Despite health literacy (HL) being recognized as a driver of health-promoting behavior, its influence on the vaccination decision-making process remains unclear. This study summarized current evidence on the association between HL and both intention to vaccinate and vaccination status. We searched PubMed, Scopus, and Web of Science, retrieving observational studies published until January 2022 that used HL-validated tools to investigate the above associations for any vaccine. Quality was assessed using the Newcastle–Ottawa scale. Twenty-one articles were included; of these, six investigated the intention to vaccinate and the remainder vaccination status. Articles on intention looked at SARS-CoV-2 vaccination using heterogeneous HL tools and were of high/fair quality. Vaccination status, mainly for influenza or pneumococcal vaccines, was explored using various HL tools; the quality was generally high. We found inconsistent results across and within vaccine types, with no clear conclusion for either vaccination intention or status. A weak but positive association was reported between a high HL level and influenza vaccination uptake for individuals aged more than 65 years. HL did not seem to significantly influence behavior towards vaccination. Differences in the methods used might explain these results. Further research is needed to investigate the role of HL in the vaccination decision-making process.

Infections caused by Acinetobacter baumannii represent a major concern for intensive care unit (ICU) patients. However, the epidemiology of these infections among COVID-19 patients has not been fully explored. The aims of this study were (i) to characterize the clonal spread of A. baumannii among COVID-19 patients admitted to the ICU of the Umberto I hospital of Rome during the first year of the pandemic and (ii) to identify risk factors for its acquisition. Isolates were analysed by pulsed-field gel electrophoresis, and a multivariable regression model was constructed. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were calculated. Overall, 193 patients were included, and 102 strains were analysed. All isolates had highly antibiotic-resistant profiles and derived from two genotypes. The cumulative incidence of A. baumannii acquisition (colonization or infection) was 36.8%. Patients with A. baumannii had higher mortality and length of stay. Multivariable analysis showed that previous carbapenem use was the only risk factor associated with A. baumannii acquisition (aOR: 4.15, 95% CI: 1.78–9.64). We documented substantial A. baumannii infections and colonization and high levels of clonal transmission. Given the limited treatment options, effective prevention and containment strategies to limit the spread of A. baumannii should be implemented.

The COVID-19 pandemic has increased the healthcare-associated infection (HAI) risk in intensive care unit (ICU) patients. However, a comparison between patients with and without COVID-19 in terms of HAI incidence has been rarely explored. In this study, we characterized the occurrence of HAI among patients with and without COVID-19 admitted to the ICU of the Umberto I hospital of Rome during the first 16 months of the pandemic and also identified risk factors for HAI acquisition. Patients were divided into four groups according to their ICU admission date. A multivariable conditional risk set regression model for multiple events was constructed for each admission period. Adjusted hazard ratios and 95% confidence intervals were calculated. Overall, 352 COVID-19 and 130 non-COVID-19 patients were included, and a total of 361 HAIs were recorded. We found small differences between patients with and without COVID-19 in the occurrence and type of HAI, but the infections in the two cohorts mostly involved different microorganisms. The results indicate that patient management was likely an important factor influencing the HAI occurrence during the pandemic. Effective prevention and control strategies to reduce HAI rates should be implemented.

Background: Post-COVID-19 Interstitial Lung Disease (PC-ILD) is characterized by fibrotic-like signs at high-resolution computed tomography (HRCT) and pulmonary function tests (PFTs) abnormalities after SARS-CoV-2 infection. It is still not clear how frequent these tests should be performed to rule out long-term consequences of COVID-19 pneumonia. Objectives: The aims of our study were to evaluate the incidence and risk factors of PC-ILD and possibly to propose a long-term follow-up program. Method: One-hundred patients, hospitalized in our ward for moderate to critical COVID-19, underwent two follow-up visits at three and 15 months in which PFTs and HRCT were performed. Results: At the 15-month follow-up, 8% of patients showed residual radiological and functional signs consistent with PC-ILD. All but one of these patients had already demonstrated PFTs and HRCT alterations at first follow-up visit, and the last 1 patient showed worsening of lung function during follow-up. These findings highlight the negative predictive value of PFTs at 3-month follow-up for the development of PC-ILD. Aging, severity of COVID-19, and degree of pulmonary involvement during acute infection proved to be significant risk factors for developing PC-ILD. Conclusions: Our study highlights the importance of PFTs in the long-term follow-up of patients affected by moderate to critical COVID-19 pneumonia. Further studies are needed to confirm our hypothesis that HRCT should be performed only in patients with PFTs abnormalities.

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.

Mandatory vaccination (MV) against COVID-19 is a contentious topic. In this study, we used logistic regression models to identify attitudes among Sapienza University students towards MV for COVID-19. We considered three different scenarios: mandatory COVID-19 vaccination (MCV) for healthcare workers (HCWs) (Model 1), for all people aged ≥ 12 years (Model 2), and for admission to schools and universities (Model 3). We collected 5287 questionnaires over a six-month period and divided these into three groups (September–October 2021, November–December 2021, and January–February 2022). MCV for HCWs was the most strongly supported policy (69.8% in favour), followed by MCV for admission to schools and universities (58.3%), and MCV for the general population (54.6%). In a multivariable analysis, the models showed both similarities and differences. There was no association of socio-demographic characteristics with the outcomes, apart from being enrolled in non-healthcare courses, which negatively affected Models 2 and 3. A greater COVID-19 risk perception was generally associated with a more positive attitude towards MCV, although heterogeneously across models. Vaccination status was a predictor of being in favour of MCV for HCWs, whereas being surveyed in November–February 2022 favoured MCV for admission to schools and universities. Attitudes towards MCV were variable across policies; thus, to avoid unintended consequences, these aspects should be carefully considered by policymakers.