Socioeconomic and Racial and/or Ethnic Disparities in Multisystem Inflammatory Syndrome

This retrospective case-control study included patients diagnosed with MIS-C between January 1, 2020, and September 1, 2020, in Massachusetts. Patients treated at Boston Children’s Hospital (BCH), Massachusetts General Hospital (MGH), and Boston Medical Center (BMC) were included.

To better evaluate the impact of SES on MIS-C, we compared children with MIS-C with the following groups (Supplemental Table 5):

1. children with COVID-19 infection (excluding patients with MIS-C), including both symptomatic and asymptomatic cases, between January 1, 2020, and July 1, 2020;

2. the MIS-C “rule-out” group of children evaluated for MIS-C at BCH who ultimately did not meet CDC criteria for MIS-C diagnosis between January 1, 2020, and September 1, 2020;

3. children hospitalized for a febrile illness (excluding KD, COVID-19, and MIS-C) at BCH between January 1, 2020, and September 1, 2020;

4. children diagnosed with KD at BCH²⁷  between 2010 and 2016; and

5. children in Massachusetts based on the 2010 US Census.

Exclusion criteria included age >21 years, a non-Massachusetts address, and congenital heart disease, except for bicommissural aortic valve, mitral valve prolapse, and hemodynamically insignificant ventricular septal defects. This study was approved or exempted by the institutional review board of each institution, and patient consent was obtained when applicable.

Electronic medical records were reviewed to obtain patients’ demographics and clinical course. MIS-C was defined, by using the CDC case definition, as an individual aged <21 years presenting with fever, laboratory evidence of inflammation, clinically severe illness with multisystem (≥2) organ involvement requiring hospitalization, no alternative plausible diagnosis, and test results positive for current or recent SARS-CoV-2 infection by reverse transcriptase polymerase chain reaction (RT-PCR), serology, or antigen testing or COVID-19 exposure within the 4 weeks before the onset of symptoms.¹⁰  All cases of MIS-C (and MIS-C rule-out) were adjudicated by a multidisciplinary team of experts, including rheumatologists, cardiologists, infectious disease specialists, and hematologists. Relevant clinical information, including age, past medical history, clinical symptoms at presentation, and laboratory and diagnostic testing results (excluding sociodemographic data), was included in a form sent to members of the expert team, who were then asked whether the patient met the CDC definition for MIS-C. The final adjudication was based on the majority vote, and any tied cases were discussed as a group to reach a consensus.

Patients’ race and ethnicity were self-reported by patients or parents at the time of hospital admission. Understanding that race and ethnicity are complex sociopolitical constructs, patients were grouped into categories according to the US Census and Office of Management and Budget.²⁸  Race was classified as white, Black, Asian American, or other (including American Indian or Alaskan native and native Hawaiian or other Pacific Islander). Patients of mixed race were classified as “other.” Ethnicity was classified as Hispanic (ie, Cuban, Mexican, Puerto Rican, South or Central American, or other Spanish culture or origin regardless of race) or non-Hispanic. Ventricular dysfunction was defined as a left ventricular ejection fraction <55%. Coronary artery z scores were calculated for the left main coronary artery, proximal right coronary artery, and proximal left anterior descending artery by using the Boston formula.²⁷  Coronary artery dilation was defined as a coronary artery z score ≥2 but <2.5, and coronary artery aneurysms were defined as having a coronary artery z score ≥2.5.²⁷ 

A neighborhood SES summary score and the social vulnerability index (SVI) were calculated by using the 2010 US Census on the basis of patient address.^29,30  The variables for the neighborhood SES summary score included the following: (1) a log of the median household income; (2) a log of the median value of housing units; (3) the percentage of households receiving interest, dividend, or net rental income; (4) the percentage of adults 25 years of age or older in the household who had completed high school; (5) the percentage of adults 25 years of age or older in the household who had completed college; and (6) the percentage of employed persons 16 years of age or older in the household in executive, managerial, or professional specialty occupations.²⁹  The SVI was calculated by using 15 variables classified in 4 themes: (1) SES; (2) housing composition and disability; (3) minority status and language; and (4) housing and transportation (Supplemental Fig 2).³⁰  Neighborhood SES summary scores were reported as z scores for each of the variables by using all census tracts in Massachusetts and then summed to determine the overall neighborhood SES summary score. SVI was reported as a percentage; a higher SVI denotes increased vulnerability of a community. For children in Massachusetts, the neighborhood SES summary score and SVI were calculated by weight-adjusting the score for each census tract on the basis of the number of children <18 years of age in each tract.

The primary outcome was the diagnosis of MIS-C, and predictor variables included SES, SVI, race, and ethnicity. As a secondary aim, we explored the impact of the neighborhood SES summary score, the SVI, race, and ethnicity on treatment needs and disease severity (days of fever, hospital length of stay, ICU admission and length of stay, inotrope requirement, positive pressure ventilation, intubation, and cardiac involvement).

In the descriptive analysis, continuous variables were summarized with median and interquartile range (IQR), and categorical variables were summarized as frequencies and percentages. The neighborhood SES summary score and SVI were compared across groups by using the Kruskal-Wallis test with pairwise comparisons. The proportion of patients in the lowest SES quartile and highest SVI quartile, as well as race and/or ethnicity in each group, was compared by using the Fisher’s exact test. Univariable and multivariable logistic regressions were used to examine the impact of neighborhood SES or SVI and race and/or ethnicity on children diagnosed with MIS-C (versus children in Massachusetts without MIS-C). Patients with MIS-C in the lowest SES quartile and highest SVI quartile were compared with those in the other quartiles by using the Kruskal-Wallis test and Fisher’s exact test. All analyses were performed with Rstudio: Integrated Development Environment for R (Rstudio, Inc, Boston, MA). A 2-tailed adjusted P value <.05 was deemed statistically significant. The P values were adjusted for false discovery rate by using the Benjamini Y and Hochberg Y method.

During the study period, a total of 43 patients (25 boys [58%]) were diagnosed with MIS-C, including 29 patients from BCH, 12 from MGH, and 2 from BMC. The median age was 9.7 years (IQR 6.5 to 16.3) at the time of diagnosis. Regarding race and/or ethnicity, 19 (44%) were Hispanic (white, n = 5; Black, n = 4; other, n = 10), and of the non-Hispanic patients, 7 (16%) were white, 7 (16%) were Black, 2 (5%) were Asian American, and 3 (7%) were classified as other. Overall, 19 patients (44%) had preexisting comorbidities, with the most common being obesity (n = 17 [40%]) and asthma (n = 6 [14%]). ICU admission was required for 19 patients (44%): fluid resuscitation and monitoring in 7 (16%) patients, vasopressors or vasoactive support in 8 (19%) patients, noninvasive positive pressure ventilation in 5 (12%) patients, and mechanical ventilation in 4 (9%) patients. Cardiac involvement was found in 33 (77%) patients, including elevated brain natriuretic peptide levels in 25 (63%) patients, elevated troponin levels in 13 (32%) patients, ventricular dysfunction in 21 (49%) patients, and coronary artery dilation or aneurysm in 10 (23%) patients. The majority of patients with MIS-C had positive antibody test results for SARS-CoV-2 (n = 29 [67%]). Results of RT-PCR tests for SARS-CoV-2 were positive for 20 patients (47%), and 15 patients (35%) had a COVID-19 exposure but negative RT-PCR and antibody test results. A majority of patients were treated with intravenous immunoglobulin (n = 34 [79%]); other treatments used included steroids for 30 (70%) patients and anakinra for 9 (21%) patients. Six patients (14%) received no immunomodulatory treatment.

Table 1 summarizes the neighborhood SES summary score, the SVI, race, and ethnicity across the comparison groups. Table 2 reveals expanded results with the specific variables and themes included in both measures of SES. There was no significant difference in the neighborhood SES summary score and the SVI between each of the 3 hospitals’ MIS-C groups. In patients with MIS-C, the neighborhood SES summary z score ranged from −12.0 to 10.2, with a median of −3.5 (IQR −6.7 to 0.3). The neighborhood SES summary score was significantly lower for patients with MIS-C than for children in Massachusetts (P = .0009), children hospitalized with febrile illness (P = .0009), children with MIS-C rule-out (P = .02), and children with KD (P < .0001); but it was similar for patients with COVID-19 (P = .23) (Fig 1, Table 2). More than half of children with MIS-C were in the lowest SES quartile (n = 22 [51%]). The SVI score, in which higher score denotes increased vulnerability, ranged from 5.8% to 98.9%, with a median of 76.5% (IQR 57.6% to 91.8%) in patients with MIS-C. SVI was significantly higher for patients with MIS-C than for children in Massachusetts (P < .0001), children hospitalized with febrile illness (P = .0006), children with MIS-C rule-out (P = .04), and children with KD (P = .0001); but it was similar for patients with COVID-19 (P = .78) (Fig 1, Table 2). This association was not only found for the overall SVI but also found for all individual categories (SES, housing composition and disability, minority status and language, and housing and transportation; Table 2). More than half of children with MIS-C were in the highest SVI quartile (n = 23 [53%]). Patients with MIS-C and COVID-19 were more likely to be from racial and/or ethnic minority groups (Black and Hispanic) compared with children hospitalized with febrile illness, MIS-C rule-out, or KD or children in Massachusetts (Table 1).

The neighborhood SES summary score, the SVI, race, and ethnicity were all associated with MIS-C diagnosis in the univariable analysis (Table 3). In the multivariable analysis (with inclusion of SES and SVI in separate models), measures of SES and race and/or ethnicity independently conferred higher risk for MIS-C diagnosis (Table 3). Specifically, Black and Hispanic children, as well as children in the lowest SES quartile and highest SVI quartile, had significantly increased odds of developing MIS-C.

There was no significant difference in demographics, hospital course, or cardiac complications in patients with MIS-C in the lowest SES quartile versus others or in the highest SVI quartile versus others (Table 4). There was also no difference in hospital course or cardiac complications based on race or ethnicity.

In this retrospective case-control study, we found that patients diagnosed with MIS-C had lower SES, had increased social vulnerability, and were more likely to be Black and/or Hispanic compared with the general population of Massachusetts children.

Previous studies have revealed that that higher rates of SARS-CoV-2 infection are found in neighborhoods with lower income and educational attainment, both of which are factors that were incorporated into the neighborhood SES summary score used in this study.²⁹  Goyal et al¹⁸  most recently showed that SARS-CoV-2 infection rates among children, in particular, were associated with lower median family income. Furthermore, the SVI was used in this study to characterize factors beyond education and income that could contribute to disparities. In Massachusetts, neighborhood proportion of foreign-born citizens, mean household size, and proportion of food service workers were associated with increased COVID-19 rate.³¹  Individuals more likely to work in essential occupations, more likely to be unable to work from home, and more likely to need to use public transportation have likely increased exposure to the virus.^32,33  Our findings revealed lower neighborhood household income and educational attainment and greater neighborhood social vulnerability regarding housing, transportation, minority status, and language among both the MIS-C and COVID-19 groups. It is therefore plausible that the elevated risk of MIS-C in the lower SES and greater SVI neighborhoods is a result of increased exposure to COVID-19.

Although SES and underrace and/or ethnicity are strongly correlated,³⁴  there appears to be an independent risk of MIS-C in Black and Hispanic patients. Our findings are consistent with data from the CDC in which the highest rates of childhood COVID-19 were found in children of Hispanic ethnicity.¹³  Two recent studies reveal that Black children have higher rates of COVID-19 infection than white children, similar to the disparities seen in adult COVID-19 cases.^18,35  However, the increased risk in patients from minority racial and/or ethnic groups appears to extend beyond their SES on the basis of our findings revealing an association of race and/or ethnicity with MIS-C even after adjustment for SES via 2 measures. Although the neighborhood SES summary score is limited to an assessment of income and education to quantify SES, the use of the SVI enabled an even more complex representation of contributing socioeconomic factors by including an array of measures ranging from housing composition to language to transportation, which were also found to be associated with MIS-C in our study.³⁰  The effect of race and ethnicity on health disparities can be related to a multitude of alternate complex factors, including increased risk of comorbidities, differential access to care, provider bias, and the effects of ongoing racism and/or discrimination and subsequent chronic stress.^14,36–38  These factors can cause racial and ethnic disparities independent of SES and could be targets for interventions to improve outcomes.^36,38 

Although we looked at race and ethnicity in our study, the role of host and environmental factors underlying differential MIS-C rates remains unclear. Despite being an early hot spot with a high incidence of patients with SARS-CoV-2 infection, China, Japan, and South Korea have not reported patients with MIS-C or similar KD-like presentations.^39,40  The cause of this variation remains unclear and may be related to genetic or environmental predisposition.^41–43  Genetic polymorphisms and environmental factors have been implicated in the development of KD and could play a similar role in MIS-C.^44–46  The artificial nature of racial and ethnic grouping as a social and political construct limits conclusions regarding the role of population genetics in our findings.^47,48  It is, however, well documented that inequities in environmental exposure exist on the basis of race and/or ethnicity and SES.^49,50  Future studies exploring genetic sequencing and environmental factors will be key to better understanding their role in MIS-C.

Our findings provide insight into avenues for interventions to reduce racial and socioeconomic disparities in MIS-C and children’s health during and beyond this pandemic. Steps to ameliorate risk for exposure to COVID-19 are key to limiting incidence of MIS-C. Identifying neighborhoods in which children are most likely to have COVID-19 exposure can inform public health efforts and may allow for proactive protection of at-risk populations. This may include increasing available testing, prioritizing vulnerable neighborhoods for vaccination, and ensuring that families can access health care resources without barriers due to language or insurance status.^14,51–53  Pediatricians also may support ongoing advocacy efforts to combat systemic racism, discrimination, and implicit bias, which can contribute to race- and ethnicity-related health disparities irrespective of SES.^54–57 

Limitations of our study include the small sample size of patients with MIS-C in a single state and the potential lack of generalizability to other states with different demographics. The inclusion of 3 large pediatric centers that cared for pediatric patients during the pandemic ensured that we captured most patients in Massachusetts admitted with MIS-C (43 of 54 reported to the Department of Public Health). The control groups consisted of patients treated at BCH, whereas patients were diagnosed with MIS-C across 3 academic centers. We did not find significant differences in SES between patients with MIS-C treated at the 3 centers, and we compared patients with MIS-C with children in Massachusetts to minimize that possible bias. Although we did not find any significant association between SES or race and/or ethnicity and treatment course or outcomes, statistical power was limited by the small number of patients, and this question would be more effectively answered through large multicenter registries. Patients reporting their race and ethnicity also may not have been given options within the confines of the electronic medical record or verbal questioning to accurately self-identify. Although all MIS-C case adjudication was performed by using forms that included only relevant clinical information, experts were not specifically blinded, and known sociodemographic information from participating in the patients’ clinical care may have influenced their decision. Lastly, this study relied on the 2010 US Census, and neighborhoods may have changed SES characteristics over the last decade.

Measures of lower SES or higher social vulnerability, Black race, and Hispanic ethnicity were independent risk factors for MIS-C in children in Massachusetts. The disproportionate number of cases of MIS-C in minority and socioeconomically disadvantaged pediatric populations may be due to increased risk of SARS-CoV-2 infection. In future studies, researchers should explore the underlying social, structural, economic, environmental, and genetic risk factors to allow for targeted interventions to support vulnerable pediatric populations most affected by MIS-C and improve health equity.

BCH

Boston Children’s Hospital

BMC

Boston Medical Center

CDC

Centers for Disease Control and Prevention

COVID-19

coronavirus disease 2019

IQR

interquartile range

KD

Kawasaki disease

MGH

Massachusetts General Hospital

MIS-C

multisystem inflammatory syndrome in children

RT-PCR

reverse transcriptase polymerase chain reaction

SARS-CoV-2

severe acute respiratory syndrome coronavirus 2

SES

socioeconomic status

SVI

social vulnerability index