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Background: Since the COVID-19 pandemic, several cases of cerebral venous sinus thrombosis (CVST) have been reported in SARS-CoV-2 infected individuals. This study provides a series of patients with CVST and SARS-CoV-2 infection.Methods: Consecutive patients with documented SARS-CoV-2 infection, as well as clinical and radiological characteristics of CVST, were reported from three teaching hospitals in the South West, North West, and the center of Iran from June to July 2020. We also searched the abstract archives until the end of August 2020 and gathered 28 reported cases. The diagnostic criteria for SARS-CoV-2 infection were determined according to SARS-CoV-2 detection in oropharyngeal or nasopharyngeal samples in clinically suspected patients. Demographics, main COVID-19 symptoms, confirmatory tests for SARS-CoV-2 infection diagnosis, the interval between the diagnosis of SARS-CoV-2 infection and CVST, clinical and radiological features of CVST, therapeutic strategies, CVST outcomes, rate of hemorrhagic transformation, and mortality rate were investigated.Results: Six patients (aged 31 to 62 years old) with confirmed CVST and SARS-CoV-2 infection were admitted to our centers. Four patients had no respiratory symptoms of SARS-CoV-2 infection. Five out of six patients developed the clinical manifestations of CVST and SARS-CoV-2 infection simultaneously. Three patients had known predisposing factors for CVST. Despite receiving CVST and SARS-CoV-2 infection treatments, four out of six patients passed away.Conclusions: The role of SARS-CoV-2 as a “cause” versus an “additive contributor” remains to be elucidated. Practitioners should be aware of the possibility of CVST in SARS-CoV-2 infection.

Laboratory data from Electronic Health Records (EHR) are often used in prediction models where estimation bias and model performance from missingness can be mitigated using imputation methods. We demonstrate the utility of imputation in two real-world EHR-derived cohorts of ischemic stroke from Geisinger and of heart failure from Sutter Health to: (1) characterize the patterns of missingness in laboratory variables; (2) simulate two missing mechanisms, arbitrary and monotone; (3) compare cross-sectional and multi-level multivariate missing imputation algorithms applied to laboratory data; (4) assess whether incorporation of latent information, derived from comorbidity data, can improve the performance of the algorithms. The latter was based on a case study of hemoglobin A1c under a univariate missing imputation framework. Overall, the pattern of missingness in EHR laboratory variables was not at random and was highly associated with patients’ comorbidity data; and the multi-level imputation algorithm showed smaller imputation error than the cross-sectional method.

Objectives There is preliminary evidence of racial and social economic disparities in the population infected by and dying from COVID-19. The goal of this study is to report the associations of COVID-19 with respect to race, health, and economic inequality in the United States. Methods We performed an ecological study of the associations between infection and mortality rate of COVID-19 and demographic, socioeconomic, and mobility variables from 369 counties (total population, 102,178,117 [median, 73,447; IQR, 30,761–256,098]) from the seven most affected states (Michigan, New York, New Jersey, Pennsylvania, California, Louisiana, Massachusetts). Results The risk factors for infection and mortality are different. Our analysis shows that counties with more diverse demographics, higher population, education, income levels, and lower disability rates were at a higher risk of COVID-19 infection. However, counties with higher proportion with disability and poverty rates had a higher death rate. African Americans were more vulnerable to COVID-19 than other ethnic groups (1981 African American infected cases versus 658 Whites per million). Data on mobility changes corroborate the impact of social distancing. Conclusion Our study provides evidence of racial, economic, and health inequality in the population infected by and dying from COVID-19. These observations might be due to the workforce of essential services, poverty, and access to care. Counties in more urban areas are probably better equipped at providing care. The lower rate of infection, but a higher death rate in counties with higher poverty and disability could be due to lower levels of mobility, but a higher rate of comorbidities and health care access. Electronic supplementary material The online version of this article (10.1007/s40615-020-00833-4) contains supplementary material, which is available to authorized users.