Cumulative burden of non-communicable diseases predicts COVID hospitalization among people with HIV: A one-year retrospective cohort study

  1. Michael D. Virata
  2. Sheela V. Shenoi
  3. Joseph Ladines-Lim
  4. Merceditas S. Villanueva
  5. Lydia A. Barakat

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Abstract

There continue to be conflicting data regarding the outcomes of people with HIV (PWH) who have COVID-19 infection with most studies describing the early epidemic. We present a single site experience spanning a later timeframe from the first report on January 21, 2020 to January 20, 2021 and describe clinical outcomes and predictors of hospitalization among a cohort of PWH in an urban center in Connecticut, USA. Among 103 PWH with controlled HIV disease, hospitalization occurred in 33% and overall mortality was 1%. HIV associated factors (CD4 count, HIV viral suppression) were not associated with hospitalization. Chronic lung disease (OR: 3.35, 95% CI:1.28–8.72), and cardiovascular disease (OR: 3.4, 95% CI:1.27–9.12) were independently associated with hospitalization. An increasing number of non-communicable comorbidities increased the likelihood of hospitalization (OR: 1.61, 95% CI:1.22–2.13).

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  1. Version published to 10.1371/journal.pone.0260251
  2. ScreenIT

    SciScore for 10.1101/2021.08.03.21261560: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The study was approved by the Yale University Institutional Review Board and the requirement for an informed consent was waived for this retrospective review.
    Consent: The study was approved by the Yale University Institutional Review Board and the requirement for an informed consent was waived for this retrospective review.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Continuous and categorical variables were summarized as medians or means, and percentages as appropriate, and bivariate analysis comparing hospitalized with nonhospitalized patients using SPSS Version 26.0 was performed to identify factors that differentiated between the two groups.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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:
    The study had several limitations. First, this was a retrospective review of PWH at a single urban institution, and this group may not be representative of the general global population of PWH. Second, hospitalized cases were identified through a health systems database with laboratory confirmation, leaving open the possibility that other PWH from the YNHH clinics were tested for SARS-CoV-2 or hospitalized elsewhere. Third, although reports show that COVID-19 disproportionately affects disadvantaged populations[22], the PWH cohort in the current study, consisting largely of minorities, and people over 50 years with multiple co-morbid conditions but with ready access to advanced treatments, achieved favorable outcomes overall. Finally, these data reflect the COVID-19 trends among PWH before the initiation of mass vaccination. As the pandemic continues to evolve and new variants emerge, these circumstances could alter the risk factors for hospitalization and severe disease.

    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:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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

    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 10.1101/2021.08.03.21261560v1 on medRxiv