Neighborhood Characteristics and Racial Disparities in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Seropositivity in Pregnancy

Abstract

Objective:

To quantify the extent to which neighborhood characteristics contribute to racial and ethnic disparities in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seropositivity in pregnancy.

Methods:

This cohort study included pregnant patients presenting for childbirth at two hospitals in Philadelphia, Pennsylvania from April 13 to December 31, 2020. SARS-CoV-2 seropositivity was determined by measuring immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies by enzyme-linked immunosorbent assay (ELISA) in discarded maternal serum samples obtained for clinical purposes. Race and ethnicity were self-reported and abstracted from medical records. Patients’ residential addresses were geocoded to obtain three census tract variables: community deprivation, racial segregation (index of the concentration of the extremes), and crowding. Multivariable mixed effects logistic regression models and causal mediation analyses were used to quantify the extent to which neighborhood variables may explain racial and ethnic disparities in seropositivity.

Results:

Among 5,991 pregnant patients, 562 (9.4%) were seropositive for SARS-CoV-2. Higher seropositivity rates were observed among Hispanic (19.3%, 104/538) and Black (14.0%, 373/2658) patients compared to Asian (3.2%, 13/406), White (2.7%, 57/2133), and another race or ethnicity (5.9%, 15/256) patients (P <0.001). In adjusted models, per standard deviation increase, deprivation (adjusted odds ratio (aOR) 1.16, 95% CI: 1.02–1.32) and crowding (aOR 1.15, 95% CI: 1.05–1.26) were associated with seropositivity, but segregation was not (aOR 0.90, 95% CI: 0.78–1.04). Mediation analyses revealed that crowded housing may explain 6.7% (95% CI: 2.0%−14.7%) of the Hispanic-White disparity and that neighborhood deprivation may explain 10.2% (95% CI: 0.5%−21.1%) of the Black-White disparity.

Conclusion:

Neighborhood deprivation and crowding were associated with SARS-CoV-2 seropositivity in pregnancy in the pre-vaccination era and may partially explain high rates of SARS-CoV-2 seropositivity among Black and Hispanic patients. Investing in structural neighborhood improvements may reduce inequities in viral transmission.

Précis:

Neighborhood deprivation and crowding were associated with racial and ethnic disparities in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seropositivity in pregnancy in the pre-vaccination era.

INTRODUCTION

The coronavirus disease 2019 (COVID-19) pandemic has disproportionately affected Hispanic and Black communities across the United States (U.S.). Hispanic and Black people are 2.8 times more likely to be hospitalized and twice as likely to die from COVID-19 compared to White people.¹ These disparities have also been reported among pregnant patients, with Hispanic and Black patients more likely to have a positive test for COVID-19 during pregnancy.² However, whether a patient is tested for COVID-19 clinically depends on illness presentation, patient and clinician choice, testing protocols, access, and availability. In contrast, population surveillance with antibody testing, prior to the availability of vaccines, enables determination of exposure and immune response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) independent of healthcare decisions and access.

A prior study of seroprevalence in pregnancy early in the pandemic (April 4- June 3, 2020) demonstrated five-fold higher rates of seropositivity for SARS-CoV-2 for Black (9.7%) and Hispanic (10.4%) pregnant patients compared with White (2.0%) patients in a hospital-based cohort in Philadelphia, Pennsylvania.³ Factors contributing to racial and ethnic disparities in seropositivity remain incompletely understood.

Many aspects of life in the United States are racially and ethnically segregated, including residential neighborhoods⁴ and workplaces.⁵ Living and working conditions affect the feasibility of reducing exposure to the SARS-CoV-2 virus through practices such as social distancing.⁶ Neighborhoods affect health,⁷ and outbreaks of infectious diseases can occur due to differences in sanitation, ventilation, employment and social activities.^8–12 Emerging data suggest that risk of COVID-19 may vary by neighborhood in the general population.^11,13–19 Our hypotheses were that, in the pre-vaccination era, neighborhood characteristics would be associated with the odds of seropositivity and might partially explain racial and ethnic disparities in seropositivity. Specifically, we assessed associations of three neighborhood characteristics (area-level community deprivation, racial segregation, and crowding) with SARS-CoV-2 seroprevalence in a pregnant population. Secondarily, we quantified the extent to which these neighborhood characteristics might explain racial and ethnic disparities in seropositivity using causal mediation analysis.²⁰

METHODS

Patients who gave birth from April 13, 2020, to December 31, 2020, in two hospitals in Philadelphia, Pennsylvania, that combined account for approximately 9,000 births annually, were eligible for this study. Demographic and clinical data were obtained from the electronic health record. Limited English proficiency was assigned using the medical record indicator for the need for interpreter.²¹ Race and ethnicity were also abstracted from the medical record, which is patient-identified at the time of registration for outpatient visits and inpatient hospitalizations. Patients can separately indicate race and Hispanic ethnicity, and select more than one race designation including Black, White, and Asian. We constructed mutually exclusive racial and ethnic categories including non-Hispanic Black, non-Hispanic White, non-Hispanic Asian and Hispanic. Due to small numbers, we combined another, multiple, and unknown racial and ethnic categories into a single group for descriptive purposes and then further added non-Hispanic Asian patients to this group when modeling disparities. For brevity, we refer to non-Hispanic Black, non-Hispanic White, and non-Hispanic Asian patients as Black, White, and Asian, respectively. Patient residential address at the time of the labor and delivery admission was geocoded using ArcMAP 10.8, Environmental Systems Research Institute (ESRI), Redlands, CA, and the ArcGIS Street Map Premium North America version 2021.1 address locator with a minimum match score of 75; 26 patients were excluded because their address was not matched. The resulting coordinates were mapped to the US Census Bureau’s 2019 cartographic boundary shapefile to assign each address a census tract. The Institutional Review Board at the University of Pennsylvania approved this study with a waiver of consent because there was no more than minimal risk to participants since the protocol called for discarded samples and existing data (Protocol # 834240). A flow chart showing development of the cohort is presented in Figure 1.

Figure 1.

Cohort development

We chose deprivation, segregation, and crowding as potential mediators of disparities in seropositivity based on reported differences in SARS-CoV-2 infection in other U.S. non-pregnant cohorts.^{16,17,19,22,23} Census tract area-level community deprivation was assigned using an index²⁴ that uses American Community Survey (ACS)²⁵ 2018 indicators for the proportion of residents with income below the federal poverty line, without a high school degree, receiving federal cash assistance and lacking medical insurance, as well as median household income and proportion of vacant housing.²⁶ The deprivation index ranges from 0 to 1 (nationwide mean 0.35, SD 0.16) with higher values indicating more deprivation. For racial residential segregation, we used the Index of the Concentration of the Extremes (ICE) for Black-White segregation which is calculated as ICE=(White(n) − Black(n)/(Total population(n)). ICE ranges from −1 to 1, where −1 would be an entirely Black census tract and 1 would indicate an entirely White census tract. Area-level crowded housing was defined using ACS data as the proportion of occupied housing units with >1 person per room.

Pregnant patients have blood drawn for rapid plasma reagin at the time of admission for childbirth for routine syphilis screening. Residual maternal serum from this testing was collected for study purposes at the time it would otherwise be discarded by the hospital laboratories. Sera were fully de-identified prior to antibody measurements; 93% had available samples for analysis of which 97% were collected during the labor and delivery admission, with 3% of those collected within the month prior. Comparisons of patients with and without sera available for testing are shown in Appendix 1 (http://links.lww.com/xxx). Sera were tested using an enzyme-linked immunosorbent assay (ELISA) with plates coated with the receptor binding domain (RBD) of the SARS-CoV-2 spike protein.³ Samples with Immunoglobulin G (IgG) or Immunoglobulin M IgG concentrations more than 0.48 arbitrary units/mL were considered seropositive.³

Unadjusted, bivariate analyses comparing baseline characteristics among seropositive and seronegative patients were performed. Multivariable logistic regression models were used to estimate adjusted odds of seropositivity by race and ethnicity. Multivariable, mixed effects logistic regression models were used to calculate adjusted odds ratios of seropositivity per standard deviation increment increase of each of the neighborhood indicators in the whole cohort as well as in race and ethnicity-stratified models. A missing indicator was used for the one variable with missingness (body mass index (BMI), 1% missing). A random effect for census tract was included to address geographical clustering. To assess the extent to which neighborhood characteristics might explain observed racial and ethnic disparities in seropositivity, we used two strategies. First, we added neighborhood factors into multilevel, multivariable regression models to determine if the association between race and ethnicity and seropositivity remained after covariate adjustment or appeared to be attenuated. Second, to address the confounding structure of race and ethnicity with the potential neighborhood mediator as well as between the mediator and seropositivity, we used formal causal mediation.²⁷ Mediation analysis quantifies the indirect effect of race and ethnicity with seropositivity that may exist due to the mediator (in this case a neighborhood factor). It also quantifies the direct effect of race and ethnicity on seropositivity which is the remaining association after the neighborhood factor is included in the analysis. The direct effect includes all of the other potential causes of the disparity. The proportion of racial or ethnic disparity mediated by the neighborhood factor is calculated with an estimate of statistical significance and degree of uncertainty (95% confidence intervals). Mediation models included adjustment for individual-level age, body mass index, insurance status, and limited English proficiency.

Given the potential for non-random referral patterns leading to births in our two hospital systems, we also performed a sensitivity analysis restricted to participants reporting a home address in Philadelphia County to determine if results were similar to those involving the whole cohort. All tests were two-tailed and P-values less than 0.05 were considered statistically significant. Analyses were performed using R version 4.1.1²⁸

RESULTS

Of the 5,991 patients in the cohort, 562 (9.4%) were seropositive for SARS-CoV-2 at the end of pregnancy (Table 1). Comparisons of patients with and without seropositivity data showed that patients without these data were older, less likely to be obese, more likely to be privately insured, more likely to self-identify as White, and more likely to have preterm birth (Appendix 1, available online at http://links.lww.com/xxx). In unadjusted, bivariate analyses, younger age, higher pre-pregnancy BMI, nulliparity, public insurance, and limited English proficiency were all associated with seropositivity (P<0.001). Higher rates of seropositivity were present among Hispanic (19.3%, 104/538) and Black (14.0%, 373/2658) patients compared to Asian (3.2%, 13/406), White (2.7%, 57/2133), and another race (5.9%, 15/256) patients (P <0.001).

Table 1.

SARS-Cov-2 seroprevalence among 5,991 patients at the end of pregnancy in the pre-vaccination era.

	Seronegative		Seropositive
Characteristic	n	row %	n	row %	P
All	5429	90.6	562	9.4
Age (years)					<0.001
< 25	899	84.8	161	15.2
25 - < 35	3143	90.9	316	9.1
≥ 35	1387	94.2	85	5.8
Pre-pregnancy body mass index (kg/m2)					<0.001
< 25	2542	93.4	180	6.6
25 - < 30	1344	89.8	152	10.2
≥ 30	1486	87.0	222	13.0
Missing	57	87.7	8	12.3
Parity					<0.001
0	2471	92.7	194	7.3
> 0	2958	88.9	368	11.1
Insurance					<0.001
Private	3235	95.1	166	4.9
Public	2194	84.7	396	15.3
Limited English proficiency					<0.001
Yes	170	73.3	62	26.7
No	5259	91.3	500	8.7
Race and ethnicity					<0.001
Hispanic	434	80.7	104	19.3
Non-Hispanic Asian	393	96.8	13	3.2
Non-Hispanic Black	2285	86.0	373	14.0
Non-Hispanic White	2076	97.3	57	2.7
None of the above, multiple, or unknown	241	94.1	15	5.9
Gestational age at delivery					0.09
Preterm (< 37 weeks)	477	88.5	62	11.5
Term (≥ 37 weeks)	4952	90.8	500	9.2

Figure 2a shows a map of seropositivity rates in census tracts with at least 20 study subjects and figures 2b–d show maps of each neighborhood factor by census tract. Associations of patient characteristics and neighborhood factors are shown in Appendix 2, available online at http://links.lww.com/xxx. All three neighborhood factors (deprivation, segregation, and crowding) were associated with seropositivity in unadjusted models (Table 2). After adjustment for age, BMI, insurance, limited English proficiency, and race, associations were attenuated but remained statistically significant for deprivation and crowding. Higher odds of seropositivity were observed with higher levels of deprivation (aOR 1.16, 95% CI: 1.02, 1.32) and crowding (aOR 1.15, 95% CI: 1.05, 1.26), but not segregation (aOR 0.90, 95% CI: 0.78, 1.04), with aOR indicating increase in odds ratio per standard deviation increase in the neighborhood factor. These effect estimates correspond to 40% and 38% increased odds of seropositivity for individuals residing in the midpoint of the highest quartile compared to the midpoint of the lowest quartile (2.3 standard deviation units apart) of deprivation and crowding, respectively.

Figure 2.

A. Map of seropositivity rates in census tracts with at least 20 study participants. Study participants lived in other tracts, but rates would be too unstable and individuals too identifiable to depict. B. Map of segregation using the index of the concentration of extremes (ICE). Higher values indicate higher proportion of White residents. C. Map of neighborhood deprivation index includes six indicators and ranges from 0 to 1, with 1 indicating more deprivation. D. Map of crowding defined as the proportion of residences with more humans than rooms in the house. Maps created using ArcGIS software by Esri. ArcGIS and ArcMap are the intellectual property of Esri and are used herein under license. Copyright Esri. All rights reserved. For more information about Esri software, please visit www.esri.com. Census tract boundaries, water features, segregation, and crowding data are from the U.S. Census Bureau (https://www.census.gov). Neighborhood deprivation index data are from Brokamp’s Nationwide Community Deprivation Index (https://github.com/geomarker-io/dep_index)

Table 2.

Census tract neighborhood factors and SARS-CoV-2 seroprevalence among 5991 patients at the end of pregnancy in the pre-vaccination era.

Neighborhood factor at the census tract level	Seronegative (n = 5429)	Seropositive (n = 562)	Associations of one standard deviation increase in the neighborhood factor with seropositivity
	Mean (SD)	Mean (SD)	Unadjusted OR (95% CI)	Adjusted§ OR (95% CI)
Deprivation*	0.40 (0.17)	0.49 (0.13)	1.84 (1.63, 2.06)	1.16 (1.02, 1.32)
Segregation†	0.05 (0.70)	−0.31 (0.60)	0.56 (0.49, 0.63)	0.90 (0.78, 1.04)
Crowding‡	2.11 (2.36)	2.98 (2.79)	1.38 (1.24, 1.55)	1.15 (1.05, 1.26)

^*Neighborhood deprivation index includes six indicators and ranges from 0 to 1 with 1 indicating more deprivation.

^†Index of the concentration of extremes (ICE), range −1 to 1 with higher values indicate higher proportion White residents.

^‡Crowding defined as the proportion of residences with more humans than rooms in the house.

^§Multilevel logistic regression models adjusted for age, body mass index, insurance, limited English proficiency, and race/ethnicity with a random effect for census tract. A missing indicator was used for the 65 patients missing body mass index.

Racial and ethnic disparities persisted after adjustment for potential confounding variables including age, BMI, insurance, and limited English proficiency. Specifically, compared with White patients, Hispanic (aOR 3.36, 95% CI: 2.10, 5.38) and Black patients (aOR 3.98, 95% CI: 2.83, 5.60) had significantly higher odds of seropositivity (Table 3). There was not a significant disparity among patients of another race (aOR 1.36, 95% CI: 0.80, 2.30). Additional adjustment for parity made no change to the models and was collinear with age so was not included in adjusted analyses.

Table 3.

Associations of race/ethnicity with SARS-CoV-2 seropositivity at the end of pregnancy in unadjusted, individual covariate-adjusted, and then neighborhood factor -adjusted models.

Models of the Hispanic-White disparity	OR	(95% CI)
M0 = Unadjusted	8.48	(5.91, 12.2)
M1 = M0 + individual covariates*	3.36	(2.10, 5.38)
M2 = M1 + deprivation†	3.16	(1.93, 5.15)
M3 = M1 + segregation‡	3.43	(2.12, 5.54)
M4 = M1 + crowding§	3.04	(1.89, 4.87)
Models of the Black-White disparity
M0 = Unadjusted	5.84	(4.35, 7.84)
M1 = M0 + individual covariates*	3.98	(2.83, 5.60)
M2 = M1 + deprivation†	3.48	(2.41, 5.02)
M3 = M1 + segregation‡	3.60	(2.42, 5.36)
M4 = M1 + crowding§	3.89	(2.77, 5.47)
Models of the Another race or ethnicity-White disparity∥
M0 = Unadjusted	1.55	(0.93, 2.60)
M1 = M0 + individual covariates*	1.36	(0.80, 2.30)
M2 = M1 + deprivation†	1.32	(0.78, 2.21)
M3 = M1 + segregation‡	1.26	(0.73, 2.17)
M4 = M1 + crowding§	1.35	(0.79, 2.28)

Abbreviations: SARS-CoV-2, severe acute respiratory syndrome coronavirus-2; OR, odds ratio calculated using multilevel logistic regression; BMI, Body mass index

^*Age, body mass index, insurance, limited English proficiency

^†Neighborhood deprivation index includes six indicators and ranges from 0 to 1 with 1 indicating more deprivation;

^‡Index of the concentration of extremes (ICE), higher values indicate higher proportion White residents.

^§Crowding defined as the proportion of residences with more humans than rooms in the house.

^∥Removed n=9 individuals with missing BMI due to lack of convergence.

Race and ethnicity were associated with differences in all three neighborhood factors (Appendix 2, http://links.lww.com/xxx). Hispanic and Black patients lived in census tracts with more deprivation, lower ICE values (i.e. lower proportion of White residents), and higher levels of crowding. In models with race or ethnicity as the independent (predictor) variable and seropositivity as the dependent (outcome) variable, adding individual age, body mass index, insurance status, and limited English proficiency substantially attenuated Hispanic-white and Black-white disparities (Table 3). The addition of neighborhood factors appeared to attenuate disparities subtly. Formal mediation analyses, adjusted for individual-level age, body mass index, insurance status, and limited English proficiency, revealed that 6.7% (95% CI: 2.0%, 14.7%) of the disparity in seropositivity between Hispanic and White patients could be explained by differences in census tract-level residential crowding (P = 0.01) (Figure 3). There was no significant mediation of the Hispanic-White disparity by deprivation or segregation. With respect to the disparity in seropositivity between Black and White patients, mediation analyses revealed that 10.2% (95% CI: 0.5%, 21.1%) could be explained by differences in deprivation (P=0.04). A significant mediation was not detected for any other factor for either Hispanic-White or Black-White disparities. In analyses restricted to patients residing in Philadelphia County (n=4,436), results were similar with crowding mediating 6.8% (95% CI: 1.7%, 14.5%) of the Hispanic-White disparity and deprivation mediating 13.8% (95% CI: 2.9%, 27%) of the Black-White disparity in seropositivity.

Figure 3.

Proportion (and 95% confidence intervals) of the racial and ethnic disparity in SARS-CoV-2 seropositivity at the end of pregnancy mediated by neighborhood factors. Neighborhood deprivation index includes six indicators and ranges from 0 to 1 with 1 indicating more deprivation; Segregation assigned using index of the concentration of extremes (ICE), higher values indicate higher proportion White residents. Crowding defined as the proportion of residences with more humans than rooms in the house.

DISCUSSION

In the pre-vaccination era of the COVID-19 pandemic, we observed large racial and ethnic disparities in SARS-CoV-2 seroprevalence. Hispanic and Black pregnant patients were more likely to be seropositive than White patients. Mediation analyses revealed that part of these disparities might be explained by differences in neighborhood conditions, specifically deprivation and crowding.

While our study is novel with respect to studying neighborhood factors in association with SARS-CoV-2 seroprevalence in pregnancy as well as interrogating them as potential mediators, it is consistent with others who have reported associations of neighborhood factors with rates of COVID-19 infection and mortality in the U.S.. Carrion et al analyzed neighborhoods in New York City, in the first nine weeks of the pandemic and found that neighborhoods with high subway ridership had a higher burden of COVID-19 mortality; these neighborhoods had a higher proportion of Hispanic and Black residents.¹⁴ Krieger et al showed that ZIP code poverty levels and crowding were significantly associated with COVID-19 mortality in Massachusetts.¹⁶ Chen and Krieger analyzed county-level variables in association with COVID-19 in New York and Illinois and found higher rates in counties with higher poverty rates, more crowding, and lower proportion of White residents.¹⁷ A study of the built environment in King County, Washington also demonstrated that fewer open spaces and more crowding were associated with COVID-19.²⁹ Another study demonstrated that residents living in Louisiana neighborhoods with high levels of deprivation had 40% higher risk of COVID-19.²² Additionally a study of 434 patients admitted for labor and delivery at a New York City hospital with universal PCR testing from the first month of the COVID-19 pandemic (March 22 through April 21, 2020) demonstrated that lower neighborhood socioeconomic status and more household crowding were associated with COVID-19 infection.³⁰ Our study differs from these prior reports in that it interrogated seroprevalence which captures information about past infections in patients who may not test positive using a PCR at the time of admission for parturition. Furthermore, we examined the extent to which neighborhood factors might explain racial and ethnic disparities in seropositivity.

While studies quantifying neighborhood factors’ contribution to disparities are rare, with respect to the magnitude of the observed mediation effect, our results with respect to deprivation’s contribution to the Black-White disparity (11.6% overall and 14.6% in Philadelphia County) are similar in magnitude to a preterm birth study in California; 16.1% of Black-White disparities were attributable to a combination of several neighborhood factors in that study.³¹ Nonetheless, it is clear that unmeasured factors are explaining the disparity in seropositivity, that persisted after adjusting for individual and neighborhood factors, with adjusted odds ratios of seropositivity among Hispanic and Black patients compared to White exceeding three. Additional work to determine the extent to which occupational, transportation, or other socioenvironmental factors explain persistent disparities in seropositivity, and many other health outcomes, is warranted.

While the effect estimates in models of associations of deprivation and crowding with seropositivity were substantially weakened by the addition of individual covariates, they remained significant. Potential mechanisms by which neighborhood factors such as deprivation and crowding might contribute to higher SARS-CoV-2 exposure include close quarters leading to shared ambient air, insufficient ventilation, and inability to quarantine. Such conditions can enable transmission of airborne infections such as tuberculosis.^32,33 It is also possible that neighborhood factors are markers of other exposures. The opportunity to socially distance at home and work may vary substantially by race and ethnicity. Hispanic and Black women are more likely, for example, to be essential personnel such as home health aides and nursing home workers³⁴ – jobs which may have placed Hispanic and Black patients at higher risk for SARS-CoV-2 exposure. Nonetheless, VoPham et al analyzed area-level indicators as potential effect modifiers of social distancing (measured with smartphone data) on COVID-19 rates and found that social distancing was less effective in counties with lower median household incomes, larger “minority” population, and more residential crowding.²³ These findings suggest that even when residents practice social distancing, neighborhood factors may play a role in virus transmission. Furthermore, in our analysis, deprivation and crowding continued be associated with seropositivity even when controlling for insurance status (a marker of socioeconomic position) and significantly mediated disparities, suggesting they may play a causal role.

Strengths of our study include a large sample size of pregnant patients in whom we analyzed seroprevalence in an unselected population. By not relying on clinician and patient decisions regarding polymerase chain reaction (PCR) or antigen testing, we were able to avoid selection bias. Importantly, by studying antibody levels, we could account for both symptomatic and asymptomatic SARS-CoV-2 infection, but did not study the disease itself, COVID-19. Prior studies have found that residents in disadvantaged neighborhoods have less testing and higher positivity ratios when tested, suggesting ascertainment bias may lead to underestimation of disparities in exposure to SARS-CoV-2 and consequently in COVID-19.³⁵ Furthermore, by testing at the time of parturition, we were able to capture exposure to the virus at any time before or during pregnancy, as opposed to the shorter time of the few weeks at the end of pregnancy during which a PCR or antigen test would still be positive. Limitations include the possibility of unmeasured confounders such as employment and detailed socioeconomic indicators such as income or education. While diverse, our study population had few cases of seropositivity among Asian patients (n=13) and patients self-designated as another, multiple, or unknown race or ethnicity (n=15) which necessitated combining this group for modeling and likely resulted in lack of power to rule out disparities. There were some differences between patients with and without seropositivity data which could affect generalizability of findings. Specifically, patients without available samples were more likely to be older, White, privately insured, and have preterm birth. We suspect that this population may have been transferred into our hospitals from other hospitals where the blood sample had already been drawn or was so sick that add-on tests used up the sample and there was no volume left for our assay. We used census tract variables which may misclassify exposures of individual patients whose local neighborhood environment may differ from the census tract indicator. There are other measures of neighborhood deprivation in the literature. We chose to use a specific material community deprivation index²⁶ because it did not have an indicators for the proportion of “minority” residents like the Centers for Disease Control and Prevention Social Vulnerability Index³⁶ does or an indicator for “crowding” which is included in the Area Deprivation Index.^37,38 We wanted to assess segregation and crowding separately. Nonetheless, findings might vary with the choice of neighborhood deprivation index. While the method we used is often called “causal mediation,” this study was observational, and thus cannot prove causal effects.³⁹

In conclusion, we found that neighborhood factors such as deprivation and crowding likely explain a portion of the racial and ethnic disparities observed in SARS-CoV-2 seropositivity in pregnancy in the pre-vaccination era. Addressing the structural racism that has led to persistently different living conditions by race and ethnicity in the U.S., and investing in residential communities, will be key to advancing health equity.