggests that targeted interventions should consider psychological components alongside lifestyle modifications mainly directed at reducing sedentary and sitting time. Promoting regular PA could serve as an effective strategy to enhance self-esteem and counteract the harmful effects of excessive Instagram use. Health education programs emphasizing digital literacy, self-esteem enhancement, and balanced SNS engagement may help mitigate PSMU’s adverse effects. Future research should explore longitudinal relationships and intervention strategies to promote healthier social media habits and psychological well-being among university students.
Copyright (c) 2025 Annalisa Belli , Erica Gobbi , Stefano Amatori , Antonio Preti , Fabrizio Perroni, Marco Bruno Luigi Rocchi , Davide Sisti https://creativecommons.org/licenses/by-sa/4.0
Epidemiological Approaches to Healthy Ageing Research Using Data from the Share Project
Introduction As populations age, public health systems face the challenge of understanding and supporting healthy ageing trajectories [1]. Life-course transitions – such as retirement – affect health behaviours, psychosocial wellbeing, and cognitive functioning. Longitudinal datasets offer key opportunities to track these changes, but require rigorous analytical strategies to fully exploit their potential in epidemiology [2]. Objectives To provide scientific evidence to support decision-making in response to the needs of an increasingly ageing population, through a Healthy Ageing and life-course research approach, by leveraging the longitudinal and multidimensional potential of SHARE data [3]. Specific objectives are: i) to analyse the short- and long-term impact of retirement on behavioural risk factors (e.g. smoking, alcohol use, food consumption, physical activity) and mental health (e.g. depressive symptoms and suicidality); ii) to evaluate individual, socioeconomic, and environmental determinants of cognitive decline using harmonised cognitive assessment protocols. Methods For the specific objective i) We implemented a longitudinal cohort design using SHARE waves 1-8 (2004–2020), selecting 8,998 individuals aged 50+ employed at baseline and retired during follow-up [3,4]. Retirement timing served as a temporal exposure to construct repeated-measures analyses. Generalised estimating equations (GEE) were applied to model selected outcomes (e.g., smoking, alcohol use, food consumption, physical activity, depressive symptoms), accounting for intra-individual correlation [5]. Time was modelled in different intervals relative to the retirement year. Covariates included sex, age, marital status, education, occupation, and chronic disease. For the specific objective ii) We adopted a retrospective design based on SHARE-HCAP Wave 9 (2021–2022), which included 2,685 individuals aged 65+ from five European countries. Respondents were classified through machine learning techniques as cognitively normal, mildly, or severely impaired based on harmonised neuropsychological protocols. To explore associations with cognitive status, we retrospectively reconstructed exposure histories using previous SHARE waves. These included educational attainment, occupational trajectories, chronic conditions, housing characteristics (e.g., presence of architectural barriers, accessibility), and perceived neighbourhood quality. Multivariable models were applied to evaluate the role of these life-course exposures in shaping late-life cognitive outcomes. Results Retirement was associated with dynamic shifts in health behaviours and mental health. Physical activity increased during the early post-retirement period, particularly among previously inactive individuals (RR=1.49, 95%CI 1.36–1.63), but declined after a decade (RR=0.90, 95%CI 0.88–0.93). Smoking prevalence and intensity decreased substantially over time, with average daily cigarette consumption falling from 27 to 9 per day, and risk of smoking dropping by 42% a decade post-retirement (RR=0.58, 95%CI 0.46–0.74). Alcohol use patterns shifted: while daily drinking increased moderately (RR=1.28 at 10+ years), binge drinking declined (RR=0.78, 95%CI 0.66–0.93). Dietary habits showed favourable trends: intake of protein-rich foods (meat, fish, legumes, eggs) increased significantly over the long term (RR=1.09, 95%CI 1.01–1.17), while fruit, vegetable, and dairy consumption remained stable. Depressive symptoms declined in the short term (RR=0.89, 95%CI 0.81–0.99), but increased again after 10 years among non-manual workers and late retirees (RR up to 1.37). Notably, suicidality ideation risk rose substantially in the long term, particularly among men (RR=1.78, 95%CI 1.11–2.86). Cognitive assessment from SHARE-HCAP data showed that 63.4% of respondents were classified as cognitively normal, 25.8% as mildly impaired, and 10.7% as severely impaired. Preliminary results suggest that environmental disadvantages Results from the SHARE HCAP wave showed that 63.4% were classified as cognitively normal, 25.8% as mildly impaired, and 10.7% as severely impaired. Retrospective reconstruction of life-course exposures showed that environmental disadvantages, including poorer perceived neighbourhood quality, may contribute to impaired cognitive impairment in later life. Conclusions Our findings offer a comprehensive view of behavioural and psychological changes during the transition to retirement, distinguishing short- and long-term effects across key health domains. SHARE data offer a powerful resource for life-course epidemiology with different epidemiological approaches and appropriate statistical modelling. The methodological strategy – centered on record linkage, repeated measures, and harmonised indicators – demonstrates how survey data traditionally used in economics and social sciences can inform public health. Future research should address compositional bias, improve data harmonisation, and explore other life-course transitions.
Copyright (c) 2025 Paola Bertuccio, Giacomo Pietro Vigezzi , Giansanto Mosconi , Anna Odone https://creativecommons.org/licenses/by-sa/4.0
Perioperative nutritional supplementation to reduce postoperative infections: a systematic review and meta-analysis
Introduction Postoperative infections, including surgical site infections (SSIs), pneumonia, bloodstream infections (BSIs), and urinary tract infections (UTIs), remain among the most frequent and impactful complications following surgery. They contribute substantially to patient morbidity, length of stay, and healthcare costs, despite improvements in surgical techniques and perioperative care. In Europe alone, healthcare-associated infections are estimated to cause over 90,000 deaths annually and impose a burden of more than 500 DALYs per 100,000 population [1]. Despite the adoption of ERAS protocols and prophylactic antibiotics, the incidence of postoperative infections has plateaued in many settings, suggesting the need for adjunctive strategies beyond standard perioperative care. In this context, perioperative nutrition has emerged as a potentially modifiable factor capable of influencing immune responses, gut microbiota, and systemic inflammation. Immunonutrition (enriched with arginine, omega-3 fatty acids, nucleotides), probiotics, synbiotics, and protein supplementation are currently being investigated for their ability to prevent infectious complications and improve surgical outcomes [2,3]. However, the literature is fragmented and results are often inconsistent across interventions and populations [4-7]. Objectives This study aimed to systematically evaluate the efficacy of perioperative nutritional supplementation in reducing postoperative infectious complications in adult surgical patients. The focus on infection stems from its clinical burden and its potential sensitivity to immune-modulating strategies. Secondary outcomes included specific infection types (SSIs, BSIs, UTIs, and pneumonia) and hospital length of stay (LOS), selected for their relevance to patient recovery and healthcare system impact. These outcomes were selected to capture both the direct impact of infections and their broader consequences on patient recovery and resource utilization. Methods A systematic review and meta-analysis were conducted in accordance with PRISMA 2020 guidelines and registered in PROSPERO (CRD42024575184) [8]. PubMed, Scopus, Cochrane Library, and Google Scholar were searched without restrictions on date or language. Eligible studies included adult surgical patients receiving perioperative nutritional interventions (immunonutrition, probiotics, synbiotics, protein supplementation) compared to standard care or placebo. The research question was structured using the PICO framework to ensure structured and clinically meaningful comparisons. Both randomized controlled trials (RCTs) and observational studies were included. The primary outcome was the incidence of infectious complications. Meta-analyses were performed using STATA SE 19, with pooled odds ratios (ORs) and 95% confidence intervals (CIs). Heterogeneity was assessed via the I² statistic, and a random- or fixed-effects model was applied accordingly. The methodological quality of included studies was assessed using NIH tools, with most RCTs classified as low or moderate risk of bias, and observational studies showing greater variability [9]. Risk of bias was independently assessed by multiple reviewers, and disagreements were resolved by consensus, ensuring rigorous evaluation. Results Thirty-nine studies (27 RCTs and 12 observational) met the inclusion criteria. Immunonutrition was the most consistently effective intervention, significantly reducing the overall incidence of infectious complications in both RCTs (OR = 0.36; 95% CI: 0.21–0.62) and observational studies (OR = 0.32; 95% CI: 0.17–0.61). The protective effect was particularly evident with oral administration and in patients aged ≤65 years. Probiotics showed a borderline protective effect overall, while synbiotics appeared more effective in younger patients and when administered orally. Protein supplementation was evaluated in too few studies to allow for pooled analysis of the primary outcome. Regarding secondary outcomes, a general trend of benefit was observed across most interventions, though with variability in effect size and consistency. Immunonutrition and protein supplementation were associated with reduced SSIs. Probiotics showed favorable effects on UTIs and pneumonia. Synbiotics were linked to a shorter LOS, though heterogeneity and limited data warrant cautious interpretation. The quality of evidence varied, with most RCTs showing low or moderate risk of bias. These findings, although promising, should be interpreted with caution due to limited data in some subgroups and moderate heterogeneity in certain analyses. Conclusions Perioperative nutritional supplementation, particularly immunonutrition, represents a promising and evidence-based strategy to reduce postoperative infectious complications. Secondary benefits on SSIs, UTIs, pneumonia, and LOS support the broader integration of nutritional protocols into surgical care. Immunonutrition showed the most consistent results, while probiotics were effective for UTIs and pneumonia. Synbiotics significantly reduced LOS, though limited data and heterogeneity warrant caution. The strength of evidence depends on study volume and quality; thus, interventions like immunonutrition for SSIs should be prioritized, while others—such as protein supplementation for non-SSI outcomes—require further evaluation. Clarifying differential impacts across surgical specialties and standardizing formulations, timing, and administration routes will be crucial for tailored and effective implementation.
Copyright (c) 2025 Marina Sartini, Alessio Carbone , Martino Oliva , Sara Pessano , Filippo Del Puente , Tania Buttiron Webber , Elisa Schinca , Gianluca Ottria , Chiara Dupont , Carolina Piccinini , Maria Luisa Cristina https://creativecommons.org/licenses/by-sa/4.0
Hospitalizations for Mental Health in Migrants and Italian Citizens in the Marche Region Between 2011 and 2023: a Population-Based Study Using Healthcare Utilization Databases (Mighty Project P2022asxkr)
Introduction International migration is considered a complex and unstoppable phenomenon. Migrant population is a heterogeneous group of people who experience migration for different reasons and that are considered to be at increased risk of developing mental disorders [1]. Scientific evidence on migrants’ mental health is limited, as is knowledge of their access to and use of specific care services and treatments. As compared to natives, higher rates of involuntary hospitalization were found among migrants in most European countries [1,2]. Aim The aim of the present study was to compare mental health hospitalization rates in Migrants and Italians in Marche Region during 2011-2023, and to investigate their differences according to demographic and clinical characteristics. Methods A cross-sectional population-based study on individuals hospitalized in psychiatric departments of all ages and resident in Marche Region in the period between 2011 and 2023 was conducted, using healthcare utilization databases (Hospital Discharge and Regional Beneficiaries databases). Residents were divided into Migrants from High Migratory Pressure Country (HMPC) and Italians, according to citizenship [3]. The primary diagnosis field of hospital discharge database was used to estimate the prevalence of different types of mental disorder using ICD-9 CM codes (290.*-319.*). Annual age-standardized hospitalization rates were calculated for HMPC Migrants and Italians using the 2019 Italian population [4] as standard, using the direct method. Rates were stratified by sex and the Standardized Rate Ratios (SRR) with 95% Confidence Interval (95%CI) were calculated by sex and year of observations as ratio between HMPC and Italian rates. All data were processed in compliance with the European (GDPR, EU 2016/679) and national privacy laws (D.lgs. 196/2003 and subsequent amendments). Results A total of 59.881 hospitalizations were analyzed, 93.3% of which were of Italians. The mean age was higher in Italians than in HMPC (43.8 y versus 32.2 y). In both populations, hospitalizations most frequently referred to unmarried individuals (62% in Italians, 64% in HMPC) and to individuals with the lowest level of education (45% in Italians, 48% in HMPC). Hospitalizations originated mainly from Emergency Departments admissions (25.4% Italians, 28.8% HMPC), medical indications (24.8% Italians 20.4% HMPC), prison (13.4% Italians, 13.7% HMPC) or hospitalization at the time of delivery (13.3% Italians 11.4% HMPC). The most frequent diagnoses were schizophrenia and other functional psychosis (29.6% in Italians vs 31.6% HMPC), alcoholism and toxicomania (15.2% in Italians vs 21.7% HMPC) in both populations. Hospitalizations for depression in Italians and HMPC had similar proportions (11.1% vs 11.2%, respectively) but the frequency was more than twice as high in HMPC women as in HMPC men (8.3% vs 2.9%); the proportions of hospitalizations for mania and bipolar affective disorders hospitalizations were higher in HMPC women than in HMPC men (7.0% vs 2.1%); alcoholism and toxicomania were more frequent in HMPC women than in Italian women (11.5% vs 4.8%), and also than in HMPC men (11.5% vs 10.1%). Excluding the 2020-2021 (the pandemic period) in which the lowest rates were recorded (2.5-2.5 and 2.3-2.0 for Italians men and women respectively, 1.4-1.7 and 1.6-2.2 for HMPC men and women respectively), the standardized rates (x1000 residents) of hospitalization ranged between 1.7 to 2.6 (in 2023 and 2019, respectively) for HMPC men and between 1.8 to 2.5 (in 2023 and 2017, respectively) for HMPC women; in Italians, rates ranged between 2.6 to 4.2 (2023 and 2011/2012, respectively) for men and between 2.1 to 3.5 (in 2023 and 2011, respectively) for women. Standardized rate ratios (Figure 1) showed that HMPC reported lower hospitalizations for mental disorders than Italians over the entire study period for both genders. In females, the differences between the two populations were less pronounced than in males. Conclusion This study based on healthcare utilization databases allowed to quantify the use of inpatient psychiatric care in both Migrants and Italians in the Marche Region. Our results showed a lower rate of hospitalization for mental disorders in Migrants than in Italians over the study period, with differences by gender and by type of mental disorder. Considering that mental disorders are characterized by chronicity, diagnostic-therapeutic difficulties and strong family and social impact, further assessments are needed to identify the reasons for the use of hospitalization versus community-based care services in both populations.
Copyright (c) 2025 Carmela Centola , Marica Iommi , Andrea Faragalli , Alessandro Fontanarosa, Marco Pompili , Elisa Barbiano di Belgiojoso , Flavia Carle, Edlira Skrami , Rosaria Gesuita https://creativecommons.org/licenses/by-sa/4.0
A Framework to Improve Data Quality and Manage Dropout in Web-Based Medical Surveys: Insights from an Ai Awareness Study among Italian Physicians
Background Ensuring data quality in self-reported online surveys remains a critical challenge in digital health research, particularly when targeting healthcare professionals [1,2]. Self-reported data are susceptible to multiple biases, including careless responding, social desirability bias, and dropout-related attrition, all of which may compromise the validity of findings [3,4]. In web-based surveys where researcher oversight is limited, structured quality control measures are essential to detect low-quality responses, minimise sampling bias, and enhance data reliability [5]. Previous studies have demonstrated that inadequate quality checks can lead to inflated error rates, reduced statistical power, and misleading conclusions [6]. Objective This study presents a comprehensive methodological framework for optimising data quality in web-based medical surveys, applied to a national study on AI awareness among Italian physicians. Integrating pre-survey validation, real-time dashboards, response-time filtering, and post-hoc careless responding detection would address key challenges in digital research, while providing a replicable model for future studies. Methods We conducted a national web-based survey using a validated instrument (doi:10.1101/2025.04.11.25325592) via the LimeSurvey platform. The survey incorporated two main sections: (1) a core module assessing knowledge, attitudes and practices regarding AI in medicine; (2) clinical scenarios evaluating diagnostic agreement with AI-generated proposals. Multiple quality control strategies were implemented throughout the survey lifecycle. In terms of survey design and logic, the questionnaire employed an adaptive flow structure, whereby respondents were routed through clinical scenarios relevant to their medical speciality. To reduce the incidence of partial completions and missing data, key questions were marked as mandatory, and completion status was actively tracked. In the monitoring and recruitment phase, a real-time dashboard monitored participant distribution (gender/geographical areas/speciality); referral links were rotated to minimise snowball bias [7]. Time-based data quality checks excluded outliers (completion time <1st or >99th percentile) [8]. Completion time for the first section was analysed for all completers to assess correlations between response speed and quality indicators. Dropout patterns were analysed using Kaplan-Meier survival analysis and logistic regression, to identify systematic attrition predictors. Data quality assessments were performed on the outlier-cleaned dataset (n=587). Response quality was assessed using complementary careless responding indicators applied specifically to opinion scale items (Likert 1-5). Two detection methods were used: low response variance analysis, identifying respondents with insufficient variability (SD < 0.5), and excessive same-response detection, flagging participants using identical responses for >75% of items. Internal consistency analysis (Cronbach's α) evaluated scale reliability across different quality levels. Results A total of 736 accesses were recorded on the survey platform. As an initial inclusion criterion, only participants who indicated current registration with the Italian Medical Council were considered eligible: 79 (10.7%) were excluded, yielding a sample of 657 eligible participants (89.3%). Among eligible respondents, 597 completed the first section, yielding a dropout rate of 9.1% (n=60). A Kaplan-Meier survival analysis using total survey time revealed that most dropouts occurred early, with critical points at 45% after demographic, 51% after personal AI knowledge items, 71% after opinion items, and 100% before clinical scenarios. Logistic regression showed no significant predictors of completion (LR χ²(6)=3.46, p= 0.7497; pseudo-R²=0.014; AUC=0.60, 95%CI: 0.50–0.70). Completion time showed no correlation with response quality (Spearman's ρ = -0.019, p = 0.645). Following outlier removal, data quality assessment among 587 who completed the first section revealed two complementary patterns of careless responding: 8.52% (n=50) exhibited low response variance, while 32 (5.45%) demonstrated excessive same response patterns. Cross-classification analysis showed 23 participants (3.92%) flagged by both indicators, with 71.88% of excessive same responders also showing low variance. Overall, 50 participants (10.05%, 95% CI: 7.9%- 12.8%) exhibited careless responding detectable by at least one indicator. Internal consistency analysis showed robust scale reliability (Cronbach's α = 0.754) that remained stable across quality levels. Conclusion The integration of real-time monitoring, adaptive design, time-based validation, and systematic careless responding detection provides a robust methodological framework for web-based medical surveys, particularly for complex topics like AI adoption. Comprehensive data quality assessment revealed a 10.05% careless responding rate among completers, which aligns with the literature. The absence of correlation between completion time and response quality shows that careless responding could reflect attentional rather than temporal factors. Our findings suggest that both phenomena likely reflect situational or contextual factors rather than systematic participant characteristics or survey design flaws. This supports the validity and generalizability of the final dataset while providing a replicable quality control framework for future web-based medical research.
Copyright (c) 2025 Vincenza Cofini, Eugenio Benvenuti , Martina Mancinelli , Ginevra Di Pangrazio, Laura Piccardi, Mario Muselli, Eleonora Cimino, Loreta Tobia, Leila Fabiani , Stefano Necozione https://creativecommons.org/licenses/by-sa/4.0
Impact of First and Further Decompensation in Metabolic-Dysfunction Associated Compensated Advanced Chronic Liver Disease
BACKGROUND Metabolic dysfunction-associated steatotic liver disease (MASLD) currently stands as one of the foremost global health challenges, with a prevalence of 38% worldwide according to the most recent estimates [1] and with a concerning upward trend due to the parallel anticipated increasing of Diabetes and Obesity epidemic in the coming years [2]. There is a long-standing agreement that the first decompensation - defined as ascites, hepatic encephalopathy (HE), variceal bleeding, and jaundice- appears the pivotal event for patients’ prognosis and marks the transition from the compensated, also known as compensated advanced chronic liver disease (cACLD), to the decompensated stage of cirrhosis [3]. Although only a small fraction of patients dies following the first decompensation episode, the risk of developing further decompensation increases and the median survival dramatically decreases [4]. The occurrence of a further decompensation event - defined according to the Baveno VII Consensus [5] as either the recurrence of the initial event or the development of a second decompensation event - represents a crucial turning point in the natural history of the liver disease, markedly increasing the risk of liver-related death (LR-D) in those patients. AIM We assessed the cumulative incidence of first and further (acute and non-acute) decompensation and evaluated their impact on LR-D in patients with compensated advanced chronic liver disease (cACLD) due to metabolic dysfunction-associated steatotic liver disease (MASLD). METHODS International multicenter retrospective study (17 centers) on 6,061 consecutive patients with clinical (LSM>10 kPa) or biopsy-proven (F3-F4 fibrosis) diagnosis of cACLD due to MASLD. First and further decompensation were defined according to Baveno VII criteria. Competing risk analyses estimated the cumulative incidence of first and further decompensations, treating liver-related death (LR-D), extra-hepatic death (EH-D), and liver transplantation (LT) as competing events. Cumulative Incidence Functions (CIFs) were compared using Gray’s test and stratified by decompensation type and cause of death. Time-to-event analyses were anchored at cACLD diagnosis (first decompensation) and at first decompensation (subsequent events), with 5-year CIFs reported. Cause-specific Cox models with time-dependent covariates assessed the impact of decompensations and HCC on LR-D. Multivariable models included age, sex, diabetes, and liver function markers when available. A seven-state multistate model estimated transitions from cACLD to better assess the clinical course of cACLD due to MASLD. Analyses were conducted in R (v4.3.3) using cmprsk, mstate, and related packages. RESULTS The cumulative incidence of the first decompensation was 3.5% (95% C.I 3.0-4.1) at 5 years, increasing 19-fold the risk of LR-D using Cox analysis (Figure 1A); the cumulative incidence of further decompensation was 43.9% (95% C.I 37.2-50.2) at 5 years among patients with first decompensation (Figure 1A), additionally increasing 1.5-times the risk of LR-D. Ascites, followed by variceal bleeding, were the most common events in both first and further decompensation. Hepatocellular carcinoma (HCC) further independently increased the risk of LR-D by 3- and 1.4-fold in the whole cohort of cACLD due to MASLD and in those who experienced first decompensation, respectively. CONCLUSIONS The first and further decompensations represent tipping points in the clinical course of patients with cACLD due to MASLD, increasing 19-times and additionally 1.5-times the risk of LR-D. HCC is an independent predictor of LR-D in patients with cACLD due to MASLD, resulting in an additional risk of LR-D when associated with both first and further decompensation.
Copyright (c) 2025 Gabriele Di Maria , Grazia Pennisi, Vincent Wai-Sun Wong, Victor de Ledinghen , Giada Sebastiani , Mauro Viganò, Anna Ludovica Fracanzani ; Luca Miele ; Elisabetta Bugianesi , Mattias Ekstedt , Roberta D’Ambrosio , Federico Ravaioli , Filippo Schepis , Fabio Marra , Alessio Aghemo , Gianluca Svegliati-Baroni , Marcello Persico , Luca Valenti , Annalisa Berzigotti , Jacob George, Angelo Armandi , Patrik Nasr , Stergios Kechagias, Antonio Liguori , Dario Saltini , Yuly P. Mendoza, Vincenza Calvaruso , Huapeng Lin , Giuseppe Infantino, Mario Masarone , Nicola Pugliese , Adele Tulone , Vito Di Marco , Calogero Cammà , Salvatore Petta , Marco Enea https://creativecommons.org/licenses/by-sa/4.0
Blood Biomarkers of Aging and Multimorbidity Prognosis in Older Adults: Preliminary Results from the Bio-Sign Project
Introduction Increased life expectancy is associated with the presence of multiple chronic diseases (i.e., multimorbidity), affecting health trajectories and care demands of older adults. The identification of a pool of accessible biomarkers that reflects specific endophenotypes of physiological dysregulation, may facilitate prognostication in older adults living with multimorbidity, offering a valuable tool for clinical decision-making and personalized interventions [1-3].The BIO-SIGN project aims to assess how a rich set of novel biomarkers, along with clinical, environmental and behavioral factors, interact in the definition of specific multimorbidity patterns and their association with negative outcomes, specifically all-cause mortality. Objectives First year of the project focused on two different objectives. First objective was the selection of a set of accessible and reliable biomarkers of multimorbidity, through extensive bibliographic research. Second objective was to identify homogeneous groups of multimorbid individuals (≥ 2 diseases), who share similar underlying disease patterns, using two Italian cohorts of randomly selected resident adult individuals. Methods Objective 1. A systematic review was conducted on PubMed and Web of Science to identify peer-reviewed studies assessing the association between individual biomarkers in human fluid samples and multimorbidity. Based on this review, a panel of significant blood biomarkers associated with multimorbidity was selected for further investigation in this study. Objective 2. The identification of multimorbidity patterns involved the two Italian cohorts of CUORE project [4] and Moli-sani project [5] (baselines 2008-2012 and 2005-2010, respectively), which share similar data collection methods. We focused on older adults aged 60-79 years. To ensure consistency and minimize data heterogeneity, a rigorous process of harmonization was applied. Population clinical characteristics were studied and homogeneous groups of individuals sharing similar underlying disease patterns were identified using Latent Class Analysis (LCA) [6]. Cox regression models were used to assess the association between multimorbidity patterns and mortality over 14 years of follow-up, with results pooled in a random-effect meta-analysis. Results Objective 1. Systematic review for biomarkers identification The systematic review identified several inflammatory and metabolic biomarkers, such as IL-6, triglycerides, LDL cholesterol and kidney and liver markers such as CyC, Aspartate aminotransferase and Alanine aminotransferase as being directly associated with multimorbidity. Additionally, neurodegeneration related biomarkers, including NfL and p-Tau 217, showed similar associations. Plasmatic level of Aβ40/42 showed a significant direct association with multimorbidity in less robust studies, while vitamin D displayed an inverse association with multimorbidity in two studies. Findings for other biomarkers such as TNFα receptor II, total cholesterol, HDL cholesterol, CRP, and Insulin-like growth factors 1 were inconsistent across studies. On the basis of the literature review, a list of blood biomarkers was selected to be assessed in relation to the aims of the BIO-SIGN project (Table 1). Furthermore, based on the researcher’s expertise, standard pancreatic (insuline, C-peptide), cardiovascular (NT-proBNP), metabolic (glycemia), hepatic and renal (creatinine, albumine) biomarkers were included to the list along with emerging candidates linked to inflammatory, vascular, and hormonal dysregulation (GDF-15, I-CAM-1, V-CAM-1, leptine, MIG, GFAP) to ensure comprehensive coverage of relevant biological pathways. Objective 2. Multimorbidity patterns identification In relation to the cohort studies, total samples of 3,695 individuals in CUORE (48% male, mean age 68.8 years [SD 5.6]) and 7,801 in Moli-sani (51% male, mean age 68.2 years [SD 5.4]) were evaluated and a total of 33 chronic diseases were considered. In both cohorts, six multimorbidity patterns were identified: hypercholesterolemia; metabolic, depression and cancer; cardiometabolic and respiratory; gastrointestinal, genitourinary and depression; respiratory; unspecific (i.e., no diseases overexpressed). A seventh pattern of multimorbidity-free participants was identified. Incidence rates of mortality were 1.7 and 1.9 per 100 person/years for CUORE and Moli-sani, respectively. When compared to participants without multimorbidity, those displaying a cardiometabolic and respiratory pattern were associated with the highest mortality (pooled HR 2.62; 95% CI 2.15-3.10), followed by unspecific (pooled HR 1.45; 95% CI 1.21-1.68), respiratory (pooled HR 1.33; 95% CI 1.01-1.64), and gastrointestinal, genitourinary, and depression (pooled HR 1.33; 95% CI 1.06-1.60). Conclusions During the first year of activity of BIOSIGN, we selected a panel of 25 blood biomarkers -across diverse diseases domains, as possible factors potentially able to define different endophenotypes of multimorbidity. Additionally, 6 different multimorbidity patterns were identified in two Italian cohorts and were differentially associated with survival. Further steps in the project will be to evaluate how biomarkers assessment can predict disease progressions and outcomes across these patterns. The identification of biomarkers as measurable prognosis factors for different homogeneous multimorbidity patterns in older adults may improve risk stratification, and prevent or reduce adverse health outcomes, including mortality. Such insights could enable personalized interventions and care plans and ensure a better allocation of health resources.
Copyright (c) 2025 Teresa Dipol , Maria Beatrice Zazzara , Cecilia Damiano , Simona Costanzo , Luigi Palmieri , Augusto Di Castelnuovo , Chiara Donfrancesco , Cinzia Lo Noce , Sara Magnacca , Benedetta Marcozzi , Tiziana Casoli , Anna Picca, Licia Iacoviello , Davide Liborio Vetrano , Graziano Onder https://creativecommons.org/licenses/by-sa/4.0
Protact-Us: A Study on the Long-Term Impact of Road Traffic Crashes in Europe
Introduction Despite advancements in emergency care and prevention, many road traffic crash (RTC) survivors suffer from enduring impairments that are insufficiently considered and registered in hospital records or existing surveillance systems. Building upon evidence from prior initiatives [1-3], this study aims to (1) assess the multidimensional long-term outcomes of RTCs, (2) identify early predictors of functional and psychological recovery, and (3) inform data-driven strategies for post-injury rehabilitation and health system planning. Aim The ProtAct-Us from Long-Term Consequences of Road Crashes (ProtAct-Us), a project funded by the European Union, addresses a critical, yet often underexplored, multidimensional aspect of road safety: the long-term consequences of road traffic crashes (RTCs) on individuals' physical, cognitive, psychological, and socio-economic well-being. Methods This multicentre, prospective, observational longitudinal study will be conducted across Germany, Greece, and Italy. The study population comprises adults (≥18 years) involved in RTCs, enrolled through hospitals, trauma centres, or on the accident scene from June to December 2025. Consecutive sampling will be performed until the minimum required sample size of 120 subjects is collected. Informed consent will be obtained in compliance with national regulations. Data will be collected at two timepoints: baseline (within 30 days after the accident) and 12 months post-injury. Validated instruments will be used, covering health-related quality of life (EQ-5D), cognitive function (MoCA), psychological status (CES-D, IES-R), social support (MOS), and economic burden (Muarc). Variables related to injury characteristics, health history, and contextual factors (e.g. access to care, social and work reintegration) will also be recorded. Statistical Analysis Descriptive analyses will summarise the sample's clinical, psychological, and socio-demographic features. Recovery trajectories and outcome prevalence at 12 months will be analyzed. Univariate analyses will explore associations between potential predictors and outcomes. Multivariate regression will identify independent predictors of poor recovery, such as persistent pain, psychological distress, or reduced participation in the daily activities. Conclusion By integrating medical, psychological, cognitive, and socio-economic data, the ProtAct-Us study will try to provide a comprehensive understanding of the long-term burden of RTCs. This multidimensional approach is expected to generate evidence-based recommendations to improve recovery pathways, tailor rehabilitation programs, and enhance policy responses. Findings will contribute to a more person-centred and sustainable management of the road traffic injury consequences for all road traffic users. Findings from ProtAct-Us will contribute to evidence-based public health and policy-making by quantifying the long-term burden of RTCs and identifying modifiable risk factors, ultimately supporting more effective post-crash care strategies.
Copyright (c) 2025 Ottavia Eleonora Ferraro , Anna Morandi , Manuela Anelli , Federico Grassi , Viviana Ciprian , Luca Ansaloni , Stefano Perlini , Cristina Montomoli https://creativecommons.org/licenses/by-sa/4.0
Psychiatric Disorders and Psychoactive Substance Use in Children and Adolescents: A Population-Based Study in Lombardy, 2016–2022
Introduction The World Health Organization (WHO) defines dual diagnosis (DD) as “the coexistence of psychoactive substance use and another psychiatric disorder in the same person” [1]. Clinicians, researchers, and policymakers are concerned with this issue due to the difficulties involved in managing dual diagnoses, including high relapse rates, reduced participation in treatment, and less favourable treatment results [2]. The literature indicates a higher risk of psychoactive substance use disorders in individuals who have grown up with psychiatric disorders [3]. Although psychiatric comorbidity in adults with substance abuse issues is well documented, it has been less thoroughly investigated in adolescents [4]. Nevertheless, it remains a highly relevant issue for national drug and mental health policies. Adolescents with dual diagnoses place a significant social and economic burden on the public healthcare system, as the interaction between substance use and psychiatric disorders creates a vicious cycle that further worsens later difficulties, such as family conflicts, academic problems, and criminal behavior [2]. The aim of the study is to estimate the prevalence of National Health Service users (0-18 years old) who have been diagnosed with psychiatric disorders related to substance abuse in Lombardy between 2016 and 2022. Methods Data Sources and Study Population The data used for this study were drawn from the administrative healthcare utilization databases of the Lombardy Region, which are routinely employed for reimbursement purposes. The observation period spanned from January 1, 2016, to December 31, 2022. The study population included all individuals aged 0–18 years as of December 31 of each year, residing in the Lombardy Region. Case Definition An individual was classified as a case if, at least once during a given year, they either accessed the Emergency Department or were hospitalized. Case identification was based on the presence of specific ICD-9-CM codes (291–292, 303–305), which indicate psychiatric conditions associated with psychoactive substance use. Emergency Department visits were included if any of the above codes were recorded as a diagnosis. Hospitalizations were considered when any of the specified codes appeared in one of the six diagnosis fields of the hospital discharge records. This definition allowed for the identification of adolescents (aged 0–18 years) who had contact with healthcare services for issues related to the comorbidity of psychiatric disorders and psychoactive substance use. Statistical Analysis Annual and monthly prevalence rates (per 1,000 individuals) were calculated by gender, by age group (0–2, 3–5, 6–10, 11–13, and 14–18 years) and observation year, using the number of identified cases as the numerator and the total population aged 0–18 in Lombardy as the denominator. A negative binomial regression model was applied to estimate the monthly number of prevalent cases, stratified by gender and age group. The model included month as a categorical variable, year as a continuous variable, and period as a categorical variable (January 2016–December 2019, January 2020–December 2021, and January 2022–December 2022). The natural logarithm of the population count was included as an offset term. Results In 2022, 87 cases involving hospital admissions or Emergency Department visits for psychiatric disorders related to substance and alcohol abuse were recorded. From 2016 to 2022, prevalence showed variable trends: an increase from 2016 to 2018, a decline until 2020, followed by fluctuations in the last two years, without returning to 2016 levels. Stratification by sex revealed fluctuating prevalence before the pandemic, with females peaking in 2018 and males in 2017 and 2019. Both sexes reached their lowest levels in 2020, with a more pronounced rebound in females during the final two years. By age group, the highest prevalence was consistently observed among adolescents aged 14–18, peaking in 2018, dipping sharply in 2020, and partially recovering in 2021. Younger age groups (0–2 and 3–5 years) showed stable or gradually decreasing prevalence, while the 6–10 and 11–13 groups maintained very low rates throughout the period. Conclusions The rise in prevalence from 2016 to 2018 may reflect increased adolescent substance use, growing attention to youth mental health, and the impact of social media and cyberbullying [5]. The 2020 decline likely relates to COVID-19, with a rebound in 2021 and slight drop in 2022 due to case re-emergence and service adjustments [6,7]. By sex, females peaked in 2018, possibly due to binge drinking and psychotropic use; males peaked in 2017 and 2019, linked to social media and cyberbullying. Post-COVID sex differences may stem from different coping strategies. Adolescents are the most affected age group, followed by infants (0–2), possibly due to prenatal exposure [8]. Children aged 6–13 show low prevalence, likely due to limited exposure. Further investigations are currently underway to assess the relationship between case distribution and socio-economic factors across Lombardy’s provinces.
Copyright (c) 2025 A. Filosa, M. Franchi , M. A. Costantino , R. Borgatti, M.M. Mensi , E. Fazzi, J. Galli , V. Poli , L.A. Reale , G. Corrao , C. Montomoli https://creativecommons.org/licenses/by-sa/4.0
Cross-Country External Validation of a Multisource Comorbidity Score
Background The increasing impact of multimorbidity is escalating clinical and economic demands on healthcare systems, underscoring the necessity for effective tools to assess clinical complexity and enhance management strategies [1-3]. The Multisource Comorbidity Score (MCS) is a population-based index based on regional healthcare Utilization databases - hospitalizations and drug prescriptions - of beneficiaries with age equal or greater than 50 years. The MCS was developed in the framework of the Monitoring and Assessing Care Pathways working group of the Italian Ministry of Health, and validated in four Italian regions, showing good performance in predicting mortality, hospitalizations, and healthcare costs [4]. Objective This study aimed to externally validate and adapt the MCS within the Catalan healthcare system, assessing its predictive capability outside the original Italian setting. This validation seeks to determine whether the MCS can serve as transferable tool in other healthcare systems with different data availability. Methods An observational longitudinal study was performed on subjects aged 50 years or older, residing in the health district of Barcelona-Esquerra (ES) continuously during 2014-2015 and followed between January 1st, 2016, and December 31st, 2019. Data were obtained from the Catalan Health Surveillance System [5] which integrates demographic, clinical and healthcare utilization information from several healthcare databases. For this study, we used Catalan healthcare system beneficiary’s, primary care, hospitalization, and pharmacy dispensation databases. First, the MCS with Italian weights (MCS-1) was applied in the Catalonia setting using the same data sources (hospitalization and pharmacy dispensation databases) as in the original Italian version. Second, new MCS weights (MCS-2) were estimated in predicting one-year mortality (primary outcome) in the Catalonia setting using the methodology and data sources as described in [4]. Finally, a third MCS version (MCS-3) was developed estimating specific weights based on hospitalization, pharmacy dispensation and primary care databases to predict the primary outcome. Secondary outcomes considered were four-year mortality, one- and four-year hospitalizations (≥1) and one- and four-year hyperfrequency primary care utilization (≥10 visits). To assess the performance of the three MCS versions, generalized linear models (GLMs) with a binomial distribution were used for each outcome. ROC curves and Area Under the Curve (AUC) with 95% Confidence Intervals (95% CI) were estimated to assess the discrimination ability of the three MCS versions. De Long's method was used to compare the AUCs [6]. Net Reclassification Improvement (NRI) [7] was also calculated to assess improvements in risk classification by comparing new MCS versions with the MCS-1. The predicted GLM values, for each outcomes, of the three MCS versions were estimated and a threshold of 0.5 was used to distinguish between high and low risk individuals. The NRI estimates the percentage of individuals who were correctly reclassified into a higher risk category (according to the threshold) if they experienced the outcome, or into a lower risk category if they did not, minus those who were incorrectly reclassified when comparing the two models. Results As of January 1st, 2016, a total of 440,790 individuals had resided in the health district of ES for at least two years. Among them, 198,753 (45%) were aged 50 or older and formed the study cohort. They were mostly women (57%), with a median age of 66 years (IQR: 57–76). Table 1 shows the MCS versions performances according to one- and four-year outcomes. All MCS versions demonstrated good discriminatory performance in primary and secondary outcomes. For one-year mortality, the MCS-1 achieved an AUC of 0.742 (95% CI: 0.734-0.750) similarly to MCS-2 (AUC=0.756, 95% CI: 0.744-0.768), while MCS-3 version showed a significant improved AUC respect to MCS-1 (AUC=0.771, 95% CI: 0.760-0.783, p<0.001). Both new MCS versions performed better in predicting four-year mortality compared to MCS-1 (p= 0.012; p<0.001, respectively). On the contrary, the MCS-1 showed better performance in predicting all secondary outcomes except one-year hospitalizations with respect to MCS-3. In addition, significant improvements in risk reclassification for both one-year and four-year mortality were observed with the MCS-2 and MCS-3 compared to the MCS-1. For one-year mortality, the NRI increased by 0.63% (95% CI: 0.14–1.17) in MCS-2 and by 2.17% (95% CI: 1.39–2.97) in MCS-3. Similarly, for four-year mortality, the NRI increased by 1.8% (95% CI: 1.33–2.25) and 2.9% (95% CI: 2.35–3.45), respectively. An increment in risk reclassification was found in four-year hospitalizations and hyper-frequency when comparing MCS-3 to MCS-1; the reclassification worsened in all secondary outcomes comparing MCS-2 to MCS-1. Conclusions The study supports the external validity of the Multisource Comorbidity Score in other healthcare systems with different data availability, such as Catalonia. The local adaptation slightly improved the ability of the score in predicting mortality, however this advantage was not maintained in the secondary outcomes, highlighting the importance of contextual adaptation of such tools. These findings provide a basis for expanding the use of the score and refining it in different health systems and population segments.
Copyright (c) 2025 Alessandro Fontanarosa, Flavia Carle, Roberta Papa, Marco Pompili, Giovanni Corrao, Valero-Bover Damià , Roca Josep, Rubèn González-Colom, Skrami Edlira https://creativecommons.org/licenses/by-sa/4.0
Evaluation of an educational intervention program designed to increase vaccination attitude in prison setting: results from the RISE-Vac project
Introduction Prison population is particularly vulnerable to communicable diseases, including vaccine-preventable infections [1], due to various factors such as low social distancing, high turnover rates, and a high proportion of migrants [2]. However, vaccination coverage among people living in prison (PLP) remains low, largely due to limited access to healthcare services, low levels of vaccine literacy and general health literacy [3]. Additional challenges include persistent vaccine hesitancy and widespread distrust in institutions. Objectives As part of the RISE-Vac project—co-funded by the 3rd EU Health Program—we developed and implemented an evidence-based educational program aimed at boosting vaccination rates in both PLP and prison staff (PS) and evaluated the effect of the intervention on vaccine hesitancy, vaccine literacy and vaccine attitude. Methods We conducted a cross-sectional non-randomised study in 24 prisons of 6 European countries. The sample included 782 PLP and 717 PS members. In participant prisons, an educational program about vaccination for preventable infection diseases was implemented; for PLP, the program consisted in the distribution of educational material (leaflet and a short video) and/or an educational event; for PS, it consisted in a 4-hours online course. Within the study sample, 387 from PLP (49%) and 285 from PS (40%) undertook the intervention. Vaccine hesitancy was measured through a scale previously validated in a sub-cohort of participants – in the following referred to as pre-test [4]. A structured questionnaire was administered to measure vaccine literacy, general health literacy and socio-demographic characteristics of participants. Vaccine attitude was determined as the willingness to accept a vaccine if offered. Linear regression model was applied to assess the effect of the intervention and pre-test on vaccine and general health literacy and vaccine hesitancy. Logistic regression was applied to assess the association between the intervention and pre-test on vaccine attitude. All analyses were stratified by group (PLP and PS) and adjusted for socio-demographic variables. Mediation analysis was conducted to quantify the proportion of the effect of the intervention on vaccine attitude mediated by vaccine hesitancy, adjusting for pre-test and socio-demographic variables. Results In both PLP and PS, the intervention was associated with higher levels of vaccine literacy and stronger associations were observed among those who undertook the pre-test (PLP: interaction between pre-test and intervention, p=0.03; intervention, p=0.005 and p<0.001 in the non-pre-test and pre-test group respectively; PS: interaction, p<0.001; intervention, p=0.02 and p<0.001 in the non-pre-test and pre-test group respectively). In PLP, the intervention was inversely associated with vaccine hesitancy only in those who did not take part of the pre-test but not in the others (interaction, p=0.02; intervention, p<0.001 and p=0.22 in the non-pre-test and pre-test group respectively). In PS, no significant interaction was observed between the intervention and pre-test (p=0.11); the intervention was significantly associated with lower vaccine hesitancy (p=0.002). In both PLP and PS, the intervention was significantly associated with a positive vaccine attitude toward vaccination with no significant interaction with pre-test: in PLP, OR=5.21 (95% CI: 2.74 to 9.91); in PS OR=2.52 (95% CI: 1.54 to 4.13). Finally, mediation analysis showed that less than 30% of the effect of the intervention on vaccination attitude was mediated by a reduction in vaccine hesitancy (24% with p<0.001 and 28% with p=0.002 in PLP and PS respectively). Conclusion Evidence-based educational interventions are effective in improving vaccine hesitancy and vaccine literacy among PLP and PS. Also, they enhance the willingness of participants to be vaccinated, through a mechanism that is only partially explained by their effect on vaccine hesitancy. Further research should be conducted to quantify the real impact of this kind of intervention on vaccine uptake in prison population.
Copyright (c) 2025 Patrizia Giangrande , Marco Fornili , Maria Paola Tramonti Fantozzi , Angela Bardelli , Erica De Vita , Lara Tavoschi , Lara Tavoschi , Laura Baglietto https://creativecommons.org/licenses/by-sa/4.0
Data Harmonization of Psychosocial Questionnaires across Population Cohorts: A Differential Item Functioning Analysis
Introduction Data harmonization is the process of achieving comparability of similar measures collected by separate cohorts by aligning their definitions and measurement formats [1]. In epidemiologic research, and in particular in predictive modelling for chronic noncommunicable diseases, it may facilitate the identification of derivation and external validation cohorts [2]. This process may be particularly challenging when psychosocial variables are amongst the predictors of interest, as different self-report questionnaires measuring the same psychosocial variable might capture different aspects, and even small differences in item wording affect responses [3]. Therefore, measurement invariance; i.e.; whether respondents from different populations (e.g.; at different time periods, or different cultures) with the same latent trait (e.g. depression) level respond similarly, should be preliminarily established to avoid bias in subsequent analyses [4]. Currently, there is little guidance on how to assess this property for data harmonization purposes. A promising framework is Item Response Theory (IRT), a probabilistic approach for modelling the relationship between a latent trait and observed item responses [5]. We designed an international project pooling Italian and German cohorts with recruitment time spanning over a 10-year period, with the aim to identify a restricted set of psychosocial items able to increase the predictive power of established risk prediction models for Cardiovascular Diseases (CVD). Here, we leverage IRT to assess Differential Item Functioning (DIF) in harmonised items, thereby testing whether item characteristics are invariant across the Italian cohorts. Aims To harmonise two questionnaires used in different cohorts to assess depressive symptoms and to evaluate the measurement invariance of the harmonised items across cohorts. Methods The MONICA Brianza study includes three independent cohorts recruited from the Brianza population, Lombardy region, over a 10-year period (MONICA87: 1986–1987; MONICA90: 1989–1990; MONICA93: 1993–1994). Each cohort includes a 10-year age- and gender-stratified random sample of the target 25- to 64-years old population. Overall, 4932 individuals participated to the study (69% of invited). Depressive symptoms were measured in the MONICA87 cohort using the Beck Depression Inventory (BDI) [6], a 22-item instrument with a 0 (absence of symptom) to 4 (severe symptom) response format, and in the MONICA90-93 cohorts using a 14-item version of the Maastricht Vital Exhaustion Questionnaire (EX) [7], coded on a scale of 0 (“No”) – 1 (“Not sure”) – 2 (“Yes”). Protocol steps for data harmonization and analysis We developed an a priori protocol for data harmonization based on the following steps. First, the content of the BDI and EX items were analysed to select item pairs measuring the same depressive symptoms. Second, for each item pair, the response format was dichotomised ensuring that the collapsed response categories have the most similar possible frequencies of endorsement across cohorts. Third, the resulting scale was analysed through a unidimensional Confirmatory Factor Analysis and, in case of departures from unidimensionality, a minimum residual exploratory factor analysis (EFA) to identify subscales. Fourth, a 2-parameter logistic IRT model was fitted on the resulting subscales to estimate for each item a discrimination parameter (ability to differentiate between individuals with close levels of the latent trait), and a difficulty parameter (level of the latent trait at which the item has a 50% probability of endorsement). Item fit was evaluated through S-X2 statistics [8]. Finally, DIF analyses were conducted to evaluate whether item parameters were invariant across cohorts through Likelihood ratio tests comparing nested models with increasing equality constraints on discrimination and difficulty parameters. Results 525 individuals in the MONICA87 and 48 individuals in the MONICA90-93 cohorts were excluded due to unavailability of the questionnaires at baseline date. The final sample (51% female, mean age ±SD: 45.9±11.3) included 1134 subjects from MONICA87 and 3225 from MONICA90-93. The item content analysis led to the selection of 10 item pairs, which were used to form a harmonised scale of depressive symptoms (Dep). One item pair was discarded due to overlap with another item pair, both assessing sleep disturbance (tetrachoric correlation: 0.78). Standard fit indices from the CFA suggested departures from unidimensionality. EFA identified two latent variables, one underpinning a “Neurovegetative and arousal disturbance” subscale (4 items; e.g., sleep problems; loss of energy), and one an “Affective and cognitive disturbance” subscale (5 items; e.g., suicidal thoughts). Both subscales met the assumptions for the IRT model and all items showed adequate model fit. DIF analysis (Figure 1) confirmed measurement invariance for six items (e.g.; Dep03, panel A). Items Dep02 (panel B) and Dep05 (panel C) exhibited different discrimination and difficulty parameters across cohorts, suggesting non-uniform DIF. Finally, the different difficulty parameters for Dep10 (panel D), indicating uniform DIF, suggests that people from different cohorts with the same latent trait have different probability to endorse the item. Conclusions The harmonization of two depression questionnaires identified 6 items with measurement invariance and revealed DIF in items that could have biased subsequent analyses if undetected. In epidemiological research, measurement invariance of psychosocial questionnaires should be thoroughly checked using comprehensive methods. Analysis of DIF within an IRT framework offers a promising approach, and can be further evaluated for cross-cultural evaluations.
Copyright (c) 2025 Emanuele Maria Giusti , Marco Mario Ferrario , Giovanni Veronesi https://creativecommons.org/licenses/by-sa/4.0
Anni di Vita Persi Aggiustati per Disabilita’ per Malattie Amianto Correlate in Italia nel Periodo 2010-2020
Introduzione: Le stime degli anni di vita aggiustati per disabilità (Disability Adjusted Life Years: DALYs) rappresentano uno strumento fondamentale per quantificare il carico globale delle malattie e dei fattori di rischio, utili alla pianificazione delle politiche sanitarie [1]. L’amianto è riconosciuto come cancerogeno certo, responsabile di mesotelioma (M), tumori del polmone, dell’ovaio e della laringe, oltre che di asbestosi e ispessimenti pleurici. Nel 2019, a livello globale, si sono stimati 4.189.000 DALYs attribuibili a patologie derivanti dall’esposizione occupazionale all’amianto. In Italia, tra il 2010 e il 2020 si sono registrati 16.993 decessi per M, mentre dal 1993 al 2021 il Registro Nazionale dei Mesoteliomi (ReNaM) ha censito oltre 35.000 casi incidenti. Obiettivo: Stimare i DALYs attribuibili ad asbestosi e mesotelioma, patologie con elevata frazione eziologica legata all’amianto (rispettivamente 80% e 100%), in Italia nel decennio 2010-2020. Metodi: Sono stati presi in considerazione i decessi per M e asbestosi, i ricoveri ospedalieri per asbestosi e i casi incidenti di M. I DALYs sono stati calcolati come somma degli anni di vita persi per morte prematura (PYLLs) e degli anni vissuti con disabilità (YLDs). I decessi per M (ICD-10: C45) e asbestosi (ICD-10: J61), così come i ricoveri per asbestosi (ICD-9: CM 501), sono stati estratti dalle banche dati elaborate dall’ISS, utilizzando fonti ISTAT e Ministero della Salute. I casi incidenti di M sono stati stimati a partire dai dati ReNaM. Le informazioni sono state stratificate per genere, classe di età e anno di calendario. I PYLLs sono stati calcolati in valore assoluto e come tassi standardizzati (per 100.000 abitanti), a livello nazionale e regionale, applicando i valori di speranza di vita ISTAT, disaggregati per genere, età e anno. Gli YLDs sono stati stimati a livello nazionale considerando i ricoveri per asbestosi e i casi incidenti di M, utilizzando un peso di disabilità pari a 0,217 e una durata di 20-30 anni per l ’asbestosi, e un peso di 0,540 con un anno di durata per il M [2, 3]. Risultati: Nel periodo 2010-2020, in Italia si stimano 204.232 DALYs per patologie amianto-correlate tra gli uomini e 72.625 tra le donne, assumendo una durata di 30 anni per l’asbestosi. I PYLLs ammontano a 161.300 nei maschi (di cui il 96,7% per M) e 67.311 nelle femmine (99,1% per M). Le regioni settentrionali, in particolare Liguria, Piemonte e Friuli-Venezia Giulia, mostrano i tassi più elevati di PYLLs per M in entrambi i sessi. Gli YLDs sono pari a 7.075 per M e 35.857 per asbestosi negli uomini, e a 2.710 per M e 2.604 per asbestosi nelle donne. Conclusioni: Per la prima volta in Italia viene proposta una stima del carico complessivo di mesotelioma e asbestosi in termini di DALYs. Questi dati, che includono gli anni vissuti con ridotta funzionalità, rappresentano un supporto rilevante per la programmazione sanitaria e l’attuazione di adeguate misure di welfare.
Copyright (c) 2025 Arianna Guaita , Carolina Romano, Giada Minelli , Alessandra Binazzi, Alessandro Marinaccio , Carolina Mensi, Amerigo Zona , Lucia Fazzo https://creativecommons.org/licenses/by-sa/4.0
Adherence to Diabetes Care Pathways and Risk of Diabetes-Related Complications by Citizenship: A Population-Based Study from The Mighty Project (Cup P2022ASXKR)
Introduction National and international guidelines on diabetes recommend continuous monitoring of this condition to prevent short- and long-term complications [1–3]. Adherence to the Diagnostic Therapeutic Care Pathway (DTCP) is evaluated by the Italian Ministry of Health using annual indicators based on five key recommendations [4], to ensure uniformity of care across all regions. However, equitable access to these services remains a public health challenge, particularly for migrant populations, who may face cultural, linguistic, or systemic barriers. Objective Within the MIGrants’ HealTh and healthcare access in ItalY (MIGHTY) project, this population-based study aimed to compare adherence to DTCP in subjects with new diagnosis of diabetes between migrant and Italian populations, and to evaluate the association between citizenship and diabetes complications in the Marche Region, between 2013–2023. Methods A population-based cohort study was conducted using Healthcare Utilization Databases from the Marche Region, including Regional Beneficiary, Hospital Discharge, Drug Prescription, Outpatient Care, and Exemption databases. The cohort of new cases of diabetes included adults (≥18 years) with a first diabetes-related event between 2013 and 2017, defined as: ≥2 prescriptions of glucose-lowering drugs (ATC: A10*) within one year, ≥1 hospitalization with diabetes as primary/secondary diagnosis (ICD-9-CM: 250.*), or ≥1 diabetes exemption (code: 013*); the date of the first event was defined as the index date. Exclusion criteria were being resident in Marche Region for less than two years prior to index date, any diabetes-related events in the two preceding years, and childbirth-related hospitalizations (Major Diagnostic Category 14) during the year of inclusion in the cohort or the two prior years. Subjects were classified as Italian or migrants from High Migratory Pressure Countries (HMPC) based on citizenship [5]. The five recommendations monitored by the Ministry of Health [4] were assessed: ≥2 HbA1c tests (PDTA-05.1), ≥1 lipid profile (PDTA-05.2), ≥1 microalbuminuria test (PDTA-05.3), ≥1 renal function test (PDTA-05.4), and ≥1 eye exam (PDTA-05.5) each per year. The adherence to each recommendation and the overall adherence, i.e. meeting at least 4 out of 5 recommendations (PDTA-05), was annually evaluated over six years from the index date for each subject. Mixed-effects logistic models for repeated measures were used to evaluate adherence to recommendations considering as independent variables citizenship (Italian vs. HMPC), year of evaluation, sex, age groups (65-74 vs. 18-44, 45-54, 55-64, 75+ years), and Multisource Comorbidity Score (MCS) [6] classes (≤4 vs. 5-9, 10-14, 15-19, ≥20). Diabetes-related complications were defined as a hospitalization with a primary diagnosis of short- or long-term diabetes complications, or uncontrolled diabetes, or non-traumatic lower limb amputation [7]. Subjects with at least one year of follow-up, were followed up from index date to a diabetes-related complication, all-cause death, emigration out of the region, or December 31, 2023, whichever came first. Cox proportional hazards model was used to estimate the association between citizenship and risk of complications, adjusting for age groups, sex, MCS classes, and including annual adherence to each DTCP’s recommendation as time-dependent covariates. Results are reported with 95% Confidence Interval (95% CI). All data were processed in compliance with the European (GDPR, EU 2016/679) and national privacy laws (D.lgs. 196/2003 and subsequent amendments). Results The study cohort comprised 28,674 adults with newly diagnosed diabetes, of whom 1,529 (5.3%) were migrants from HMPC. At index date, migrants from HMPC were younger (mean age 50 vs. 66 years), more frequently female (59% vs. 48%), and had lower comorbidity scores (MCS median: 2 vs. 6) compared to Italians. Migrants from HMPC compared to Italians showed lower observed adherence to all DTCP recommendations: HbA1c (26.1%, 95%CI 25.1-27.1 vs. 36.0%, 95%CI 35.7-36.2), lipids (43.4%, 95%CI 42.2-44.5 vs. 61.3%, 95%CI 61.1-61.6), microalbuminuria (27.0%, 95%CI 25.9-28.0 vs. 31.3%, 95%CI 31.0-31.5), renal function (46.8%, 95%CI 45.7-47.9 vs. 66.2%, 95%CI 66.0-66.5), eye exam (6.7%, 95%CI 6.1-7.3 vs. 8.4%, 95%CI 8.3-8.6), and overall adherence (15.6%, 95%CI 14.7-16.4 vs. 20.3%, 95%CI 20.1-20.6). Mixed-effect models confirmed lower adherence among migrants, except for eye exams (Figure 1). Of 24,992 with ≥1 year follow-up, 3,229 experienced complications (3,161 Italians, 68 migrants). Six-year complication-free survival was 85.7% (Italians) vs. 94.1% (migrants). In the adjusted Cox regression model, citizenship was not significantly associated with the risk of developing diabetes complications (HR=0.98; 95% CI: 0.77–1.26; p=0.896). Conclusions This study highlights differences in adherence to recommended diabetes care pathways between Italians and migrant populations. However, these differences did not translate into a higher risk of developing diabetes-related complications. Longer period of observation might be required to evaluate the impact of citizenship on adverse diabetes- related outcomes. Sustained monitoring and culturally tailored interventions remain essential to ensure equitable access and prevent future health inequalities.
Copyright (c) 2025 Marica Iommi , Andrea Faragalli , Alessandro Fontanarosa Alessandro, Marco Pompili , Flavia Carle, Barbiano di Belgiojoso Elisa, Edlira Skrami , Rosaria Gesuita https://creativecommons.org/licenses/by-sa/4.0
Evaluation of Vaccination Status of Patients Diagnosis of Multiple Myeloma or Monoclonal Gammopathy of Uncertain Significance: Analysis of Coverage in the Province of Catania
Introduction : The patient diagnosed with multiple myeloma or monoclonal gammopathy of undetermined significance (MGUS) is considered a fragile patient, at risk of severe post-infectious outcomes [1] for which adequate vaccination prevention against pneumococcus and meningococcus, Herpes Zoster virus , influenza virus, SARS-CoV2 is recommended [2]. Despite existing recommendations, there is limited evidence regarding actual vaccination coverage of these patients [3]. Objectives: This study aims to evaluate the vaccination status in a cohort of haemato-oncology patients and to propose strategies to improve their vaccination coverage. Methods : We conducted a retrospective cohort study on 737 patients with multiple myeloma or MGUS diagnosed in the province of Catania in the period 2000-2023, coded by the Integrated Cancer Registry. Vaccination status was retrieved by cross-referencing cases with data in the Provincial Vaccination Registry. Medical records and vaccination data collected during outpatient visits, hospital admissions for possible hospitalization for infectious disease were examined and the presence of infectious disease diagnoses among the causes of death was verified. The data will be presented in aggregate form. The variables considered were: tumor diagnosis, last follow-up date, date of death, date and components of vaccinations performed. Relative risk with 95% confidence intervals on deaths with respect to the vaccination stratum analyzed by type of disease was calculated. Analyses were performed with Stata 17. For all statistical tests, the significance level was set at a p value <0.05. Results : Preliminary results indicate poor adherence to recommended vaccinations. Frequency distribution of variables of vaccination status, post-diagnosis vaccination, cycle completeness and timing of vaccinations were calculated. The observed coverage is 30.9% for influenza, 6% for Varicella Zoster, 39% for SARS-CoV2, 1.5% for meningococcus and 10% for pneumococcus of vaccinable subjects. The observed all-cause mortality rate among never-vaccinated subjects is 45.4%; among subjects with at least one dose of vaccine, it is 18.4%. Delays in meeting recommended intervals and incomplete cycles have been observed. Relative risk (RR) analysis shows that vaccination is significantly associated with a reduction in mortality in the overall cohort (including patients with MGUS and multiple myeloma). In particular, subjects who have received at least one of the recommended vaccinations have a reduction in the risk of death of approximately 60% compared to unvaccinated subjects. In detail: In patients with MGUS, vaccination is associated with an approximately 74% reduction in the risk of death. In patients with multiple myeloma, the risk reduction is around 42.7%. Focusing the analysis on individual vaccines: Vaccination against COVID-19 is associated with an approximately 63% reduction in the risk of death. The one against the flu shows a reduction of 59.2%. For pneumococcal, meningococcal, and Herpes Zoster vaccines, no statistically significant association with mortality was observed, either in the overall cohort or in subgroups. However, potentially relevant trends emerge: In patients with MGUS, meningococcal vaccine showed a RR of 1.65 (95% CI: 0.91–3.02; p = 0.10), while herpes zoster vaccine showed a RR of 0.65 (95% CI: 0.35–1.21; p = 0.18). The wide confidence intervals suggest a possible lack of statistical power to detect significant effects. Conclusions : Protection provided by vaccination is present in both subgroups, but is more marked in MGUS patients (RR ≈ 0.26) than in those with myeloma (RR ≈ 0.57). The study provides an overview of the vaccination status in haemato-oncology patients, highlighting the need for personalized strategies: vaccination recommendations in the discharge letter, promotion of training of hospital staff and the attending physician. The organization of vaccination sessions in the hospital outpatient setting has been activated in conjunction with periodic or follow-up checks.
Copyright (c) 2025 A. Ippolito, V. Grieco, A. Romano, M.A. Aiello, A. Torrisi, A. Di Prima, C. Chillari, G. Giorgianni, R. Ragusa https://creativecommons.org/licenses/by-sa/4.0
Determinants of Carbon and Water Footprints of Food Consumption in an Italian Population
Introduction: The environmental impacts of food consumption, specifically carbon footprint (CF) and water footprint (WF), are gaining increasing importance in the context of global sustainability and public health. As global food demand is expected to increase by 35% to 56% between 2010 and 2050 [1] substantial shifts in dietary habits are required to sustain a growing global population while aligning with the One Health approach ​[2]​, and the 2030 agenda for the accomplishment of Sustainable Development Goals (SDGs) established by the Food and Agriculture Organization (FAO) [3]. However, dietary choices are influenced by a complex interplay of factors, including socioeconomic status, cultural traditions, and individual preferences ​[4]. This study evaluates the contribution of different food groups to environmental footprints and examines how health determinants such as gender, age, BMI, geographic location, and adherence to the Mediterranean diet (MD) influence these impacts. The goal is to better understand how dietary habits affect environmental sustainability and to provide data-driven recommendations for policy development. Objectives: To estimate the average daily CF and WF of the study sample, leveraging estimates from the SU-EATABLE LIFE (SEL) Database ​[5]. Furthermore, the study will identify the food groups with the highest and lowest environmental impact and explore how dietary impact varies based on gender, age, Body Mass Index (BMI), education level, geographic region, and adherence to the MD measured through the Mediterranean Adequacy Index (MAI). Methods: This cross-sectional study utilised data from 2,831 participants in the third INRAN-SCAI Survey, conducted across various regions of Italy in 2005-2006. We calculated the environmental footprints by multiplying the daily consumption of each food item (n = 878), classified using the European Food Safety Authority (EFSA) FoodEx2 system, by the footprint coefficients from the SEL database. For items without direct matches (e.g., canned beer), we assigned median values from related food commodity subcategories as proxy conversion factors. We excluded approximately 35% of items lacking reliable conversion data, leaving 617 food items with assigned footprint values. In addition to dietary data, we considered sociodemographic and anthropometric variables including age, sex, BMI, education level, and geographical area of residence. We evaluated adherence to the MD using the MAI score ​[6,7]​. We excluded foods incompatible with the MAI framework (e.g., alcoholic beverages other than wine, mixed processed dishes, coffee), representing less than 7% of total energy intake in the population. We performed the comparisons of CF and WF across sociodemographic variables using two-sided t-tests and one-way analysis of variance (ANOVA) to examine differences by sex, BMI, geographical area of residence, and education level. We used multiple linear regression models to investigate associations between environmental impact indicators and individual characteristics, with CF and WF as the dependent variables in separate models. Independent variables included age, sex, BMI, educational level, geographical area, total energy intake, MAI score, and energy density of food consumed (kcal/kg). Results: The average CF was 3.53 kg CO₂eq/day, and the average WF was 3,330.96 L/day. The mean daily CO₂ emissions per kcal consumed were 1.88 g CO₂eq (SD = 0.05), while the average WF per kcal was 177.12 L (SD = 34.87). Red meats (1.08 kg CO₂eq; 657.24 L) and dairy products (0.28 kg CO₂eq; 708.62 L), were the largest contributors to CF and WF. Overall, meat accounted for 68.7% of the total CF and 27.5% of the total WF, and dairy products, contributing 20.0% to CF and 21.3% to WF. Moreover, subjects with the highest adherence to MD (above the population mean) showed reduced CF (9.84 vs. 11.01 kg CO₂eq) and WF (9,356.0 vs. 10,348.3 L) compared to lower adherence. Males had higher environmental impacts (3.92 kg CO₂eq; 3,691 L) than females (3.21 kg CO₂eq; 3,037 L) (p<0.001), as well as younger individuals (3.76 kg CO₂eq; 3,491 L) versus older adults (3.12 kg CO₂eq; 2,948 L) (p<0.001). In multiple linear analysis, beyond sex, age, education and geographic area of residence, subjects with higher adherence to the MD were linked to a lower environmental impact both in term of CF (β = -0.239) and WF (β = -206.4), independently on kcal intake. Conclusions: Animal-based foods, particularly red meat and dairy products are the primary drivers of the environmental impact of diets in Italy. Health determinants, including gender, age, and adherence to the MD, significantly influence individuals' environmental footprints. Specifically, males and younger individuals exhibit higher environmental impacts, whereas stronger adherence to the MD is associated with lower environmental burdens. Transitioning towards more plant-based, sustainable dietary patterns is crucial not only for reducing the environmental burden of diets but also for addressing pressing global challenges such as climate change and water scarcity. These findings further highlight the complexity of environmental impacts linked to dietary behaviours and emphasises the importance of considering multiple factors, including further socio-demographic and lifestyle variable. Dietary shifts, when combined with targeted policy intervention aimed at specific populations and strategic modifications in food systems, have the potential to promote more sustainable diets and address the urgent environmental challenges facing our planet.
Copyright (c) 2025 Doriana Lacalaprice , Nicolò Scarsi , Francesco Gianfagna , Andrea Paladini, Stefania Boccia , Roberta Pastorino https://creativecommons.org/licenses/by-sa/4.0
Sleep Disorders, Smartphone Use and Mental Health: A Cross-Sectional Study on a Sample of Students from the University of Palermo – MORPHEO
Introduction Sleep disorders constitute a significant public health concern recognized by the World Health Organization (WHO) in the ICD (International Classification of Diseases), with notable implications for young populations. Research demonstrates that disrupted sleep patterns significantly impair mental recovery processes and emotional stability [1]. Poor sleep quality contributes to mental health deterioration through disruption of emotional regulation and neurobiological mechanisms. Inadequate sleep compromises hypothalamic-pituitary-adrenal axis function, increasing cortisol production and stress perception, potentially leading to depressive symptoms [2]. Young adults represent the population stratum with the highest smartphone and electronic device usage rates, sometimes developing behavioural dependencies. Studies show that light exposure to these devices before falling asleep significantly disrupts sleep quality [3]. Moreover, excessive smartphone use is associated with reduced cognitive performance, negatively affecting work efficiency and academic achievement [4]. This study investigates the interactions between sleep disorders, mental health, electronic device usage, and academic performance among university students. We specifically examine how sleep quality and quantity influence students' psychological functioning, with particular attention to psychological distress. Methods The Pittsburgh Sleep Quality Index (PSQI) [5], the Kessler Psychological Distress Scale (K10) [6] and the Smartphone Application-Based Addiction Scale (SABAS) [7] were used to assess sleep quality, mental distress and problematic smartphone use, respectively. Descriptive statistics were expressed as Mean (SD), for continuous variables, and as count/percentages for categorical variables. “Good sleepers” and “Poor sleepers” were compared using Chi-square test or Fisher's exact test for categorical variables, and Student's t-test or the Wilcoxon-Mann-Whitney test for continuous variables, with significance at P < 0.05. Logistic regression identified independent predictors of poor sleep quality (PSQI > 5). Variables with significant univariate association (p < 0.05) were included in the multivariate model, with results expressed as odds ratios (OR) with 95% confidence intervals (95% CI). Results This cross-sectional study involved 208 students from the University of Palermo, with 58.7% (n=122) enrolled in medical degree programs. The average age of the sample was 22±1.99 years, and 71.6% were female. The analysis revealed that 61.54% (n=128) of students had inadequate sleep quality. Univariate analysis showed that their exam completion rate (80.1%) was lower than that one reported for good sleepers (83.5%) (p < 0.05). On average, daily smartphone use was higher among poor sleepers (6.46±3.03 vs 5.57±2.22 hour/day, p < 0.05), and a significant association was found between poor sleep quality and the risk of problematic smartphone use (OR=2.83, 95%CI [ 1.27-7.00], p < 0.05). Furthermore, results from K10 revealed that reporting severe psychological distress was significantly associated to poor sleep quality (OR=13.25, 95%CI= [5.34-37.28], p < 0.001). The multivariate analysis confirmed that higher daily smartphone usage, measured in hours, is associated with poor sleep quality (AdjOR=1.21; 95% CI [1.02-1.45]) and, notably, subjects with high probability of severe psychological distress have significantly higher likelihood of being classified as poor sleepers (AdjOR = 9.59, 95% CI = [3.57-28.82]). Discussion Our analysis revealed a strong association between psychological distress (K10 scale) and poor sleep quality among university students. Students experiencing significant psychological distress showed markedly higher likelihood of being poor sleepers, confirming bidirectional relationships between mental health and sleep, as documented in previous research. Daily smartphone uses also emerged as a significant predictor of poor sleep quality, aligning with literature on electronic devices' detrimental effects on sleep hygiene. Smartphone light emissions, particularly blue light, suppress melatonin production and disrupt circadian rhythms [8]. These findings emphasize the importance of addressing sleep health within university mental health and academic support initiatives. The strong psychological distress-sleep quality association suggests interventions targeting either aspect may benefit the other. Universities should consider implementing screening programs to identify students at risk of sleep disorders, especially those reporting psychological distress symptoms. Additionally, digital hygiene education should be incorporated into student wellness programs to mitigate electronic devices' negative impact on sleep. Conclusions The study highlights the link between psychological distress, smartphone use, and sleep quality in university students. The strong connection between mental health struggles and sleep issues underscores the need to integrate sleep health into mental health services. Universities should promote well-being and responsible technology use to enhance academic performance and overall student health.
Copyright (c) 2025 Manuela Lodico , Laura Maniscalco , Silvana Miceli , Marco Enea , Domenica Matranga https://creativecommons.org/licenses/by-sa/4.0
Evaluating the Use of Cluster Analysis to Detect and Correct Selection Bias in Single-Center Observational Research on Voluntary Terminations of Pregnancy
Introduction Voluntary termination of pregnancy (VTP) has always been debated within the general population. The circumstances leading women to choose VTP are multifaceted, and it remains challenging to identify patterns leading women to opt for this procedure [1, 2]. Up to now, numerous studies have focused on characterizing demographic or psychological profiles of women within specific geographic regions [3, 4]. Due to the sensitive nature of the decision and privacy requirements, studying population clusters in VTP presents inherent methodological limitations. While these challenges can be partially mitigated, they risk a potential selection bias during cohort formation. In these cases, a monocentric study may introduce biases that could arise primarily from the selection of the study centre and secondarily from the difficulty of properly recruiting a sample representative of the entire population. In this context, cluster analysis conducted first on the general population and subsequently applied to the selected sample could clarify the nature of the selection process that occurred, thereby enabling better estimation of the final conclusions [1, 2]. In general, clustering demonstrated efficiency and feasibility to handle large datasets [5, 6]. In particular, the use of hierarchical clustering procedure is able to choose the optimal number of clusters in the population with a readily available stopping rules, without a priori selection of number of clusters [7]. This approach aligns with contemporary efforts to leverage data-driven methodologies in reproductive health research while addressing ethical considerations inherent to studies of private medical decisions. Objective The aim is to evaluate the effectiveness of a clustering method in detecting selection bias within a sample from a monocentric observational study, to generalize the findings from the small sample to the broader population and draw valid inferences from the results. Methods We used a retrospective observational database of 6559 women that carried out VTP in Apulia, from January 2023 to January 2024. Data was collected according to the regulations established by the National Surveillance System and were taken from the Health Information System of the Apulia region. The second database was collected, as a cross-sectional, observational study, conducted on 122 women > 18 years who underwent VTP in the Family Planning Unit of the "Di Venere-Fallacara" hospital, between November 2023 and January 2024. The assumption made were that the women of the selected sample are drawn from the same population. Listwise deletion was applied, to handle missing value. A Multiple Correspondence Analysis (MCA) was first conducted on the population sample to identify the structure of relationships among variables, reducing dimensionality to two principal dimensions. The socio-demographic characteristics and the characteristic of the VTP event were chosen as variables. We did a hierarchical cluster analysis using Hierarchical Clustering on Principle Components (HCPC) procedure [8,9]. We use the Euclidean metric for calculating distances between observations and the Ward's method. The procedure was applied only on the population dataset. When the cluster has been defined, we predicted the MCA output on the selected sample [11,12]. We selected 10 casual sample of the 111 women from the entire population to compare differences in percentage among clusters. Chi-square tests were employed to compare the proportion of women in each cluster in both samples. We use the z-tests to account the difference in proportions among clusters between the selected sample of women and the population sample [13]. Results with a two-sided p-value < 0.05 were considered statistically significant. The statistical comparisons were done using SAS/STAT® Statistics version 9.4. The cluster analyses were developed using R software version 4.5 [8, 9, 14, 15]. Results Following listwise deletion, MCA was conducted on a total of 6353 women. The total women in the selected sample with no missing value were 111. The optimal number of clusters was determined using hierarchical clustering procedures implemented through built-in package functions. The p-values across all examined variables are highly significant (p << 0.001), providing robust evidence that the HCPC analysis has identified clinically meaningful clusters, representing authentic subgroups within the population. Table 1 shows the percentage distribution of women across cluster. Cluster 1 is overrepresented in both samples and subsamples (38% vs. 24%, median 41%). Cluster 2 shows minimal deviation between population and sample (8% vs. 9%), but the median percentage in 10 random subsamples deviates substantially (26%). Cluster 3 is significantly overrepresented in the sample selected (53% vs. 28%, median 27%). Cluster 4 is severely underrepresented in both the samples (0.9% vs. 39%, median 8%). Conclusions The selected sample overrepresents Cluster 3 (53% vs. 28%) and nearly excludes Cluster 4 (0.9% vs. 39%). Random subsamples did not align with population proportions, suggesting possible random deviations in the selected sample or from sample size. The observed selection bias might originate from the a priori selection in the monocentric study design. This methodological limitation introduces systematic differences between the sampled cohort and the target population, particularly affecting the generalizability of findings to underrepresented clusters. Beyond ensuring transparency regarding the sample's generalizability, a solution involves implementing covariate balancing techniques [16, 17], balancing alongside synthetic oversampling methods [19] to address overrepresentation of specific variables. This dual approach aligns with causal inference frameworks while mitigating selection bias inherent in monocentric observational designs [20, 21].
Copyright (c) 2025 Letizia Lorusso, Bartolomeo Nicola , Paolo Trerotoli https://creativecommons.org/licenses/by-sa/4.0
Trajectories of Adherence to Biologics in Patients with Rheumatoid Arthritis and Risk of a Secondary Immuno-Mediated Inflammatory Disease: A Large Multi-Database Italian Study
Background Rheumatoid arthritis (RA) is an immune-mediated inflammatory disease (IMID) with a global prevalence of approximately 1%. The therapeutic strategy, aiming at achieving low disease activity, includes both conventional and biologic disease-modifying anti-rheumatic drugs (bDMARDs). Evidence from the literature suggests that patients with one IMID are at higher risk of developing another. However, data are lacking on the association between the occurrence of secondary IMIDs and longitudinal adherence to bDMARDs. Aim To evaluate the association between adherence trajectories to bDMARDs and the occurrence of secondary IMIDs in patients with RA. Methods We conducted a population-based retrospective observational cohort study using administrative data [1]. We included residents of the participating regions from 2010 to 2023 who had at least one biologic dispensing approved for RA, and a diagnosis of RA identified though a validated algorithm. We excluded individuals younger than 18 years, had less than one year of continuous enrolment (look-back), were prevalent users of RA bDMARDs, were treated with rituximab as index drug, or had a diagnosis of any IMID other than RA during the look-back period. The first dispensing date of a bDMARD was defined as the index date. Patients were observed over a two-year period: the first year (exposure period) to assess adherence, and the second year (follow-up period) to monitor the occurrence of secondary IMIDs, death, or end of the study period—whichever came first. During the exposure period, patients were censored if they developed cancer, became pregnant, or died. Treatment coverage was estimated assuming a daily intake of one Defined Daily Dose. We assessed adherence to bDMARDS monthly over the exposure period using the Medication Possession Ratio. Adherence trajectories were identified using Group-Based Trajectory Modeling (GBTM) [2,3]. We used a Cox proportional hazards model to estimate the hazard ratio, and corresponding 95% confidence interval, for developing a secondary IMIDs. Results We identified a cohort of 35,600 individuals, with a higher proportion of females (78%), and a mean age of 56.5 (standard deviation: 14.0). We identified four distinct adherence trajectories over a 12-month period. Group 3, labeled as the High Adherent group and comprising 49.7% of participants, maintains nearly 100% adherence consistently throughout the entire follow-up period, indicating stable and optimal adherence. Group 1, referred to as the Declining Adherent group and accounting for 11.0% of the sample, starts with high adherence but shows a gradual and marked decline, particularly after the fifth month, reaching levels around 0.2 by month 12. Group 2, named the Moderate Adherent group and comprising 19.5% of the sample, experiences an initial drop in adherence in the early months, followed by an increase and stabilization around 0.6 to 0.7. Group 4, described as the Low Adherent group and representing 19.8% of participants, demonstrates a steep and continuous decline in adherence from the beginning, falling below 0.2 within the first few months and remaining low for the rest of the follow-up period, highlighting significant variability in adherence behaviors across the groups. During the one-year of follow-up, 205 events of secondary IMIDs were observed. The Cox proportional hazards model did not reveal statistically significant differences in the risk of developing a secondary IMID across the adherence trajectory groups (overall p = 0.20). Compared to the High Adherent group (Group 3, reference), the Declining Adherent group (Group 1) had a hazard ratio (HR) of 1.28 (95% CI: 0.83–1.99), the Moderate Adherent group (Group 2) had an HR of 0.67 (95% CI: 0.43–1.04), and the Low Adherent group (Group 4) showed a non-significant increase in risk with an HR of 1.11 (95% CI: 0.78–1.59). Conclusion The study identified four distinct trajectories of bDMARD adherence, showing marked heterogeneity in patients' behaviors. However, there were no statistically significant differences in the risk of developing a second IMID between the groups. These findings suggest that factors other than adherence may influence the occurrence of autoimmune comorbidities.
Copyright (c) 2025 S. Giometto, I. Convertino, G. Pellegrini , R. Campelo , G. Limoncella , A. Spini , M. Massari , S. Spila-Alegiani , E. Carrasquinha , M. Tuccori , G. Trifirò , E. Lucenteforte https://creativecommons.org/licenses/by-sa/4.0
A Randomized Controlled Field Trial: A Gamified Training Course on Workplace Health and Safety Prevention for Middle School Students – "Let's Play 81!"
Introduction Injury prevention and the promotion of health in the workplace are priority objectives starting from school age. Early and effective training can help develop a safety culture in future workers. However, traditional methods often fail to engage students. Gamification, the integration of game elements into educational contexts, has emerged as a promising strategy to increase engagement, improve learning, and enhance working memory [1-5]. Objectives To evaluate the effectiveness of the gamified training course "Let's Play 81!" (“Giochiamo a 81!”) in promoting and improving knowledge of workplace health and safety, in reference to the contents of Italian Legislative Decree 81/2008 [6], among middle school students. Methods A teaching kit was created, consisting of a box (30×20×10 cm) containing an operational manual and the materials necessary for three different types of games: a role-playing game ("81 si gira!"), a board game ("Riduci il premio") and a card game ("Mettici la mano") [7]. The games were designed to reinforce the key concepts of Legislative Decree 81/2008, with particular attention to the duties and responsibilities of key figures in prevention (company owner, safety executive, operator in charge, Prevention and Protection Service Manager, security officer, inspector, occupational health physician, workers’ safety representative and a simple worker). The study was conducted following CONSORT guidelines [8]. The study was set in third-year classes from three middle schools of Rome. A 15-question quiz was administered to both the intervention group (IG) and the control group (CG) at the beginning (T0) and at the end of the training day (T1). "Score81" was calculated (range 0–15), based on the number of correct answers of the quiz. The quiz, validated in a previous study with an adult population (Cronbach’s α > 0.70), required one correct answer among four alternatives, including the "don’t know" option. The intervention included a 1-hour lecture, with materials (slides and theoretical content) available in the manual included in the kit, followed by 2.5 hours of playful-formative activities with the three games. Students were divided into groups of 7–9 participants, with about 40 minutes dedicated to each game. The activities were led by graduates in Prevention Techniques, specializing in Occupational Medicine, competent doctors, and university professors. The project received ethical approval from the Universitas Mercatorum Ethics Committee on March 13, 2025. Qualitative variables were described using absolute frequencies and percentages, while quantitative ones were described using means and standard deviation (SD). The comparison between IG and CG was performed using chi-square test for qualitative variables and independent t-test or Mann-Whitney test in the case of non-normality. The pre-post Score81 change was analyzed with paired t-tests or the equivalent non-parametric test. Normality was checked using skewness and kurtosis coefficients and the Kolmogorov-Smirnov test. The level of statistical significance was set at p<0.05. Results Ten classes (n = 226 students) were invited to participate (100% response rate). On the day of the intervention, 36 students were absent, resulting in 190 students participating (mean age 13.08 ± 0.36 years; 49.5% female), evenly distributed between the intervention (IG: n = 97) and the control groups (CG: n = 93). Three students from the CG were excluded from the analysis due to missing the T1 questionnaire. The groups were comparable for age, gender, and school of origin (p > 0.05). Based on the sample size obtained of both groups, it was possible to define the hypothesis of being able to observe a mean difference in the Score81 between CG and IG at time T1 of at least 3.96, with 80% power and a 95% level of significance, assuming SD=2. At the start of the study (T0), there were no significant differences in the Score81 between the groups (IG: 4.87 ± 2.41; CG: 4.69 ± 2.14; p=0.597). By the end (T1), the IG showed a significant increase in the mean score (9.30 ± 2.89), while the CG maintained values similar to T0 (4.60 ± 2.41) (Table1). The difference within groups was highly significant (p<0.001) only in the IG. When analyzing the change (Δ=post-pre) within the groups, the IG showed an average increase of Δ=+4.43 points (SD=2.98; p<0.001), whereas the CG showed no significant change (Δ=+0.09 points; SD=1.68; p=0.617). Conclusions The gamified training course "Let's Play 81!" proved effective in significantly improving knowledge of workplace health and safety among middle school students. Students in the intervention group achieved a significant average increase in Score81 of more than 4 points, while no changes were observed in the control group. The teaching kit "Let's Play 81!" was found to be easy to manage and use by trainers. The manual allows for the independent delivery of the theoretical lesson by teachers and trainers. The integration of theoretical content and gamified activities represents a replicable and sustainable strategy to promote a prevention culture among young people. Despite encouraging results, the study has limitations. Group assignment at the class level may have introduced selection bias, and the sample was limited to Roman schools, affecting generalizability. The absence of long-term follow-up prevented assessment of knowledge retention over time. The project is currently being extended to high school students to evaluate effectiveness in older age groups and broader educational contexts.
Copyright (c) 2025 Alice Mannocci , Guendalina Capece , David Shaholli, Francesco Belsito, Ilaria Frantellizzi, Bruno Gerace , Floranna Guarente Lombardo , Jessica Prataviera , Mren Quesada Ramos , Claudia Sarcoli , Ginevra Duraturo , Antonietta Monteduro , Giuseppe La Torre https://creativecommons.org/licenses/by-sa/4.0
Validation Protocol of the Italian Version of the Internet Related Measures (IRM) Questionnaire and Assessment of Internet Use Among High School Students in Italy: A Multicenter Study
Introduction Internet use has become an integral part of adolescents’ daily lives, a trend that intensified during and after the COVID-19 pandemic [1]. Alongside the increase in online activity, adolescents are now more exposed to digital threats such as harmful content (e.g., self-harm, radicalization), sexual exploitation, and cyberviolence [2]. Scientific literature highlights that some groups of adolescents—particularly those with psychological, familial, or social vulnerabilities—are more likely to encounter these risks [3,4]. In the Italian context, however, validated tools to assess Internet use and related risks are often outdated and lack cultural and technological relevance. This gap hinders effective screening and prevention efforts in schools. The present study addresses this need by validating an internationally adopted tool—the Internet Related Measures (IRM) questionnaire—for use with Italian adolescents, ensuring both scientific accuracy and contextual relevance. Objectives The study aims to: Validate the Italian version of the Internet Related Measures (IRM) questionnaire through linguistic, cultural, and psychometric adaptation; Explore and describe patterns of Internet use and perceived online risks among high school students in Lombardy. Methods This study will adopt a multicenter design and will involve students attending upper secondary schools (scuole secondarie di secondo grado) across the Lombardy region in Northern Italy. Participants will complete a structured questionnaire comprising sociodemographic items (age, gender, ethnicity, school attended) along with the Italian version of the IRM questionnaire. Descriptive statistics will summarize participant characteristics and Internet use patterns. Means and standard deviations will be used for normally distributed variables; medians and interquartile ranges for non-normally distributed data; and frequencies and percentages for categorical variables. Psychometric properties of the IRM will be evaluated through both exploratory and confirmatory factor analyses. Internal consistency will be assessed using Cronbach’s alpha and corrected item-total correlations. To explore associations between IRM responses and sociodemographic variables, generalized additive models for location, scale, and shape (GAMLSS) will be employed. All statistical analyses will be performed using R software (version 4.4.1), with significance set at p < 0.05. Expected Results The Italian version of the IRM is expected to show robust psychometric properties, including high internal consistency and structural validity in line with its original version. The study is also expected to reveal a relevant prevalence of problematic Internet use among high school students in Lombardy. Additionally, the analysis is anticipated to identify associations between certain sociodemographic characteristics—such as age and gender—and increased perceptions of digital risk. These may include exposure to harmful or inappropriate content, compulsive or excessive use of the Internet, and experiences of cybervictimization. The findings will offer valuable insights for designing evidence-based digital health promotion strategies in the educational setting. Conclusions This study will provide an updated and in-depth picture of Internet use and digital risk perception among Italian adolescents. By validating the Italian version of the Internet Related Measures (IRM) questionnaire, it will offer a reliable and culturally adapted tool for use in educational and public health contexts. The results are expected to support the development of targeted prevention programs, digital literacy initiatives, and evidence-based policies aimed at promoting safer and healthier online behaviors among youth.
Copyright (c) 2025 Carlo Marinoni, Simona Carla Silvia Caravita , Maria Assunta Zanetti , Simona Villani , Camilla Torriani https://creativecommons.org/licenses/by-sa/4.0