Social and Clinical Determinants of COVID-19 Outcomes: Modeling Real-World Data from a Pandemic Epicenter

  1. Jyothi Manohar
  2. Sajjad Abedian
  3. Rachel Martini
  4. Scott Kulm
  5. Mirella Salvatore
  6. Kaylee Ho
  7. Paul Christos
  8. Thomas Campion
  9. Julianne Imperato-McGinley
  10. Said Ibrahim
  11. Teresa H. Evering
  12. Erica Phillips
  13. Rulla Tamimi
  14. Vivian Bea
  15. Onyinye D. Balogun
  16. Andrea Sboner
  17. Olivier Elemento
  18. Melissa Boneta Davis

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Abstract

IMPORTANCE

As the United States continues to accumulate COVID-19 cases and deaths, and disparities persist, defining the impact of risk factors for poor outcomes across patient groups is imperative.

OBJECTIVE

Our objective is to use real-world healthcare data to quantify the impact of demographic, clinical, and social determinants associated with adverse COVID-19 outcomes, to identify high-risk scenarios and dynamics of risk among racial and ethnic groups.

DESIGN

A retrospective cohort of COVID-19 patients diagnosed between March 1 and August 20, 2020. Fully adjusted logistical regression models for hospitalization, severe disease and mortality outcomes across 1-the entire cohort and 2-within self-reported race/ethnicity groups.

SETTING

Three sites of the NewYork-Presbyterian health care system serving all boroughs of New York City. Data was obtained through automated data abstraction from electronic medical records.

PARTICIPANTS

During the study timeframe, 110,498 individuals were tested for SARS-CoV-2 in the NewYork-Presbyterian health care system; 11,930 patients were confirmed for COVID-19 by RT-PCR or covid-19 clinical diagnosis.

MAIN OUTCOMES AND MEASURES

The predictors of interest were patient race/ethnicity, and covariates included demographics, comorbidities, and census tract neighborhood socio-economic status. The outcomes of interest were COVID-19 hospitalization, severe disease, and death.

RESULTS

Of confirmed COVID-19 patients, 4,895 were hospitalized, 1,070 developed severe disease and 1,654 suffered COVID-19 related death. Clinical factors had stronger impacts than social determinants and several showed race-group specificities, which varied among outcomes. The most significant factors in our all-patients models included: age over 80 (OR=5.78, p= 2.29×10 −24 ) and hypertension (OR=1.89, p=1.26×10 −10 ) having the highest impact on hospitalization, while Type 2 Diabetes was associated with all three outcomes (hospitalization: OR=1.48, p=1.39×10 −04 ; severe disease: OR=1.46, p=4.47×10 −09 ; mortality: OR=1.27, p=0.001). In race-specific models, COPD increased risk of hospitalization only in Non-Hispanics (NH)-Whites (OR=2.70, p=0.009). Obesity (BMI 30+) showed race-specific risk with severe disease NH-Whites (OR=1.48, p=0.038) and NH-Blacks (OR=1.77, p=0.025). For mortality, Cancer was the only risk factor in Hispanics (OR=1.97, p=0.043), and heart failure was only a risk in NH-Asians (OR=2.62, p=0.001).

CONCLUSIONS AND RELEVANCE

Comorbidities were more influential on COVID-19 outcomes than social determinants, suggesting clinical factors are more predictive of adverse trajectory than social factors.

KEY POINTS

QUESTION

What is the impact of patient self-reported race, ethnicity, socioeconomic status, and clinical profile on COVID-19 hospitalizations, severity, and mortality?

FINDINGS

In patients diagnosed with COVID-19, being over 50 years of age, having type 2 diabetes and hypertension were the most important risk factors for hospitalization and severe outcomes regardless of patient race or socioeconomic status.

MEANING

In this large sample pf patients diagnosed with COVID-19 in New York City, we found that clinical comorbidity, more so than social determinants of health, was associated with important patient outcomes.

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  1. ScreenIT

    SciScore for 10.1101/2021.04.06.21254728: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was approved by the WCM Institutional Review Board (IRB).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Experimental Models: Organisms/Strains
    SentencesResources
    Thus, we designated patients to mutually exclusive self-identified categories: Hispanic (HISP); Non-Hispanic Black (NH-Black); Non-Hispanic White (NH-White); and Non-Hispanic Asian (NH-Asian) (Table 1.
    Non-Hispanic White
    suggested: None

    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:
    There are important limitations to consider in interpreting our results. First, our study has the typical limitations RWD data collection from EHR systems. However, use of RWD is advantageous because in the acute and fast-paced context of a deadly pandemic, urgency of saving lives precedes the priority of appropriate study designs and specialized data collection. However, completeness and accuracy of some variables that are typically part of self-reported intake surveys, including smoking status, medical history and loss of follow-up is debatable. The NDI application of SDOH is a powerful indicator of patients’ ‘placemats’ or integrated neighborhood-level determinants, which is often more predictive of health outcomes than direct measures of socioeconomic status. However, the NDI does not directly measure some key factors, such as systemic racism or social capital12 though the measures included in calculating the NDI are certainly influenced by these factors. (Supplemental Figure 3).

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.
    • Thank you for including a protocol registration statement.

    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 10.1101/2021.04.06.21254728v1 on medRxiv