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OBJECTIVE Progressive multifocal leukoencephalopathy (PML) is a serious viral infection associated with disease-modifying therapies (DMT) for multiple sclerosis (MS) including sphingosine 1-phosphate receptor (S1PR) modulators. The objective of this review was to investigate the characteristics of PML in MS patients associated with drugs of the S1PR modulator. METHODS We conducted a literature review and analysis of 24 patients from 12 publications in PubMed, SCOPUS and EMBASE. This is a descriptive analysis and study of characteristics of PML associated fingolimod and related S1PR modulator group of DMT. RESULTS A total of 24 cases of PML in MS patients treated with fingolimod were identified. Of these, 21 cases contained data regarding changes in the expanded disability status scale (EDSS). One case of PML in association with ozanimod treatment in a clinical trial was also identified. In PML cases associated with fingolimod, the mean age at the time of PML diagnosis was 50.91 ± 11.5 years. All patients were treated with fingolimod for more than 24 months. Compared to patients who improved or were stable, in terms of EDSS, after symptomatic management of PML, the non-improved groups were significantly older. There were no fatalities in either group during the reported follow-up period. CONCLUSION The incidence of PML appears to be extremely low in MS patients treated with S1PR modulators. Risk of PML increases with increase in duration of treatment with S1PR modulators like fingolimod, and increased age at the time of PML diagnosis is associated with worse prognosis.

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.