How COVID-19 Pandemic Has Influenced Public Interest in Foods: A Google Trends Analysis of Italian Data.

Controversy exists about the impact of the COVID-19 pandemic on dietary habits, with studies demonstrating both benefits and drawbacks of this period. We analyzed Google Trends data on specific terms and arguments related to different foods (i.e., fruits, vegetables, legumes, whole grains, nuts and seeds, milk, red meat, processed meat, and sugar-sweetened beverages) in order to evaluate the interest of Italian people before and during the COVID-19 pandemic. Joinpoint regression models were applied to identify the possible time points at which public interest in foods changed (i.e., joinpoints). Interestingly, public interest in specific food categories underwent substantial changes during the period under examination. While some changes did not seem to be related to the COVID-19 pandemic (i.e., legumes and red meat), public interest in fruit, vegetables, milk, and whole grains increased significantly, especially during the first lockdown. It should be noted, however, that the interest in food-related issues returned to prepandemic levels after the first lockdown period. Thus, more efforts and ad hoc designed studies should be encouraged to evaluate the duration and direction of the COVID-19 pandemic's influence.

How Antimicrobial Resistance Is Linked to Climate Change: An Overview of Two Intertwined Global Challenges.

Globally, antimicrobial resistance (AMR) and climate change (CC) are two of the top health emergencies, and can be considered as two interlinked public health priorities. The complex commonalities between AMR and CC should be deeply investigated in a One Health perspective. Here, we provided an overview of the current knowledge about the relationship between AMR and CC. Overall, the studies included pointed out the need for applying a systemic approach to planetary health. Firstly, CC increasingly brings humans and animals into contact, leading to outbreaks of zoonotic and vector-borne diseases with pandemic potential. Although it is well-established that antimicrobial use in human, animal and environmental sectors is one of the main drivers of AMR, the COVID-19 pandemic is exacerbating the current scenario, by influencing the use of antibiotics, personal protective equipment, and biocides. This also results in higher concentrations of contaminants (e.g., microplastics) in natural water bodies, which cannot be completely removed from wastewater treatment plants, and which could sustain the AMR spread. Our overview underlined the lack of studies on the direct relationship between AMR and CC, and encouraged further research to investigate the multiple aspects involved, and its effect on human health.

The Impact of the COVID-19 Pandemic on Dietary Patterns of Pregnant Women: A Comparison between Two Mother-Child Cohorts in Sicily, Italy.

A maternal diet, before and during pregnancy, plays a key role in ensuring maternal and newborn health. The COVID-19 pandemic, however, may have compromised dietary habits in the general population and in specific subgroups of individuals. Here, we evaluated the impact of COVID-19 on the diet of pregnant women, using data from two mother-child cohorts in Sicily (Italy). Dietary data were collected using a food frequency questionnaire and analyzed through the Mediterranean diet (MD) score and principal component analysis (PCA). The comparison of maternal dietary consumption before and during the COVID-19 pandemic showed differences in terms of vegetables (p < 0.001), fruit (p < 0.001), dairy products (p < 0.001), fish (p < 0.001), and legumes (p = 0.001). Accordingly, after adjusting for covariates, mothers enrolled during the pandemic were more likely to report low adherence to MD than those enrolled before (OR = 1.65; 95%CI = 1.12-2.42; p = 0.011). A similar result was obtained by analyzing the adherence to a prudent dietary pattern, derived through PCA and characterized by high intake of cooked and row vegetables, legumes, fruit, fish, and soup. Overall, these findings suggested that the COVID-19 pandemic may have influenced maternal diet during pregnancy. However, further efforts are needed to investigate the main causes and consequences of this change.

HIV infection does not affect the risk of death of COVID-19 patients: A systematic review and meta-analysis of epidemiological studies.

Background: Even during the current Coronavirus Disease 2019 (COVID-19) pandemic, the infection with the Human Immunodeficiency Virus (HIV) continues to pose a major threat, worldwide. In fact, the World Health Organization (WHO) defined the HIV infection as a risk factor for both severe COVID-19, at hospital admission, and in-hospital mortality. Despite this evidence, however, there remains the need for investigating whether SARS-CoV-2 infection could increase the risk of death among people living with HIV (PLHIV). Thus, we conducted a systematic review and meta-analysis to assess the impact of the SARS-CoV-2 infection on the risk of death among PLHIV and HIV- seronegative people. Methods: The literature search was carried out on PubMed, Embase and Web of Science databases, from the inception to February 2022. Epidemiological studies on patients tested positive for SARS-CoV-2 infection, which compared the proportion of deaths between PLHIV and HIV-seronegative people, were considered eligible for the inclusion. The pooled odds ratio (OR) was obtained through meta-analysis of the comparison between PLHIV and HIV-seronegative people. Study quality was assessed by using the Newcastle-Ottawa Quality Assessment. Results: On a total of 1001 records obtained from the literature search, the present systematic review and meta-analysis included 28 studies on 168 531 PLHIV and 66 712 091 HIV-seronegative patients with SARS-CoV-2 infection. The meta-analysis showed no difference in the risk of death between PLHIV and HIV-seronegative patients (OR = 1.09; 95% confidence interval (CI) = 0.93-1.26; P > 0.001). However, a significant heterogeneity was found for this comparison (I2 = 88.8%, P < 0.001). Conclusions: Although our meta-analysis suggests no difference in the risk of death of PLHIV with SARS-CoV-2 infection, if compared with HIV-seronegative patients, further research should be encouraged to improve the current knowledge about the impact of SARS-CoV-2 and HIV co-infection.

In-vitro NET-osis induced by COVID-19 sera is associated to severe clinical course in not vaccinated patients and immune-dysregulation in breakthrough infection.

Since neutrophil extracellular traps formation (NET-osis) can be assessed indirectly by treating healthy neutrophils with blood-derived fluids from patients and then measuring the NETs response, we designed a pilot study to convey high-dimensional cytometry of peripheral blood immune cells and cytokines, combined with clinical features, to understand if NET-osis assessment could be included in the immune risk profiling to early prediction of clinical patterns, disease severity, and viral clearance at 28 days in COVID-19 patients. Immune cells composition of peripheral blood, cytokines concentration and in-vitro NETosis were detected in peripheral blood of 41 consecutive COVID-19 inpatients, including 21 mild breakthrough infections compared to 20 healthy donors, matched for sex and age. Major immune dysregulation in peripheral blood in not-vaccinated COVID-19 patients compared to healthy subjects included: a significant reduction of percentage of unswitched memory B-cells and transitional B-cells; loss of naïve CD3+CD4+CD45RA+ and CD3+CD8+CD45RA+ cells, increase of IL-1ß, IL-17A and IFN-γ. Myeloid compartment was affected as well, due to the increase of classical (CD14++CD16-) and intermediate (CD14++CD16+) monocytes, overexpressing the activation marker CD64, negatively associated to the absolute counts of CD8+ CD45R0+ cells, IFN-γ and IL-6, and expansion of monocytic-like myeloid derived suppressor cells. In not-vaccinated patients who achieved viral clearance by 28 days we found at hospital admission lower absolute counts of effector cells, namely CD8+T cells, CD4+ T-cells and CD4+CD45RO+ T cells. Percentage of in-vitro NET-osis induced by patients' sera and NET-osis density were progressively higher in moderate and severe COVID-19 patients than in mild disease and controls. The percentage of in-vitro induced NET-osis was positively associated to circulating cytokines IL-1ß, IFN-γ and IL-6. In breakthrough COVID-19 infections, characterized by mild clinical course, we observed increased percentage of in-vitro NET-osis, higher CD4+ CD45RO+ and CD8+ CD45RO+ T cells healthy or mild-COVID-19 not-vaccinated patients, reduced by 24 h of treatment with ACE inhibitor ramipril. Taken together our data highlight the role of NETs in orchestrating the complex immune response to SARS-COV-2, that should be considered in a multi-target approach for COVID-19 treatment.

How COVID-19 Has Influenced Public Interest in Antimicrobials, Antimicrobial Resistance and Related Preventive Measures: A Google Trends Analysis of Italian Data.

Google Trends analytics is an innovative way to evaluate public interest in antimicrobial resistance (AMR) and related preventive measures. In the present study, we analyzed Google Trends data in Italy, from 2016 to 2021. A joinpoint analysis was performed to assess whether and how annual campaigns and the COVID-19 pandemic affected public interest in antimicrobials, AMR, hand hygiene, and the use of disinfectant. For the terms "antimicrobials" and "antimicrobial resistance", no joinpoints were detected around the time of the World Antimicrobial Awareness Week. Similarly, the COVID-19 pandemic seems to have had no effect on public interest in this term. For the term "handwashing", no joinpoints were detected around World Hand Hygiene Day or Global Handwashing Day. However, three joinpoints were detected around the peak of interest observed in March 2020, after the beginning of the COVID-19 pandemic. Comparable results were obtained for the term "disinfectant". These findings show that the influence of annual campaigns on public interest in AMR and preventive measures was modest and not long-term. The COVID-19 pandemic, meanwhile, had no effect on AMR but raised awareness on preventive measures. However, this was a temporary rather than long-term outcome. Thus, different policies, strategies, and measures should be designed to advocate prevention of AMR in the COVID-19 era.

Development of a Web-App for the Ecological Momentary Assessment of Dietary Habits among College Students: The HEALTHY-UNICT Project.

The transition from adolescence to adulthood is a critical period for the development of healthy behaviors. Yet, it is often characterized by unhealthy food choices. Considering the current pandemic scenario, it is also essential to assess the effects of coronavirus disease-19 (COVID-19) on lifestyles and diet, especially among young people. However, the assessment of dietary habits and their determinants is a complex issue that requires innovative approaches and tools, such as those based on the ecological momentary assessment (EMA). Here, we describe the first phases of the "HEALTHY-UNICT" project, which aimed to develop and validate a web-app for the EMA of dietary data among students from the University of Catania, Italy. The pilot study included 138 students (mean age 24 years, SD = 4.2; 75.4% women), who used the web-app for a week before filling out a food frequency questionnaire with validation purposes. Dietary data obtained through the two tools showed moderate correlations, with the lowest value for butter and margarine and the highest for pizza (Spearman's correlation coefficients of 0.202 and 0.699, respectively). According to the cross-classification analysis, the percentage of students classified into the same quartile ranged from 36.9% for vegetable oil to 58.1% for pizza. In line with these findings, the weighted-kappa values ranged from 0.15 for vegetable oil to 0.67 for pizza, and most food categories showed values above 0.4. This web-app showed good usability among students, assessed through a 19-item usability scale. Moreover, the web-app also had the potential to evaluate the effect of the COVID-19 pandemic on students' behaviors and emotions, showing a moderate impact on sedentary activities, level of stress, and depression. These findings, although interesting, might be confirmed by the next phases of the HEALTHY-UNICT project, which aims to characterize lifestyles, dietary habits, and their relationship with anthropometric measures and emotions in a larger sample of students.

Using Google Trends to Predict COVID-19 Vaccinations and Monitor Search Behaviours about Vaccines: A Retrospective Analysis of Italian Data.

Google Trends data are an efficient source for analysing internet search behaviour and providing valuable insights into community dynamics and health-related problems. In this article, we aimed to evaluate if Google Trends data could help monitor the COVID-19 vaccination trend over time and if the introduction of COVID-19 vaccines modified the interest of pregnant women in vaccination. Data related to Google internet searches and the number of vaccine doses administered in Italy were used. We found moderate to strong correlations between search volumes of vaccine-related terms and the number of vaccines administered. In particular, a model based on Google Trends with a 3-week lag showed the best performance in fitting the number of COVID-19 vaccinations over time. We also observed that the introduction of COVID-19 vaccines affected the search interest for the argument "vaccination in pregnancy" both quantitatively and qualitatively. There was a significant increase in the search interest after the launch of the COVID-19 vaccination campaign in Italy. Qualitative analysis suggested that this increase was probably due to concerns about COVID-19 vaccines. Thus, our study suggests the benefits of using Google Trends data to predict the number of COVID-19 vaccine doses administered, and to monitor feelings about vaccination.

Applying a hierarchical clustering on principal components approach to identify different patterns of the SARS-CoV-2 epidemic across Italian regions.

Italy has experienced the epidemic of Severe Acute Respiratory Syndrome Coronavirus 2, which spread at different times and with different intensities throughout its territory. We aimed to identify clusters with similar epidemic patterns across Italian regions. To do that, we defined a set of regional indicators reflecting different domains and employed a hierarchical clustering on principal component approach to obtain an optimal cluster solution. As of 24 April 2020, Lombardy was the worst hit Italian region and entirely separated from all the others. Sensitivity analysis-by excluding data from Lombardy-partitioned the remaining regions into four clusters. Although cluster 1 (i.e. Veneto) and 2 (i.e. Piedmont and Emilia-Romagna) included the most hit regions beyond Lombardy, this partition reflected differences in the efficacy of restrictions and testing strategies. Cluster 3 was heterogeneous and comprised regions where the epidemic started later and/or where it spread with the lowest intensity. Regions within cluster 4 were those where the epidemic started slightly after Veneto, Emilia-Romagna and Piedmont, favoring timely adoption of control measures. Our findings provide policymakers with a snapshot of the epidemic in Italy, which might help guiding the adoption of countermeasures in accordance with the situation at regional level.

Estimation of unreported SARS-CoV-2 cases in Italy using a Susceptible-Exposed-Infectious-Recovered-Dead model.

BACKGROUND: An important epidemiological characteristic that might modulate the pandemic potential of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the proportion of undocumented cases. METHODS: Here, we employed a Susceptible-Exposed-Infectious-Recovered-Dead (SEIRD) model to estimate the proportion of unreported SARS-CoV-2 cases in Italy from the reported number of deaths prior to the adoption of national control measures. RESULTS: We estimated 115 894 infectious individuals (95% confidence interval (CI) = 95 318-140 455) and a total of 144 116 cases (95% CI = 119 030-173 959) on 20 March, 2020. These estimates resulted in 67.3% (95% CI = 60.3%-73.0%) unreported infectious individuals and in 67.4% (95% CI = 60.5%-73.0%) total cases. As such, given the substantial volume of undocumented cases, the case fatality risk would drop from an apparent 8.6% to an estimated 2.6% (95% CI = 2.2%-2.9%). CONCLUSIONS: Our findings partially explain the case fatality risk observed in Italy with a high proportion of unreported SARS-CoV-2 cases. Moreover, we underline that the fraction of undocumented infectious individuals is a critical epidemiological characteristic that needs to be taken into for a better understanding of the SARS-CoV-2 epidemic.

A Clustering Approach to Classify Italian Regions and Provinces Based on Prevalence and Trend of SARS-CoV-2 Cases.

While several efforts have been made to control the epidemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Italy, differences between and within regions have made it difficult to plan the phase two management after the national lockdown. Here, we propose a simple and immediate clustering approach to categorize Italian regions working on the prevalence and trend of SARS-CoV-2 positive cases prior to the start of phase two on 4 May 2020. Applying both hierarchical and k-means clustering, we identified three regional groups: regions in cluster 1 exhibited higher prevalence and the highest trend of SARS-CoV-2 positive cases; those classified into cluster 2 constituted an intermediate group; those in cluster 3 were regions with a lower prevalence and the lowest trend of SARS-CoV-2 positive cases. At the provincial level, we used a similar approach but working on the prevalence and trend of the total SARS-CoV-2 cases. Notably, provinces in cluster 1 exhibited the highest prevalence and trend of SARS-CoV-2 cases. Provinces in clusters 2 and 3, instead, showed a median prevalence of approximately 11 cases per 10,000 residents. However, provinces in cluster 3 were those with the lowest trend of cases. K-means clustering yielded to an alternative cluster solution in terms of the prevalence and trend of SARS-CoV-2 cases. Our study described a simple and immediate approach to monitor the SARS-CoV-2 epidemic at the regional and provincial level. These findings, at present, offered a snapshot of the epidemic, which could be helpful to outline the hierarchy of needs at the subnational level. However, the integration of our approach with further indicators and characteristics could improve our findings, also allowing the application to different contexts and with additional aims.

Modeling the Novel Coronavirus (SARS-CoV-2) Outbreak in Sicily, Italy

Italy was the first country in Europe which imposed control measures of travel restrictions, quarantine and contact precautions to tackle the epidemic spread of the novel coronavirus (SARS-CoV-2) in all its regions. While such efforts are still ongoing, uncertainties regarding SARS-CoV-2 transmissibility and ascertainment of cases make it difficult to evaluate the effectiveness of restrictions. Here, we employed a Susceptible-Exposed-Infectious-Recovered-Dead (SEIRD) model to assess SARS-CoV-2 transmission dynamics, working on the number of reported patients in intensive care unit (ICU) and deaths in Sicily (Italy), from 24 February to 13 April. Overall, we obtained a good fit between estimated and reported data, with a fraction of unreported SARS-CoV-2 cases (18.4%;95%CI = 0-34.0%) before 10 March lockdown. Interestingly, we estimated that transmission rate in the community was reduced by 32% (95%CI = 23-42%) after the first set of restrictions, and by 80% (95%CI = 70-89%) after those adopted on 23 March. Thus, our estimates delineated the characteristics of SARS-CoV2 epidemic before restrictions taking into account unreported data. Moreover, our findings suggested that transmission rates were reduced after the adoption of control measures. However, we cannot evaluate whether part of this reduction might be attributable to other unmeasured factors, and hence further research and more accurate data are needed to understand the extent to which restrictions contributed to the epidemic control.

Estimation of Unreported Novel Coronavirus (SARS-CoV-2) Infections from Reported Deaths: A Susceptible-Exposed-Infectious-Recovered-Dead Model.

In the midst of the novel coronavirus (SARS-CoV-2) epidemic, examining reported case data could lead to biased speculations and conclusions. Indeed, estimation of unreported infections is crucial for a better understanding of the current emergency in China and in other countries. In this study, we aimed to estimate the unreported number of infections in China prior to the 23 January 2020 restrictions. To do this, we developed a Susceptible-Exposed-Infectious-Recovered-Dead (SEIRD) model that estimated unreported infections from the reported number of deaths. Our approach relied on the fact that observed deaths were less likely to be affected by ascertainment biases than reported infections. Interestingly, we estimated that the basic reproductive number (R0) was 2.43 (95%CI = 2.42-2.44) at the beginning of the epidemic and that 92.9% (95%CI = 92.5%-93.1%) of total cases were not reported. Similarly, the proportion of unreported new infections by day ranged from 52.1% to 100%, with a total of 91.8% (95%CI = 91.6%-92.1%) of infections going unreported. Agreement between our estimates and those from previous studies proves that our approach is reliable for estimating the prevalence and incidence of undocumented SARS-CoV-2 infections. Once it has been tested on Chinese data, our model could be applied to other countries with different surveillance and testing policies.