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BackgroundGlobal efforts are needed to elucidate the epidemiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the underlying cause of coronavirus disease 2019 (COVID-19), including seroprevalence, risk factors, and long-term sequelae, as well as immune responses after vaccination across populations and the social dimensions of prevention and treatment strategies.MethodsIn the United States, the National Cancer Institute in partnership with the National Institute of Allergy and Infectious Diseases, established the SARS-CoV-2 Serological Sciences Network (SeroNet) as the nation's largest coordinated effort to study coronavirus disease 2019. The network comprises multidisciplinary researchers bridging gaps and fostering collaborations among immunologists, epidemiologists, virologists, clinicians and clinical laboratories, social and behavioral scientists, policymakers, data scientists, and community members. In total, 49 institutions form the SeroNet consortium to study individuals with cancer, autoimmune disease, inflammatory bowel diseases, cardiovascular diseases, human immunodeficiency virus, transplant recipients, as well as otherwise healthy pregnant women, children, college students, and high-risk occupational workers (including healthcare workers and first responders).ResultsSeveral studies focus on underrepresented populations, including ethnic minorities and rural communities. To support integrative data analyses across SeroNet studies, efforts are underway to define common data elements for standardized serology measurements, cellular and molecular assays, self-reported data, treatment, and clinical outcomes.ConclusionsIn this paper, we discuss the overarching framework for SeroNet epidemiology studies, critical research questions under investigation, and data accessibility for the worldwide scientific community. Lessons learned will help inform preparedness and responsiveness to future emerging diseases.

Background: Studies on the impact of workplace safety inspections on work injuries have found mixed effectiveness. Most studies are from the United States, examining Occupational Health and Safety Administration (OSHA) regulatory inspections in manufacturing firms with more than 10 employees. This study examines whether regulatory inspections in Alberta, Canada, result in reductions in workers' compensation claims rates for inspected firms relative to comparable non‐inspected firms. Methods: Firm and claim‐level data from the Workers' Compensation Board of Alberta were linked with regulatory enforcement data from the Government of Alberta for construction, manufacturing, and transportation firms with at least one full‐time employee for 37 consecutive months. A matched difference‐in‐differences study design was used to estimate changes in lost‐time claim rates for work‐related injuries and musculoskeletal diseases of inspected and comparable non‐inspected firms between the year pre‐inspection and 2 years, post‐inspection, controlling for firm‐level characteristics. Results: Inspections were not effective in reducing firm‐level claim rates, with the exception of transportation firms with more than one inspection experiencing a 28% decrease in their claim rate in the second year post‐inspection, relative to the change in non‐inspected firms. In construction, inspected firms experienced a 12% increase in their claim rate in the first year post‐inspection. No effect was observed in the manufacturing sector. Conclusions: Regulatory workplace safety inspections in Alberta generally do not result in greater reductions in firm‐level claim rates in the construction, manufacturing, and transportation sectors. Inspections alone may not be sufficient to induce compliance or hazard management changes that lead to reductions in firm‐level injuries. [ABSTRACT FROM AUTHOR]

Background and Aims: Longitudinal change in income is crucial in explaining cardiovascular health inequalities. However, there is limited evidence for cardiovascular disease (CVD) risk associated with income dynamics over time among individuals with type 2 diabetes (T2D). Methods: Using a nationally representative sample from the Korean National Health Insurance Service database, 1 528 108 adults aged 30–64 with T2D and no history of CVD were included from 2009 to 2012 (mean follow-up of 7.3 years). Using monthly health insurance premium information, income levels were assessed annually for the baseline year and the four preceding years. Income variability was defined as the intraindividual standard deviation of the percent change in income over 5 years. The primary outcome was a composite event of incident fatal and nonfatal CVD (myocardial infarction, heart failure, and stroke) using insurance claims. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated after adjusting for potential confounders. Results: High-income variability was associated with increased CVD risk (HRhighest vs. lowest quartile 1.25, 95% CI 1.22–1.27; Ptrend <.001). Individuals who experienced an income decline (4 years ago vs. baseline) had increased CVD risk, which was particularly notable when the income decreased to the lowest level (i.e. Medical Aid beneficiaries), regardless of their initial income status. Sustained low income (i.e. lowest income quartile) over 5 years was associated with increased CVD risk (HRn = 5 years vs. n = 0 years 1.38, 95% CI 1.35–1.41; Ptrend <.0001), whereas sustained high income (i.e. highest income quartile) was associated with decreased CVD risk (HRn = 5 years vs. n = 0 years 0.71, 95% CI 0.70–0.72; Ptrend <.0001). Sensitivity analyses, exploring potential mediators, such as lifestyle-related factors and obesity, supported the main results. Conclusions: Higher income variability, income declines, and sustained low income were associated with increased CVD risk. Our findings highlight the need to better understand the mechanisms by which income dynamics impact CVD risk among individuals with T2D. Structured Graphical Abstract This study investigates the association between income dynamics over five years and incident composite cardiovascular outcomes in non-elderly individuals with type 2 diabetes. Our graphical abstract highlights significant associations between cardiovascular risk and income metrics, including income variability (standard deviation of percent change), changes in income status (rise and decline), and cumulative number of years in low- or high-income status. [ABSTRACT FROM AUTHOR]