COVID-19: Highlighting Health Disparities in the Los Angeles Latinx Community

Abstract

Objective: Characterization of COVID-19 in the Latinx community is necessary for guiding public health initiatives, health system policy, clinical management practices, and improving outcomes. Our aim was to describe the socioeconomic background and clinical profile of patients with COVID-19 at a large public hospital in Los Angeles to improve health disparities leading to poor outcomes during the pandemic.

Design, Setting and Participants: A single center retrospective cross-sectional study of all patients with a positive polymerase chain reaction (PCR) test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) who presented to Los Angeles County (LAC)+University of Southern California (USC) Medical Center between March 15, 2020 and April 30, 2020.

Methods: We describe patient characteristics, socioeconomic factors, laboratory findings, and outcomes of the first 278 patients to present to LAC+USC Medical Center with COVID-19.

Results: Patients self-identified as Hispanic (82.4%) or non-Hispanic (17.6%). Hispanic patients presented later from symptom onset (6 days vs 3 days, P = 0.027) and had higher post-intubation mortality (40.9% vs. 33.3%, P = 1), intensive care unit (ICU) mortality (31.1% vs. 22.2%, P = 0.87), and overall mortality (11.1% vs 10.2%, P = 1). However, the difference in admission rates, mechanical ventilation rates, and overall mortality rates were not statistically significant. A majority of patients, 275/278 (98.9%), reported residency ZIP codes in areas of higher population density, higher percentage of Latinx, born outside the United States, lower median income, and lower high school graduation rate when compared to the rest of Los Angeles County. Regression analysis within the Hispanic cohort found that age, history of hypertension, history of diabetes, lactate dehydrogenase (LDH), and C-reactive protein (CRP) were predictors of mechanical ventilation and mortality.

Conclusion: We show the Latinx community has been disproportionally affected by the pandemic in Los Angeles and we identified multiple socioeconomic and clinical characteristics that predispose this population to COVID-19 infection. This study highlights the need for change in local and national strategies to protect vulnerable communities during public health outbreaks.

In December 2019, the first cluster of patients with SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infections were identified in Wuhan, China.¹ The syndrome, known as coronavirus disease 2019 (COVID-19), has led to a worldwide pandemic with over 120 million cases and over 2.6 million deaths to date.² No disease in recent history has had such a tremendous effect on society. As the pandemic advanced in the United States, surveillance epidemiological data made it clear that Black and Latinx populations were disproportionately affected by COVID-19.^3-8 Historically, communities of color and those from lower socioeconomic backgrounds have been disparately affected during global and regional health outbreaks. During the 1918 Spanish flu, low-income countries had higher mortality than high-income countries.⁹ The 2009 H1N1 influenza pandemic again highlighted the need for national strategies for addressing health outbreaks within vulnerable communities.^10-12 In Los Angeles, the second most populous city in the United States, health systems struggled to implement effective H1N1 vaccination strategies in communities of color. This resulted in higher morbidity and mortality for these communities.¹³ Evidence clearly shows communities of color are disproportionately affected by COVID-19. However, there is lack of information regarding social determinants of health and clinical characteristics of this patient population that may contribute to this health disparity.

In this study, we aimed to describe the socioeconomic background and clinical profile of the first 278 COVID-19 patients to present to Los Angeles County (LAC) + University of Southern California (USC) Medical Center. LAC+USC Medical Center is one of the largest public hospitals in the United States and the largest single provider of healthcare in Los Angeles (LA) County. It is a 676-bed, level 1 trauma, tertiary referral safety-net hospital that serves a predominantly Latinx population in LA County.

Methods

This retrospective cross-sectional study was approved with exempt status by the USC institutional review board (IRB). Informed consent was waived, and no study participant was contacted for the purposes of this study.

Data were obtained from LAC+USC Medical Center’s electronic medical record. Study data were collected and managed using Research Electronic Data Capture software (REDCap) hosted at USC.^14,15 The data dictionary was developed independently by the research team to collect demographics, medical history, presenting symptomatology, vital signs, laboratory data, complications, and discharge information for each study participant. A patient’s discharge date was defined as the day the patient left the hospital or the day the medical team documented a status of “medically stable for discharge,” such as those awaiting placement to acute rehabilitation or skilled nursing facilities and had no active inpatient medical needs.

All patients from March 15, 2020 until April 30, 2020 with nasopharyngeal or bronchoalveolar lavage samples with confirmed SARS-CoV-2 polymerase chain reaction (PCR) assays were included in this study. Patients who had a negative test for SARS-CoV-2 PCR were excluded from this study. The study group included all confirmed cases requiring inpatient hospitalization and those discharged directly from the emergency department.

Comparisons between our study population and historical data for LAC+USC Medical Center were made using data for all inpatient admissions from July 1, 2019 to December 31, 2019, released by the Office of Statewide Health Planning and Development.¹⁶ To create a reference for demographics of LA County as a whole, publicly accessible U.S. Census data (www.census.gov/quickfacts, accessed 7/28/2020) were obtained for ZIP codes associated with each inpatient admission. Cohort-specific ZIP code data were compared with ZIP code data for LA County as a whole.

Statistical analyses were performed using R version 4.0.2 statistical software and RStudio v1.1.456. We focused on two patient outcomes in keeping with previously published guidelines on standardized outcome measures relevant to COVID-19 infection: (1) severe illness, defined as transfer to the intensive care unit (ICU) for either non-invasive positive pressure ventilation (NIPPV) or mechanical ventilation, which would correspond to an illness severity score of 6 or above on the World Health Organization (WHO) Clinical Progression Scale,¹⁷ and (2) mortality at discharge (ie, if the patient was deceased on the date of discharge from the hospital). Univariate logistic regression models were built using variables of interest as predictors for these two outcomes. Odds ratios (OR) and 95% confidence intervals (CI) were estimated from these univariate models. To adjust for covariance of different variables, multivariate models were built for each outcome using only predictor variables that reached statistical significance in univariate models (P < 0.05). Revised ORs and 95% CIs were estimated for all predictor variables in multivariate models. Normality, heteroscedasticity, and multicollinearity were assessed by visually inspecting the distribution and partial residual plots for each variable. Outliers were excluded after identifying influential observations using a combination of standardized residuals, leverage, and Cook’s distance.

Results

Demographics and Clinical Characteristics

In this study of 278 patients, 229 (82.4%) self-identified as Hispanic, and 49 (17.6%) self-identified as non-Hispanic. Patients who identified as Hispanic were younger, presented later from symptoms onset, and had higher median body mass index (BMI) compared to non-Hispanic patients (Table 1). A higher percentage of Hispanics reported no known past medical history (33.6% vs. 24.5%, P = 0.2823). However, in Hispanic patients that did have pre-existing conditions, diabetes was more prevalent (39.9% vs. 32.7%, P = 0.4792), while hypertension was less prevalent in comparison to non-Hispanic patients (28.8% vs. 34.7%, P = 0.52). These differences were not significant at the P < 0.005 threshold. Among inflammatory markers, C-reactive protein (CRP) and white blood cell count (WBC) on admission were significantly more elevated in Hispanic patients than in non-Hispanic patients, while lactate dehydrogenase (LDH) was not (Table 1). The admission rate, re-hospitalization rate, transfer to ICU rate, need for mechanical ventilation, ICU mortality, and overall mortality did not significantly differ between Hispanic vs non-Hispanic patients (Table 2). Additionally, while not statistically significant, post-hospitalization, post-ICU transfer, and post-intubation mortality were all higher in Hispanic patients. The overall mortality appeared to increase with age, starting from the 4^th until the 9^th decade of life (2.99%, 11.76%, 15.22%, 31.58%, 62.50%, and 100% mortality, respectively).

Table 1:

Baseline clinical characteristics of 278 COVID-19 patients at LAC+USC Medical Center

Characteristic	Hispanic (n=229)	Non-Hispanic (n=49)	95% CI	P value
Age in years – Mean (Range)	51.0 (17-91)	53.5 (12-97)	(-8.4, +3.5)	0.41
Sex, n (%)	Male: 150 (65%)	Male: 38 (78%)	(-26%, +3.1%)	0.16
	Female: 79 (34%)	Female: 11 (22%)	(-3.1%, +26%)	0.16
Body mass index	29.2 (25.7-33.3)	27.51 (21.8 -31.3)	(+0.38, +4.9)	0.026*
Days of symptoms	6 (3-7)	3 (1-7)	(+0, +3.0)	0.027*
Coexisting condition, n (%)
Diabetes	90 (39.3%)	16 (32.7%)	(-9.2%, + 22%)	0.48
Hypertension	66 (28.8%)	17 (34.7%)	(-9.9%, +21%)	0.52
Chronic kidney disease	11 (4.8%)	4 (8.2%)	(-6.0%, +13%)	0.55
Congestive heart failure	11 (4.8%)	3 (6.1%)	(-7.2%, +9.8%)	0.98
Asthma	10 (4.4%)	3 (6.1%)	(-6.7%, +10%)	0.88
Malignancy	10 (4.4%)	2 (4.1%)	(-6.7%, +6.1%)	1
No past medical history	77 (33.6%)	10 (24.5%)	(-5.6%, +24%)	0.28
Vital signs on admission
Temperature (°C)	38 (37-39)	37 (37-38)	(+0, +0.6)	0.024*
Heart rate (beats/min)	101 (91-112)	92 (85-106)	(-1.0, +11)	0.094
Systolic blood pressure (mmHg)	132 (122-142)	128 (120-137)	(-3.0, +8.0)	0.30
Diastolic blood pressure (mmHg)	83 (78-92)	78 (69-85)	(-2.0, +7.0)	0.22
Respiratory rate (breaths/min)	24 (20-30)	20 (16-25)	(+2.0, +6.0)	0.00028*
Oxygen saturation on room air (%)	94 (89-97)	96 (90-99)	(-3.0, -0)	0.035*
Laboratory data on admission (reference range)
White blood cell count (4.5–10 K/cu mm)	9.1 (6.5-12.1)	6.4 (4.8-8.4)	(+0.20, +2.2)	0.020*
Absolute lymphocyte count (1.2–3.3 K/cu mm)	0.9 (0.7-1.2)	0.9 (0.6-1.3)	(-0.10, +0.20)	0.40
Hemoglobin (12–14.6 g/dL)	14.8 (13.5-15.5)	13.6 (12.4-14.6)	(-0.10, +1.1)	0.091
Platelet count (160–360 K/cu mm	229.5 (176.0-332.3)	190.5 (157.3-317.0)	(-25, +38)	0.63
Sodium (135–145 mmol/L	136.0 (133.0-138.0)	137.5 (135.0-140.0)	(-2.0, +0)	0.11
Creatinine (0.5–1 mg/dL)	0.8 (0.7-1.1)	1.1 (0.8-1.5)	(-0.33, -0.060)	0.0054*
D-dimer (≤0.49 mcg/mL)	0.9 (0.6-1.4)	0.8 (0.5-1.7)	(-0.29, +0.34)	0.92
Ferritin (10-330 ng/mL)	786.0 (460.8-1274.5)	668.0 (220.3-834.3)	(-59, +475)	0.13
C-reactive protein (≤4.9 (mg/L)	129.6 (85.7-189.4)	66.1 (24.1-111.9)	(+25, +89)	0.0012*
Lactate dehydrogenase (135–225 U/L)	351.5 (294.8-485.8)	350.0 (219-417)	(-46, +101)	0.44
Procalcitonin (≤0.25 ng/mL)	0.2 (0.1-0.4)	0.2 (0.1-0.5)	(-0.09, +0.06)	0.86
Hemoglobin A1 (<5.6 %)	7.3 (6.0-10.2)	6.5 (5.7-9.7)	(-0.70, +2.1)	0.39

Values given in each column correspond to means with interquartile ranges (IQR) given in parenthesis.

Confidence intervals refer to true difference between means.

Two-sample test of proportions used to analyze percentages.

Two-sample t test used to analyze age, given the normal distribution.

Wilcoxon rank-sum test used to analyze all other non-normally distributed variables.

^*P < 0.05

Table 2:

Hospitalization outcomes for all COVID-19 patients from March 15 to April 30, 2020

Outcome	Hispanic	Non-Hispanic	95% CI	P value
Admission rate	81.6%	81.6%	(-12%, +12%)	1
Re-admission rate	8.04%	12.5%	(-16%, + 6.8%)	0.49
Transfer to intensive care unit	37.6%	22.5%	(-1.0%, +31%)	0.10
Mechanical ventilation	19.3%	12.5%	(-5.1%, +19%)	0.36
Post-intubation mortality	40.9%	33.3%	(-40%, +55%)	1
Intensive care unit mortality	31.1%	22.2%	(-26%, + 44%)	0.87
Overall mortality	11.4%	10.2%	(-9.4%, +11%)	1

Two-sample test of proportions used to analyze percentages. Confidence intervals refer to true difference between proportions in Hispanic patients compared to non-Hispanic patients.

ZIP Code and Insurance Data

A majority of patients reported ZIP codes in LA County. Only three patients reported ZIP codes outside LA County (Brentwood and Gilroy, CA, as well as Blue Gap, AZ) and were excluded from further analyses. U.S. Census data (www.census.gov/quickfacts, accessed 7/28/2020) were compiled for each of these ZIP codes. Average population density and percentage of those that were Latinx, born outside the United States, uninsured, and living in poverty were all significantly higher in the ZIP codes of our study cohort when compared to the entirety of LA County (Table 3). Median household income and percentage of people that completed high school and college were all significantly lower in the ZIP codes of our study cohort. When compared to all inpatient admissions from July to December 2019, our study cohort had a higher proportion of male patients (67.5% vs 57.1%, 95% CI for true difference = 4.4-15.9%, P = 8.7x10^-4 by two-proportion Z test), Hispanic patients (82.4% vs 68.1%, CI = 9.5-19%, P = 5.6x10^-7), uninsured patients (12.2% vs 3.0%, CI = 4.6-12.5%, P = 1.9 x 10^-15), and lower proportion of patients on a state-sponsored Medi-Cal insurance plan (53.2% vs 72.8%, CI = 13.5-25.7%, P = 6.7 x 10^-13). The probability of seeing similar or higher proportion of self-identified Hispanic patients in a random sample of the same size from all admissions from July to December 2019 is P = 8 x 10^-6, based on Fisher’s exact test, which is significant at the P < 0.05 threshold, suggesting enrichment of this subset of patients in our cohort. Male patients and uninsured patients were similarly enriched (respectively, P = 3.1 x 10^-4, P = 1.4 x 10^-4), while Medi-Cal patients were under-enriched in the 2020 COVID-19 cohort (P = 9.0 x 10^-13).

Table 3:

Census demographics for our patient cohort based on reported address compared to LA County

Demographic	Patient cohort (95% CI)	LA County	P value
Population density / sq mile	9,475 (8,985-9,963)	2,419	< 0.001
Latinx	57.92% (55.38-60.47%)	48.60%	< 0.001
Born outside the United States	36.73% (36.13-37.33%)	34.20%	< 0.001
Persons living in same household	3.12 (3.05-3.19)	3.00	< 0.005
Completed high school	72.18% (70.73-73.62%)	78.70%	< 0.001
Completed college	27.55% (25.93-29.16%)	31.80%	< 0.001
Uninsured	14.36% (14.01-14.71%)	10.20%	< 0.001
Median household income	$58,385 (IQR $53,596 – 58,385)	$64,251	< 0.001
Living in poverty	18.89% (18.39-19.39%)	14.20%	< 0.001

Two-sample test of proportions used to analyze percentages.

Two-sample t test used to analyze population density and persons living in same household, given the normal distribution.

Wilcoxon rank-sum test used to analyze median income which was a non-normally distributed variable.

Regression Analysis and Outcomes

We focused our regression analysis on 16 predictor variables of interest within the Latinx cohort including age, gender, history of hypertension, history of diabetes mellitus, BMI, and laboratory data (WBC, D-dimer, ferritin, CRP, LDH) on admission. Table 4 illustrates unadjusted and adjusted OR, CI, and P values for each predictor variable in both univariate and multivariate analyses. For continuous predictor variables, the OR can be interpreted as the increased odds of the outcome associated with a unit increase in the predictor variable. Among inflammatory markers, CRP and LDH on admission were significant predictors of both severe illness (defined earlier as illness severity score of 6 or above on the WHO Clinical Progression Scale) and mortality at discharge. WBC on admission was a significant predictor of severe illness. Ferritin and d-dimer were not significant predictors of any outcome even after log-scale normalization. Age, history of hypertension, and history of diabetes were significant predictors of both severe illness and mortality at discharge (Table 4). In our multivariate models, age was the strongest predictor of both severe illness and mortality at discharge, associated with approximately 16% increase in odds of mortality at discharge per each additional year of age. LDH on admission was also a significant predictor of both severe illness and mortality, while CRP was not. A history of diabetes was a predictor of severe illness (Table 4).

Table 4:

Unadjusted and adjusted Odds Ratios (ORs) from regression models for the Latinx cohort

Univariate regression	OR (95% CI)	P value
Severe illness
Age	1.025 (1.0060-1.045)	0.011*
Male sex	1.94 (1.063-3.63)	0.034*
Body mass index	1.0035 (0.97-1.040)	0.84
White blood cell count	1.13 (1.050-1.22)	0.0012*
D-dimer	1.10 (0.97-1.30)	0.18
Ferritin	1.00032 (1.00-1.0010)	0.12
C-reactive protein	1.0065 (1.0029-1.010)	0.00060*
Lactate dehydrogenase	1.0069 (1.0040-1.010)	8.87879×10-6*
Hypertension	2.32 (1.28-4.21)	0.0053*
Diabetes mellitus	1.94 (1.11-3.41)	0.020*
Mortality
Age	1.11 (1.073-1.16)	8.9×10-8*
Male sex	0.99 (0.43-2.44)	0.99
Body mass index	0.98 (0.92-1.04)	0.98
White blood cell count	1.075 (0.97-1.18)	0.16
D-Dimer	1.08 (0.95-1.22)	0.18
Ferritin	1.00027 (1.00-1.00068)	0.10
C-reactive protein	1.0084 (1.0035-1.014)	0.0011*
Lactate dehydrogenase	1.0046 (1.0014-1.0080)	0.0053*
Hypertension	3.39 (1.47-7.91)	0.0042*
Diabetes mellitus	3.36 (1.46-8.26)	0.0055*

Multivariate regression	OR (95% CI)	P value
Severe illness
Age	1.053 (1.00 -1.078)	0.071*
Male sex	2.21 (0.69-7.79)	0.19
White blood cell count	0.98 (0.85-1.12)	0.73
C-reactive protein	1.0013 (1.00-1.0076)	0.67
Lactate dehydrogenase	1.012 (1.0069-1.019)	3.9×10-5*
Hypertension	1.073 (0.31-3.56)	0.91
Diabetes mellitus	3.51 (1.16-11.52)	0.039*
Mortality
Age	1.16 (1.082-1.27)	0.00027*
C-reactive protein	1.0056 (1.00-1.014)	0.14
Lactate dehydrogenase	1.0080 (1.0019-1.015)	0.017*
Hypertension	0.75 (0.14-3.58)	0.73
Diabetes mellitus	1.95 (0.41-10.83)	0.40

^*P<0.05

Discussion

This study examined the clinical and socioeconomic features of the first 278 patients with COVID-19 at a large tertiary public safety-net hospital in LA County. We found that Latinx patients were significantly overrepresented at 82.4%, with Latinx males representing 54% of the entire cohort. This observation is out of proportion for the usual demographics of the medical center, which shows that Latinx patients typically represent 68.1% of hospital visits.¹⁶ There may be several reasons for this finding.

First, the role of Latinx patients in the workforce likely plays a large factor in this observation. Latinx males are employed at a rate of approximately 80%, compared to 60% of Latinx females.¹⁸ Labor force participation of Latinx males between the ages of 24-54 years is 90.8%, higher than that of other racial/ethnic groups in this age range.¹⁹ The mean age of our Latinx patient cohort was 51 years, which represents the peak employed age group. Additionally, the Latinx population in Los Angeles are more likely to have occupations with increased contact with others and represent a large proportion of occupations deemed essential during the pandemic; 68% of the agricultural labor force, 65% of the construction workers, and 40% of the food service workers.²⁰ These occupations result in increased potential exposure to the virus.

Secondly, occupation is closely linked to health care access in the United States. It is well known that the Latinx community faces a variety of barriers to obtaining routine care. Latinx patients who are born outside of the United States and have limited English proficiency are less likely to have health insurance and less likely to see a healthcare provider for routine care, when compared to other ethnic groups.²¹ The lack of access to preventative care results in a reliance on hospital-based services, which is disproportionately provided by safety-net centers.²² Furthermore, the occupations that employ Latinx populations often lack employer sponsored health coverage, which provides most private (non-government) health coverage in the United States.²³ Additionally, it has been shown that in California, Latinx populations have the lowest employer sponsored health coverage rates of all racial and ethnic groups at 31.6%.²⁴

A third factor that may be contributing to the vulnerability of Latinx to COVID in Los Angeles is urban density and housing. Household overcrowding and population density is associated with an increased susceptibility to infectious respiratory diseases such as TB, viral pneumonia, and infectious gastroenteritis.²⁵ Latinx populations tend to live in higher density neighborhoods and crowded homes, and therefore, they are less able to practice shelter-in-place, work remotely, and isolate from their families.²⁶ This was reflected in our studied patient cohort where there were statistically significant differences in both neighborhood population density (9,475 vs 2,419 per square mile) as well as household size (3.12 vs 3.0 persons per household) compared to the population of Los Angeles at large.

Another finding in our study was that Latinx patients in this cohort also presented significantly later in their disease course compared to non-Latinx patients — 6 days after the start of symptoms compared to 3 days. The factors influencing this observation are also likely multifactorial. Though not recorded in the electronic medical record, undocumented immigrants make up a significant part of the Latinx community in Los Angeles. This segment of society is known to delay seeking care due to fears of interacting with authority and fears of deportation of themselves or their family;²⁷ the authors have all anecdotally experienced this firsthand with our patients. This later presentation increases Latinx patients’ susceptibility to severe disease, which correlated with significantly higher inflammatory markers on hospital admission compared to those of non-Latinx patients.

In addition to these multiple social determinants, we found several clinical characteristics of this population that also predispose this patient population to be more vulnerable to COVID-19. In addition to having less access to care and a primary care provider, this population also has a higher burden of chronic diseases such as hypertension, diabetes, chronic kidney disease, and cardiovascular disease.²⁸ Further analysis of our Latinx cohort showed that age, history of hypertension, and diabetes were strong predictors of disease severity and mortality in univariate analyses as were the inflammatory markers CRP and LDH. In multivariate analyses, age seems to be the single largest contributor to both disease severity and mortality when adjusted for covariance with other statistically significant predictor variables. Of the two inflammatory markers mentioned above, only LDH seems to retain an association with both disease severity and mortality even when adjusted for covariance with age. In a systematic review of meta-analyses of prognostic factors for COVID-19 disease,²⁹ both of these laboratory measures were associated with disease severity and mortality, with CRP being associated with a greater increase in risk of severe illness than mortality (13.2% increase in severe disease, 7.9% increase in mortality for CRP > 100 mg/L) when compared to LDH (16.2% increase in severe disease, 10.4% increase in mortality for LDH > 250 U/L). Additionally, this study also found similar findings to ours in terms of socio-demographic risk factors for severe disease. Our findings were less comprehensive, however, likely because of the smaller nature of our study as compared to the large population size captured in a systematic review. We also focused our analysis mostly on one demographic, the Latinx population, which likely also played a part in the differences noted.

There were several limitations identified in our study. First, this was a single center study at a public hospital in LA County. Second, the narrow time frame resulted in a small sample size, which limited direct comparison between Latinx and other populations. Third, community testing during the early phase of the pandemic was limited. Fourth, during the enrollment period of the study, the hospital often diverted admissions due to having reached surge capacity in the medical ICU, which also decreased our sample size. Lastly, the study’s time frame was during the early period of the pandemic when only patients who were highly suspicious for COVID-19 were tested, which may have led to under diagnosis. Future directions include efforts to collect data from a larger patient population to identify differences in outcomes between specific ethnic and racial groups (eg, White vs Latinx vs Black).

Conclusion

The COVID-19 pandemic has exposed many of the systemic issues in the U.S. healthcare system that have resulted in health inequity in our society. COVID-19 has magnified the need for change in the healthcare system so vulnerable communities are protected and not constrained to a disproportionate burden of disease during health outbreaks.