Antonio Pinto

Data centers are a critical component of digital infrastructure, and their expansion in the artificial intelligence era raises concerns about environmental pressures and potential implications for human health. This systematic review and evidence map synthesized evidence on data-center-related environmental pressures, exposures and health impacts. Bibliographic databases, Google Scholar, preprint servers, gray literature and citation searching were used to identify eligible studies. Methodological quality was assessed using QualSyst or the JBI checklist, and each result was classified along a four-level pathway from environmental pressure to population exposure, modeled health impact and observed health outcome (PROSPERO CRD420261502966). Thirty-six studies yielding 112 quantitative estimates were included. The most downstream level reached was environmental pressure only in 18 studies (50.0%), population exposure in 12 (33.3%) and modeled health impact in only six (16.7%); no study reached the observed-health-outcome level. Across heterogeneous satellite, modeling, and field-monitoring studies, reported localized temperature differences ranged from 0.35 to 2.07 °C, with preliminary evidence of vegetation loss. Exposure assessment was predominantly model-based (26/36), and all six studies reaching the health-impact level estimated rather than observed health outcomes; four of these six studies were preprints. Environmental pressures attributable to data-center activity have been quantified across multiple domains, but evidence linking these pressures to human health remains limited to modeled health impacts. No included study evaluated observed human-health outcomes using epidemiological or clinical data. Therefore, the current evidence base does not establish whether data-center-related exposures are associated with health effects in exposed populations.

Food insecurity is increasingly recognised as a relevant public health issue (Nucci et al. 2025) not only in low- and middle-income countries (Grace 2015; Turner et al. 2020), but also in high-inco...

Background: European health systems face growing pressure from population ageing, post-pandemic service backlogs, and fiscal constraints. Yet substantial cross-country differences in health outcomes persist despite comparable levels of healthcare expenditure. This study evaluated the relative efficiency of European health systems using a multidimensional framework that integrates expenditure, prevention, and population health outcomes. Methods: A cross-sectional analysis was conducted on 25 European countries using 2022 data or the nearest available year. An output-oriented constant returns to scale Data Envelopment Analysis (DEA) model was estimated with two inputs, public and private healthcare expenditure per capita, and five outputs, life expectancy at birth, inverse infant mortality, healthy life years at birth, breast cancer screening coverage, and poliomyelitis vaccination coverage. A robustness specification added physician density as an additional input. Bootstrap bias correction with 1000 replications was applied to the baseline model. A second-stage Simar–Wilson truncated regression with 2000 bootstrap replications examined the association between inefficiency and selected contextual variables, including GDP per capita, population ageing, obesity prevalence, and tobacco use prevalence. Results: In the baseline DEA model, 8 of 25 countries were located on the technical efficiency frontier (Croatia, Czechia, Estonia, Greece, Hungary, Latvia, Lithuania, and Poland; output-oriented DEA inefficiency score = 1.000 for each country), while inefficiency scores among the remaining countries ranged from 1.042 to 2.617. The highest inefficiency scores were observed for Germany (2.617), Austria (2.283), Belgium (2.230), Ireland (2.219), and France (2.167). When physician density was added as an additional input, 12 countries were located on the estimated frontier. Bootstrap correction of the baseline model increased the estimated output-oriented inefficiency scores, with bias-corrected values ranging from 1.100 to 2.941. In the second-stage analysis, higher log GDP per capita was positively associated with bias-corrected inefficiency (coefficient 1.993; 95% bootstrap CI 0.219 to 4.197), whereas population ageing, adult obesity prevalence, and tobacco use prevalence were not statistically associated with bias-corrected inefficiency. Conclusions: In this cross-sectional sample of 25 European countries, higher healthcare expenditure was not consistently associated with frontier performance when health outcomes and preventive coverage were considered jointly. The results were sensitive to the inclusion of physician density and to bootstrap correction, supporting the interpretation of Data Envelopment Analysis as an exploratory benchmarking tool rather than a definitive ranking of health systems. These findings highlight the importance of assessing how financial and workforce resources are converted into measurable health and prevention-related outputs.

Andes virus (ANDV) is the only hantavirus with well-documented evidence of person-to-person transmission. However, key parameters related to transmission timing, viral shedding, exposure contexts, and public health management remain incompletely defined. We conducted a systematic review in accordance with PRISMA 2020. MEDLINE/PubMed, Scopus, and Web of Science were searched from database inception up to 14 May 2026. Eligible studies reported epidemiological, virological, clinical, or public health data relevant to ANDV infection, person-to-person transmission, viral shedding, and/or outbreak control. Thirty-three studies, including 17,204 individuals, 2221 laboratory-confirmed ANDV cases, and 135 documented secondary cases, were included. Person-to-person transmission was identified as a primary or co-occurring route in 20 papers. The median incubation period among ANDV cases was 20.8 days, and the median serial interval was 21.8 days (upper bounds near 40 days). Secondary attack rates were higher among sexual and other close contacts. ANDV RNA was consistently detected in blood and occasionally in saliva, respiratory secretions, urine, breast milk, and semen, although RNA detection alone does not necessarily imply infectious virus. Rare reports of culture-confirmed isolation of replication-competent virus support the biological plausibility of transmission via close mucosal or respiratory exposure. Unlike other hantaviruses, Andes virus can spread person to person through close contact, supporting prolonged monitoring and risk-stratified follow-up of high-risk contacts based on ANDV-specific epidemiological evidence. Possible recommendations, including post-discharge counselling regarding possible sexual transmission, remain provisional and require further evidence. Preparedness activities against outbreaks should also be implemented in non-endemic regions, while future research should prioritize prospective contact studies, standardized virological sampling, and genomic confirmation.

Cooking-related skills, encompassing practical food preparation skills and behaviors associated with food management, have been increasingly recognized as potential determinants of healthier and more sustainable dietary behaviors. However, the extent to which cooking skills and related practices contribute to food sustainability-related practices remains unclear. This systematic review aimed to assess the relationship between cooking-related skills and food sustainability-related practices. The literature search was conducted in PubMed/MEDLINE, Scopus and Embase and updated on 25 February 2026. Eligible studies included peer-reviewed studies examining associations between cooking-related skills (including cooking skills, home cooking, culinary education, and teaching kitchen interventions) and food sustainability-related practices (such as diet-related greenhouse gas emissions). Eligible studies included quantitative and qualitative designs conducted among adult populations. Risk of bias was assessed using design-specific appraisal tools. Results were reported according to PRISMA 2020 guidelines. Overall, 28 studies published between 2015 and 2026 (comprising 32,211 participants) were included. Of these, 16 studies were cross-sectional, 9 used pre–post intervention designs, and the remaining evidence included qualitative, mixed-methods, and randomized or cluster-randomized designs. Food waste outcomes were assessed in 19 studies, of which 15 reported favorable associations between cooking-related skills, home cooking, or cooking-based interventions and lower food waste or better food waste prevention behaviors. Diet quality outcomes were assessed in 12 studies and generally suggested favorable associations with Mediterranean diet adherence, fresh food consumption, and lower processed or ultra-processed food intake. Cooking-related skills may represent a promising behavioral pathway to promote healthier and more sustainable food practices. Nevertheless, stronger evidence from well-designed longitudinal and experimental studies is needed to clarify causal relationships and better inform public health strategies aimed at supporting sustainable food systems.

Background/Objectives: Heat waves are increasingly frequent and intense climate events with significant implications for public health, particularly among frail older adults. While most evidence has focused on mortality and morbidity, healthcare service utilization represents an additional and potentially more sensitive indicator of heat-related health burden. Methods: A systematic review and meta-analysis was conducted following the PRISMA guidelines and prospectively registered in PROSPERO (CRD420251107598). PubMed/MEDLINE, Scopus, and Web of Science were searched up to August 2025. This study aimed to systematically review and quantitatively synthesize the evidence on the association between heat wave exposure and healthcare utilization—including hospitalizations, emergency department (ED) visits, and outpatient care—among frail older adults. Pooled effect estimates (RRs, IRRs, and ORs) were calculated using random-effects models. Heterogeneity was assessed using the I² statistic, and sensitivity analyses were performed by outcome type, effect measure, and risk of bias. Results: Fifty-five studies met the inclusion criteria. Heat wave exposure was consistently associated with increased healthcare utilization. Both hospitalizations and ED visits showed significant increases during heat wave periods, with results remaining robust across sensitivity analyses. Evidence on outpatient care was limited but suggested a similar pattern. Substantial heterogeneity was observed across studies, reflecting variability in exposure definitions, populations, and study designs. Overall, the methodological quality of the included studies was acceptable, with most presenting a low-to-moderate risk of bias. Conclusions: Heat waves are associated with increased healthcare utilization among frail older adults, indicating a relevant burden on healthcare systems. Healthcare utilization may represent a sensitive indicator of heat wave impact, complementing traditional clinical outcomes.

Background/Objectives: To assess the prevalence of eating disorder (ED) risk in a sample of Italian university students and to examine its independent associations with mental health indicators, self-rated health, body mass index (BMI), lifestyle behaviours, and engagement with digital food-related applications. Methods: Of the 2779 Italian university students who accessed the survey, 2691 completed and were included in the analysis. ED risk was assessed with the validated 5-item SCOFF questionnaire. Exposure included socio-demographics, BMI, depressive symptoms (PHQ-9), self-rated health, adherence to the Mediterranean diet (Medi-Lite), smoking, alcohol use (AUDIT-C), and use of food delivery and food waste apps. Multivariable logistic regression models, stratified by sex, and adjusted by age and education, estimated associations with ED risk. Results: Overall, 34.6% of participants screened positive for ED risk (women 39.5%, men 21.8%). Smoking and use of food delivery apps and food waste apps were independently associated with ED risk. Clinically relevant depressive symptoms (PHQ-9 ≥ 10) (aOR 3.37, 95% CI 2.82–4.02) and poor/fair self-rated health (aOR 2.45, 95% CI 1.93–3.11) showed the strongest association. Overweight (aOR 1.47, 95% CI 1.06–2.03) and obesity (aOR 2.48, 95% CI 1.53–4.01) increased the likelihood of ED risk. Risky alcohol use was also associated (aOR 1.42, 95% CI 1.15–1.75). Conclusions: More than one in three Italian university students is at risk for an ED, highlighting a substantial public health concern. Strong links with depression, perceived poor health, digital food app use, and unhealthy behaviours underscore the need for early screening and integrated mental health and nutrition interventions within university settings.

Background: University students are at increased risk for unhealthy behaviours, including poor diet and hazardous alcohol use. Mediterranean Diet (MD) adherence has been linked to favourable health outcomes, but evidence in young adults is scarce. This study examined associations between MD adherence, self-rated health (SRH), body mass index (BMI), and alcohol-related risk in Italian university students, considering also food delivery app use. Methods A cross-sectional study was con-ducted among 2,697 students at the University of Milan. MD adherence was assessed with the validated Medi-Lite questionnaire, SRH with a single-item indicator, BMI from self-reported data, and alcohol-related risk with the AUDIT-C. Multivariable logistic and multinomial regression models were applied. Results: High MD adherence (≥12 score) was associated with higher odds of good SRH (aOR = 3.16, 95% CI = 1.23–8.11) and lower odds of overweight/obesity (aOR = 0.66, 95% CI = 0.52–0.85), with no significant association with alcohol risk. Greater consumption of fruits, vegetables, legumes, cereals, and olive oil predicted better SRH and lower BMI. Food delivery app use was associated with overweight/obesity and alcohol risk. Discussion: Higher MD adherence is linked to improved perceived health and healthier weight status among university students. Public health strategies should promote MD and address digital food environments.

Background: Early differentiation of mosquito-borne viral infections from other causes of acute febrile illness remains challenging, particularly in endemic and resource-limited settings. Artificial intelligence (AI) models have been proposed to improve early diagnosis, but their incremental value over conventional approaches is unclear. Methods: We conducted a systematic review and meta-analysis of comparative studies evaluating AI/machine learning models versus conventional approaches (clinical assessment, laboratory-based pathways, or traditional statistical models) for early detection of mosquito-borne viral infections. PubMed, Embase, and Scopus were searched through August 2025. Paired performance metrics were synthesized using fixed- and random-effects models. Outcomes included AUC, sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). Risk of bias was assessed using PROBAST. Results: Thirteen studies met inclusion criteria. Under random-effects models, AI improved sensitivity (ES = 2.64, p = 0.028), specificity (ES = 5.55, p < 0.001), accuracy (ES = 3.19, p < 0.001), and NPV (ES = 13.84, p < 0.001). No consistent advantage was observed for AUC, and PPV findings were inconsistent. Substantial heterogeneity was present across outcomes (I² = 100%). Most studies relied on internal validation, and PROBAST identified high risk of bias in the analysis domain in over half. Conclusions: AI-based models may enhance threshold-dependent performance metrics, supporting their use as adjunctive decision-support tools for early triage and case exclusion, while external validation and implementation-focused research remain essential.