Racial and ethnic disparity in clinical outcomes among patients with confirmed COVID-19 infection in a large US electronic health record database

Buikema, Ami; Buzinec, Paul; Paudel, Misti L.; Andrade, Katherine E; Johnson, Jonathan; Edmonds, Yvette; Jhamb, Sumit K.; Chastek, Benjamin; Raja, Harish; Cao, Feng; Hulbert, E.; Korrer, Stephanie; Mazumder, D; Seare, Jerald; Solow, Brian K.; Currie, U. Michael

doi:10.1016/j.eclinm.2021.101075

Cited by 35 publications

(22 citation statements)

References 41 publications

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“…Previous research has shown the importance of active solicitation of information [68] and EMRs are now being used to help evaluate the effectiveness [69] and potential side effects [70,71] of COVID-19 vaccines. Similarly, EMR-based data has been utilized previously in studies looking at the effects of the COVID-19 pandemic as a stressor on people with bipolar disorder [72], the safety of the COVID-19 vaccine in pregnant women [73], the effect of COVID-19 on pregnancy outcomes [74], the effects of clinical characteristics on outcomes of COVID-19 patients [75][76][77], the factors that influence pharmacotherapy for tobacco dependence in the VA system [78], the effect of mental health disorders on COVID-19 mortality [79], COVID-19 vaccine adverse effects [68], and the effects of the COVID-19 pandemic on body mass index in children [80]. Thus, the present study builds upon a growing body of research examining similar effects of the COVID-19 pandemic.…”

Section: Discussionmentioning

confidence: 99%

Changes in Tobacco Use Patterns among Veterans in San Diego during the Recent Peak of the COVID-19 Pandemic

Fatollahi

Bentley

Doran

et al. 2021

IJERPH

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The prevalence of tobacco use increases in times of stress; however, during the initial stage of the COVID-19 pandemic, tobacco use rates stayed the same in most populations. Previous work focused on the initial months of the pandemic, while this study examined the changes in tobacco use during a later peak period of the pandemic. We used data from 61,852 visits to the VA San Diego Healthcare System from November 2019 to February 2021, divided into pre-, early, and peak pandemic periods. Multinomial logistic regression was used to test whether the odds of being a daily or non-daily tobacco user varied over time, by demographic group, or with the presence of specific psychiatric diagnoses. Younger Veterans had a greater reduction in the prevalence of non-daily tobacco use between the early and peak periods, while older Veterans had a rise in daily use from pre- to the early pandemic, which returned to baseline during the peak. Individuals with substance use disorder and serious mental illness diagnoses were more likely to report tobacco use, but psychiatric diagnoses did not predict change over time. These findings demonstrate factors that potentially contribute to changes in tobacco use during a public health crisis and may help guide future targeted cessation efforts.

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Section: Discussionmentioning

confidence: 99%

Changes in Tobacco Use Patterns among Veterans in San Diego during the Recent Peak of the COVID-19 Pandemic

Fatollahi

Bentley

Doran

et al. 2021

IJERPH

View full text Add to dashboard Cite

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“…This is consistent with the limited availability of diagnostic tests in that period. Moreover, our racially and ethnically diverse study population allowed our study to specifically evaluate differential performance of diagnosis codes for case surveillance in racial/ethnic minority groups and women, especially due to the disproportionately large effect of the pandemic on racial/ethnic minorities 25 . We found modest differences in performance of codes across these groups, with a lower performance of diagnosis codes in non-Hispanic White patients relative to Hispanic and non-Hispanic Black individuals.…”

Section: Discussionmentioning

confidence: 99%

A multicenter evaluation of computable phenotyping approaches for SARS-CoV-2 infection and COVID-19 hospitalizations

et al. 2022

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Diagnosis codes are used to study SARS-CoV2 infections and COVID-19 hospitalizations in administrative and electronic health record (EHR) data. Using EHR data (April 2020–March 2021) at the Yale-New Haven Health System and the three hospital systems of the Mayo Clinic, computable phenotype definitions based on ICD-10 diagnosis of COVID-19 (U07.1) were evaluated against positive SARS-CoV-2 PCR or antigen tests. We included 69,423 patients at Yale and 75,748 at Mayo Clinic with either a diagnosis code or a positive SARS-CoV-2 test. The precision and recall of a COVID-19 diagnosis for a positive test were 68.8% and 83.3%, respectively, at Yale, with higher precision (95%) and lower recall (63.5%) at Mayo Clinic, varying between 59.2% in Rochester to 97.3% in Arizona. For hospitalizations with a principal COVID-19 diagnosis, 94.8% at Yale and 80.5% at Mayo Clinic had an associated positive laboratory test, with secondary diagnosis of COVID-19 identifying additional patients. These patients had a twofold higher inhospital mortality than based on principal diagnosis. Standardization of coding practices is needed before the use of diagnosis codes in clinical research and epidemiological surveillance of COVID-19.

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“…Second, upstream social determinants of health may lead to minority groups having higher rates of preexisting conditions associated with risk for severe COVID-19 infections [ 6 ]. Third, minority populations may present later for care, as noted by multiple studies finding delays in presentation among minority individuals after symptom onset and an increased likelihood of acute care needs at presentation compared to White individuals [ 7 , 8 ]. Biomarkers for inflammation (C-reactive protein, d -dimer, procalcitonin, lactate dehydrogenase, ferritin) and organ system dysfunction (neutrophil/lymphocyte ratio, lymphocyte percentage, troponin, creatinine) have been associated with COVID-19 illness severity and proposed as a means of measuring clinical severity at time of presentation [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…Biomarkers for inflammation (C-reactive protein, d -dimer, procalcitonin, lactate dehydrogenase, ferritin) and organ system dysfunction (neutrophil/lymphocyte ratio, lymphocyte percentage, troponin, creatinine) have been associated with COVID-19 illness severity and proposed as a means of measuring clinical severity at time of presentation [ 9 , 10 ]. Finally, minority group-related disparities in COVID-19 outcomes could result from differential or suboptimal care experiences once admitted, although multiple studies have not found differences in clinical outcomes during hospitalization after adjusting for clinical risk factors [ 8 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Is the Gap Closing? Comparison of Sociodemographic Disparities in COVID-19 Hospitalizations and Outcomes Between Two Temporal Waves of Admissions

Wang

Brisbon

Hubbell

et al. 2022

J. Racial and Ethnic Health Disparities

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Objective The COVID-19 pandemic has disproportionately impacted minority communities, yet little data exists regarding whether disparities have improved at a health system level. This study examined whether sociodemographic disparities in hospitalization and clinical outcomes changed between two temporal waves of hospitalized COVID-19 patients. Methods This is a retrospective cohort study of primary care patients at Mass General Brigham (a large northeastern health system serving 1.27 million primary care patients) hospitalized in-system with COVID-19 between March 1, 2020, and March 1, 2021, categorized into two 6-month “wave” periods. We used chi-square tests to compare demographics between waves, and regression analysis to characterize the association of race/ethnicity and language with in-hospital severe outcomes (death, hospice discharge, intensive unit care need). Results Hispanic/Latino, Black, and non-English-speaking patients constituted 30.3%, 12.5%, and 29.7% of COVID-19 admissions in wave 1 ( N = 5844) and 22.2%, 9.0%, and 22.7% in wave 2 ( N = 4007), compared to 2019 general admission proportions of 8.8%, 6.3%, and 7.7%, respectively. Admissions from highly socially vulnerable census tracts decreased between waves. Non-English speakers had significantly higher odds of severe illness during wave 1 (OR 1.35; 95% CI: 1.10, 1.66) compared to English speakers; this association was non-significant during wave 2 (OR 1.01; 95% CI: 0.76, 1.36). Conclusions Comparing two COVID-19 temporal waves, significant sociodemographic disparities in COVID-19 admissions improved between waves but continued to persist over a year, demonstrating the need for ongoing interventions to truly close equity gaps. Non-English-speaking language status independently predicted worse hospitalization outcomes in wave 1, underscoring the importance of targeted and effective in-hospital supports for non-English speakers. Supplementary Information The online version contains supplementary material available at 10.1007/s40615-022-01249-y.

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Racial and ethnic disparity in clinical outcomes among patients with confirmed COVID-19 infection in a large US electronic health record database

Cited by 35 publications

References 41 publications

Changes in Tobacco Use Patterns among Veterans in San Diego during the Recent Peak of the COVID-19 Pandemic

Changes in Tobacco Use Patterns among Veterans in San Diego during the Recent Peak of the COVID-19 Pandemic

A multicenter evaluation of computable phenotyping approaches for SARS-CoV-2 infection and COVID-19 hospitalizations

Is the Gap Closing? Comparison of Sociodemographic Disparities in COVID-19 Hospitalizations and Outcomes Between Two Temporal Waves of Admissions

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