Background: Strengthening community-based healthcare is a valuable strategy to reduce health inequalities and improve the integration of migrants and refugees into local communities in the European Union. However, little is known about how to effectively develop and run community-based healthcare models for migrants and refugees. Aiming at identifying the most-promising best practices, we performed a scoping review of the international academic literature into effective community-based healthcare models and interventions for migrants and refugees as part of the Mig-HealthCare project. Methods: A systematic search in PubMed, EMBASE, and Scopus databases was conducted in March 2018 following the PRISMA methodology. Data extraction from eligible publications included information on general study characteristics, a brief description of the intervention/model, and reported outcomes in terms of effectiveness and challenges. Subsequently, we critically assessed the available evidence per type of healthcare service according to specific criteria to establish a shortlist of the most promising best practices. Results: In total, 118 academic publications were critically reviewed and categorized in the thematic areas of mental health (n = 53), general health services (n = 36), noncommunicable diseases (n = 13), primary healthcare (n = 9), and women’s maternal and child health (n = 7). Conclusion: A set of 15 of the most-promising best practices and tools in community-based healthcare for migrants and refugees were identified that include several intervention approaches per thematic category. The elements of good communication, the linguistic barriers and the cultural differences, played crucial roles in the effective application of the interventions. The close collaboration of the various stakeholders, the local communities, the migrant/refugee communities, and the partnerships is a key element in the successful implementation of primary healthcare provision.

Τhe COVID-19 pandemic highly impacted the circulation, seasonality, and morbidity burden of several respiratory viruses. We reviewed published cases of SARS-CoV-2 and respiratory virus co-infections as of 12 April 2022. SARS-CoV-2 and influenza co-infections were reported almost exclusively during the first pandemic wave. It is possible that the overall incidence of SARS-CoV-2 co-infections is higher because of the paucity of co-testing for respiratory viruses during the first pandemic waves when mild cases might have been missed. Animal models indicate severe lung pathology and high fatality; nevertheless, the available literature is largely inconclusive regarding the clinical course and prognosis of co-infected patients. Animal models also indicate the importance of considering the sequence timing of each respiratory virus infection; however, there is no such information in reported human cases. Given the differences between 2020 and 2023 in terms of epidemiology and availability of vaccines and specific treatment against COVID-19, it is rational not to extrapolate these early findings to present times. It is expected that the characteristics of SARS-CoV-2 and respiratory virus co-infections will evolve in the upcoming seasons. Multiplex real-time PCR-based assays have been developed in the past two years and should be used to increase diagnostic and infection control capacity, and also for surveillance purposes. Given that COVID-19 and influenza share the same high-risk groups, it is essential that the latter get vaccinated against both viruses. Further studies are needed to elucidate how SARS-CoV-2 and respiratory virus co-infections will be shaped in the upcoming years, in terms of impact and prognosis.

The aim of this study is to investigate the circulating variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from Athens and from rural areas in Greece during July and August 2021. We also present a rapid review of literature regarding significant SARS-CoV-2 mutations and their impact on public health. A total of 2500 nasopharyngeal swab specimens were collected from suspected COVID-19 cases (definition by WHO 2021b). Viral nucleic acid extraction was implemented using an automatic extractor and the RNA recovered underwent qRT-PCR in order to characterize the specimens as positive or negative for SARS-CoV-2. The positive specimens were then used to identify specific Spike gene mutations and characterize the emerging SARS-CoV-2 variants. For this step, various kits were utilized. From the 2500 clinical specimens, 220 were tested positive for SARS-CoV-2 indicating a prevalence of 8.8% among suspected cases. The RT-PCR Ct (Cycle threshold) Value ranged from 19 to 25 which corresponds to medium to high copy numbers of the virus in the positive samples. From the 220 positive specimens 148 (67.3%) were from Athens and 72 (32.7%) from Greek rural areas. As far as the Spike mutations investigated: N501Y appeared in all the samples, D614G mutation appeared in 212 (96.4%) samples with a prevalence of 87.2% in Athens and 98.6% in the countryside, E484K had a prevalence of 10.8% and 12.5% in Athens and the rural areas, respectively. K417N was found in 18 (12.2%) samples from Athens and four (5.6%) from the countryside, P681H was present in 51 (34.5%) Athenian specimens and 14 (19.4%) specimens from rural areas, HV69-70 was carried in 32.4% and 19.4% of the samples from Athens and the countryside, respectively. P681R had a prevalence of 87.2% in Athens and 98.6% in rural areas, and none of the specimens carried the L452R mutation. 62 (28.2%) samples carried the N501Y, P681H, D614G and HV69-70 mutations simultaneously and the corresponding variant was characterized as the Alpha (UK) variant (B 1.1.7). Only six (2.7%) samples from the center of Athens had the N501Y, E484K, K417N and D614G mutations simultaneously and the virus responsible was characterized as the Beta (South African) variant (B 1.351). Our study explored the SARS-CoV-2 variants using RT-PCR in a representative cohort of samples collected from Greece in July and August 2021. The prevalent mutations identified were N501Y (100%), D614G (96.4%), P681R (90.1%) and the variants identified were the Delta (90.1%), Alpha (28.2%) and Beta (2.7%).