I am a Public Health resident at Università Vita-Salute San Raffaele (Milan), currently completing training at ATS Bergamo (Public Health & Environmental Health Unit) and at the Prevention Directorate of Regione Lombardia. My work combines epidemiology, environmental health, and applied data analysis, with experience in systematic reviews, health-surveillance projects and quantitative assessment of environmental and infectious risks. I collaborate with regional health authorities on environmental risk assessment (radon, asbestos, chemical hazards) and contribute to multi-centre research projects on vaccination, chronic diseases and machine-learning applications in epidemiology.
I am looking to collaborate on systematic reviews meta-analyses and original-data studies in public health environmental health and epidemiology. My interests include infectious-disease prediction (trend peaks outbreaks) AI/ML applications for health intelligence environmental risk assessment and quantitative evaluation of public-health interventions. I am particularly motivated to join multi-author projects that require rigorous methods structured data analysis and clear scientific synthesis.
Antimicrobial resistance (AMR) poses a critical global health threat, necessitating innovative approaches in antimicrobial stewardship (AMS). Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in this domain, enabling data-driven interventions to optimize antibiotic use and combat resistance. This comprehensive review explores the multifaceted role of AI and ML models in enhancing antimicrobial stewardship efforts across healthcare systems. AI-powered predictive analytics can identify patterns of resistance, forecast outbreaks, and guide personalized antibiotic therapies by leveraging large-scale clinical and epidemiological data. ML algorithms facilitate rapid pathogen identification, resistance profiling, and real-time monitoring, enabling precise decision making. These technologies also support the development of advanced diagnostic tools, reducing the reliance on broad-spectrum antibiotics and fostering timely, targeted treatments. In public health, AI-driven surveillance systems improve the detection of AMR trends and enhance global monitoring capabilities. By integrating diverse data sources—such as electronic health records, laboratory results, and environmental data—ML models provide actionable insights to policymakers, healthcare providers, and public health officials. Additionally, AI applications in antimicrobial stewardship programs (ASPs) promote adherence to prescribing guidelines, evaluate intervention outcomes, and optimize resource allocation. Despite these advancements, challenges such as data quality, algorithm transparency, and ethical considerations must be addressed to maximize the potential of AI and ML in this field. Future research should focus on developing interpretable models and fostering interdisciplinary collaborations to ensure the equitable and sustainable integration of AI into antimicrobial stewardship initiatives. © 2025 by the authors.
The increasing threat of antimicrobial resistance has prompted a need for more effective antimicrobial stewardship programs (AMS). Artificial intelligence (AI) and machine learning (ML) tools have emerged as potential solutions to enhance decision-making and improve patient outcomes in AMS. This systematic review and meta-analysis aims to evaluate the impact of AI in AMS and to assess its predictive performance and diagnostic accuracy. We conducted a comprehensive literature search across PubMed/MEDLINE, Scopus, EMBASE, and Web of Science to identify studies published up to July 2024. Studies included were observational, cohort, or retrospective, focusing on the application of AI/ML in AMS. The outcomes assessed were the area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). We calculated the mean pooled effect size (ES) and its 95% confidence interval (CI) using a random-effects model. The risk of bias was assessed using the QUADAS-AI tool, and the protocol was registered in PROSPERO. Out of 3,458 retrieved articles, 80 studies met the inclusion criteria. Our meta-analysis demonstrated that ML models exhibited strong predictive performance and diagnostic accuracy, with the following results: AUC [ES: 72.28 (70.42–74.14)], accuracy [ES: 74.97 (73.35–76.58)], sensitivity [ES: 76.89; (71.90–81.89)], specificity [ES: 73.77; (67.87–79.67)], NPV [ES:79.92 (76.54–83.31)], and PPV [ES: 69.41 (60.19–78.63)] across various AMS settings. AI and ML tools offer promising enhancements due to their strong predictive performance. The integration of AI into AMS could lead to more precise antimicrobial prescribing, reduced antimicrobial resistance, and better resource utilization. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.
Background: Climate change has intensified the frequency and severity of extreme weather events, disproportionately affecting vulnerable populations, including older people for which the literature is still limited. This systematic review investigated the impact of extreme weather events on malnutrition and food security among individuals aged 60 and older. Methods: A systematic search of PubMed/MEDLINE, Scopus, and Web of Science was conducted without restrictions (October 2024), and following PRISMA guidelines. Observational studies examining older adults exposed to extreme weather events (e.g., droughts, floods, heatwaves, hurricanes) and their effects on malnutrition or food security were included. The Newcastle-Ottawa Scale assessed study quality. Protocol was registered in PROSPERO (ID: CRD42024596910). Results: From 1,709 articles, six observational studies involving 265,000 participants (aged 60 years and over) were included. These studies spanned multiple geographies, with a concentration in the United States. Findings revealed a dual impact: while some studies reported protective factors, such as social support and economic stability, others highlighted increased malnutrition risk due to disrupted food supply, economic hardship, and inadequate adaptive responses. Heterogeneity in study designs, exposure definitions, and outcome measures limited comparability. Conclusion: Extreme weather events significantly impact malnutrition and food security among older adults, with outcomes influenced by socio-economic and geographical factors. Further longitudinal studies are needed to clarify causal pathways and inform targeted public health interventions to enhance resilience in aging populations. © The Author(s) 2025.
Background/Objectives: Adult vaccination remains suboptimal, particularly among older adults and individuals with chronic conditions. Hospitals represent a strategic setting for improving vaccination coverage among these high-risk populations. This systematic review and meta-analysis evaluated hospital-based interventions aimed at enhancing vaccine uptake in adults aged ≥60 years or 18–64 years with at-risk medical conditions. Methods: We conducted a systematic review and meta-analysis following PRISMA and MOOSE guidelines. Searches in PubMed, EMBASE, and Scopus identified studies published in the last 10 years evaluating hospital-based interventions reporting vaccination uptake. The risk of bias was assessed using validated tools (NOS, RoB 2, ROBINS-I, QI-MQCS). A meta-analysis was conducted for categories with ≥3 eligible studies reporting pre- and post-intervention vaccination coverage in the same population. Results: We included 44 studies. Multi-component strategies (n = 21) showed the most consistent results (e.g., pneumococcal uptake from 2.2% to 43.4%, p < 0.001). Reminder-based interventions (n = 4) achieved influenza coverage increases from 31.0% to 68.0% and a COVID-19 booster uptake boost of +38% after SMS reminders. Educational strategies (n = 11) varied in effectiveness, with one study reporting influenza coverage rising from 1.6% to 12.2% (+662.5%, OR 8.86, p < 0.01). Standing order protocols increased pneumococcal vaccination from 10% to 60% in high-risk adults. Hospital-based catch-up programs improved DTaP-IPV uptake from 56.2% to 80.8% (p < 0.001). For patient education, the pooled OR was 2.11 (95% CI: 1.96–2.27; p < 0.001, I2 = 97.2%) under a fixed-effects model, and 2.47 (95% CI: 1.53–3.98; p < 0.001) under a random-effects model. For multi-component strategies, the OR was 2.39 (95% CI: 2.33–2.44; p < 0.001, I2 = 98.0%) with fixed effects, and 3.12 (95% CI: 2.49–3.92; p < 0.001) with random effects. No publication bias was detected. Conclusions: Hospital-based interventions, particularly those using multi-component approaches, effectively improve vaccine coverage in older and high-risk adults. Embedding vaccination into routine hospital care offers a scalable opportunity to reduce disparities and enhance population-level protection. Future policies should prioritize the institutional integration of such strategies to support healthy aging and vaccine equity. © 2025 by the authors.
Objectives: The growing challenge of antimicrobial resistance (AMR) has underscored the urgent need for robust antimicrobial stewardship programs (AMS). Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to support enhanced decision-making in AMS. This systematic review and meta-analysis aims to evaluate the impact of AI in AMS and compare its effectiveness with traditional risk systems. Methods: PubMed/MEDLINE, Scopus, EMBASE, and Web of Science were searched to identify studies published up to July 2024. Any studies that evaluated the use of AI/ML in AMS compared with conventional decision-making approaches were eligible. Outcomes of interested were predictive performance metrics and diagnostic accuracy. The meta-estimate was performed pooling standardized mean difference, and effect size (ES) measured as Cohen's d with a 95% confidence interval (CI). The risk of bias was assessed using the QUADAS-AI tool. Results: Out of 3,458 studies, 27 were included, demonstrating that ML models outperform traditional methods in terms of sensitivity [1.93 (0.48–3.39) p = 0.009], and negative predictive value [1.66 (0.86–2.46), p < 0.001] but not in terms of area under the curve, accuracy, specificity, positive predictive value, when random effect models were applied. Conclusions: Our results revealed that ML tools offer promising enhancements to traditional AMS strategies. However, high heterogeneity, inconsistent results between fixed and random effect models, and limited use of external validation in retrieved studies raise concerns about the generalizability of the findings. Furthermore, the lack of representation from outpatient and pediatric settings highlights a critical equity gap in the application of these technologies. © 2025
Background: Adherence to the Mediterranean diet (MD) is associated with improved health outcomes, however limited evidence exists on the socio-demographic and behavioral determinants of MD adherence among university students, a population at risk of developing unhealthy habits during a critical life stage. Methods: A cross-sectional study was conducted among 2697 students (70.6% female) enrolled at a university in Northern Italy. MD adherence was measured using the validated Medi-Lite score. Multivariable logistic and linear regression models were used to identify socio-demographic and behavioral associations with high adherence to the MD (score ≥12). Principal component analysis was performed to explore multivariate patterns across dietary components and participant characteristics. Results: Overall, 25.6% of participants were classified as having high adherence to the MD. Higher adherence was more frequent among women, non-smokers, older students, and those living with their families. Students in health sciences showed greater adherence compared to those in other fields of study. Conversely, frequent users of mobile food ordering applications and smokers were less likely to adhere to the MD. These associations remained consistent after adjusting for age and sex. Conclusions: Adherence to the MD is suboptimal among university students and influenced by socio-demographic and behavioral factors. Targeted interventions should prioritize younger males, smokers, and convenience food users, while promoting sustainability and social support as facilitators of healthier dietary patterns. © 2025 by the authors.
Background: Older adults are highly vulnerable to infectious diseases due to immunosenescence, multimorbidity, and atypical presentations. Artificial intelligence (AI) offers promising opportunities to improve diagnosis, prognosis, treatment, and continuity of care in this population. This review summarizes current applications of AI in the management of infections in older adults across diagnostic, prognostic, therapeutic, and preventive domains. Methods: We conducted a narrative review of peer-reviewed studies retrieved from PubMed, Scopus, and Web of Science, focusing on AI-based tools for infection diagnosis, risk prediction, antimicrobial stewardship, prevention of healthcare-associated infections, and post-discharge care in individuals aged ≥65 years. Results: AI models, including machine learning, deep learning, and natural language processing techniques, have demonstrated high performance in detecting infections such as sepsis, pneumonia, and healthcare-associated infections (Area Under the Curve AUC up to 0.98). Prognostic algorithms integrating frailty and functional status enhance the prediction of mortality, complications, and readmission. AI-driven clinical decision support systems contribute to optimized antimicrobial therapy and timely interventions, while remote monitoring and telemedicine applications support safer hospital-to-home transitions and reduced 30-day readmissions. However, the implementation of these technologies is limited by the underrepresentation of frail older adults in training datasets, lack of real-world validation in geriatric settings, and the insufficient explainability of many models. Additional barriers include system interoperability issues and variable digital infrastructure, particularly in long-term care and community settings. Conclusions: AI has strong potential to support predictive and personalized infection management in older adults. Future research should focus on developing geriatric-specific, interpretable models, improving system integration, and fostering interdisciplinary collaboration to ensure safe and equitable implementation. © 2025 by the authors.
(1) Background: Domestic violence (DV), including intimate partner violence (IPV) during pregnancy and the puerperium, represents a major public health issue, significantly affecting maternal and child health. (2) Methods: This systematic review and meta-analysis, conducted according to PRISMA 2020 guidelines, aimed to identify screening tools used to detect DV and IPV among pregnant and postpartum women and to estimate DV prevalence. The protocol was published in PROSPERO in advance (CRD42023473392). (3) Results: A comprehensive literature search across PubMed, EMBASE, Scopus, and Web of Science was conducted on 1 January 2024, resulting in 34,720 records; 98 studies met the inclusion criteria. The included studies were conducted in over 40 countries, and most were cross-sectional. Commonly used screening tools included the WHO Women’s Health and Life Experiences Questionnaire, the Abuse Assessment Screen, and the WHO Violence Against Women Instrument. Meta-analyses showed that 10% of women experienced physical violence, 26% psychological violence, 9% sexual violence, 16% verbal violence, and 13% economic violence. The overall prevalence of IPV during pregnancy and the puerperium was 26%. Despite the widespread use of validated instruments, substantial heterogeneity was observed, underscoring the need for standardization. (4) Conclusion: These findings underline the urgent need to integrate routine IPV screening into maternal care pathways using validated, culturally adapted tools, ensuring women’s safety and confidentiality. © 2025 by the authors.
We are seeking 1–2 collaborators, preferably with a background in the medical or health sciences, to join our project focusing on a systema…
