Epidemiological and immunological features of obesity and SARS-CoV-2 (preprint)

Background: Obesity is established as a key correlate of severe SARS-CoV-2 outcomes. Multiple other epidemiological and immunological features are less well-defined including whether obesity increases susceptibility to SARS-CoV-2, influences symptom phenotype, or impedes or alters the immune response to infection. Given the substantial global burden of obesity and given these uncertainties, we examined the epidemiology and immunology of obesity and SARS-CoV-2. Methods: Industry employees were invited to participate in a prospective SARS-CoV-2 serology-based cohort study. Blood and baseline survey measures that included demographics, comorbidities, and prior COVID-19 compatible symptoms were collected. Serological testing and interim symptom reporting were conducted monthly. SARS-CoV-2 immunoassays included an IgG ELISA targeting the spike RBD, multiarray Luminex targeting 20 viral antigens, pseudovirus neutralization, and T cell ELISPOT assays. Unadjusted and adjusted analyses were used to identify differences in seroprevalence, clinical features, and immune parameters by BMI. Results: Of 4469 individuals enrolled, 322 (7.21%) were seropositive. Adjusted seroprevalence was non-significantly lower with higher BMI. Obesity was associated with increased reporting of fever (OR 3.43 [95% CI 1.58-7.60]) and multiple other symptoms and aggregate measures. There were no identifiable differences in immune response between obese and non-obese individuals. Discussion: We present benchmark data that obesity is not linked to increased risk of SARS-CoV-2 infection; that symptom phenotype is strongly influenced by obesity; and that despite evidence of obesity-associated immune dysregulation in severe infections, there is no evidence of muted or dysfunctional immune response across multiple immune measures among non-severe infections.

Mental Health Status of Adolescents During the COVID-19 Pandemic: A Cross-sectional Survey among the Bangladeshi Graduate Students at Dhaka City (preprint)

ObjectivesTo identify the level of Mental Health Status of Adolescents During the COVID-19 Pandemic among the Bangladeshi Graduate Student at Dhaka MethodA cross-sectional survey was conducted with 330 students from different public and Private Universities in Dhaka, Bangladesh between April 01, 2020 and July 31, 2020 amid the COVID-19 lockdown period in Bangladesh. A standard, self-administered online questionnaire consisting of questions on socio-demographic variables, mental health status, as well as stress management sent to the respondents through social networking platforms. Data were analyzed using descriptive statistics, t-test, one-way ANOVA and correlation tests. ResultsThe mean score of mental health status was 2.08 based on four points scale. They felt problem in decision making (3.04), in doing the things well (2.92), in enjoying normal day to day life (2.88), in playing a useful part in life (2.85), in doing their task (2.75), living in perfectly well and in good health (2.70). The respondents also developed a suicidal tendency (2.55), felt nervous in strung-up (2.24), took longer time to do things (2.14), felt tightness and pressure in head (2.12), and found themselves pressurized by various stuff (2.05). This study also found a significant positive relationship between mental health status and age, living with parents, and parents attitude. Finally, this study revealed that the respondents managed their stress by chatting with their friends, parents and siblings, and by sleeping. ConclusionMental health status of adolescents was found moderate in this study. This study suggests further large-scale study including different socio-economic settings in order to figure out the real scenario of adolescents mental health status of the country during the pandemic.

A retrospective cluster analysis of COVID-19 cases by county (preprint)

The COVID-19 pandemic in the U.S. has exhibited distinct waves, the first beginning in March 2020, the second beginning in early June, and additional waves currently emerging. Paradoxically, almost no county has exhibited this multi-wave pattern. We aim to answer three research questions: (1) How many distinct clusters of counties exhibit similar COVID-19 patterns in the time-series of daily confirmed cases?; (2) What is the geographic distribution of the counties within each cluster? and (3) Are county-level demographic, socioeconomic and political variables associated with the COVID-19 case patterns? We analyzed data from counties in the U.S. from March 1 to October 24, 2020. Time series clustering identified clusters in the daily confirmed cases of COVID-19. An explanatory model was used to identify demographic, socioeconomic and political variables associated the cluster patterns. Four patterns were identified from the timing of the outbreaks including counties experiencing a spring, an early summer, a late summer, and a fall outbreak. Several county-level demographic, socioeconomic, and political variables showed significant associations with the identified clusters. The timing of the outbreak is related both to the geographic location within the U.S. and several variables including age, poverty distribution, and political association. These results show that the reported pattern of cases in the U.S. is observed through aggregation of the COVID-19 cases, suggesting that local trends may be more informative. The timing of the outbreak varies by county, and is associated with important demographic, socioeconomic and geographic factors.