IMPORTANCEThe National COVID Cohort Collaborative (N3C) is a centralized, harmonized, highgranularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy.OBJECTIVES To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity. DESIGN, SETTING, AND PARTICIPANTSIn a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen <1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation). MAIN OUTCOMES AND MEASURESPatient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression. RESULTSThe cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472(18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, (continued) Key Points Question In a US data resource large enough to adjust for multiple confounders, what risk factors are associated with COVID-19 severity and severity trajectory over time, and can machine learning models predict clinical severity? Findings In this cohort study of 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized and 6565 (20.2%) were severely ill, and first-day machine learning models accurately predicted clinical severity. Mortality was 11.6%

Food insecurity is a major social determinant of health and an assessment of how it may impact college students’ mental health is imperative, as well as differential associations by self-identified gender. A cross-sectional survey was used among college students of a mid-size minority-serving institution with a final sample size of 302 participants aged 18 years or above. Descriptive, bivariate, and multivariable regressions were conducted, by gender, to assess the role of food insecurity (United States Department of Agriculture (USDA) six-item questionnaire), on mental health outcomes (Kessler-6 scale and self-perception). All the statistical analyses were conducted in SPSS version 24 (IBM, Corp.; Armonk, NY, USA) with an alpha less than 0.05 used to denote significance. Among those with food insecurity, the odds of reporting psychological distress (odds ratio (OR) = 3.645, p < 0.05) and an average to very poor self-perceived mental health status (OR = 2.687, p <0.05) were higher compared to their food-secure counterparts, with the results consistent in a gender-specific analysis as well. Compared to men, however, women had higher odds of psychological distress (OR = 2.280, p < 0.05), as well as reporting average to very poor self-perceived mental health statuses (OR = 2.700, p < 0.05). Among women, any alcohol use in the past 12 months (OR = 2.505, p < 0.05) and a low self-perceived physical health status (OR = 3.601, p < 0.05) were associated with an average to very poor self-perceived mental health status. Among men, a low perceived physical health status was associated with higher odds of psychological distress (OR = 3.477, p < 0.05). The results of our study highlight that food insecurity should be considered a social determinant of mental health wellbeing. In addition, gender-specific trends in mental health highlight the need for targeted interventions for prevention and treatment.

Low health literacy is a significant barrier to healthcare access and service utilization; however, there are few studies that have evaluated the factors associated with having low health literacy, especially among immigrant minority populations. This exploratory study aimed to assess the key determinants of low health literacy among immigrant Hispanic adults in California using the California Health Interview Survey, the largest population-based state health survey in the United States. Analysis accounted for complex survey design, allowing generalizations to the entire state of California. Low health literacy was associated with living in poverty (OR = 1.63), lacking consistent health insurance (OR = 1.40), and limited English language proficiency (OR = 3.22), while women were less likely than men (OR = 0.59) to report low health literacy. The results of this study demonstrate that language proficiency, in addition to other key sociodemographic variables, can significantly posit limitations to health literacy. Future efforts should address providing linguistically competent health literacy interventions in order to improve associated health outcomes.