Comorbidity and Sociodemographic determinants in COVID-19 Mortality in an US Urban Healthcare System

  1. An-Li Wang
  2. Xiaobo Zhong
  3. Yasmin L Hurd

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Abstract

Background

New York City is the US epicenter of the coronavirus disease 2019 (COVID-19) pandemic. Early international data indicated that comorbidity contributes significantly to poor prognosis and fatality in patients infected with SARS-CoV-2. It is not known to what degree medical comorbidity and sociodemographic determinants impact COVID-19 mortality in the US.

Methods

Evaluation of de-identified electronic health records of 7,592 COVID-19 patients confirmed by SARS-CoV-2 lab tests in New York City. Medical comorbidites and outcome of mortality, and other covariates, including clinical, sociodemographic, and medication measures were assessed by bivariate and multivariate logistic regression models.

Results

Of common comorbid conditions (hypertension, chronic kidney disease, chronic obstructive pulmonary disease, asthma, obesity, diabetes, HIV, cancer), when adjusted for covariates, chronic kidney disease remained significantly associated with increased odds of mortality. Patients who had more than one comorbidities, former smokers, treated with Azithromycin without Hydroxychloroquine, reside within the boroughs of Brooklyn and Queens Higher had higher odds of death.

Conclusions

Increasing numbers of comorbid factors increase COVID-19 mortality, but several clinical and sociodemographic factors can mitigate risk. Continued evaluation of COVID-19 in large diverse populations is important to characterize individuals at risk and improve clinical outcomes.

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  1. ScreenIT

    SciScore for 10.1101/2020.06.11.20128926: (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
    All analyses were conducted using SAS statistical software (SAS Institute. 2019).
    SAS
    suggested: (SASqPCR, RRID:SCR_003056)

    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 current study has several limitations especially as related to a retroactive evaluation of de-identified electronic health records. We were unable to explore in-depth individual medical cases or to access to all aspects of clinical care. Data regarding vital sign measures were limited to the initial clinical encounter and not available for the course of hospitalization for inpatients. Although the cohort studied represents a heterogenous population, whether the results generalize to other large metropolitan communities needs to be addressed as more data becomes available across the US and other countries. Another limitation is that SES cannot be accurately determined only by zip code, and caution should be undertaken in interpreting the geographic data. In summary, the current findings confirm the critical nature of comorbid factors, as well as the increasing number of comorbidities (hypertension, chronic kidney disease, COPD, and diabetes) to mortality risk and also suggest that geographical location, which potentially relates to SES within a large metropolitan area, may increase mortality. Although females, African Americans, and Hispanics often have a greater number of comorbidities, these groups were not associated with increased mortality risk in the COVID-19 population after covarying for other factors. Continued analysis of larger populations locally and globally is essential as the pandemic evolves. Moreover, it will be important to monitor the long-term health im...

    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.

    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.

  2. Version published to 10.1101/2020.06.11.20128926 on medRxiv