Association of Obesity and Diabetes with SARS-Cov-2 Infection and Symptoms in the COVID-19 Community Research Partnership.

OBJECTIVE: Obesity and diabetes are established risk factors for severe SARS-CoV-2 outcomes, but less is known about their impact on susceptibility to COVID-19 infection and general symptom severity. We hypothesized that those with obesity or diabetes would be more likely to self-report a positive SARS-CoV-2 test, and among those with a positive test, have greater symptom severity and duration. METHODS: Among 44,430 COVID-19 Community Research Partnership participants, we evaluated the association of self-reported and electronic health record obesity and diabetes with a self-reported positive COVID-19 test at any time. Among the 2,663 participants with a self-reported positive COVID-19 test during the study, we evaluated the association of obesity and diabetes with self-report of symptom severity, duration, and hospitalization. Logistic regression models were adjusted for age, sex, race/ethnicity, socioeconomic status, and healthcare worker status. RESULTS: We found a positive graded association between Body Mass Index (BMI) category and positive COVID-19 test (Overweight OR = 1.14 [1.05-1.25]; Obesity I OR = 1.29 [1.17-2.42]; Obesity II OR = 1.34 [1.19-1.50]; Obesity III OR = 1.53 [1.35-1.73]), and a similar but weaker association with COVID-19 symptoms and severity among those with a positive test. Diabetes was associated with COVID-19 infection but not symptoms after adjustment, with some evidence of an interaction between obesity and diabetes. CONCLUSIONS: While the limitations of this health system convenience sample include generalizability and selection around test-seeking, the strong graded association of BMI and diabetes with self-reported COVID-19 infection suggests that obesity and diabetes may play a role in risk for symptomatic SARS-CoV-2 beyond co-occurrence with socioeconomic factors.

Randomization tests for multiarmed randomized clinical trials.

We examine the use of randomization-based inference for analyzing multiarmed randomized clinical trials, including the application of conditional randomization tests to multiple comparisons. The view is taken that the linkage of the statistical test to the experimental design (randomization procedure) should be recognized. A selected collection of randomization procedures generalized to multiarmed treatment allocation is summarized, and generalizations for two randomization procedures that heretofore were designed for only two treatments are developed. We explain the process of computing the randomization test and conditional randomization test via Monte Carlo simulation, developing an efficient algorithm that makes multiple comparisons possible that would not be possible using a standard algorithm, demonstrate the preservation of type I error rate, and explore the relationship of statistical power to the randomization procedure in the presence of a time trend and outliers. We distinguish between the interpretation of the p-value in the randomization test and in the population test and verify that the randomization test can be approximated by the population test on some occasions. Data from two multiarmed clinical trials from the literature are reanalyzed to illustrate the methodology.