Worldwide, racial and ethnic minorities have been disproportionately impacted by COVID-19 with increased risk of infection, its related complications, and death. In the initial phase of population-based vaccination in the United States (U.S.) and United Kingdom (U.K.), vaccine hesitancy may result in differences in uptake. We performed a cohort study among U.S. and U.K. participants who volunteered to take part in the smartphone-based COVID Symptom Study (March 2020-February 2021) and used logistic regression to estimate odds ratios of vaccine hesitancy and uptake. In the U.S. (n = 87,388), compared to white participants, vaccine hesitancy was greater for Black and Hispanic participants and those reporting more than one or other race. In the U.K. (n = 1,254,294), racial and ethnic minority participants showed similar levels of vaccine hesitancy to the U.S. However, associations between participant race and ethnicity and levels of vaccine uptake were observed to be different in the U.S. and the U.K. studies. Among U.S. participants, vaccine uptake was significantly lower among Black participants, which persisted among participants that self-reported being vaccine-willing. In contrast, statistically significant racial and ethnic disparities in vaccine uptake were not observed in the U.K sample. In this study of self-reported vaccine hesitancy and uptake, lower levels of vaccine uptake in Black participants in the U.S. during the initial vaccine rollout may be attributable to both hesitancy and disparities in access.

Given the continued burden of COVID-19 worldwide, there is a high unmet need for data on the effect of social distancing and face mask use to mitigate the risk of COVID-19. We examined the association of community-level social distancing measures and individual face mask use with risk of predicted COVID-19 in a large prospective U.S. cohort study of 198,077 participants. Individuals living in communities with the greatest social distancing had a 31% lower risk of predicted COVID-19 compared with those living in communities with poor social distancing. Self-reported ‘always’ use of face mask was associated with a 62% reduced risk of predicted COVID-19 even among individuals living in a community with poor social distancing. These findings provide support for the efficacy of mask-wearing even in settings of poor social distancing in reducing COVID-19 transmission. Despite mass vaccination campaigns in many parts of the world, continued efforts at social distancing and face mask use remain critically important in reducing the spread of COVID-19.

We paired high-resolution travel-time metrics with a SARS-CoV-2 testing location database in the United States. Median travel time to testing sites is longer in counties with lower population density, and a higher percent of minority and uninsured individuals. Differential geographic accessibility to testing can recapitulate healthcare disparities and bias transmission estimates.

Simultaneously tracking the global impact of COVID-19 is challenging because of regional variation in resources and reporting. Leveraging self-reported survey outcomes via an existing international social media network has the potential to provide standardized data streams to support monitoring and decision-making worldwide, in real time, and with limited local resources. The University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), in partnership with Facebook, has invited daily cross-sectional samples from the social media platform's active users to participate in the survey since its launch on April 23, 2020. We analyzed UMD-CTIS survey data through December 20, 2020, from 31,142,582 responses representing 114 countries/territories weighted for nonresponse and adjusted to basic demographics. We show consistent respondent demographics over time for many countries/territories. Machine Learning models trained on national and pooled global data verified known symptom indicators. COVID-like illness (CLI) signals were correlated with government benchmark data. Importantly, the best benchmarked UMD-CTIS signal uses a single survey item whereby respondents report on CLI in their local community. In regions with strained health infrastructure but active social media users, we show it is possible to define COVID-19 impact trajectories using a remote platform independent of local government resources. This syndromic surveillance public health tool is the largest global health survey to date and, with brief participant engagement, can provide meaningful, timely insights into the global COVID-19 pandemic at a local scale.

Objective: Rapid‐onset Obesity with Hypothalamic Dysfunction, Hypoventilation and Autonomic Dysregulation (ROHHAD), is a severe pediatric disorder of uncertain etiology resulting in hypothalamic dysfunction and frequent sudden death. Frequent co‐occurrence of neuroblastic tumors have fueled suspicion of an autoimmune paraneoplastic neurological syndrome (PNS); however, specific anti‐neural autoantibodies, a hallmark of PNS, have not been identified. Our objective is to determine if an autoimmune paraneoplastic etiology underlies ROHHAD.Methods: Immunoglobulin G (IgG) from pediatric ROHHAD patients (n = 9), non‐inflammatory individuals (n = 100) and relevant pediatric controls (n = 25) was screened using a programmable phage display of the human peptidome (PhIP‐Seq). Putative ROHHAD‐specific autoantibodies were orthogonally validated using radioactive ligand binding and cell‐based assays. Expression of autoantibody targets in ROHHAD tumor and healthy brain tissue was assessed with immunohistochemistry and mass spectrometry, respectively.Results: Autoantibodies to ZSCAN1 were detected in ROHHAD patients by PhIP‐Seq and orthogonally validated in 7/9 ROHHAD patients and 0/125 controls using radioactive ligand binding and cell‐based assays. Expression of ZSCAN1 in ROHHAD tumor and healthy human brain tissue was confirmed.Interpretation: Our results support the notion that tumor‐associated ROHHAD syndrome is a pediatric PNS, potentially initiated by an immune response to peripheral neuroblastic tumor. ZSCAN1 autoantibodies may aid in earlier, accurate diagnosis of ROHHAD syndrome, thus providing a means toward early detection and treatment. This work warrants follow‐up studies to test sensitivity and specificity of a novel diagnostic test. Last, given the absence of the ZSCAN1 gene in rodents, our study highlights the value of human‐based approaches for detecting novel PNS subtypes. ANN NEUROL 2022;92:279–291

Background: COVID-19 vaccine distribution is at risk of further propagating the inequities of COVID-19, which in the United States (US) has disproportionately impacted the elderly, people of color, and the medically vulnerable. We sought to measure if the disparities seen in the geographic distribution of other COVID-19 healthcare resources were also present during the initial rollout of the COVID-19 vaccine. Methods: Using a comprehensive COVID-19 vaccine database (VaccineFinder), we built an empirically parameterized spatial model of access to essential resources that incorporated vaccine supply, time-willing-to-travel for vaccination, and previous vaccination across the US. We then identified vaccine deserts—US Census tracts with localized, geographic barriers to vaccine-associated herd immunity. We link our model results with Census data and two high-resolution surveys to understand the distribution and determinates of spatially accessibility to the COVID-19 vaccine. Results: We find that in early 2021, vaccine deserts were home to over 30 million people, >10% of the US population. Vaccine deserts were concentrated in rural locations and communities with a higher percentage of medically vulnerable populations. We also find that in locations of similar urbanicity, early vaccination distribution disadvantaged neighborhoods with more people of color and older aged residents. Conclusion: Given sufficient vaccine supply, data-driven vaccine distribution to vaccine deserts may improve immunization rates and help control COVID-19.