Trends in COVID-19 cases and clinical management in Veterans Health Administration medical facilities: A national cohort study

  1. Maya Aboumrad
  2. Brian Shiner
  3. Natalie Riblet
  4. Hugh Huizenga
  5. Nabin Neupane
  6. Yinong Young-Xu

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Abstract

We explored longitudinal trends in sociodemographic characteristics, reported symptoms, laboratory findings, pharmacological and non-pharmacological treatment, comorbidities, and 30-day in-hospital mortality among hospitalized patients with coronavirus disease 2019 (COVID-19).

Methods

This retrospective cohort study included patients diagnosed with COVID-19 in the United States Veterans Health Administration between 03/01/20 and 08/31/20 and followed until 09/30/20. We focused our analysis on patients that were subsequently hospitalized, and categorized them into groups based on the month of hospitalization. We summarized our findings through descriptive statistics. We used Cuzick’s Trend Test to examine any differences in the distribution of our study variables across the six months.

Results

During our study period, we identified 43,267 patients with COVID-19. A total of 8,240 patients were hospitalized, and 13.1% (N = 1,081) died within 30 days of admission. Hospitalizations increased over time, but the proportion of patients that died consistently declined from 24.8% (N = 221/890) in March to 8.0% (N = 111/1,396) in August. Patients hospitalized in March compared to August were younger on average, mostly black, urban-dwelling, febrile and dyspneic. They also had a higher frequency of baseline comorbidities, including hypertension and diabetes, and were more likely to present with abnormal laboratory findings including low lymphocyte counts and elevated creatinine. Lastly, there was a decline from March to August in receipt of mechanical ventilation (31.4% to 13.1%) and hydroxychloroquine (55.3% to <1.0%), while treatment with dexamethasone (3.7% to 52.4%) and remdesivir (1.1% to 38.9%) increased.

Conclusion

Among hospitalized patients with COVID-19, we observed a trend towards decreased disease severity and mortality over time.

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  1. Version published to 10.1371/journal.pone.0246217
  2. ScreenIT

    SciScore for 10.1101/2021.01.18.427092: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study received institutional review board approval from the Veteran’s Institutional Review Board of Northern New England at the White River Junction Veterans Affairs Medical
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All analyses were performed using Stata/MP version 15.1 software (StataCorp, 2015).
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    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:
    Limitations: We acknowledge important limitations to our study. The VHA typically treats a population that is older, medically complex, and have greater risk behaviors compared to the general U.S. population.41 However, prior work has found no evidence of differences in comorbidity burden between VHA and non-VHA users after controlling for age, sex, race, geographic region, and urbanicity of residence.24 Additionally, the frequency and type of comorbidities identified in this study are similar to those reported among non-VHA patients hospitalized with COVID-19.15–17 Therefore, our findings may still be generalizable to the larger U.S. population. Our analysis was limited to patients diagnosed and hospitalized for COVID-19 within the VHA health care system and may not capture cases, hospitalizations, treatments received, or deaths occurring at non-VHA facilities. We reported the month-to-month and cumulative disease burden on the VHA, which may not account for regional differences in sociodemographic characteristics, government restrictions and policies, or the magnitude and timing of case peaks. Future work on the regional intensity and timing of outbreaks may be beneficial for identifying specific areas with elevated disease burden and help inform allocation of limited resources.

    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 rtransparent:
    • No conflict of interest statement was detected. If there are no conflicts, we encourage authors to explicit state so.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2021.01.18.427092v1 on bioRxiv