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ICU Admission Risk Factors for Latinx COVID-19 Patients at a U.S.-Mexico Border Hospital
Faith C Quenzer1,2, Christopher J Coyne3, Karen Ferran4
1Division of Epidemiology and Biostatistics, School of Public Health, San Diego State University, San Diego, CA, USA. fquenzer@gmail.com.
Objective:
Few studies have examined the impact of coronavirus disease 2019 (COVID-19) on the primarily Latinx community along the U.S.-Mexico border. This study explores the socioeconomic impacts which contribute to strong predictors of severe COVID-19 complications such as intensive care unit (ICU) hospitalization in a primarily Latinx/Hispanic U.S.-Mexico border hospital.
Methods:
A retrospective, observational study of 156 patients (≥ 18 years) Latinx/Hispanic patients who were admitted for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection at a U.S.-Mexico border hospital from April 10, 2020, to May 30, 2020. Descriptive statistics of sex, age, body mass index (BMI), and comorbidities (coronary artery disease, hypertension, diabetes, cancer/lymphoma, current use of immunosuppressive drug therapy, chronic kidney disease/dialysis, or chronic respiratory disease). Multivariate regression models were produced from the most significant variables and factors for ICU admission.
Results:
Of the 156 hospitalized Latinx patients, 63.5% were male, 84.6% had respiratory failure, and 45% were admitted to the ICU. The average age was 67.2 (± 12.2). Those with body mass index (BMI) ≥ 25 had a higher frequency of ICU admission. Males had a 4.4 (95% CI 1.58, 12.308) odds of ICU admission (p = 0.0047). Those who developed acute kidney injury (AKI) and BMI 25-29.9 were strong predictors of ICU admission (p < 0.001 and p = 0.0020, respectively). Those with at least one reported comorbidity had 1.98 increased odds (95% CI 1.313, 2.99) of an ICU admission.
Conclusion:
Findings show that age, AKI, and male sex were the strongest predictors of COVID-19 ICU admissions in the primarily Latinx population at the U.S.-Mexico border. These predictors are also likely driven by socioeconomic inequalities which are most apparent in border hospitals.
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