Seroprevalence of SARS-CoV-2 antibodies, associated factors, experiences and attitudes of nursing home and home healthcare employees in Switzerland.

BACKGROUND: Many studies in hospital settings exist and have shown healthcare employees to be particularly exposed to SARS-CoV-2. While research focused on hospital staff, little evidence exists for employees in nursing homes and home care. The aims of this study were to assess the seroprevalence in nursing homes and home care employees in the Canton of Zurich, compare it to the general population, assess factors associated with seropositivity and explore the perspective of the employees regarding how the pandemic changed their daily work. METHODS: This cross-sectional study is part of the national Corona Immunitas research program of coordinated, seroprevalence studies in Switzerland. Six nursing homes and six home healthcare organizations providing at home care services in Zurich were selected and 296 and 131 employees were recruited, respectively. Assessments included standardized questionnaires, blood sampling for antibodies, and additional work-specific questions. All participants were recruited between 21st September and 23rd October 2020, before the second wave of the pandemic hit Switzerland, and were possibly exposed to SARS-CoV-2 at their work during the first wave in spring 2020. RESULTS: Seroprevalence of SARS-CoV-2 was 14.9% (95% CI 11.1%-19.6%; range 3.8% to 24.4%) for nursing home employees and 3.8% (95% CI 1.4-9.1%; range 0% to 10%) for home healthcare employees, compared to the general population of Zurich at 3.5% in September 2020 for those aged 20-64. Nurses were 2.6 times more likely to have SARS-CoV-2 antibodies than those employees who were not nurses (95% CI 1.1-6.2). The employees (nursing homes vs. home healthcare) perceived the implementation of general safety measures (44.9% vs. 57.3%) and wearing masks during work (36.8% vs. 43.5%), especially due to the limited communication with residents/clients, as the most crucial changes. CONCLUSIONS: Nursing home employees who worked through SARS-CoV-2 outbreaks at their work were substantially more affected by SARS-CoV-2 infection compared to the general population and to home healthcare employees who similarly worked through outbreaks in their communities. Employees reported that important resources to cope with the burdensome changes they perceived in their daily work were personal resources and team support. TRIAL REGISTRATION: Current Controlled Trials ISRCTN18181860 dated 09/07/2020. Retrospectively registered.

Predicting Fine-Scale Daily NO2 for 2005-2016 Incorporating OMI Satellite Data Across Switzerland.

Nitrogen dioxide (NO2) remains an important traffic-related pollutant associated with both short- and long-term health effects. We aim to model daily average NO2 concentrations in Switzerland in a multistage framework with mixed-effect and random forest models to respectively downscale satellite measurements and incorporate local sources. Spatial and temporal predictor variables include data from the Ozone Monitoring Instrument, Copernicus Atmosphere Monitoring Service, land use, and meteorological variables. We derived robust models explaining ∼58% (R2 range, 0.56-0.64) of the variation in measured NO2 concentrations using mixed-effect models at a 1 × 1 km resolution. The random forest models explained ∼73% (R2 range, 0.70-0.75) of the overall variation in the residuals at a 100 × 100 m resolution. This is one of the first studies showing the potential of using earth observation data to develop robust models with fine-scale spatial (100 × 100 m) and temporal (daily) variation of NO2 across Switzerland from 2005 to 2016. The novelty of this study is in demonstrating that methods originally developed for particulate matter can also successfully be applied to NO2. The predicted NO2 concentrations will be made available to facilitate health research in Switzerland.

Sample size considerations for clinical research studies in nuclear cardiology.

Sample size calculation is an important element of research design that investigators need to consider in the planning stage of the study. Funding agencies and research review panels request a power analysis, for example, to determine the minimum number of subjects needed for an experiment to be informative. Calculating the right sample size is crucial to gaining accurate information and ensures that research resources are used efficiently and ethically. The simple question "How many subjects do I need?" does not always have a simple answer. Before calculating the sample size requirements, a researcher must address several aspects, such as purpose of the research (descriptive or comparative), type of samples (one or more groups), and data being collected (continuous or categorical). In this article, we describe some of the most frequent methods for calculating the sample size with examples from nuclear cardiology research, including for t tests, analysis of variance (ANOVA), non-parametric tests, correlation, Chi-squared tests, and survival analysis. For the ease of implementation, several examples are also illustrated via user-friendly free statistical software.

Long COVID Through a Public Health Lens: An Umbrella Review.

Objectives: To synthesize existing evidence on prevalence as well as clinical and socio-economic aspects of Long COVID. Methods: An umbrella review of reviews and a targeted evidence synthesis of their primary studies, including searches in four electronic databases, reference lists of included reviews, as well as related article lists of relevant publications. Results: Synthesis included 23 reviews and 102 primary studies. Prevalence estimates ranged from 7.5% to 41% in non-hospitalized adults, 2.3%-53% in mixed adult samples, 37.6% in hospitalized adults, and 2%-3.5% in primarily non-hospitalized children. Preliminary evidence suggests that female sex, age, comorbidities, the severity of acute disease, and obesity are associated with Long COVID. Almost 50% of primary studies reported some degree of Long COVID-related social and family-life impairment, long absence periods off work, adjusted workloads, and loss of employment. Conclusion: Long COVID will likely have a substantial public health impact. Current evidence is still heterogeneous and incomplete. To fully understand Long COVID, well-designed prospective studies with representative samples will be essential.

Increase in Airway Obstruction between 1993 and 2012 in Switzerland. An Observational Study.

Rationale: Most studies determining the prevalence of airway obstruction are limited to short time periods.Objectives: Because temporal trends of obstruction in populations are largely unknown, we determined the prevalence of airway obstruction over 20 years in yearly general population samples in Switzerland between 1993 and 2012.Methods: We analyzed data of 85,789 participants aged 35 years and older who provided spirometric measurements as part of the LuftiBus lung function campaign. We linked data from the 2003-2012 period to the Swiss National Cohort to adjust for annual population differences. Spirometry was performed without bronchodilation, according to American Thoracic Society guidelines. We used Global Lung Initiative (GLI) and Hankinson reference equations to identify obstruction.Results: Obstruction prevalence increased between 1993 and 2012 from 6.1% (95% confidence interval [CI], 5.5 to 6.7) to 15.6% (95% CI, 13.8 to 17.3) based on GLI estimates and from 5.3% (95% CI, 4.7 to 5.9) to 15.4% (95% CI, 13.6 to 17.1) based on Hankinson estimates. When adjusted for participant demographics, air pollutant and occupational exposures, altitude, and season, the prevalence ratios of obstruction were 1.54 (95% CI, 1.22 to 1.93) and 1.65 (95% CI, 1.33 to 2.04) for GLI- and Hankinson-defined airway obstruction, respectively, for 2012 compared with 2003.Conclusions: Though prebronchodilator measurements likely overestimate the prevalence of airway obstruction in absolute terms compared with post-bronchodilator measurements, we found an increase in airway obstruction prevalence. Even with adjustment for several well-known risk factors for obstruction to make the populations across the years more comparable, we still saw a statistically significant increase in prevalence over this time period.

Corona Immunitas: study protocol of a nationwide program of SARS-CoV-2 seroprevalence and seroepidemiologic studies in Switzerland.

OBJECTIVES: Seroprevalence studies to assess the spread of SARS-CoV-2 infection in the general population and subgroups are key for evaluating mitigation and vaccination policies and for understanding the spread of the disease both on the national level and for comparison with the international community. METHODS: Corona Immunitas is a research program of coordinated, population-based, seroprevalence studies implemented by Swiss School of Public Health (SSPH+). Over 28,340 participants, randomly selected and age-stratified, with some regional specificities will be included. Additional studies in vulnerable and highly exposed subpopulations are also planned. The studies will assess population immunological status during the pandemic. RESULTS: Phase one (first wave of pandemic) estimates from Geneva showed a steady increase in seroprevalence up to 10.8% (95% CI 8.2-13.9, n = 775) by May 9, 2020. Since June, Zurich, Lausanne, Basel City/Land, Ticino, and Fribourg recruited a total of 5973 participants for phase two thus far. CONCLUSIONS: Corona Immunitas will generate reliable, comparable, and high-quality serological and epidemiological data with extensive coverage of Switzerland and of several subpopulations, informing health policies and decision making in both economic and societal sectors. ISRCTN Registry: https://www.isrctn.com/ISRCTN18181860 .