Background Pregnant women are potentially a high-risk population during infectious disease outbreaks such as COVID-19, because of physiologic immune suppression in pregnancy. However, data on the morbidity and mortality of COVID-19 among pregnant women, compared to nonpregnant women, are sparse and inconclusive. We sought to assess the impact of pregnancy on COVID-19 associated morbidity and mortality, with particular attention to the impact of pre-existing comorbidity. Methods We used retrospective data from January through June 2020 on female patients aged 18–44 years old utilizing the Cerner COVID-19 de-identified cohort. We used mixed-effects logistic and exponential regression models to evaluate the risk of hospitalization, maximum hospital length of stay (LOS), moderate ventilation, invasive ventilation, and death for pregnant women while adjusting for age, race/ethnicity, insurance, Elixhauser AHRQ weighted Comorbidity Index, diabetes history, medication, and accounting for clustering of results in similar zip-code regions. Results Out of 22,493 female patients with associated COVID-19, 7.2% (n = 1609) were pregnant. Crude results indicate that pregnant women, compared to non-pregnant women, had higher rates of hospitalization (60.5% vs. 17.0%, P < 0.001), higher mean maximum LOS (0.15 day vs. 0.08 day, P < 0.001) among those who stayed < 1 day, lower mean maximum LOS (2.55 days vs. 3.32 days, P < 0.001) among those who stayed ≥1 day, and higher moderate ventilation use (1.7% vs. 0.7%, P < 0.001) but showed no significant differences in rates of invasive ventilation or death. After adjusting for potentially confounding variables, pregnant women, compared to non-pregnant women, saw higher odds in hospitalization (aOR: 12.26; 95% CI (10.69, 14.06)), moderate ventilation (aOR: 2.35; 95% CI (1.48, 3.74)), higher maximum LOS among those who stayed < 1 day, and lower maximum LOS among those who stayed ≥1 day. No significant associations were found with invasive ventilation or death. For moderate ventilation, differences were seen among age and race/ethnicity groups. Conclusions Among women with COVID-19 disease, pregnancy confers substantial additional risk of morbidity, but no difference in mortality. Knowing these variabilities in the risk is essential to inform decision-makers and guide clinical recommendations for the management of COVID-19 in pregnant women.

Objective To assess the risk of new-onset type 1 diabetes mellitus (T1D) diagnosis following COVID-19 diagnosis and the impact of COVID-19 diagnosis on the risk of diabetic ketoacidosis (DKA) in patients with prior T1D diagnosis. Research design and methods Retrospective data consisting of 27,292,879 patients from the Cerner Real-World Data were used. Odds ratios, overall and stratified by demographic predictors, were calculated to assess associations between COVID-19 and T1D. Odds ratios from multivariable logistic regression models, adjusted for demographic and clinical predictors, were calculated to assess adjusted associations between COVID-19 and DKA. Multiple imputation with multivariate imputation by chained equations (MICE) was used to account for missing data. Results The odds of developing new-onset T1D significantly increased in patients with COVID-19 diagnosis (OR: 1.42, 95% CI: 1.38, 1.46) compared to those without COVID-19. Risk varied by demographic groups, with the largest risk among pediatric patients ages 0–1 years (OR: 6.84, 95% CI: 2.75, 17.02) American Indian/Alaskan Natives (OR: 2.30, 95% CI: 1.86, 2.82), Asian or Pacific Islanders (OR: 2.01, 95% CI: 1.61, 2.53), older adult patients ages 51–65 years (OR: 1.77, 95% CI: 1.66, 1.88), those living in the Northeast (OR: 1.71, 95% CI: 1.61, 1.81), those living in the West (OR: 1.65, 95% CI: 1.56, 1.74), and Black patients (OR: 1.59, 95% CI: 1.47, 1.71). Among patients with diagnosed T1D at baseline (n = 55,359), 26.7% (n = 14,759) were diagnosed with COVID-19 over the study period. The odds of developing DKA for those with COVID-19 were significantly higher (OR 2.26, 95% CI: 2.04, 2.50) than those without COVID-19, and the largest risk was among patients with higher Elixhauser Comorbidity Index. Conclusions COVID-19 diagnosis is associated with significantly increased risk of new-onset T1D, and American Indian/Alaskan Native, Asian/Pacific Islander, and Black populations are disproportionately at risk. In patients with pre-existing T1D, the risk of developing DKA is significantly increased following COVID-19 diagnosis.

Cytokine storm syndrome in patients with COVID-19 is mediated by pro-inflammatory cytokines resulting in acute lung injury and multiorgan failure. Elevation in serum ferritin and D-dimer is observed in COVID-19 patients. To determine prognostic values of optimal serum cutoff with trajectory plots for both serum ferritin and D-dimer in COVID-19 patients with invasive ventilator dependence and in-hospital mortality. We used retrospective longitudinal data from the Cerner COVID-19 de-identified cohort. COVID-19 infected patients with valid repeated values of serum ferritin and D-dimer during hospitalization were used in mixed-effects logistic-regression models. Among 52,411 patients, 28.5% (14,958) had valid serum ferritin and 28.6% (15,005) D-dimer laboratory results. Optimal cutoffs of ferritin (714 ng/mL) and D-dimer (2.1 mg/L) revealed AUCs ≥ 0.99 for in-hospital mortality. Optimal cutoffs for ferritin (502 ng/mL) and D-dimer (2.0 mg/L) revealed AUCs ≥ 0.99 for invasive ventilator dependence. Optimal cutoffs for in-house mortality, among females, were lower in serum ferritin (433 ng/mL) and D-dimer (1.9 mg/L) compared to males (740 ng/mL and 2.5 mg/L, respectively). Optimal cutoffs for invasive ventilator dependence, among females, were lower in ferritin (270 ng/mL) and D-dimer (1.3 mg/L) compared to males (860 ng/mL and 2.3 mg/L, respectively). Optimal prognostic cutoffs for serum ferritin and D-dimer require considering the entire trajectory of laboratory values during the disease course. Females have an overall lower optimal cutoff for both serum ferritin and D-dimer. The presented research allows health professionals to predict clinical outcomes and appropriate allocation of resources during the COVID-19 pandemic, especially early recognition of COVID-19 patients needing higher levels of care.

Background: Both opioid use and COVID-19 affect respiratory and pulmonary health, potentially putting individuals with opioid use disorders (OUD) at risk for complications from COVID-19. We examine the relationship between OUD and subsequent hospitalization, length of stay, risk for invasive ventilator dependence (IVD), and COVID-19 mortality. Methods: Multivariable logistic and exponential regression models using electronic health records data from the Cerner COVID-19 De-Identified Data Cohort from January through June 2020. Findings: Out of 52,312 patients with COVID-19, 1.9% (n=1,013) had an OUD. COVID-19 patients with an OUD had higher odds of hospitalization (aOR=3.44, 95% CI=2.81À4.21), maximum length of stay (eb=1.16, 95% CI=1.09À1.22), and odds of IVD (aOR=1.26, 95% CI=1.06À1.49) than patients without an OUD, but did not differ with respect to COVID-19 mortality. However, OUD patients under age 45 exhibited greater COVID-19 mortality (aOR=3.23, 95% CI=1.59À6.56) compared to patients under age 45 without an OUD. OUD patients using opioid agonist treatment (OAT) exhibited higher odds of hospitalization (aOR=5.14, 95% CI=2.75À10.60) and higher maximum length of stay (eb=1.22, 95% CI=1.01À1.48) than patients without OUDs; however, risk for IVD and COVID-19 mortality did not differ. OUD patients using naltrexone had higher odds of hospitalization (aOR=32.19, 95% CI=4.29À4,119.83), higher maximum length of stay (eb=1.59, 95% CI=1.06À2.38), and higher odds of IVD (aOR=3.15, 95% CI=1.04À9.51) than patients without OUDs, but mortality did not differ. OUD patients who did not use treatment medication had higher odds of hospitalization (aOR=4.05, 95% CI=3.32À4.98), higher maximum length of stay (eb=1.14, 95% CI=1.08À1.21), and higher odds of IVD (aOR=1.25, 95% CI=1.04À1.50) and COVID-19 mortality (aOR=1.31, 95% CI=1.07À1.61) than patients without OUDs. Interpretation: This study suggests people with OUD and COVID-19 often require higher levels of care, and OUD patients who are younger or not using medication treatment for OUDs are particularly vulnerable to death due to COVID-19.

With the emergence of the novel SARS-CoV-2 and the disease it causes; COVID-19, compliance with/adherence to protective measures is needed. Information is needed on which measures are, or are not, being undertaken. Data collected from the COVID Impact Survey, conducted by the non-partisan and objective research organization NORC at the University of Chicago on April, May, and June of 2020, were analyzed through weighted Quasi-Poisson regression modeling to determine the association of demographics, socioeconomics, and health conditions with protective health measures taken at the individual level in response to COVID-19. The three surveys included data from 18 regional areas including 10 states (CA, CO, FL, LA, MN, MO, MT, NY, OR, and TX) and 8 Metropolitan Statistical Areas (Atlanta, GA; Baltimore, MD; Birmingham, AL; Chicago, IL; Cleveland and Columbus, OH; Phoenix, AZ; and Pittsburgh, PA). Individuals with higher incomes, insurance, higher education levels, large household size, age 60+, females, minorities, those who have asthma, have hypertension, overweight or obese, and those who suffer from mental health issues during the pandemic were significantly more likely to report taking precautionary protective measures relative to their counterparts. Protective measures for the three subgroups with a known relationship to COVID-19 (positive for COVID-19, knowing an individual with COVID-19, and knowing someone who had died from COVID-19) were strongly associated with the protective health measures of washing hands, avoiding public places, and canceling social engagements. This study provides first baseline data on the response to the national COVID-19 pandemic at the individual level in the US. The found heterogeneity in the response to this pandemic by different variables can inform future research and interventions to reduce exposure to the novel SARS-CoV-2 virus.