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Dietary intake to trace elements may represent the most relevant source of exposure for the general, non-occupationally population, but some of them have been rarely evaluated. We measured content of fifteen trace elements (antimony, barium, beryllium, boron, cobalt, lithium, molybdenum, nickel, silver, strontium, tellurium, thallium, titanium, uranium, and vanadium) in 908 food and beverage samples through inductively coupled plasma mass spectrometry. We estimated their dietary intake using a validated semi-quantitative food frequency questionnaire collected from a population of the Emilia-Romagna Region in Northern Italy. We compared our estimates with tolerable upper intake levels reported by international agencies and we assessed the non-carcinogenic risk through calculation of total hazard quotient for each trace element according to the US-EPA approach. Overall, estimates of their dietary intake were substantially similar to those reported from other countries, and they fell below the tolerable upper intake levels provided by international agencies. The total hazard quotient for each trace element was below 1. Our findings provide updated estimates of food levels and dietary intake of trace elements far frequently evaluated in a sample of Italian adult consumers. They also suggest that any non-carcinogenic risk associated with intake of investigated trace elements may be ruled out in our population.

Children obesity is a serious public health issue. This study aimed to investigate physical/sedentary activities of first-year primary schools children in Modena, and their association with overweight/obesity and dietary habits of children and family characteristics to identify the risk factors for unhealthy lifestyles. Child physical/sedentary activities were gathered through an anonymous questionnaire administered to parents, as well as family characteristics and weight/height of child and parents. Logistic regression models, eventually adjusted for parents’ sociodemographic characteristics, were used to analyze data. Questionnaires were delivered by 660 families (74.2%), of which 72 without anthropometric data were excluded. Three out of four children spent in physical activities less than 7 h/week, while 63.9% dedicated to sedentary activities two or more hours/day. From multivariate analysis, the habit significantly affecting children’s overweight/obesity was spending time on tablets/Personal Computers/mobile phones/videogames. Higher parental education level resulted in a protective factor for implementing unhealthy lifestyles in terms of time dedicated to physical/sedentary activities. Our results suggest the need of interventions to increase time for physical activity and to promote a responsible use of digital media involving the entire families to reach all parents regardless of their education and nationality with a possible relapse on other family members.

Objectives: To assess the association between students’ financial loss and depressive symptoms during the first wave of the coronavirus disease 2019 (COVID-19) pandemic and whether this association varied by countries having different levels of lockdown measures.Methods: This cross-sectional survey, conducted in spring 2020, included 91,871 students from 23 countries. Depressive symptoms were measured using the shortened Center for Epidemiological Studies Depression Scale and information on lockdowns retrieved from the COVID-19 government response tracker. The association between financial loss and depressive symptoms was investigated estimating prevalence ratios (PR) with multilevel Poisson models.Results: Some 13% of students suffered financial loss during the lockdown and 52% had a relatively high depression score, with large between-countries differences. Minimally and maximally adjusted models showed a 35% (PR = 1.35, 95% Confidence Interval (CI) = 1.29–1.42) and 31% (PR = 1.31, 95% CI = 1.26–1.37) higher prevalence of depressive symptoms in students who lost economic resources compared to students with stable economic resources. No substantial differences in the association were found across countries.Conclusion: Depressive symptoms were more frequent among students who suffered financial loss during the pandemic. Policy makers should consider this issue in the implementation of COVID-19 mitigating measures.

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Background We aimed to determine whether living in a household with children is associated with SARS-CoV-2 seropositivity in adults and investigated interacting factors that may influence this association. Methods SARS-CoV-2 serology testing was performed in randomly selected individuals from the general population between end of October 2020 and February 2021 in 11 cantons in Switzerland. Data on sociodemographic and household characteristics, employment status, and health-related history was collected using questionnaires. Multivariable logistic regression was used to examine the association of living with children <18 years of age (number, age group) and SARS-CoV-2 seropositivity. Further, we assessed the influence of reported non-household contacts, employment status, and gender. Results Of 2393 working age participants (18–64 years), 413 (17.2%) were seropositive. Our results suggest that living with children and SARS-CoV-2 seropositivity are likely to be associated (unadjusted odds ratio (OR) 1.22, 95% confidence interval [0.98–1.52], adjusted OR 1.25 [0.99–1.58]). A pattern of a positive association was also found for subgroups of children aged 0–11 years (OR 1.21 [0.90–1.60]) and 12–17 years (OR 1.14 [0.78–1.64]). Odds of seropositivity were higher with more children (OR 1.14 per additional child [1.02–1.27]). Men had higher risk of SARS-CoV-2 infection when living with children than women (interaction: OR 1.74 [1.10–2.76]). Conclusions In adults from the general population living with children seems associated with SARS-CoV-2 seropositivity. However, child-related infection risk is not the same for every subgroup and depends on factors like gender. Further factors determining child-related infection risk need to be identified and causal links investigated. Trial registration https://www.isrctn.com/ISRCTN18181860 .

Surveillance bias occurs when looking at health conditions that have differential intensity across populations, over time, or according to care setting or type of patient. 1 As a result, any difference in frequency of the condition might not reflect a change in the actual risk of this condition but rather differences in the modality of detection or patient characteristics. 2

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