Risk Factors for Healthcare Personnel Infection With Endemic Coronaviruses (HKU1, OC43, NL63, 229E): Results from the Respiratory Protection Effectiveness Clinical Trial (ResPECT).

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents a large risk to healthcare personnel (HCP). Quantifying the risk of coronavirus infection associated with workplace activities is an urgent need. METHODS: We assessed the association of worker characteristics, occupational roles and behaviors, and participation in procedures with the risk of endemic coronavirus infection among HCP who participated in the Respiratory Protection Effectiveness Clinical Trial (ResPECT), a cluster randomized trial to assess personal protective equipment to prevent respiratory infections and illness conducted from 2011 to 2016. RESULTS: Among 4689 HCP seasons, we detected coronavirus infection in 387 (8%). HCP who participated in an aerosol-generating procedure (AGP) at least once during the viral respiratory season were 105% (95% confidence interval, 21%-240%) more likely to be diagnosed with a laboratory-confirmed coronavirus infection. Younger individuals, those who saw pediatric patients, and those with household members <5 years of age were at increased risk of coronavirus infection. CONCLUSIONS: Our analysis suggests that the risk of HCP becoming infected with an endemic coronavirus increases approximately 2-fold with exposures to AGPs. Our findings may be relevant to the coronavirus disease 2019 (COVID-19) pandemic; however, SARS-CoV-2, the virus that causes COVID-19, may differ from endemic coronaviruses in important ways. CLINICAL TRIALS REGISTRATION: NCT01249625.

Structural factors contributing to SARS-CoV-2 infection risk in the urban slum setting.

BACKGROUND: The structural environment of urban slums, including physical, demographic and socioeconomic attributes, renders inhabitants more vulnerable to SARS-CoV-2 infection. Yet, little is known about the specific determinants that contribute to high transmission within these communities. METHODS AND FINDINGS: We performed a serosurvey of an established cohort of 2,035 urban slum residents from the city of Salvador, Brazil between November 2020 and February 2021, following the first COVID-19 pandemic wave in the country. We identified high SARS-CoV-2 seroprevalence (46.4%, 95% confidence interval [CI] 44.3-48.6%), particularly among female residents (48.7% [95% CI 45.9-51.6%] vs. 43.2% [95% CI 39.8-46.6%] among male residents), and among children (56.5% [95% CI 52.3-60.5%] vs. 42.4% [95% CI 39.9-45.0%] among adults). In multivariable models that accounted for household-level clustering, the odds ratio for SARS-CoV-2 seropositivity among children was 1.96 (95% CI 1.42-2.72) compared to adults aged 30-44 years. Adults residing in households with children were more likely to be seropositive; this effect was particularly prominent among individuals with age 30-44 and 60 years or more. Women living below the poverty threshold (daily per capita household income <$1.25) and those who were unemployed were more likely to be seropositive. CONCLUSIONS: During a single wave of the COVID-19 pandemic, cumulative incidence as assessed by serology approached 50% in a Brazilian urban slum population. In contrast to observations from industrialized countries, SARS-CoV-2 incidence was highest among children, as well as women living in extreme poverty. These findings emphasize the need for targeted interventions that provide safe environments for children and mitigate the structural risks posed by crowding and poverty for the most vulnerable residents of urban slum communities.

Ensemble forecast modeling for the design of COVID-19 vaccine efficacy trials.

To rapidly evaluate the safety and efficacy of COVID-19 vaccine candidates, prioritizing vaccine trial sites in areas with high expected disease incidence can speed endpoint accrual and shorten trial duration. Mathematical and statistical forecast models can inform the process of site selection, integrating available data sources and facilitating comparisons across locations. We recommend the use of ensemble forecast modeling - combining projections from independent modeling groups - to guide investigators identifying suitable sites for COVID-19 vaccine efficacy trials. We describe an appropriate structure for this process, including minimum requirements, suggested output, and a user-friendly tool for displaying results. Importantly, we advise that this process be repeated regularly throughout the trial, to inform decisions about enrolling new participants at existing sites with waning incidence versus adding entirely new sites. These types of data-driven models can support the implementation of flexible efficacy trials tailored to the outbreak setting.