Disparities in COVID-19 hospitalizations and mortality among black and Hispanic patients: cross-sectional analysis from the greater Houston metropolitan area.

BACKGROUND: Disparate racial/ethnic burdens of the Coronavirus Disease 2019 (COVID-19) pandemic may be attributable to higher susceptibility to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) or to factors such as differences in hospitalization and care provision. METHODS: In our cross-sectional analysis of lab-confirmed COVID-19 cases from a tertiary, eight-hospital healthcare system across greater Houston, multivariable logistic regression models were fitted to evaluate hospitalization and mortality odds for non-Hispanic Blacks (NHBs) vs. non-Hispanic Whites (NHWs) and Hispanics vs. non-Hispanics. RESULTS: Between March 3rd and July 18th, 2020, 70,496 individuals were tested for SARS-CoV-2; 12,084 (17.1%) tested positive, of whom 3536 (29.3%) were hospitalized. Among positive cases, NHBs and Hispanics were significantly younger than NHWs and Hispanics, respectively (mean age NHBs vs. NHWs: 46.0 vs. 51.7 years; p < 0.001 and Hispanic vs. non-Hispanic: 44.0 vs. 48.7 years; p < 0.001). Despite younger age, NHBs (vs. NHWs) had a higher prevalence of diabetes (25.2% vs. 17.6%; p < 0.001), hypertension (47.7% vs. 43.1%; p < 0.001), and chronic kidney disease (5.0% vs. 3.3%; p = 0.001). Both minority groups resided in lower median income (median income [USD]; NHBs vs. NHWs: 63,489 vs. 75,793; p < 0.001, Hispanic vs. non-Hispanic: 59,104 vs. 68,318; p < 0.001) and higher population density areas (median population density [per square mile]; NHBs vs. NHWs: 3257 vs. 2742; p < 0.001, Hispanic vs. non-Hispanic: 3381 vs. 2884; p < 0.001). In fully adjusted models, NHBs (vs. NHWs) and Hispanics (vs. non-Hispanic) had higher likelihoods of hospitalization, aOR (95% CI): 1.42 (1.24-1.63) and 1.61 (1.46-1.78), respectively. No differences were observed in intensive care unit (ICU) utilization or treatment parameters. Models adjusted for demographics, vital signs, laboratory parameters, hospital complications, and ICU admission vital signs demonstrated non-significantly lower likelihoods of in-hospital mortality among NHBs and Hispanic patients, aOR (95% CI): 0.65 (0.40-1.03) and 0.89 (0.59-1.31), respectively. CONCLUSIONS: Our data did not demonstrate racial and ethnic differences in care provision and hospital outcomes. Higher susceptibility of racial and ethnic minorities to SARS-CoV-2 and subsequent hospitalization may be driven primarily by social determinants.

Rapid Response to Drive COVID-19 Research in a Learning Health Care System: Rationale and Design of the Houston Methodist COVID-19 Surveillance and Outcomes Registry (CURATOR).

BACKGROUND: The COVID-19 pandemic has exacerbated the challenges of meaningful health care digitization. The need for rapid yet validated decision-making requires robust data infrastructure. Organizations with a focus on learning health care (LHC) systems tend to adapt better to rapidly evolving data needs. Few studies have demonstrated a successful implementation of data digitization principles in an LHC context across health care systems during the COVID-19 pandemic. OBJECTIVE: We share our experience and provide a framework for assembling and organizing multidisciplinary resources, structuring and regulating research needs, and developing a single source of truth (SSoT) for COVID-19 research by applying fundamental principles of health care digitization, in the context of LHC systems across a complex health care organization. METHODS: Houston Methodist (HM) comprises eight tertiary care hospitals and an expansive primary care network across Greater Houston, Texas. During the early phase of the pandemic, institutional leadership envisioned the need to streamline COVID-19 research and established the retrospective research task force (RRTF). We describe an account of the structure, functioning, and productivity of the RRTF. We further elucidate the technical and structural details of a comprehensive data repository-the HM COVID-19 Surveillance and Outcomes Registry (CURATOR). We particularly highlight how CURATOR conforms to standard health care digitization principles in the LHC context. RESULTS: The HM COVID-19 RRTF comprises expertise in epidemiology, health systems, clinical domains, data sciences, information technology, and research regulation. The RRTF initially convened in March 2020 to prioritize and streamline COVID-19 observational research; to date, it has reviewed over 60 protocols and made recommendations to the institutional review board (IRB). The RRTF also established the charter for CURATOR, which in itself was IRB-approved in April 2020. CURATOR is a relational structured query language database that is directly populated with data from electronic health records, via largely automated extract, transform, and load procedures. The CURATOR design enables longitudinal tracking of COVID-19 cases and controls before and after COVID-19 testing. CURATOR has been set up following the SSoT principle and is harmonized across other COVID-19 data sources. CURATOR eliminates data silos by leveraging unique and disparate big data sources for COVID-19 research and provides a platform to capitalize on institutional investment in cloud computing. It currently hosts deeply phenotyped sociodemographic, clinical, and outcomes data of approximately 200,000 individuals tested for COVID-19. It supports more than 30 IRB-approved protocols across several clinical domains and has generated numerous publications from its core and associated data sources. CONCLUSIONS: A data-driven decision-making strategy is paramount to the success of health care organizations. Investment in cross-disciplinary expertise, health care technology, and leadership commitment are key ingredients to foster an LHC system. Such systems can mitigate the effects of ongoing and future health care catastrophes by providing timely and validated decision support.

Racial and ethnic disparities in SARS-CoV-2 pandemic: analysis of a COVID-19 observational registry for a diverse US metropolitan population.

INTRODUCTION: Data on race and ethnic disparities for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are limited. We analysed sociodemographic factors associated with higher likelihood of SARS-CoV-2 infection and explore mediating pathways for race and ethnic disparities in the SARS-CoV-2 pandemic. METHODS: This is a cross-sectional analysis of the COVID-19 Surveillance and Outcomes Registry, which captures data for a large healthcare system, comprising one central tertiary care hospital, seven large community hospitals and an expansive ambulatory/emergency care network in the Greater Houston area. Nasopharyngeal samples for individuals inclusive of all ages, races, ethnicities and sex were tested for SARS-CoV-2. We analysed sociodemographic (age, sex, race, ethnicity, household income, residence population density) and comorbidity (Charlson Comorbidity Index, hypertension, diabetes, obesity) factors. Multivariable logistic regression models were fitted to provide adjusted OR (aOR) and 95% CI for likelihood of a positive SARS-CoV-2 test. Structural equation modelling (SEM) framework was used to explore three mediation pathways (low income, high population density, high comorbidity burden) for the association between non-Hispanic black (NHB) race, Hispanic ethnicity and SARS-CoV-2 infection. RESULTS: Among 20 228 tested individuals, 1551 (7.7%) tested positive. The overall mean (SD) age was 51.1 (19.0) years, 62% were females, 22% were black and 18% were Hispanic. NHB and Hispanic ethnicity were associated with lower socioeconomic status and higher population density residence. In the fully adjusted model, NHB (vs non-Hispanic white; aOR, 2.23, CI 1.90 to 2.60) and Hispanic ethnicity (vs non-Hispanic; aOR, 1.95, CI 1.72 to 2.20) had a higher likelihood of infection. Older individuals and males were also at higher risk of infection. The SEM framework demonstrated a significant indirect effect of NHB and Hispanic ethnicity on SARS-CoV-2 infection mediated via a pathway including residence in densely populated zip code. CONCLUSIONS: There is strong evidence of race and ethnic disparities in the SARS-CoV-2 pandemic that are potentially mediated through unique social determinants of health.