COVID-19 hospitalizations in five California hospitals: a retrospective cohort study

  1. Miriam Nuño
  2. Yury García
  3. Ganesh Rajasekar
  4. Diego Pinheiro
  5. Alec J. Schmidt

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Abstract

Background

The novel coronavirus pandemic has had a differential impact on communities of color across the US. The University of California hospital system serves a large population of people who are often underrepresented elsewhere. Data from hospital stays can provide much-needed localized information on risk factors for severe cases and/or death.

Methods

Patient-level retrospective case series of laboratory-confirmed COVID-19 hospital admissions at five UC hospitals (N = 4730). Odds ratios of ICU admission, death, and a composite of both outcomes were calculated with univariate and multivariate logistic regression based on patient characteristics, including sex, race/ethnicity, and select comorbidities. Associations between comorbidities were quantified and visualized with a correlation network.

Results

Overall mortality rate was 7.0% (329/4,730). ICU mortality rate was 18.8% (225/1,194). The rate of the composite outcome (ICU admission and/or death) was 27.4% (1298/4730). Comorbidity-controlled odds of a composite outcome were increased for age 75–84 (OR 1.47, 95% CI 1.11–1.93) and 85–59 (OR 1.39, 95% CI 1.04–1.87) compared to 18–34 year-olds, males (OR 1.39, 95% CI 1.21–1.59) vs. females, and patients identifying as Hispanic/Latino (OR 1.35, 95% CI 1.14–1.61) or Asian (OR 1.43, 95% CI 1.23–1.82) compared to White. Patients with 5 or more comorbidities were exceedingly likely to experience a composite outcome (OR 2.74, 95% CI 2.32–3.25).

Conclusions

Males, older patients, those with multiple pre-existing comorbidities, and those identifying as Hispanic/Latino or Asian experienced an increased risk of ICU admission and/or death. These results are consistent with reported risks among the Hispanic/Latino population elsewhere in the United States, and confirm multiple concerns about heightened risk among the Asian population in California.

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  1. Version published to 10.1186/s12879-021-06640-4
  2. ScreenIT

    SciScore for 10.1101/2021.01.29.21250788: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analyses was conducted using SAS software, version 9.4 (SAS Institute, Cary, NC, USA), changepoint package (binary segmentation algorithm) in R version 4.0.1 (R Core Team 2020), and Python.
    SAS
    suggested: (SASqPCR, RRID:SCR_003056)
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    This study has several limitations. Our findings represent the experience of five hospitals within the UC Health system and therefore may have limited external generalizability to other health care settings, especially outside of California. Since our sample was drawn from electronic medical records and not full chart reviews, our data was also missing reported symptoms and the patients’ stated reasons for seeking COVID-19 tests, which could be one or a combination of the presence of COVID-19 symptoms, notification of exposure through contact tracing, or seeking a test in lieu of observing a 14-day quarantine period after travel. Such a large cohort followed over an extended time of period will necessarily include aggregation biases due to differential test availability, hospital admission practices, and documentation across the five medical centers, especially as public health advice evolved over the observation period. This could very well have obscured evolving demographic trends as the pandemic developed, or introduced a confounding bias in crude measures. Finally, cases who were tested at UC Health and sought care outside the system were not captured in this database, meaning any subsequent mortality or ICU admission of these cases outside the UC Health system would not be captured either. Notwithstanding these limitations, this study provides comparative epidemiologic characteristics of a diverse and underrepresented population of patients admitted to the hospital with ...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  3. Version published to 10.1101/2021.01.29.21250788v1 on medRxiv