|Year : 2021 | Volume
| Issue : 2 | Page : 81-88
COVID-19 clinical course and outcomes in a predominantly black, vulnerable patient population in New York City
Priyanka Parmar, Alecia James, Sabrina Rosengarten, Alvin Oommen, Michael A Joseph, Clara Wilson, Rohan Maini, Max Mecklenburg, Julie Kim, Jodi-Ann Edwards, Mohamed Nakeshbandi, Igal Breitman, Bonnie Arquilla, Pia Daniel
Department of Emergency Medicine, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
|Date of Submission||03-Sep-2020|
|Date of Acceptance||25-Mar-2021|
|Date of Web Publication||29-Jun-2021|
Dr. Priyanka Parmar
571 E New York Ave, Apt 3C, Brooklyn NY 11225
Source of Support: None, Conflict of Interest: None
Introduction: A study examining the impact of social determinants of health on COVID-19 outcomes is necessary to identify which aspects of a vulnerable population make it more susceptible. We describe the epidemiological and clinical characteristics of a patient population disproportionately impacted by COVID-19 and situate the findings within the broader context of health determinants.
Materials and Methods: A descriptive study of 527 laboratory-confirmed COVID-19 patients hospitalized from March 12 to April 13, 2020, evaluated patient demographics, comorbidities, presentation, and outcomes. The study took place at an academic medical center serving a low-income, Black community (predominantly Caribbean-born) in Brooklyn, New York.
Results: Compared to previous studies, we report a higher median age of 70 (interquartile range 59–80), a higher percentage of Blacks (91%), a higher prevalence of comorbidities (hypertension [83%], diabetes [53%], and obesity [42%]), a higher prevalence of abnormal findings on presentation (altered mental status [30%], Quick sequential organ failure assessment score ≥2 [27%], elevated random-glucose [77%], elevated creatinine [57%]), and a higher incidence of negative in-hospital outcomes (intensive care unit admission [21%], mechanical ventilation [21%], acute kidney injury [31%], acute respiratory distress syndrome [17%] and acute cardiac injury [18%], and age-adjusted fatality rate [40%.]).
Conclusions: This study shows the characteristics of a patient population disproportionately impacted by COVID-19. The intersectionality of the Black race, older age, a high prevalence of comorbidities, and residency in a locale severely affected by COVID-19, deserves further consideration to better address health outcomes in vulnerable patient groups.
The following core competencies are addressed in this article: Practice-based learning and improvement, Patient care and Procedural skills, Systems-based practice.
Keywords: Case series, COVID-19 health outcomes, COVID-19 in African Americans, COVID-19 in New York City, COVID-19, health disparities and COVID-19
|How to cite this article:|
Parmar P, James A, Rosengarten S, Oommen A, Joseph MA, Wilson C, Maini R, Mecklenburg M, Kim J, Edwards JA, Nakeshbandi M, Breitman I, Arquilla B, Daniel P. COVID-19 clinical course and outcomes in a predominantly black, vulnerable patient population in New York City. Int J Acad Med 2021;7:81-8
|How to cite this URL:|
Parmar P, James A, Rosengarten S, Oommen A, Joseph MA, Wilson C, Maini R, Mecklenburg M, Kim J, Edwards JA, Nakeshbandi M, Breitman I, Arquilla B, Daniel P. COVID-19 clinical course and outcomes in a predominantly black, vulnerable patient population in New York City. Int J Acad Med [serial online] 2021 [cited 2021 Oct 26];7:81-8. Available from: https://www.ijam-web.org/text.asp?2021/7/2/81/319796
| Introduction|| |
Despite comprising 22% of the population in NY, Blacks lead in the number of confirmed COVID-19 cases, hospitalizations, and deaths despite Black race not being an independent risk factor for COVID-19 mortality.,,,, Variations in social factors also influence the health of patient populations and contribute to health outcomes such as morbidity and mortality.
This study presents the clinical characteristics and outcomes of a primarily Black, elderly, hospitalized COVID-19 patient population in the county with the third-highest number of COVID-19 deaths. We propose that the intersectionality between variations in social determinants of health and Black race worsen the hospitalized COVID-19 outcomes seen in this patient population.
| Materials and Methods|| |
The SUNY Downstate Health Sciences University Institutional Review Board approved this study as minimal-risk research, which waived the requirement for informed consent.
A retrospective, single-center, consecutive, case series of all hospitalized laboratory-confirmed COVID-19 positive patients at an urban academic medical center in Brooklyn, NY.
University Hospital of Brooklyn (UHB) at SUNY Downstate Health Sciences University is a 239-bed public hospital. It is the only academic hospital serving the approximately 2.6 million residents of Brooklyn, New York. Data were collected from March 12, 2020, to April 13, 2020, and all patients were followed up till either discharge from the hospital or death during hospital admission. Data were obtained from a retrospective review of the electronic medical record system.
Potentially eligible participants were those who were tested positive for COVID-19 infection and were admitted from March 12 to April 13, 2020. A positive COVID-19 test was defined by severe acute respiratory syndrome coronavirus-2 on reverse-transcriptase-polymerase-chain reaction with sample collected using nasopharyngeal swab. These patients were examined for eligibility, and a total of 684 patients were tested for the COVID test, 110 patients resulted negative and were excluded from this study. Of the 574 eligible patients, 47 patients had a high level of missing data/variables. A total of 527 patients met the inclusion criteria for the final analysis [Figure 1]. All patients were included in the study.
Patient demographics (age, gender, race, and zip code), smoking status, and baseline comorbidities were collected by self-reported data in prespecified fixed categories. These variables were included to characterize admitted patients. The primary outcome measure was mortality. Secondary outcomes include triage vitals, presenting symptoms, admission laboratory results, admission radiologic imaging, treatments (inpatient medications, ventilation, and intubation), and hospital outcomes (total hospital length of stay, total intensive care unit length of stay, total days intubated, onset of acute kidney injury, acute respiratory distress syndrome, secondary bacterial pneumonia, acute cardiac injury, and case-fatality).
Initial laboratory testing was defined as the first test results available, typically within 24 h of admission. For any values that were not completed, a total percentage of patients with completed tests was shown. Secondary bacterial pulmonary infection was defined with the following criteria: positive tracheal aspirate/sputum culture or procalcitonin >0.25 ng/mL and consolidation on chest X-ray. Acute kidney injury was defined according to the KDIGO guidelines: increase in creatinine by 0.3 mg/dL within 48 h, or an increase in creatinine of 1.5 mg/dL from baseline within 7 days, or a urine output volume of <0.4 mL/kg/h for 6 h. Acute respiratory distress syndrome was diagnosed based on the Berlin guidelines: PaO2/FiO2 <300 and imaging findings indicating bilateral opacities consistent with pulmonary edema that is not explained by congestive heart failure or fluid overload. Acute cardiac injury was defined as troponin greater than the 99th percentile of reference range in <30 days of COVID-19 diagnosis. The Quick sequential organ failure assessment (qSOFA) score was based on three parameters, each worth one point: respiratory rate ≥22/min, change in mental status (GCS <15), and systolic blood pressure ≤100 mmhg.
The data were inputted by clinicians as part of routine medical care; there were no interventions in patient care by the researchers for this study. All imputed data from the EMR system was verified by two people from the (institution name redacted) COVID research collection team that was taught a standardized approach for data collection to reduce potential sources of bias.
De-identified patient data were collected and analyzed using descriptive statistical methods. Results are expressed as means, or medians and inter-quartile ranges. Categorical variables are summarized as counts, and proportions are summarized as percentages. No imputation was made for missing data. Analysis was performed with Statistical Analysis System Studio 3.8 software (SAS Institute Inc., Cary, North Carolina, USA).
| Results|| |
Participants and demographics
A total of 527 patients were eligible and included in the final study [Figure 1]. Each participant was followed up till either their hospital discharge or due to their death during hospital admission. The median age was 70 with a range of 3–99 and interquartile range (IQR) of 59–80. Males comprised 52% and Black patients 91% [Table 1]. Most patients were from zip codes 11226 (29%) and 11203 (22%) [Figure 2].
|Figure 2: University hospital at Brooklyn is located in 11203. zip codes 10027, 10301, and 10302 are not shown in this figure. Both are located outside the borough of Brooklyn; each reported one patient per zip code|
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Clinical characteristics on admission
The most prevalent comorbidities were coronary vascular disease (86%; 455/527), hypertension (83%; 435/527), diabetes (53%; 278/527), and obesity (42%; 214/515). There was also a high prevalence of patients with ≥3 coexisting comorbidities (55%; 291/527) [Table 2]. The findings of hypertension, diabetes, and obesity were found to be elevated compared to other major studies thus far [Table 3].
|Table 2: Clinical characteristics of hospitalized patients with COVID-19 (original)|
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|Table 3: Comparison of demographics and outcomes between major studies (original)|
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The most common presenting symptoms were respiratory (79%; 414/527), gastrointestinal (34%; 181/527), and neurological (33%; 176/527). Prior to admission, the median days of symptom onset was 4 days (range of 0–30 days, and IQR of 2–7 days) [Table 4].
|Table 4: Presenting symptoms of hospitalized patients with COVID-19 (original)|
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Vital signs at admission were fever (29%; 141/486), hypoxemia with an SpO2 <90% (22%; 98/444), tachypnea with a respiratory rate >24 (21%; 103/485), hypotension with a systolic blood pressure ≤90 (9%; 46/497), and qSOFA ≥2 (27%; 133/486) [Table 5].
|Table 5: Admission vitals, imaging, and laboratories of hospitalized patients with COVID-19 (original)|
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Laboratory findings and radiologic imaging
The most common abnormalities on laboratory tests were elevated C-reactive protein (98%; 441/450), ferritin (87%; 363/418), random glucose (77%; 408/527), procalcitonin (68%; 278/410), D-dimer (62%; 91/146), creatinine (57%; 302/526), blood urea nitrogen (52%; 275/527), and absolute lymphocyte count (49%; 237/482). Infiltrates were present on initial chest radiography in 78% (414/527) [Table 5].
In-hospital treatments and outcomes
Supplemental oxygen was given in the form of high-flow oxygen via nasal cannula (20%; 104/527), continuous positive pressure ventilation at any point during their admission (22%; 118/527), or mechanical ventilation (21%; 110/527) [Table 5]. The median length of hospital admission was 5 days. There were 109 (21%) patients admitted to the intensive care unit and the median intensive care unit length of stay was 3 days. The most common adverse outcomes were acute kidney injury, seen in 161 (31%), acute cardiac injury (18%; 97/527), and acute respiratory distress syndrome (17%; 91/527) [Table 6]. The total number of expirations was 225 (age-adjusted fatality rate of 40%). In the expired patients, the prevalence of DNR/DNI orders was 46% (103/225). The median age of death in the group without a DNR/DNI was 74 (IQR: 65–82) [Table 6].
|Table 6: Treatment and outcomes of all hospitalized patients with COVID-19 (expired and discharged) (original)|
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| Discussion|| |
This study provides vital data concerning key epidemiological features and outcomes of a vulnerable population highly impacted by COVID-19 in Brooklyn, NY. Almost all (98%) of the study population resides in Kings County (Brooklyn), the county with the third-highest number of COVID-19 deaths nationwide. We report hospitalized COVID-19 patients in a predominately Black patient population (91%) with an older median age, elevated rates of comorbidities, greater prevalence of severe outcomes, and an increased case-fatality rate compared to previous COVID-19 studies in surrounding zip codes, but similar to those in overrepresented Black studies [Table 3].
The disproportionate burden of disease seen in predominantly Black counties might be explained by racial-specific contextual factors in our patient population. In Suleyman et al.'s Michigan study with a 72.1% African American patient profile, they explained that factors such as a 27% poverty rate among African Americans (compared to 11% for whites) in that state result in a lack of health insurance and access to care which may contribute to a disproportionately greater burden of disease. A similar trend can be seen in New York City: 22% of Black New Yorkers live in poverty, while this percentage decreases to 12% for Whites. Beyond access to care, this data is suggestive of other elements such as overcrowding, poor access to healthy foods, and the dependence on public transportation that can factor into poor COVID-19 health outcomes.
In addition, in this study, the most common comorbidities were cardiovascular disease (86%), hypertension (83%), diabetes (53%), and obesity (42%). In comparison to previous COVID-19 NY studies from patients in Long Island, Manhattan, and Staten Island, the rates of hypertension, diabetes, and obesity were much lower: Goyal et al.'s study, for example, reported rates of 50%, 25%, 36%, respectively. However, the higher rates of disease burden were comparable to some of the other studies with a higher Black population [Table 6].,,, The higher rates of comorbidities in this study need to be examined in the context of health care access, avoidable hospitalizations, and cultural norms. Brooklyn has a higher rate of uninsured adults, and avoidable hospitalizations than Manhattan (another locale in NYC), for example, and Manhattan saw significantly fewer COVID-19 cases and deaths., While this may not confirm a causal linkage, these statistics warrant further consideration. The population served by UHB is of a predominantly Afro-Caribbean origin, and primarily resides in the East Flatbush and Flatbush sections of Brooklyn. In cultural groups such as these that generally view added bodyweight as a desired feature, the comorbidity of obesity is considered “normal.” Kalligeros et al. suggest that an elevated BMI is associated with an increased rate of intubation, and we report more frequent intubations compared to Richardson et al.'s findings in another patient population in NYC., Thus, the UHB patients' underlying health conditions appear to have worsened their COVID-19 health outcomes.
Worse outcomes are also seen in our patient presentations, as there were higher rates of fever, hypotension, hypoxemia, and dyspnea in this study compared to other NY studies by Richardson et al., and Goyal et al., In addition, we are among the few studies in the U.S. that characterize altered mental status as a major (30%) presenting symptom on admission. In addition, this study notes a qSOFA score ≥2 in 27% of the patients. These findings may be suggestive of the chronic baseline comorbidities as described above. The diabetes in this community is often characterized as ketosis-prone diabetes, also termed “Flatbush diabetes”, where Lebovitz and Banerji explained that diabetes Type 2 could present with diabetic ketoacidosis. This disease phenomenon has been clinically seen in this patient population of African American and Afro-Caribbean individuals, but also worldwide in Sub-Saharan African, Asian and Indian, and Hispanic populations. This could help to explain the increased prevalence of altered mental status and consequently, a high qSOFA score on admission.
Our study population is older than previous studies, with a median age of 70 years and 35% of the patients were over 74 years. This study reports a 40% age-adjusted fatality rate. To better account for UHB's predominantly older patient population, it is important that we adjusted for age. Most (49%) of the COVID-19 deaths in NYC are in the 75 and over age group. This study's expired group revealed that 48% are in the 75 and over age group, further confirming that “older age” is a risk factor. In addition, of those that expired, 46% had DNR/DNI orders. Thus, the study's fatality rate needs to be examined in the context of the significant percentage of patients opting not to be resuscitated or intubated – a firmly-held preference of older individuals in certain cultural groups.
This study is subject to several limitations. The data collected for this study were extracted from electronic medical records through manual chart review which may be prone to retrieval errors. Eight percent of patients were still hospitalized and were not included in this study after censoring the data on April 13, therefore the results and outcomes for those patients are excluded. Our study population is not generalizable to the U.S. population because it is predominantly African American. Yet, this allows it to be generalizable to similar communities. Further studies are needed to establish the association between COVID-19 health outcomes and patient-specific demographic and contextual factors. Despite these limitations, this study provides vital data concerning key epidemiological features, clinical course, and outcomes of a vulnerable patient population to COVID-19. Underscoring these characteristics and outcomes are absolutely necessary to guide social policies that directly affect healthcare and allow improved outcomes in similar communities.
| Conclusions|| |
In summary, this majority Black, older Afro-Caribbean immigrant community had poor COVID-19 health outcomes, and very extreme clinical characteristics, that are suggestive of existing factors such as limited access to social determinants of health, and the impact of health disparities. Our findings are indicative that the intersectionality of the Black race, older age, comorbidities, and county of residence – King's County (individual-level, high prevalence COVID-19 associative factors in this patient population), deserves close analysis and further consideration if we are to understand and adequately address the observed poor health outcomes. In preparation for future COVID-19 and similar diseases, resources should be allocated to areas where they are most needed while population-driven interventions should be designed to benefit vulnerable communities.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
Research quality and ethics statement
The authors of this manuscript declare that this scientific work complies with reporting quality, formatting, and reproducibility guidelines set forth by the EQUATOR Network (http://www.equator-network.org). The authors also attest The SUNY Downstate Health Sciences University Institutional Review Board approved this study as minimal-risk research (protocol/approval number 115,21), which waived the requirement for informed consent.
| References|| |
COVID-19: Data. New York City Department of Health and Mental Hygiene Website. Available from:
. [Last accessed on 2020 Jun 10].
Gold JA, Wong KK, Szablewski CM, Patel PR, Rossow J, DaSilva J, et al
. Characteristics and clinical outcomes of adult patients hospitalized with COVID-19 – Georgia, March 2020 | MMWR. Morb Mortal Wkly Rep 2020;69:545-550 [doi: 10.15585/mmwr.mm6918e1external icon].
Price-Haywood EG, Burton J, Fort D, Seoane L. Hospitalization and mortality among black patients and white patients with COVID-19. N Engl J Med 2020;382:2534-43.
Suleyman G, Fadel RA, Malette KM, Hammond C, Abdulla H, Entz A, et al
. Clinical characteristics and morbidity associated with coronavirus disease 2019 in a series of patients in metropolitan Detroit. JAMA Netw Open 2020;3:e2012270.
Marmot M, Friel S, Bell R, Houweling TA, Taylor S. Closing the gap in a generation: Health equity through action on the social determinants of health. Lancet 2008;372:1661-9.
KDIGO. KDIGO 2012 clinical practice guideline for acute Kidney injury. Kidney Int Suppl 2012;2:1-138 [doi: 10.1038/kisup. 2012.1].
Fan E, Brodie D, Slutsky AS. Acute respiratory distress syndrome advances in diagnosis and treatment. J Am Med Assoc 2018;319:698-710.
Singer M, Deutschman CS, Seymour C, Shankar-Hari M, Annane D, Bauer M, et al
. The third international consensus definitions for sepsis and septic shock (sepsis-3). J Am Med Assoc 2016;315:801-10.
Snowden LR, Graaf G. COVID-19, Social Determinants Past, Present, and Future, and African Americans' Health. J Racial Ethn Health Disparities. 2021;8:12-20. doi:10.1007/s40615-020-00923-3.
Goyal P, Choi JJ, Pinheiro LC, Schenck EJ, Chen R, Jabri A, et al
. Clinical characteristics of Covid-19 in New York City. N Engl J Med 2020;382:2372-4.
Shah P, Owens J, Franklin J, Mehta A, Heymann W, Sewell W, et al
. Demographics, comorbidities and outcomes in hospitalized Covid-19 patients in rural southwest Georgia. Ann Med 2020;52:354-60.
Muñoz-Price LS, Nattinger AB, Rivera F, Hanson R, Gmehlin CG, Perez A, et al
. Racial disparities in incidence and outcomes among patients with COVID-19. JAMA Netw Open 2020;3:e2021892.
Hinterland K, Naidoo M, King L, Lewin V, Myerson G, Noumbissi B, et al
. Community Health Profiles 2018, Manhattan Community District 1: Financial District; 2018;1:1-20.
Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW, et al
. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City Area. JAMA 2020;323:2052-9.
Kalligeros M, Shehadeh F, Mylona EK, Benitez G, Beckwith C, Chan P, et al.
Association of obesity with disease severity among patients with COVID-19. Obesity 2020;28 (7):1200-1204. [doi: 10.1002/oby. 22859].
Lebovitz HE, Banerji MA. Ketosis-prone diabetes (Flatbush Diabetes): An emerging worldwide clinically important entity. Curr Diab Rep 2018;18:120.
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]