Search

Background Unbiased assessment of the risks associated with acquisition of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is critical to informing mitigation efforts during pandemics. The objective of our study was to understand the risk factors for acquiring coronavirus disease 2019 (COVID-19) in a large prospective cohort of adult residents in a large US metropolitan area. Methods We designed a fully remote longitudinal cohort study involving monthly at-home SARS-CoV-2 polymerase chain reaction (PCR) and serology self-testing and monthly surveys. Results Between October 2020 and January 2021, we enrolled 10 289 adults reflective of the Boston metropolitan area census data. At study entry, 567 (5.5%) participants had evidence of current or prior SARS-CoV-2 infection. This increased to 13.4% by June 15, 2021. Compared with Whites, Black non-Hispanic participants had a 2.2-fold greater risk of acquiring COVID-19 (hazard ratio [HR], 2.19; 95% CI, 1.91–2.50; P <.001), and Hispanics had a 1.5-fold greater risk (HR, 1.52; 95% CI, 1.32–1.71; P <.016). Individuals aged 18–29, those who worked outside the home, and those living with other adults and children were at an increased risk. Individuals in the second and third lowest disadvantaged neighborhood communities were associated with an increased risk of acquiring COVID-19. Individuals with medical risk factors for severe disease were at a decreased risk of SARS-CoV-2 acquisition. Conclusions These results demonstrate that race/ethnicity and socioeconomic status are the biggest determinants of acquisition of infection. This disparity is significantly underestimated if based on PCR data alone, as noted by the discrepancy in serology vs PCR detection for non-White participants, and points to persistent disparity in access to testing. Medical conditions and advanced age, which increase the risk for severity of SARS-CoV-2 disease, were associated with a lower risk of COVID-19 acquisition, suggesting the importance of behavior modifications. These findings highlight the need for mitigation programs that overcome challenges of structural racism in current and future pandemics. [ABSTRACT FROM AUTHOR]

Longitudinal clinical studies traditionally require in-person study visits which are well documented to pose barriers to participation and contribute challenges to enrolling representative samples. Remote trial models may reduce barriers to research engagement, improve retention, and reach a more representative cohort. As remote trials become more common following the COVID-19 pandemic, a critical evaluation of this approach is imperative to optimize this paradigm shift in research. The TestBoston study was launched to understand prevalence and risk factors for COVID-19 infection in the greater Boston area through a fully remote home-testing model. Participants (adults, within 45 miles of Boston, MA) were recruited remotely from patient registries at Brigham and Women's Hospital and the general public. Participants were provided with monthly and "on-demand" at-home SARS-CoV-2 RT-PCR and antibody testing using nasal swab and dried blood spot self-collection kits and electronic surveys to assess symptoms and risk factors for COVID-19 via an online dashboard. Between October 2020 and January 2021, we enrolled 10,289 participants reflective of Massachusetts census data. Mean age was 47 years (range 18–93), 5855 (56.9%) were assigned female sex at birth, 7181(69.8%) reported being White non-Hispanic, 952 (9.3%) Hispanic/Latinx, 925 (9.0%) Black, 889 (8.6%) Asian, and 342 (3.3%) other and/or more than one race. Lower initial enrollment among Black and Hispanic/Latinx individuals required an adaptive approach to recruitment, leveraging connections to the medical system, coupled with community partnerships to ensure a representative cohort. Longitudinal retention was higher among participants who were White non-Hispanic, older, working remotely, and with lower socioeconomic vulnerability. Implementation highlighted key differences in remote trial models as participants independently navigate study milestones, requiring a dedicated participant support team and robust technology platforms, to reduce barriers to enrollment, promote retention, and ensure scientific rigor and data quality. Remote clinical trial models offer tremendous potential to engage representative cohorts, scale biomedical research, and promote accessibility by reducing barriers common in traditional trial design. Barriers and burdens within remote trials may be experienced disproportionately across demographic groups. To maximize engagement and retention, researchers should prioritize intensive participant support, investment in technologic infrastructure and an adaptive approach to maximize engagement and retention. [ABSTRACT FROM AUTHOR]

On-road emissions are a major contributor to rising concentrations of atmospheric greenhouse gases. In this study, we applied a downscaling methodology based on commonly available spatial parameters to model on-road CO2 emissions at the 1 × 1 km scale for the Boston, MA region and tested our approach with surface-level CO2 observations. Using two previously constructed emissions inventories with differing spatial patterns and underlying data sources, we developed regression models based on impervious surface area and volume-weighted road density that could be scaled to any resolution. We found that the models accurately reflected the inventories at their original scales (R 2 = 0.63 for both models) and exhibited a strong relationship with observed CO2 mixing ratios when downscaled across the region. Moreover, the improved spatial agreement of the models over the original inventories confirmed that either product represents a viable basis for downscaling in other metropolitan regions, even with limited data. [Copyright &y& Elsevier]