1 INTRODUCTION

The severe acute respiratory syndrome coronavirus 2 virus (SARS-CoV-2), responsible for COVID-19, has caused a pandemic with far-reaching implications worldwide. US-based studies have demonstrated a disproportionate number of infections and hospitalizations for COVID-19 in people of certain racial and ethnic backgrounds, such as Black and Hispanic persons, and people residing in low-income areas or experiencing homelessness.^{1, 3, 4, 2, 5-7} Additionally, worldwide studies have attempted to identify other independent risk factors, such as comorbidities and patient demographics, for COVID-19 mortality and severe COVID-19. However, many of these studies utilized the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) codes, rather than chart review to assess for comorbidities, which facilitates data collection by integrating hospital billing data, but may not reflect the true number of patients’ comorbidities.⁸ This may have led to inaccurate proportions of covariates utilized in the studies’ multivariable analysis.⁸

Philadelphia, a diverse city where 44% of the population identifies as Black or African American (hereafter “Black”), 34% non-Hispanic White (“White”), 15% Hispanic, and 8% Asian, has been significantly affected by COVID-19.⁹ Through July 28, 2021, Black Philadelphians represented 45% of the known positive cases in which race/ethnicity was reported (83% of cases), while White, Hispanic, and Asian populations accounted for 34%, 14%, and 6%, respectively.^{10, 11} Hospitalization trends occurred in similar proportions.¹² COVID-19 mortality for Black, White, Hispanic, and Asian patients was 49%, 35%, 11%, and 5% of deaths, respectively.

Philadelphia has the highest poverty rate among the top 10 largest cities, with 23.3% of Philadelphians living below the poverty level with a median household income of $47,474.^{9, 13, 14} In 2016, the poverty rate among Hispanics was 37.9%, the highest among racial and ethnic groups, followed by Blacks, which had the second-highest poverty rate at 30.8%; both groups are also more likely to live in areas of racially or ethnically concentrated high-poverty areas.¹⁴ This highlights the complex interaction between race/ethnicity and poverty in Philadelphia, although it is important to extricate, as they both have distinct implications for policy and intervention. Furthermore, comparing case positivity rates and poverty level by zip code, data suggest that poorer Philadelphians are also disproportionately affected by COVID-19.^{12, 15} The extent to which SES, which is a combined factor of income, education, and occupation, affects mortality for hospitalized Philadelphians is not known. The objective of this study is to assess if Black, White, Hispanic, or Asian race/ethnicity, and/or SES are independent risk factors for mortality for patients hospitalized with COVID-19 at one Philadelphia hospital using manual chart review rather than ICD-10 codes.

2 METHODS

3 RESULTS

A total of 426 patients were admitted for COVID-19 from March 17 to June 6, 2020, at Thomas Jefferson University Hospital (Philadelphia, PA), an academic tertiary care center. Primary and secondary outcomes, subcategorized by demographic and clinical characteristics, are presented in Table 2 and Table S1, respectively. For all patients admitted to the hospital with COVID-19, 16.7% (n = 71) died, 3.3% (n = 14) were discharged to hospice, and 20% (n = 85) were admitted to the ICU. Of the 83 patients who died or were discharged to hospice, 45.8% (n = 38) were Black, 36.1% (n = 30) were White, 4.8% (n = 4) were Hispanic, 9.6% (n = 8) were Asian, and 3.6% (n = 3) were other, which was statistically significant in the unadjusted analysis (p = 0.029).

Table 3 displays the bivariate and multivariable analyses for the variables of interest to assess for association with the 30-day mortality or discharge to hospice. For the bivariate analysis, age 65–74 and ≥75 had increased ORs (Table 3). This association persisted for age ≥75 in Models 1–4 when progressively adjusting for additional factors, with an OR of 11.01 (95% CI: 1.96–61.97) versus age <45 in Model 4. Risk factors of coronary artery disease, renal disease, cerebrovascular disease, chronic obstructive pulmonary disease, and heart failure were associated with increased OR, and active smoking was associated with a decreased OR with bivariate analysis, but only renal disease was significant in Model 4 with an OR of 2.78 (95% CI: 1.31–5.90). BMI 30–35 had decreased odds with OR in Model 4 of 0.24 (95% CI: 0.08–0.69) versus BMI < 30. In Model 4, there was no significant difference in mortality comparing the four different admission time frames nor presentation timing.

Although Black and Hispanic race/ethnicity were both associated with decreased odds of the death or discharge to hospice in bivariate analysis, in all adjusted models, there was no association. The effect noted in the bivariate analysis was attenuated in Model 1 when controlling for age and sex as well as in Model 2 when accounting for SES features. Living in very-low-income areas and residences before admission at a shelter or street were both associated with decreased OR of the primary outcome in bivariate analysis. This effect was attenuated in Model 2 when accounting for age, sex, and other SES variables. In Models 3 and 4, however, for patients living in very-low-income areas, the effect returned with an OR of 0.29 (95% CI: 0.12–0.71) in Model 4.

For the secondary outcome, admission to the ICU, diabetes had an OR of 3.09 (95% CI: 1.57–6.09) in Model 4. Living in a median or high-population-density area (OR: 0.33, 95% CI: 0.15–0.73) (OR: 0.40, 95% CI: 0.18–0.90), respectively, and admission date of April 27 to May 14 (OR: 0.37, 95% CI: 0.15–0.87) were protective in Model 4. Race/ethnicity and SES were not associated with the secondary outcome in bivariate nor multivariable analysis (Table S2).

4 DISCUSSION

In this retrospective cohort study including 426 racially and ethnically diverse patients at a tertiary academic center in Philadelphia, we demonstrate that race/ethnicity is not independently associated with 30-day mortality or discharge to hospice from COVID-19 when using multivariable analysis controlling for age, sex, living in a low-income area, population density, type of residence before hospitalization, the time frame of admission, days since symptom onset, and comorbidities. From our review of the literature, this is one of a limited number of studies thus far to assess risk factors for COVID-19 using a more accurate approach with manual chart review rather than ICD-10 codes.³¹ Furthermore, this is one of the first studies to assess the effects of race/ethnicity on 30-day mortality for hospitalized patients with COVID-19 in the Philadelphia region.³

A major strength of this study is the use of manual chart review to determine comorbidities, which distinguishes this study from most others that used ICD-10 codes in analyses of 30-day mortality from COVID-19. A post hoc analysis of our data suggested that the manual chart review was more sensitive than ICD-10 based methods with ICD-10-based data set comorbidity agreement ranging from 44% (renal disease) to 87% (diabetes). An additional strength is the use of census tract-level data rather than zip code for mean area income, our proxy for SES. This may be a better representation of SES as it is comparatively more homogenous regarding the economic position and living conditions, which is especially important in Philadelphia given its unique urban geography in which very poor and affluent census tracts are frequently adjacent. A limitation of our study is that we did not collect patient data on other aspects of SES, such as occupation, highest education, number of household members, or access to healthcare; these factors may elucidate further disparities in COVID-19 infection and mortality and should be considered in future studies. Another limitation of our study is that deaths that occurred outside of the hospital setting or at hospitals not participating in our EHR sharing service, Care Everywhere, were not captured in this study. This may lead to an underestimation of mortality that occurred within the study period. Both a strength and a limitation of this study is that it occurred at a single institution. This is a limitation regarding sample size and generalizability, but this also is a strength as it allowed for homogeneity of care received by our patients pertaining to protocols for treatments, escalation of care, and timing of noninvasive and mechanical ventilation.

To best target our efforts at reducing disparate COVID-19 infection rates in the United States, it is helpful to know where along the disease course the disparities occur most frequently. National and city data have repeatedly shown that Black people in the United States have borne the largest burden of COVID-19 cases and fatalities. From the Department of Public Health data, Black citizens in Philadelphia, like other areas of the country, have been similarly affected by COVID-19, representing 45% of positive cases and 49% of deaths through July 28, 2021, while composing 44% of the Philadelphia population.¹² Our findings confirm similar trends at hospital presentation given 54% of patients were Black. Once hospitalized, however, poor outcomes did not vary by race/ethnicity when corrected for covariates; this is consistent with similar prior studies in the United States.^{4, 6, 26, 34} Our findings indicate that variation in mortality by race/ethnicity in Philadelphians is related to factors occurring before hospitalization that affect exposure to COVID-19 and worse outcomes. For example, a study by Mutambudzi et al. showed that essential workers in healthcare professionals, medical support, social care, and transportation had the highest risk of severe COVID-19, which was even greater in non-White essential workers.³⁵ Other factors that could contribute to disparities are the number of household members, limited access to healthcare, weathering, structural and systemic racism, and discrimination.^36-38 A subsequent study could investigate the role of these and similar upstream effects on rates of infection and hospitalization.

Although Black patients in the present study were disproportionately representative of COVID-19 hospitalizations (p < 0.002), 16.4% of Black patients died in the hospital compared to 27.6% of Asian patients, 27.5% of White patients, and 8.9% of Hispanic patients. Black and Hispanic race/ethnicity were found to be protective in bivariate analysis. This protective effect, however, was no longer significant in Model 1 when controlling for age and sex or Models 2–4 when controlling for SES and comorbidities. Our analysis suggests this is because hospitalized Black and Hispanic patients were younger (mean age: 62.6 and 57.1, respectively; White patients, 71.1), leading to the protective effect in bivariate analysis that was no longer significant when controlling for age.

We were surprised to find that living in very low-income neighborhoods was protective for death or discharge to hospice, but not ICU admission across Models 3 and 4. This may be an artifact of the level of data used (census tract rather than household and/or individual). For instance, in a preprint study, Cerami et al. demonstrate that high household living density is associated with a higher risk of secondary household transmission, which also occurred more frequently in non-White and Hispanic households than White households.³⁹ Alternatively, if this effect is not truly protective, it is possible that the aspect of SES we chose for analysis (income) might have other prehospital effects and/or hospitalization effects that warrant further research in combination with other aspects of SES. Our study similarly found that homelessness conferred a protective effect for poor outcomes in the bivariate analysis (OR: 0.10, 95% CI: 0.01–0.76).

We included homelessness/shelter status to account for the practice to admit patients experiencing homelessness to the hospital while awaiting safe housing placement to slow the spread of COVID-19 in the shelter system. The apparent protective effect in the bivariate analysis may be a result of how many of them likely did not meet the same admission standard of patients not experiencing homelessness. The average hospital stay for patients experiencing homelessness was 4.6 days (range: 1–32; interquartile range: 4.0) for 51 patients, which highlights the difficulty of discharging patients to safe housing due to resource and logistical constraints. Despite the well-intended nature of this intervention, admitting patients for the sole purpose of quarantine is not an efficient use of hospital resources, poses an increased risk of COVID-19 infection for healthcare workers and fellow patients and hospital-acquired infections for these patients, and, during a pandemic in which acute-care hospital bed capacity has at times been insufficient, effectively reduces the capacity to care for patients at higher risk for morbidity and mortality. This highlights the need for more effective multidisciplinary emergency preparedness, particularly as it pertains to sheltering vulnerable individuals, and highlights the broader need for effective shelter interventions during a pandemic and beyond in Philadelphia.

With respect to age and comorbidities, our study demonstrated an OR of 11.01 (95% CI: 1.96–61.97) for the composite primary outcome for patients age ≥75, which is similar to prior research.^{21, 23, 24, 26, 27, 29, 32, 33} We do note, however, that the 45–64 and 65–74 age groups did not show a distinct risk as expected. Those with renal disease had an OR of 2.78 (95% CI: 1.31–5.90) for the composite primary outcome, which was similarly demonstrated by Yehia et al. and Williamson et al.^{26, 29} We note that several comorbidities found in other studies did not have clear effects in our multivariable models including coronary artery disease, cancer, cerebral vascular disease, chronic obstructive pulmonary disease, heart failure, and hypertension.^{21, 23, 24, 26, 27, 29, 32, 33} Other studies have demonstrated an increased odds of death in those with elevated BMIs, but the present study reflects a protective effect with an OR: 0.24 (95% CI: 0.08–0.69) in those with BMI: 30–35, and a trend toward a U-shaped curve (e.g., BMI: 30–40 lower risk, BMI < 30 or >40 higher risk), which may be a product of grouping all BMIs < 30 but warrants further investigation.^{21, 25, 29} Finally, while living in a high-density area has previously been demonstrated to increase risk of hospitalization in COVID-19, in the present study we did not demonstrate increase odds of death or discharge to hospice for patients living in densely populated areas who were hospitalized with COVID-19; this outcome may also have been an artifact of census-level tract data as noted above.^{34, 39}

5 CONCLUSIONS

Our study demonstrates that among patients hospitalized for COVID-19, after controlling for mediating variables, self-reported race and ethnicity were not independent predictors of mortality while living in very-low income neighborhoods was protective against death or hospice discharge from COVID-19.

CONFLICT OF INTERESTS

The authors declare that there are no conflict of interests.

AUTHOR CONTRIBUTIONS

Dianna R. Cheney-Peters, Robert B. Jones, and Alan A. Kubey conceived of the presented idea and contributed to study design. Data collection and analyses were performed by Dianna R. Cheney-Peters, Crystal Y. Lee, Shuji Mitsuhashi, Dina S. Zaret, Joshua M. Riley, Chantel M. Venkataraman, Joseph W. Schaefer, Brandon J. George, Chris J. Li, Christa M. Smaltz, Conor G. Bradley, Danielle M. Fitzpatrick, David B. Ney, Divya M. Chalikonda, Joshua D. Mairose, Kashyap Chauhan, Margaret V. Szot, Robert B. Jones, Rukaiya Bashir-Hamidu, and Alan A. Kubey. Dianna R. Cheney-Peters, Crystal Y. Lee, Shuji Mitsuhashi, Dina S. Zaret, and Alan A. Kubey led the writing of the initial manuscript. Dianna R. Cheney-Peters, Crystal Y. Lee, Shuji Mitsuhashi, Dina S. Zaret, Joshua M. Riley, Chantel M. Venkataraman, Joseph W. Schaefer, Brandon J. George, Chris J. Li, Christa M. Smaltz, Conor G. Bradley, Danielle M. Fitzpatrick, David B. Ney, Divya M. Chalikonda, Joshua D. Mairose, Kashyap Chauhan, Margaret V. Szot, Robert B. Jones, Rukaiya Bashir-Hamidu, and Alan A. Kubey reviewed, edited, and finalized the manuscript, providing critical feedback and changes before submission.