key: cord-0979204-3whrp9wh
authors: Millet, Christopher; Jimenez, Humberto; Racoosin, Emily; Horani, George; Shamoon, Yezin; Narvaneni, Spandana; Roman, Sherif; Chaudhry, Arslan; Chaudhry, Sohail; Farokhian, Alisa; Aron, Polina; Kmiecik, Christina; Faheem, Beenish; Ashkar, Hamdallah; Shafeek, Fady; Michael, Patrick; Suh, Jin
title: 456. The Racial Divide: A Follow Up Study on Racial Disparity Amongst COVID-19 Survivors in an Urban Community in New Jersey
date: 2021-12-04
journal: Open Forum Infect Dis
DOI: 10.1093/ofid/ofab466.655
sha: de67659622f3f747af105da5c7013da5be624612
doc_id: 979204
cord_uid: 3whrp9wh

BACKGROUND: We conducted a follow up study on patients previously diagnosed with COVID-19 one year ago in an urban community in Paterson, New Jersey. The purpose of the study was to evaluate the socioeconomic impact of COVID-19 as well as assess for receptiveness towards COVID-19 vaccination amongst various ethnic groups. METHODS: This was a prospective cohort study consisting of patients who had COVID-19 in the months of March and April of 2020. This was a single institutional study conducted at St. Joseph’s Hospital in Paterson, NJ from March to April of 2021. Patients included were either male or female aged 18 years or older. Patients were contacted by telephone to participate to completed the survey. Chi-square testing and multivariable logistic regression analysis were utilized for statistical analysis. RESULTS: Of the 170 patients enrolled in the study, the most common ethnicity was Hispanic (79/170 [46.47%]), followed by African American (46/170 [27.05%]). 83 patients were male (83/170 [48.82%]). Caucasians were the most willing to receive a COVID-19 vaccine (28/30 [93.3%]), followed by Asians (13/14 [92.8%]), Hispanics (63/78 [80.7%]) and African Americans (29/46 [63.0%]). Hispanics had the highest rate of job loss (31/79 [39.24%]), followed by African Americans (16/46 [34.7%]). Hispanics were found to be in the most financial distress (31/79 [39.2%]), followed by African Americans (17/46 [36.9%]). Hispanics and African Americans were more likely to refuse COVID-19 vaccination (p: 0.02). Hispanics were more likely to lose their jobs compared to Caucasians (odds ratio,4.456; 95% CI, 1.387 to 14.312; p: 0.0121). African Americans were also more likely to lose their jobs when compared to Caucasians (odds ratio, 4.465; 95% CI, 1.266 to 15.747; p: 0.0200). Willingness amongst COVID-19 survivors to get vaccinated based on ethnicity [Image: see text] COVID-19 survivors who lost their jobs following diagnosis with COVID-19 based on ethnicity [Image: see text] COVID-19 survivors who are experiencing financial distress following diagnosis with COVID-19 based on ethnicity [Image: see text] CONCLUSION: Hispanics reported the most financial distress and with nearly 40% losing their jobs, the highest in our study group. 37% of African Americans experienced job loss and financial distress following their diagnosis with COVID-19. Only 63% of African Americans and 80.7% of Hispanics were willing to get vaccinated, mostly due to lack of trust in the vaccine. Statistical analysis showed Hispanics and African Americans were more likely to lose their jobs and refuse COVID-19 vaccination following diagnosis with COVID-19. DISCLOSURES: All Authors: No reported disclosures

Background. We conducted a follow up study on patients previously diagnosed with COVID-19 one year ago in an urban community in Paterson, New Jersey. The purpose of the study was to evaluate the socioeconomic impact of COVID-19 as well as assess for receptiveness towards COVID-19 vaccination amongst various ethnic groups.

Methods. This was a prospective cohort study consisting of patients who had COVID-19 in the months of March and April of 2020. This was a single institutional study conducted at St. Joseph's Hospital in Paterson, NJ from March to April of 2021. Patients included were either male or female aged 18 years or older. Patients were contacted by telephone to participate to completed the survey. Chi-square testing and multivariable logistic regression analysis were utilized for statistical analysis.

Results. Of the 170 patients enrolled in the study, the most common ethnicity was Hispanic ( Willingness amongst COVID-19 survivors to get vaccinated based on ethnicity COVID-19 survivors who lost their jobs following diagnosis with COVID-19 based on ethnicity COVID-19 survivors who are experiencing financial distress following diagnosis with COVID-19 based on ethnicity Conclusion. Hispanics reported the most financial distress and with nearly 40% losing their jobs, the highest in our study group. 37% of African Americans experienced job loss and financial distress following their diagnosis with COVID-19. Only 63% of African Americans and 80.7% of Hispanics were willing to get vaccinated, mostly due to lack of trust in the vaccine. Statistical analysis showed Hispanics and African Americans were more likely to lose their jobs and refuse COVID-19 vaccination following diagnosis with COVID-19.

Disclosures. Background. Up until this day, over 3.5 million fatalities related to coronavirus disease 2019 (COVID-19) have been registered worldwide by the World Health Organization. Healthcare professionals require prognostic tools for COVID-19 patients in order to guide treatment strategies. Elevated troponin levels, a biomarker of cardiac injury, have been detected among patients with COVID-19, hence associating it with cardiac injury. Although several studies have mentioned it, the role of troponin as a prognosis biomarker is unclear. Elevation in troponin levels has been observed in patients with community-acquired pneumonia (CAP). However, its association with mortality is scarcely mentioned in literature. Thus, we sought to determine the utility of serum troponin I levels as a mortality predictor for patients with COVID-19 and CAP.

Methods. A prospective observational study was carried out at Clinica Universidad de La Sabana, Colombia, with patients hospitalized due to CAP and COVID-19. Troponin biomarker was quantified in serum samples using the PATHFAST system within the first 24 hours of hospital admission. Serum concentrations of troponin were compared among study groups. To assess the biomarker´s capacity to predict mortality, ROC curves were used, quantifying their differences through the DeLong´s test.

Results. A total of 88 patients with CAP and 152 with COVID-19 were included in the study. In all cohort the median [IQR] serum concentration of troponin (ng/ml) was higher in those who died (34.2, [9.74-384] A. Serum troponin I and mortality due to lower respiratory tract infections B. Serum troponin I to predict mortality in patients with lower tract infections C. ROC curve for serum troponin I to predict risk of mortality Conclusion. Overall, troponin levels were higher among deceased patients. Our findings suggest that high troponin levels are a mortality predictor for patients with COVID-19.

Disclosures. All Authors: No reported disclosures

A Machine Learning Approach Identifies Distinct Early-Symptom Cluster Phenotypes Which Correlate with Severe SARS-CoV

Rupal Mody, MD 8 ; Cristian Madar

MHS 18 ; 1 HJF, Bethesda, Maryland; 2 Support to National Institute of Allergy and Infectious Disease

Infectious Disease Clinical Research Program and the Henry M. Jackson Foundation for the Advancement of Military Medicine and Walter Reed National Military Medical Center

Jackson Foundation for the Advancement of Military Medicine

Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics

Background. The novel coronavirus disease 2019 (COVID-19) pandemic remains a global challenge. Accurate COVID-19 prognosis remains an important aspect of clinical management. While many prognostic systems have been proposed, most are derived from analyses of individual symptoms or biomarkers. Here, we take a machine learning approach to first identify discrete clusters of early stage-symptoms which may delineate groups with distinct symptom phenotypes. We then sought to identify whether these groups correlate with subsequent disease severity.Methods. The Epidemiology, Immunology, and Clinical Characteristics of Emerging Infectious Diseases with Pandemic Potential (EPICC) study is a longitudinal cohort study with data and biospecimens collected from nine military treatment facilities over 1 year of follow-up. Demographic and clinical characteristics were measured with interviews and electronic medical record review. Early symptoms by organ-domain were measured by FLU-PRO-plus surveys collected for 14 days post-enrollment, with surveys completed a median 14.5 (Interquartile Range, IQR = 13) days post-symptom onset. Using these FLU-PRO-plus responses, we applied principal component analysis followed by unsupervised machine learning algorithm k-means to identify groups with distinct clusters of symptoms. We then fit multivariate logistic regression models to determine how these early-symptom clusters correlated with hospitalization risk after controlling for age, sex, race, and obesity.Results. Using SARS-CoV-2 positive participants (n = 1137) from the EPICC cohort (Figure 1 ), we transformed reported symptoms into domains and identified three groups of participants with distinct clusters of symptoms. Logistic regression demonstrated that cluster-2 was associated with an approximately three-fold increased odds [3.01 (95% CI: 2-4.52); P < 0.001] of hospitalization which remained significant after controlling for other factors [2.97 (95% CI: 1.88-4.69); P < 0.001].