Burden of illness associated with overweight and obesity in patients hospitalized with COVID-19 in the United States: analysis of the premier healthcare database from April 1, 2020 to October 31, 2020.

BACKGROUND: SARS-CoV-2 (COVID-19) continues to be a major public health issue. Obesity is a major risk factor for disease severity and mortality associated with COVID-19. OBJECTIVE: This study sought to estimate the healthcare resource use and cost outcomes in patients hospitalized with COVID-19 in the United States (US) according to body mass index (BMI) class. METHODS: Retrospective cross-sectional study analyzing data from the Premier Healthcare COVID-19 database for hospital length-of-stay (LOS), intensive care unit (ICU) admission, ICU LOS, invasive mechanical ventilator use, invasive mechanical ventilator use duration, in-hospital mortality, and total hospital costs from hospital charge data. RESULTS: After adjustment for patient age, gender, and race, patients with COVID-19 and overweight or obesity had longer durations for mean hospital LOS (normal BMI = 7.4 days, class 3 obesity = 9.4 days, p < .0001) and ICU LOS (normal BMI = 6.1 days, class 3 obesity = 9.5 days, p < .0001) than patients with normal weight. Patients with normal BMI had fewer days on invasive mechanical ventilation compared to patients with overweight and obesity classes 1-3 (6.7 days vs. 7.8, 10.1, 11.5, and 12.4, respectively, p < .0001). The predicted probability of in-hospital mortality was nearly twice that of patients with class 3 obesity compared to patients with normal BMI (15.0 vs 8.1%, p < .0001). Mean (standard deviation) total hospital costs for a patient with class 3 obesity is estimated at $26,545 ($24,433-$28,839), 1.5 times greater than the mean for a patient with a normal BMI at $17,588 ($16,298-$18,981). CONCLUSIONS: Increasing levels of BMI class, from overweight to obesity class 3, are significantly associated with higher levels of healthcare resource utilization and costs in adult patients hospitalized with COVID-19 in the US. Effective treatment of overweight and obesity are needed to reduce the burden of illness associated with COVID-19.

The COVID-19 pandemic has caused many people to be seriously ill. People who are overweight are more likely to get sicker from COVID-19 infection and to require hospitalization.In our study, we compared patients who have normal weight to people who have overweight or obesity to understand how excess weight affects their experiences with COVID-19. We looked at: (1) how overweight and obesity is related to how long patients with COVID-19 stay in the hospital, (2) if they stayed in the intensive care unit (ICU) and how long they spent there, (3) whether they needed help breathing with the use of a ventilator and how long they needed a ventilator, (4) if they died during their hospital stay, and (5) how much their hospital stay cost.We found that people who have overweight or obesity stayed in the hospital longer, were more likely to need to stay in the ICU, and were in the ICU longer. They were also more likely to need help breathing with the use of a ventilator and needed that help for a longer time. People who have overweight or obesity died during their hospital stay more often than people with a normal BMI. The costs associated with people who have overweight or obesity were higher than people who have a normal BMI.Overall, this study shows that having overweight or obesity is a significant risk factor for poor outcomes from COVID-19 infection. Treatment for obesity and overweight is needed to help improve outcomes from future pandemics.

Race Versus Social Determinants of Health in COVID-19 Hospitalization Prediction.

INTRODUCTION: Including race as a biological construct in risk prediction models may guide clinical decisions in ways that cause harm and widen racial disparities. This study reports on using race versus social determinants of health (SDoH) in predicting the associations between cardiometabolic disease severity (assessed using cardiometabolic disease staging) and COVID-19 hospitalization. METHODS: Electronic medical record data on patients with a positive COVID-19 polymerase chain reaction test in 2020 and a previous encounter in the electronic medical record where cardiometabolic disease staging clinical data (BMI, blood glucose, blood pressure, high-density lipoprotein cholesterol, and triglycerides) were available from 2017 to 2020, were analyzed in 2021. Associations between cardiometabolic disease staging and COVID-19 hospitalization adding race and SDoH (individual and neighborhood level [e.g., Social Vulnerability Index]) in different models were examined. Area under the curve was used to assess predictive performance. RESULTS: A total of 2,745 patients were included (mean age of 58 years, 59% female, 47% Black). In the cardiometabolic disease staging model, area under the curve was 0.767 vs 0.777 when race was included. Adding SDoH to the cardiometabolic model improved the area under the curve to 0.809 (p<0.001), whereas the addition of SDoH and race increased the area under the curve to 0.811. In race-stratified models, the area under the curve for non-Hispanic Blacks was 0.781, whereas the model for non-Hispanic Whites performed better with an area under the curve of 0.821. CONCLUSIONS: Cardiometabolic disease staging was predictive of hospitalization after a positive COVID-19 test. Adding race did not markedly increase the predictive ability; however, adding SDoH to the model improved the area under the curve to ≥0.80. Future research should include SDoH with biological variables in prediction modeling to capture social experience of race.

Associations between HbA1c, Area Level Social Vulnerability, and Poor COVID-Outcomes

Background: HbA1c has been associated with COVID-poor outcomes in diabetic and non-diabetic populations, while patients living in census tracts with high levels of social vulnerability [measured using the CDC's Social Vulnerability Index (SVI) ] have experienced poorer outcomes. Objective: To examine associations between HbA1c, area level social vulnerability and poor COVID outcomes in patients who tested positive for COVID and were captured in the electronic medical record (EMR) at a large academic institution in the Southeastern United States. Methods: HbA1c and SVI, collected up to 3 years prior to a positive COVID test, were extracted from the EMR. HbA1c and SVI were compared by poor outcome status [hospitalization, intensive care unit (ICU) admission, and mortality]. Bayesian logistic regression was used to examine associations between HbA1c [≤5.7%;5.7%-<6.5%;and ≥6.5%], SVI [living in high SVI census tract vs low SVI tract] and each COVID outcome separately. Multivariable models were adjusted for sex, age, race, body mass index and diabetes status. Results presented as odds ratios (OR) and 95% confidence intervals (95% CI) . Results: N=3,7patients were identified [mean age: 54 years (SD 16.3) , 60% female, 47% Black]. Patients with HbA1c ≥ 6.5% and those living in a high SVI census tract were more likely to experience a poor COVID outcome (p's<0.0001) . In multivariable models, patients with HbA1c ≥ 6.5% had higher odds of hospitalization (OR 1.79, 95% CI 1.44-2.22) ;ICU admission (OR 2.13, 95% CI 1.78-2.55) ;and mortality (OR 1.60, 95% CI 1.12-2.28) . Patients living in a high SVI census tract had higher odds of hospitalization (OR 2.47, 95% CI 1.94-3.15) ;ICU admission (OR 2.58, 95% CI 2.12-3.14) ;and mortality (OR 2.07, 95% CI 1.39-3.09) . Conclusion: HbA1c ≥6.5% and living in a census tract with high social vulnerability were independently associated with poor COVID-outcomes. Findings highlight the need to assess HbA1c and area level social determinants in the context of COVID.

Associations between cardiometabolic disease severity, social determinants of health (SDoH), and poor COVID-19 outcomes.

OBJECTIVE: This study aimed to determine the ability of retrospective cardiometabolic disease staging (CMDS) and social determinants of health (SDoH) to predict COVID-19 outcomes. METHODS: Individual and neighborhood SDoH and CMDS clinical parameters (BMI, glucose, blood pressure, high-density lipoprotein, triglycerides), collected up to 3 years prior to a positive COVID-19 test, were extracted from the electronic medical record. Bayesian logistic regression was used to model CMDS and SDoH to predict subsequent hospitalization, intensive care unit (ICU) admission, and mortality, and whether adding SDoH to the CMDS model improved prediction was investigated. Models were cross validated, and areas under the curve (AUC) were compared. RESULTS: A total of 2,873 patients were identified (mean age: 58 years [SD 13.2], 59% were female, 45% were Black). CMDS, insurance status, male sex, and higher glucose values were associated with increased odds of all outcomes; area-level social vulnerability was associated with increased odds of hospitalization (odds ratio: 1.84, 95% CI: 1.38-2.45) and ICU admission (odds ratio 1.98, 95% CI: 1.45-2.85). The AUCs improved when SDoH were added to CMDS (p < 0.001): hospitalization (AUC 0.78 vs. 0.82), ICU admission (AUC 0.77 vs. 0.81), and mortality (AUC 0.77 vs. 0.83). CONCLUSIONS: Retrospective clinical markers of cardiometabolic disease and SDoH were independently predictive of COVID-19 outcomes in the population.

Covid-19 & Obesity: Beyond Bmi.

The pandemic of novel coronavirus disease 2019 (COVID-19) has triggered an international crisis resulting in excess morbidity and mortality with adverse societal, economic, and geopolitical consequences. Like other disease states, there are patient characteristics that impact clinical risk and determine the spectrum of severity. Obesity, or adiposity-based chronic disease, has emerged as an important risk factor for morbidity and mortality due to COVID-19. It is imperative to further stratify risk in patients with obesity to determine optimal mitigation and perhaps therapeutic preparedness strategies. We suspect that insulin resistance is an important pathophysiologic cause of poor outcomes in patients with obesity and COVID-19 independent of body mass index. This explains the association of type 2 diabetes mellitus (T2DM), hypertension (HTN), and cardiovascular disease with poor outcomes since insulin resistance is the main driver of both dysglycemia-based chronic disease and cardiometabolic-based chronic disease towards end-stage disease manifestations. Staging the severity of adiposity-related disease in a "complication-centric" manner (HTN, dyslipidemia, metabolic syndrome, T2DM, obstructive sleep apnea, etc.) among different ethnic groups in patients with COVID-19 should help predict the adverse risk of adiposity on patient health in a pragmatic and actionable manner during this pandemic.