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Generally, seafarers face a higher risk of illnesses and accidents than land workers. In most cases, there are no medical professionals on board seagoing vessels, which makes disease diagnosis even more difficult. When this occurs, onshore doctors may be able to provide medical advice through telemedicine by receiving better symptomatic and clinical details in the health abstracts of seafarers. The adoption of text mining techniques can assist in extracting diagnostic information from clinical texts. We applied lexicon sentimental analysis to explore the automatic labeling of positive and negative healthcare terms to seafarers’ text healthcare documents. This was due to the lack of experimental evaluations using computational techniques. In order to classify diseases and their associated symptoms, the LASSO regression algorithm is applied to analyze these text documents. A visualization of symptomatic data frequency for each disease can be achieved by analyzing TF-IDF values. The proposed approach allows for the classification of text documents with 93.8% accuracy by using a machine learning model called LASSO regression. It is possible to classify text documents effectively with tidy text mining libraries. In addition to delivering health assistance, this method can be used to classify diseases and establish health observatories. Knowledge developed in the present work will be applied to establish an Epidemiological Observatory of Seafarers’ Pathologies and Injuries. This Observatory will be a collaborative initiative of the Italian Ministry of Health, University of Camerino, and International Radio Medical Centre (C.I.R.M.), the Italian TMAS.

Early detection and accurate diagnosis are crucial for effective management and prevention of complications associated with atrial fibrillation (AF). The advent of wearable technology has revolutionized the way we detect and monitor cardiac conditions. This case report describes a patient who requested medical assistance from the Centro Internazionale Radio Medico (CIRM), the Italian Telemedical Maritime Assistance Service (TMAS) center and was diagnosed with AF while on board a merchant ship using a smartwatch Electrocardiogram (ECG).

ObjectivesHigh blood pressure is a common health concern among seafarers. However, due to the remote nature of their work, it can be difficult for them to access regular monitoring of their blood pressure. Therefore, the development of a risk prediction model for hypertension in seafarers is important for early detection and prevention. This study developed a risk prediction model of self-reported hypertension for telemedicine.DesignA cross-sectional epidemiological study was employed.SettingThis study was conducted among seafarers aboard ships. Data on sociodemographic, occupational and health-related characteristics were collected using anonymous, standardised questionnaires.ParticipantsThis study involved 8125 seafarers aged 18–70 aboard 400 vessels between November 2020 and December 2020. 4318 study subjects were included in the analysis. Seafarers over 18 years of age, active (on duty) during the study and willing to give informed consent were the inclusion criteria.Outcome measuresWe calculated the adjusted OR (AOR) with 95% CIs using multiple logistic regression models to estimate the associations between sociodemographic, occupational and health-related characteristics and self-reported hypertension. We also developed a risk prediction model for self-reported hypertension for telemedicine based on seafarers’ characteristics.ResultsAmong the 4318 participants, 55.3% and 44.7% were non-officers and officers, respectively. 20.8% (900) of the participants reported having hypertension. Multivariable analysis showed that age (AOR: 1.08, 95% CI 1.07 to 1.10), working long hours per week (AOR: 1.02, 95% CI 1.01 to 1.03), work experience at sea (10+ years) (AOR: 1.79, 95% CI 1.33 to 2.42), being a non-officer (AOR: 1.75, 95% CI 1.44 to 2.13), snoring (AOR: 3.58, 95% CI 2.96 to 4.34) and other health-related variables were independent predictors of self-reported hypertension, which were included in the final risk prediction model. The sensitivity, specificity and accuracy of the predictive model were 56.4%, 94.4% and 86.5%, respectively.ConclusionA risk prediction model developed in the present study is accurate in predicting self-reported hypertension in seafarers’ onboard ships.

Despite the pandemic, 34,154 migrants, refugees or asylum-seekers landed in Sicily (Italy) in 2020, representing the main point of entry by sea into Europe. The SARS-CoV-2 surveillance program among migrants arriving to Sicily via the Mediterranean Sea, made by the combination of clinical examination and molecular testing, has been integrated by full-genome sequencing strains using the NGS technology from the last week of February. To date, more than one hundred full-genome strains have been sequenced and 8 different lineages have been identified mostly belonging to the lineages B.1.1.7 and B.1.525. As global access to COVID-19 vaccines should be ensured, the need to provide more detailed information to inform policies and to drive the possible re-engineering of vaccines needed to deal with the challenge of new and future variants should be highlighted.

Introduzione: il trasporto su strada in biocontenimento è essenziale per la gestione di pazienti affetti da patologie altamente trasmissibili, tuttavia nella letteratura civile mancano strumenti decisionali standardizzati a supporto dei trasferimenti su strada. Questo studio si propone di sviluppare un modello operativo per supportare le decisioni cliniche e logistiche in tali trasporti. Materiali e Metodi: è stato elaborato uno schema decisionale attraverso un'analisi retrospettiva di 1.994 trasferimenti effettuati in regime di biocontenimento in Sicilia tra dicembre 2020 e marzo 2022. Le variabili principali includevano le condizioni del paziente, l’impiego di dispositivi di supporto vitale e le caratteristiche logistiche del trasferimento. Il modello è stato perfezionato attraverso briefing strutturati post-missione, arrivando a classificare i trasferimenti in tre livelli di complessità (C1-C3) in base a criteri clinici e logistici. Risultati: i pazienti sono stati classificati in funzione della capacità di camminare, della necessità di monitoraggio o supporto vitale e del rischio infettivo. Sono stati identificati 590 trasferimenti ad alta complessità eseguiti mediante l’utilizzo di isolatori, mentre 1.403 casi a bassa complessità sono stati gestiti con un minibus di biocontenimento. Non sono stati registrati guasti alle apparecchiature critiche. Lo schema decisionale ha supportato una pianificazione coerente e ha migliorato la sicurezza operativa. Discussione: Il confronto con le principali raccomandazioni presenti in letteratura ha confermato la validità del modello e ha messo in evidenza criticità operative come la comunicazione inefficace e l’integrazione delle apparecchiature. Si propone l'integrazione dell’indice NEWS2 per migliorare la classificazione del rischio clinico. Conclusioni: lo schema decisionale proposto fornisce uno strumento riproducibile per classificare la complessità del trasporto sanitario in biocontenimento. È necessaria una validazione prospettica del modello al fine di garantirne l’applicabilità in contesti sanitari più ampi.   Introduction: ground biocontainment transport is essential for managing highly transmissible diseases, yet standardized decision-making tools for road-based transfers are lacking in civilian literature. This study aimed to develop an operational model to support clinical and logistical decisions during such transports. Materials and Methods: a decision tree was developed through retrospective analysis of 1.994 biocontainment transfers performed in Sicily between December 2020 and March 2022. Key variables included patient condition, use of life-support equipment, and transfer characteristics. The model was refined through structured post-mission briefings and categorized transfers into three complexity levels (C1–C3) based on clinical and logistical criteria. Results: patients were stratified by walking ability, need for monitoring or life support, and infectious risk. A total of 590 high-complexity transfers used isolators, while 1.403 lower-complexity cases were managed with a biocontainment minibus. No critical equipment failures were recorded. The decision tree supported consistent planning and improved safety. Discussion: Comparison with literature-based recommendations confirmed the model’s validity and highlighted operational issues such as miscommunication and equipment integration. Integration with NEWS2 is proposed to enhance risk stratification. Conclusions: the proposed decision tree provides a reproducible framework for classifying biocontainment transport complexity. Prospective validation is needed to support its application in broader healthcare contexts.

Introduction: ground biocontainment transport is essential for managing highly transmissible diseases, yet standardized decision- making tools for road-based transfers are lacking in civilian literature. This study aimed to develop an operational model to support clinical and logistical decisions during such transports. Materials and Methods: a decision tree was developed through retrospective analysis of 1.994 biocontainment transfers performed in Sicily between December 2020 and March 2022. Key variables included patient condition, use of life-support equipment, and transfer characteristics. The model was refined through structured post-mission briefings and categorized transfers into three complexity levels (C1-C3) based on clinical and logistical criteria. Results: patients were stratified by walking ability, need for monitoring or life support, and infectious risk. A total of 590 highcomplexity transfers used isolators, while 1.403 lower-complexity cases were managed with a biocontainment minibus. No critical equipment failures were recorded. The decision tree supported consistent planning and improved safety. Discussion: comparison with literature-based recommendations confirmed the model's validity and highlighted operational issues such as miscommunication and equipment integration. Integration with NEWS2 is proposed to enhance risk stratification. Conclusions: the proposed decision tree provides a reproducible framework for classifying biocontainment transport complexity. Prospective validation is needed to support its application in broader healthcare contexts.

IntroductionEURO2020 generated a growing media and population interest across the month period, that peaked with large spontaneous celebrations across the country upon winning the tournament.MethodsWe retrospectively analysed data from the national surveillance system (indicator-based) and from event-based surveillance to assess how the epidemiology of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) changed in June–July 2021 and to describe cases and clusters linked with EURO2020.ResultsWidespread increases in transmission and case numbers, mainly among younger males, were documented in Italy, none were linked with stadium attendance. Vaccination coverage against SARS-CoV-2 was longer among cases linked to EURO2020 than among the general population.ConclusionsTransmission increased across the country, mainly due to gatherings outside the stadium, where, conversely, strict infection control measures were enforced. These informal ‘side’ gatherings were dispersed across the entire country and difficult to control. Targeted communication and control strategies to limit the impact of informal gatherings occurring outside official sites of mass gathering events should be further developed.

Objectives: The incidence of acute cardiac events is one of the main reasons for medical consultation, disembarkation, repatriation, and death among seafarers at sea. Managing cardiovascular risk factors, particularly those that can be modified, is the key to preventing cardiovascular disease. Therefore, this review estimates the pooled prevalence of major CVD risk factors among seafarers. Methods: We conducted a comprehensive search of studies published between 1994 and December 2021 in four international databases, namely PubMed/Medline, Scopus, Google Scholar, and Web of Science (WOS). Each study was evaluated for methodological quality using the Joanna Briggs Institute (JBI) critical appraisal tool for prevalence studies. The DerSimonian–Laird random-effects model with logit transformations was used to estimate the pooled prevalence of major CVD risk factors. The results were reported in accordance with the Preferred Items for Systematic Review and Meta-analysis (PRISMA) guidelines. Results: Out of all 1484 studies reviewed, 21 studies with 145,913 study participants met the eligibility criteria and were included in the meta-analysis. In the pooled analysis, the prevalence of smoking was found to be 40.14% (95% CI: 34.29 to 46.29%) with heterogeneity between studies (I2 = 98%, p < 0.01). The prevalence of hypertension, overweight, obesity, diabetes mellitus, and alcohol consumption was 45.32%, 41.67%, 18.60%, 12.70%, and 38.58%, respectively. However, the sensitivity analysis after excluding studies showed a pooled prevalence of hypertension, overweight, obesity, and diabetes mellitus of 44.86%, 41.87%, 15.99%, and 16.84%, respectively. The subgroup analysis demonstrated that smoking prevalence among seafarers had decreased significantly after 2013. Conclusion: This study demonstrated that CVD risk factors, particularly hypertension, overweight, smoking, alcohol consumption, and obesity, are prevalent among seafarers. These findings may serve as a guide for shipping companies and other responsible bodies in order to prevent CVD risk factors among seafarers. PROSPERO Registration: CRD42022300993.

The dual/global value of SARS-COV-2 genome surveillance on migrants arriving to Europe via the mediterranean routes. Ann Glob Health 2021; 87: 71. First detection of SARS-CoV-2 A. 23.1 sub-lineage in migrants arriving to Italy via the Mediterranean Sea and public health implications. Quarantine vessels and irregular migration: new public health measures against SARS-CoV-2.

The implementation genomic-based surveillance on emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants in low-income countries, which have inadequate molecular and sequencing capabilities and limited vaccine storage, represents a challenge for public health. To date, there is little evidence on molecular investigations of SARS-CoV-2 variants in areas where they might emerge. We report the findings of an experimental SARS-CoV-2 molecular surveillance programme for migrants, refugees, and asylum seekers arriving to Europe via Italy through the Mediterranean Sea. We descriptively analysed data on migrants collected at entry points in Sicily from February 2021 to May 2022. These entry points are integrated with a network of laboratories fully equipped for molecular analyses, which performed next-generation sequencing and used Nextclade and the Pangolin coronavirus disease 2019 (COVID-19) tools for clade/lineage assignment. We obtained 472 full-length SARS-CoV-2 sequences and identified 12 unique clades belonging to 31 different lineages. The delta variant accounted for 43.6% of all genomes, followed by clades 21D (Eta) and 20A (25.4% and 11.4%, respectively). Notably, some of the identified lineages (A.23.1, A.27, and A.29) predicted their introduction into the migration area. The mutation analysis allowed us to identify 617 different amino acid substitutions, 156 amino acid deletions, 7 stop codons, and 6 amino acid insertions. Lastly, we highlighted the geographical distribution patterns of some mutational profiles occurring in the migrants' countries of origin. Genome-based molecular surveillance dedicated to migrant populations from low-resource areas may be useful for forecasting new epidemiological scenarios related to SARS-CoV-2 variants or other emerging pathogens, as well as for informing the updating of vaccination strategies.