Racial and Ethnic Disparities in Rates of COVID-19–Associated Hospitalization, Intensive Care Unit Admission, and In-Hospital Death in the United States From March 2020 to February 2021

Acosta, Anna M.; Garg, Shikha; Pham, Huong; Whitaker, Michael; Anglin, Onika; O’Halloran, Alissa; Milucky, Jennifer; Patel, Kadam; Taylor, Christopher A.; Wortham, Jonathan M.; Chai, Shua J.; Kirley, Pam Daily; Alden, Nisha; Kawasaki, Breanna; Meek, James; Yousey‐Hindes, Kimberly; Anderson, Evan J.; Openo, Kyle P.; Weigel, Andrew; Monroe, Maya; Ryan, Patricia; Reeg, Libby; Kohrman, Alexander; Lynfield, Ruth; Bye, Erica; Torres, Salina; Salazar-Sanchez, Yadira; Muse, Alison; Barney, Grant; Bennett, Nancy M.; Bushey, Sophrena; Billing, Laurie M; Shiltz, Eli; Sutton, Melissa; Abdullah, Nasreen; Talbot, H. Keipp; Schaffner, William; Ortega, Jake; Price, Andrea; Fry, Alicia M.; Hall, Aron J.; Kim, Lindsay; Havers, Fiona

doi:10.1001/jamanetworkopen.2021.30479

Cited by 193 publications

(171 citation statements)

References 35 publications

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“…Adverse COVID-19 outcomes have been linked to the ethnic origin of the population under study and its socio-economic characteristics; this has also been associated with the presence of vitamin D deficiency. Asians, African Americans, and ethnic minorities are at an increased risk of mortality from COVID-19 ( 34 ). This may partly be due to a decrease in the production of vitamin D dependent on UV rays.…”

Section: Discussionmentioning

confidence: 99%

Serum Vitamin D Levels Are Associated With Increased COVID-19 Severity and Mortality Independent of Whole-Body and Visceral Adiposity

et al. 2022

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IntroductionCoronavirus disease (COVID-19) is a global pandemic. Vitamin D deficiency has been associated with susceptibility to infectious disease. In this study, the association between COVID-19 outcomes and vitamin D levels in patients attending a COVID-19 reference center in Mexico City are examined.MethodsConsecutive patients with confirmed COVID-19 were evaluated. All patients underwent clinical evaluation and follow-up, laboratory measurements and a thoracic computerized tomography, including the measurement of epicardial fat thickness. Low vitamin D was defined as levels <20 ng/ml (<50nmol/L) and deficient Vitamin D as a level ≤12 ng/ml (<30 nmol/L).ResultsOf the 551 patients included, low vitamin D levels were present in 45.6% and deficient levels in 10.9%. Deficient Vitamin D levels were associated with mortality (HR 2.11, 95%CI 1.24–3.58, p = 0.006) but not with critical COVID-19, adjusted for age, sex, body-mass index and epicardial fat. Using model-based causal mediation analyses the increased risk of COVID-19 mortality conferred by low vitamin D levels was partly mediated by its effect on D-dimer and cardiac ultrasensitive troponins. Notably, increased risk of COVID-19 mortality conferred by low vitamin D levels was independent of BMI and epicardial fat.ConclusionVitamin D deficiency (≤12 ng/ml or <30 nmol/L), is independently associated with COVID-19 mortality after adjustment for visceral fat (epicardial fat thickness). Low vitamin D may contribute to a pro-inflammatory and pro-thrombotic state, increasing the risk for adverse COVID-19 outcomes.

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Section: Discussionmentioning

confidence: 99%

Serum Vitamin D Levels Are Associated With Increased COVID-19 Severity and Mortality Independent of Whole-Body and Visceral Adiposity

et al. 2022

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“…Our combined use of logistic regression and random forest analyses, as applied to this very large Medicare population, allowed us to further assess the current CDC listing of clinical and socio-demographic risk factors for severe COVID-19 [30,31]. Logistic regression and random forest analyses provide two different perspectives: the odds ratios from the logistic regression help identify individual risk while the Feature Importance from the random forest analyses identified the most important variables for predicting severe COVID-19 outcomes at the population-level for the entire cohort.…”

Section: Discussionmentioning

confidence: 99%

A Predictive Model for Severe COVID-19 in the Medicare Population: A Tool for Prioritizing Primary and Booster COVID-19 Vaccination

Experton¹,

Tetteh

Lurie

et al. 2021

Biology

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Recommendations for prioritizing COVID-19 vaccination have focused on the elderly at higher risk for severe disease. Existing models for identifying higher-risk individuals lack the needed integration of socio-demographic and clinical risk factors. Using multivariate logistic regression and random forest modeling, we developed a predictive model of severe COVID-19 using clinical data from Medicare claims for 16 million Medicare beneficiaries and socio-economic data from the CDC Social Vulnerability Index. Predicted individual probabilities of COVID-19 hospitalization were then calculated for population risk stratification and vaccine prioritization and mapping. The leading COVID-19 hospitalization risk factors were non-white ethnicity, end-stage renal disease, advanced age, prior hospitalization, leukemia, morbid obesity, chronic kidney disease, lung cancer, chronic liver disease, pulmonary fibrosis or pulmonary hypertension, and chemotherapy. However, previously reported risk factors such as chronic obstructive pulmonary disease and diabetes conferred modest hospitalization risk. Among all social vulnerability factors, residence in a low-income zip code was the only risk factor independently predicting hospitalization. This multifactor risk model and its population risk dashboard can be used to optimize COVID-19 vaccine allocation in the higher-risk Medicare population.

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“…Importantly, we evaluated our model real-time in PUI patients seeking acute care in ED after the score became available in the EHR and the model performance remained strong. The difference in ICU admission rate, ventilator use, and mortality rate between the training set and the temporal, PUI, and real-time validation sets can be explained by the temporal improvement in COVID-19 patients’ outcomes that was noted in other studies [ 4 , 32 , 33 ]. The COVID-19 ICU admission and patient survival improved in our study over time as it did in other reports, perhaps because of better understating of the diseases and improvement of treatment as the pandemic progresses [ 4 , 33 ] In our study, for a cutoff of 0.1 for COVID-19 severity, our model had a sensitivity of 73.7% and specificity 79.9% in the prospective validation set, 52.2% and 88.1%, respectively in the PUI set, and 78% and 71%, respectively in the real-time validation set.…”

Section: Discussionmentioning

confidence: 84%

“…A multihospital United States (U.S.) based cohort study identified that the 30-day mean risk standardized event rate of hospital mortality and hospice referral among patients with COVID-19 varied from 9% to 16%, with better outcomes occurring in community’s with lower disease prevalence [ 3 ]. A large cross-sectional study found racial and ethnic disparities in rates of COVID-19 hospital and ICU admission and in-hospital mortality in the US [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19

Lupei

Ingraham

et al. 2022

PLoS ONE

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Objective To prospectively evaluate a logistic regression-based machine learning (ML) prognostic algorithm implemented in real-time as a clinical decision support (CDS) system for symptomatic persons under investigation (PUI) for Coronavirus disease 2019 (COVID-19) in the emergency department (ED). Methods We developed in a 12-hospital system a model using training and validation followed by a real-time assessment. The LASSO guided feature selection included demographics, comorbidities, home medications, vital signs. We constructed a logistic regression-based ML algorithm to predict “severe” COVID-19, defined as patients requiring intensive care unit (ICU) admission, invasive mechanical ventilation, or died in or out-of-hospital. Training data included 1,469 adult patients who tested positive for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within 14 days of acute care. We performed: 1) temporal validation in 414 SARS-CoV-2 positive patients, 2) validation in a PUI set of 13,271 patients with symptomatic SARS-CoV-2 test during an acute care visit, and 3) real-time validation in 2,174 ED patients with PUI test or positive SARS-CoV-2 result. Subgroup analysis was conducted across race and gender to ensure equity in performance. Results The algorithm performed well on pre-implementation validations for predicting COVID-19 severity: 1) the temporal validation had an area under the receiver operating characteristic (AUROC) of 0.87 (95%-CI: 0.83, 0.91); 2) validation in the PUI population had an AUROC of 0.82 (95%-CI: 0.81, 0.83). The ED CDS system performed well in real-time with an AUROC of 0.85 (95%-CI, 0.83, 0.87). Zero patients in the lowest quintile developed “severe” COVID-19. Patients in the highest quintile developed “severe” COVID-19 in 33.2% of cases. The models performed without significant differences between genders and among race/ethnicities (all p-values > 0.05). Conclusion A logistic regression model-based ML-enabled CDS can be developed, validated, and implemented with high performance across multiple hospitals while being equitable and maintaining performance in real-time validation.

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Racial and Ethnic Disparities in Rates of COVID-19–Associated Hospitalization, Intensive Care Unit Admission, and In-Hospital Death in the United States From March 2020 to February 2021

Cited by 193 publications

References 35 publications

Serum Vitamin D Levels Are Associated With Increased COVID-19 Severity and Mortality Independent of Whole-Body and Visceral Adiposity

Serum Vitamin D Levels Are Associated With Increased COVID-19 Severity and Mortality Independent of Whole-Body and Visceral Adiposity

A Predictive Model for Severe COVID-19 in the Medicare Population: A Tool for Prioritizing Primary and Booster COVID-19 Vaccination

A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19

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