Antonio Sciurti

BACKGROUND: Following substantial progress towards measles elimination after the COVID-19 pandemic, Italy experienced a marked increase in measles cases in 2024. AIM: Our aim was to describe measles epidemiology in Italy, October 2023–March 2025, and assess vaccination coverage, surveillance performance at national and regional levels, and temporal patterns of transmission. METHODS: Numbers of measles cases and outbreaks were reviewed. Vaccination coverage and surveillance indicators were described at national and regional levels. Measles variants were classified by genotype and Distinct Sequence Identifier (DSId). Temporal transmission patterns of measles variants were assessed nationally and regionally to identify sustained transmission chains (≥ 12 consecutive months). RESULTS: In total, 1,311 measles cases were reported, with 178 outbreaks. Cases’ median age was 30.9 years (IQR: 15.6–39.8); 601 were female (45.8%) and 710 male (54.2%). Only 3.1% (38/1,230) were fully vaccinated. In 2024, the first-dose vaccination coverage increased to 94.77% (95% CI: 94.70–94.84%) nationally, while second-dose coverage declined to 84.45% (95% CI: 84.35–84.55%). Among all cases, 12.1% (n = 158) were imported/importation-related, 64.1% (n = 841) were of unknown origin and 52.8% had DSId data available (n = 692), with high regional heterogeneity. Twenty-five measles variants were identified. One variant (D8–5963) showed sustained national transmission for over 12 months, meeting criteria for re-established endemic transmission, although no single region showed uninterrupted ≥ 12-month circulation. CONCLUSION: Measles resurgence in Italy was marked by persistent immunity gaps, heterogeneous surveillance performance, and sustained national circulation of a D8 variant, indicating loss of near-elimination status. Strengthening vaccination strategies and enhancing molecular surveillance is urgently needed to restore progress towards measles elimination.

Background and Aims: Owing to their distinct features, genetic/genomic technologies have traditionally been assessed using tailored approaches, with no standardized methodology emerging to date. The Health Technology Assessment (HTA) Core Model (version 3.0), adopted within the European Union HTA Regulation, is a rigorous framework for health technology assessment, but its suitability for genetic/genomic testing remains unclear. This study examined whether the Model could serve as a standardized framework for the evaluation of genetic/genomic applications. Methods: A search was conducted to identify evaluation frameworks for genetic/genomic applications (PROSPERO CRD42023429403), and a critical analysis was performed. The methodological characteristics of the identified frameworks, including their assessment domains and components, were systematically compared to the domains and issues of the HTA Core Model. Findings were synthesized narratively. Results: Overall, 24 evaluation frameworks for genetic/genomic testing were analysed: half were based on the ACCE model, four on the HTA approach, one combined both methodologies, and seven were heterogeneous in structure. Only one framework covered all relevant assessment aspects but lacked validation. Conversely, the HTA Core Model captured all core questions and addressed every identified assessment component, although with varying degrees of coverage (Cohen's κ between researchers: 0.71, 95% CI: 0.59–0.82). Conclusion: Existing frameworks for genetic/genomic technologies are fragmented and mostly incomplete. As the HTA Core Model encompasses all relevant components of these technologies, it may offer a suitable basis for standardizing their evaluation process. Future research could integrate it with a few genomic‐specific items to maximize its exhaustiveness.

Background and Aims: Despite widespread Infection Prevention and Control (IPC) measures, Healthcare‐Associated Infections (HAIs) remain a major concern in Intensive Care Units (ICUs), leading to increased mortality, prolonged hospital stays, and additional healthcare costs. Prognostic prediction models support identification of patients at high infection risk, and Machine Learning and Artificial Intelligence (ML/AI) methods offer the potential to improve such predictions. However, no systematic review has specifically examined ML/AI‐based prognostic models for HAI prediction in adult ICU populations. This review aims to identify ML/AI‐based prognostic models for estimating the HAI risk in adult ICU‐patients, investigate their methodological characteristics, risk of bias and reporting quality. Methods: A systematic search will be conducted in PubMed, Scopus, and Web of Science to identify studies developing and/or validating ML/AI‐based prognostic models of HAI prediction in adult ICU‐admitted patients. Any study developing or evaluating supervised ML/AI‐based models for the prediction of any HAI occurring after ICU admission will be included. Three reviewers will independently screen articles, extract data, and assess risk of bias using the Risk Of Bias ASsessment Tool for prediction models using Artificial Intelligence (PROBAST + AI). Extracted information will include study characteristics, patients and setting, predictors, sample size, missing data, analysis methods, performance, and model presentation and interpretation. A narrative synthesis will be conducted for all included studies. Finally, the SPIN‐Prediction Models (SPIN‐PM) framework will be used to assess quality of reporting. This systematic review protocol was registered on the Open Science Framework (osf. io/bzqfp). Conclusion: This systematic review's findings on methodological aspects, risk of bias, and applicability of ML/AI‐based prognostic models for HAI prediction in adult ICU patients will help inform researchers, clinicians, and IPC decision‐makers about these models' reliability and readiness for clinical use.

Eurosurveillance.org is the online home of Eurosurveillance, Europe's journal on infectious disease surveillance, epidemiology, prevention and control.

BACKGROUND: Community-acquired Legionnaires’ disease (LD) has increased globally, including Europe. Climatic exposures may contribute to this rise, yet evidence from high-incidence countries, such as Italy, is limited. AIM: We aimed to assess LD seasonality at the national level and the effects of temperature, relative humidity and precipitation on LD incidence at the municipality level. METHODS: We analysed data on notified community-acquired LD cases and national climatic data from 2005 to 2023. Seasonality was assessed (peak-to-trough ratio (PTR)). A case time-series design with distributed lag nonlinear models was applied to estimate 1–10 week lagged effects of climatic exposures on LD incidence at the municipality level, as incidence rate ratios (IRRs). RESULTS: Of the 28,662 notified LD cases, 27,458 (95.8%) were included. A clear seasonality was observed in the incidence, with a trough in early spring and a peak in autumn (PTR = 3.62; 95% confidence interval (CI): 2.45–5.34). Higher temperature and relative humidity had the strongest effects 10–9 weeks before disease onset (for 5°C increase above 15°C: IRR = 1.45; 95% CI: 1.33–1.58 and for 5% increase above 60%: IRR = 1.19; 95% CI: 1.12–1.26). Precipitation showed more immediate effects, particularly with a 1-week lag (for 5 mm increase above 10 mm: IRR = 1.07; 95% CI: 1.06–1.09). Findings were consistent among individuals aged ≥ 60 years. CONCLUSION: A clear seasonal pattern was seen in the LD incidence, peaking in summer and autumn. Temperature and humidity increased LD risk over longer lags, while precipitation had shorter-term effects, suggesting climatic exposures influence environmental proliferation and immediate transmission of Legionella .

Systematic reviews play a critical role in evidence-based research but are labor-intensive, especially during title and abstract screening. Compact large language models (LLMs) offer potential to automate this process, balancing time/cost requirements and accuracy. The aim of this study is to assess the feasibility, accuracy, and workload reduction by three compact LLMs (GPT-4o mini, Llama 3.1 8B, and Gemma 2 9B) in screening titles and abstracts. Records were sourced from three previously published systematic reviews and LLMs were requested to rate each record from 0 to 100 for inclusion, using a structured prompt. Predefined 25-, 50-, 75-rating thresholds were used to compute performance metrics (balanced accuracy, sensitivity, specificity, positive and negative predictive value, and workload-saving). Processing time and costs were registered. Across the systematic reviews, LLMs achieved high sensitivity (up to 100%) and low precision (below 10%) for records included by full text. Specificity and workload savings improved at higher thresholds, with the 50- and 75-rating thresholds offering optimal trade-offs. GPT-4o-mini, accessed via application programming interface, was the fastest model (~40 minutes max.) and had usage costs ($0.14–$1.93 per review). Llama 3.1-8B and Gemma 2-9B were run locally in longer times (~4 hours max.) and were free to use. LLMs were highly sensitive tools for the title/abstract screening process. High specificity values were reached, allowing for significant workload savings, at reasonable costs and processing time. Conversely, we found them to be imprecise. However, high sensitivity and workload reduction are key factors for their usage in the title/abstract screening phase of systematic reviews.

Background: The World Health Organization (WHO) recently advocated an urgent need for implementing national surveillance systems for the timely detection and reporting of emerging antimicrobial resistance (AMR). However, public information on the existing national early warning systems (EWSs) is often incomplete, and a comprehensive overview on this topic is currently lacking. Objective: This review aimed to map the availability of EWSs for emerging AMR in high-income countries and describe their main characteristics. Methods: A systematic review was performed on bibliographic databases, and a targeted search was conducted on national websites. Any article, report, or web page describing national EWSs in high-income countries was eligible for inclusion. EWSs were identified considering the emerging AMR-reporting WHO framework. Results: We identified 7 national EWSs from 72 high-income countries: 2 in the East Asia and Pacific Region (Australia and Japan), 3 in Europe and Central Asia (France, Sweden, and the United Kingdom), and 2 in North America (the United States and Canada). The systems were established quite recently; in most cases, they covered both community and hospital settings, but their main characteristics varied widely across countries in terms of the organization and microorganisms under surveillance, with also different definitions of emerging AMR and alert functioning. A formal system assessment was available only in Australia. Conclusions: A broader implementation and investment of national surveillance systems for the early detection of emerging AMR are still needed to establish EWSs in countries and regions lacking such capabilities. More standardized data collection and reporting are also advisable to improve cooperation on a global scale. Further research is required to provide an in-depth analysis of EWSs, as this study is limited to publicly available data in high-income countries.

Introduction: Frameworks based on the ACCE model, which have a limited health technology assessment (HTA) approach, have long been used in the assessment of genetic and genomic applications (GGAs). While ACCE frameworks are mainly focused on technical aspects, HTA assessments include economic and organizational aspects. The aim of this study was to develop a framework able to address a comprehensive assessment of GGAs.Methods: We analyzed the most currently used HTA model in Europe, the EUnetHTA HTA Core Model®, to investigate its suitability as an HTA framework for GGAs and to identify any possible shortcomings in its evaluation. The Sapienza evaluation framework, which is based on a previous systematic review of assessment frameworks for GGAs, was used as the comparison. The HTA Core Model applications were then used as a basis for an assessment framework for GGAs, classified according to “GGA function/use setting” pairs.Results: The domains included in the Sapienza framework fully corresponded with the HTA Core Model. In addition, the “Screening Technology” application of the HTA Core Model was found to be suitable for diagnostic, pre-symptomatic, predictive, and carrier GGAs when used in screening settings. The “Diagnostic Technologies” application was deemed appropriate for the pairs “Diagnostic/Diagnosis”, “Pre-symptomatic/Diagnosis”, “Prognostic/Staging”, and “Pharmacogenetic/Staging”, although it may require additional considerations such as the need for screening in the case of germline mutations, the use of appropriate accuracy measures for prognostic GGAs, and the evaluation of the clinical utility of pharmacogenetic or prognostic GGAs.Conclusions: The proposed approach is a flexible tool that allows assessment of GGAs using a shared and validated methodology that considers the technical aspect, clinical utility, economic value, and delivery models. We found that the HTA Core Model fits the needs of GGA assessment, although some GGA peculiarities should be further explored in the assessment process.

Introduction: Evaluating genetic and genomic applications is critical for their adoption in healthcare practice, but this has several challenges, including limited evidence on clinical and non-clinical benefits and rapid development processes. Since these challenges lack comprehensive discussion, this study aimed to identify and analyze all the barriers emerging in the health technology assessment (HTA) of genetic and genomic tests.Methods: The protocol of this study was registered in the Open Science Framework database. A systematic search of the PubMed, Scopus, and Web of Science bibliographic databases was undertaken to identify all studies that specifically discussed any difficulty, gap, or barrier in the HTA evaluation of genetic or genomic technologies. No restrictions were imposed on HTA domain, study type, or publication date. Gaps and challenges were then grouped into domains outlined in the EUnetHTA HTA Core Model®. A narrative synthesis of the results was performed for each HTA domain.Results: Of the 457 publications identified, 23 were included. Article designs exhibited considerable diversity, ranging from systematic reviews to semi-structured interviews, and stakeholder involvement varied from universities to national research groups. The challenges we found referred to the general HTA framework or, in most cases, to specific domains. More than 25 percent of the challenges were associated with economic aspects, with cost-effectiveness analysis being the focal point of debate. In addition, clinical effectiveness, in terms of non-health outcomes, lack of evidence, and clinical utility, was also frequently discussed, accounting for 23 percent of the challenges identified.Conclusions: Our study systematically summarized gaps and barriers in the HTA of genetic and genomic applications, providing a thorough analysis and categorization of these issues. Various challenges surfaced across different domains, notably related to costs, economic evaluation, and clinical effectiveness. There is a need for exhaustive discussions on potential solutions to facilitate and enhance the assessment process for these genetic and genomic applications.