Applying mixtures methodology to analyze how exposure to structural racism and economic disadvantage affect perinatal health outcomes: An ECHO study

Dana E Goin; Ronel Ghidey; Holly Schuh; Lori Dean; Emily Barrett; Theresa M Bastain; Jessie Buckley; Nicole R Bush; Marie Camerota; Kecia N Carroll; Nicholas Cragoe; Lara J Cushing; Dana Dabelea; Anne L Dunlop; Stephanie Eick; Amy J Elliot; Tali Felson; Sarah Dee Geiger; Frank D Gilliland; Tamarra James-Todd; Linda G Kahn; Matt Kasman; Jordan Kuiper; Bennett Leventhal; Maristella Lucchini; Morgan Nelson; Gwendolyn Norman; Chaela Nutor; T Michael O’Shea; Amy M Padula; Susan Schantz; Mehta-Lee Shilpi; Benjamin Steiger; Tracey Woodruff; Rosalind J Wright; Rachel Morello-Frosch

doi:10.1093/aje/kwaf224

. Author manuscript; available in PMC: 2026 Apr 10.

Published in final edited form as: Am J Epidemiol. 2026 Feb 5;195(2):464–476. doi: 10.1093/aje/kwaf224

Applying mixtures methodology to analyze how exposure to structural racism and economic disadvantage affect perinatal health outcomes: An ECHO study

Dana E Goin ^1,^2,^*, Ronel Ghidey ³, Holly Schuh ^4,⁵, Lori Dean ⁴, Emily Barrett ⁶, Theresa M Bastain ⁷, Jessie Buckley ⁸, Nicole R Bush ⁹, Marie Camerota ¹⁰, Kecia N Carroll ¹¹, Nicholas Cragoe ¹², Lara J Cushing ¹³, Dana Dabelea ¹⁴, Anne L Dunlop ¹⁵, Stephanie Eick ¹⁶, Amy J Elliot ¹⁷, Tali Felson ², Sarah Dee Geiger ¹⁸, Frank D Gilliland ⁷, Tamarra James-Todd ¹⁹, Linda G Kahn ²⁰, Matt Kasman ²¹, Jordan Kuiper ²², Bennett Leventhal ²³, Maristella Lucchini ²⁴, Morgan Nelson ¹⁷, Gwendolyn Norman ²⁵, Chaela Nutor ²⁶, T Michael O’Shea ²⁷, Amy M Padula ², Susan Schantz ¹², Mehta-Lee Shilpi ²⁸, Benjamin Steiger ²⁹, Tracey Woodruff ², Rosalind J Wright ³⁰, Rachel Morello-Frosch ^31,^*, for the ECHO Cohort Consortium

¹Columbia University Mailman School of Public Health, Department of Epidemiology, New York, NY

²UCSF Program on Reproductive Health and the Environment, Department of Obstetrics, Gynecology, and Reproductive Sciences, San Francisco, CA

³Johns Hopkins Bloomberg School of Public Health, ECHO Data Analysis Center, Baltimore, MD

⁴Johns Hopkins Bloomberg School of Public Health, Department of Epidemiology, Baltimore, MD

⁵Johns Hopkins Bloomberg School of Public Health, Institute for Vaccine Safety, Baltimore, MD

⁶Rutgers School of Public Health, Environmental and Occupational Health Sciences Institute, Rutgers University, Department of Biostatistics and Epidemiology, Piscataway, NJ

⁷University of Southern California, Department of Population and Public Health Sciences, Los Angeles, CA

⁸University of North Carolina at Chapel Hill, Department of Epidemiology, Chapel Hill, NC

⁹UCSF, Department of Psychiatry and Behavioral Sciences and Department of Pediatrics, San Francisco, CA

¹⁰Alpert Medical School of Brown University, Department of Psychiatry and Human Behavior, Providence, RI

¹¹Icahn School of Medicine at Mount Sinai, Division of General Pediatrics, Department of Pediatrics and Department of Environmental Medicine and Climate Science, New York, NY

¹²University of Illinois Urbana-Champaign, Beckman Institute, Urbana, IL

¹³University of California Los Angeles, Department of Environmental Health Sciences, Los Angeles, CA

¹⁴University of Colorado Anschutz Medical Campus, Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, Aurora, CO

¹⁵Emory University School of Medicine, Atlanta, GA

¹⁶Emory University Rollins School of Public Health, Gangarosa Department of Environmental Health, Atlanta, GA

¹⁷Avera Research Institute, Sioux Falls, SD

¹⁸University of Illinois at Urbana-Champaign, Department of Health and Kinesiology, Urbana, IL

¹⁹Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA

²⁰New York University Grossman School of Medicine, Department of Pediatrics, New York, NY

²¹Brookings Institution, Economic Studies, Washington, D.C.

²²The George Washington University Milken Institute School of Public Health, Department of Environmental and Occupational Health, Washington, D.C.

²³University of Chicago, Psychiatry & Behavioral Neuroscience, Chicago, IL

²⁴Department of Psychiatry, Columbia University, New York, NY

²⁵Wayne State University, Department of Obstetrics and Gynecology, Detroit, MI

²⁶Emory University, Department of Psychology, Atlanta, GA

²⁷University of North Carolina School of Medicine, Department of Pediatrics, Chapel Hill, NC

²⁸NYU Langone Health, Department of Obstetrics and Gynecology, Brooklyn, NY

²⁹Columbia University, Department of Environmental Health Sciences, New York, NY

³⁰Icahn School of Medicine at Mount Sinai, Department of Public Health, New York, NY

³¹University of California, Berkeley, Department of Environmental Science, Policy, and Management & School of Public Health, Berkeley, CA

⁺

See Appendix for full list of collaborators

Contact: Rachel Morello-Frosch rmf@berkeley.edu, Dana Goin dg3369@cumc.columbia.edu

PMCID: PMC13063399 NIHMSID: NIHMS2149144 PMID: 41111261

Abstract

Our objective was to examine the role of structural racism and economic disadvantage in perinatal health inequities using the Environmental influences on Child Health Outcomes Cohort. Participants’ addresses were linked to area-level measures of life expectancy, education, unemployment, health insurance, jail rate, segregation, and housing cost burden. We created absolute measures to represent economic disadvantage and relative measures comparing values for Black or Latinx people to White people in the same area to represent structural racism. We used quantile G-computation to estimate the effects of a one-quartile increase in all exposures simultaneously on fetal growth and gestational age measures. A one-quartile increase in economic disadvantage was associated with a reduction in birthweight [(−25.65 grams, 95% CI (−45.83, −5.48)], but not gestational age [−0.02 weeks, 95% CI (−0.13, 0.09)]. With a one-quartile increase in Latinx–White structural racism, we observed reductions in birthweight [−80.83, 95% CI (−143.42, −18.23)) among Latinx participants. A one-quartile increase in Black–White structural racism was weakly associated with lower birthweight among Black participants [−15.70, 95% CI (−82.89, 51.48)] but was associated with higher birthweight among White participants [57.47, 95% CI (13.26, 101.67)]. Our findings suggest co-occurring forms of structural inequity likely influence racialized disparities in fetal growth outcomes.

Introduction

Persistent inequities in adverse pregnancy outcomes across racial and ethnic groups are a significant public health problem. Differences are not adequately explained by individual-level risk factors or health behaviors, pointing to the need to examine upstream structural forces that shape risk (1–3). In particular, the scientific, clinical, and public understanding of structural racism and its implications for the health of birthing people is growing (4). Frameworks such as Ecosocial Theory have guided approaches to studying how structural racism causes inequities in health and disease via embodiment (5,6). Indeed, structural racism represents mutually reinforcing policies, laws, institutions, and norms that disproportionately privilege the well-being and success of White people to the detriment and disadvantage of marginalized racial and ethnic groups (7). While structural racism has been studied most often in the context of Black-White disparities, histories of discrimination, exclusion, racialized hierarchies, and oppression exist for other racial and ethnic groups, including Latinx, Asian, and American Indian populations (8,9). Structural racism emanates from historical policies with embedded legacies that shape contemporary social and physical environments and discriminatory actions by those currently in power (10,11). Often, a historically discriminatory policy or practice intersects with current decision-making in ways that perpetuate inequities between racial groups (12,13).

Structural racism affects health across the lifecourse, and can be especially impactful during pregnancy given the physical, emotional, and economic demands of reproduction and increased interaction with the health care system (14). Specific mediators could include chronic exposure to interpersonal racism, socioeconomic hardship, inadequate medical care, disproportionate exposure to environmental hazards, lack of social support, and fear (15). For example, living in an area with more severe structural racism has been associated with higher rates of poverty, a larger Black-White wealth gap (16), higher firearm homicide rates (17), higher non-fatal shootings (18), less green space (19,20), more experiences of interpersonal discrimination (21), higher rates of police stops (11), and higher body mass index (BMI) (22). Previous research has documented several ways in which racism-linked stress can become biologically embodied (5,23). For example, psychological stress resulting from interpersonal or structural racism or related stressors has been linked to higher oxidative stress during pregnancy (24–26), which can increase the risk of preterm birth, preeclampsia, and decreased fetal growth (27–29). Structural racism also increases the risk of developing chronic conditions before pregnancy, such as diabetes and hypertension (30,31), which are associated with adverse birth outcomes (32,33). Notably, structural racism can operate without an individual’s perception of it, making self-reported measures of experiences of discrimination insufficient to fully capture the effects of racism on health (34,35).

Prior studies of structural racism and birth outcomes have yielded mixed results. Some indicators of structural racism have been associated with lower birthweight, but only among specific groups (36–38). One study analyzed the ratio of the predicted probability of employment between Black and White people and found associations with infant birthweight only among United States (US)-born Black people living in the South (36). A California study found associations between county-level measures of segregation, including the dissimilarity index, isolation index, and the delta index, and lower gestational age and lower birthweight among Black and White women (37). Another study examined state-level structural racism and socioeconomic inequality and found an increased risk for small-for-gestational age (SGA) births when both racial inequality and socioeconomic inequality were high (38). This study also found the ratio of Black-to-White people who were incarcerated for a felony, and the ratio of Black-to-White people who were elected to a county board of supervisors were associated with lower birthweight.

Structural racism has also been associated with preterm birth (39–41). A study in New York found structural racism, measured using an index of the built and social environment and Black-White residential segregation, was associated with preterm birth but not small-for-gestational age (SGA) (39). A Philadelphia study found that living in an area with higher levels of residential segregation was associated with a higher risk of preterm birth (40). Another study applied a latent class approach to derive structural racism typologies based on residential segregation, education, employment, income, homeownership, and criminal justice (41). This method was not able to identify a hierarchy of classes (e.g., high, medium, and low racism), and relationships with SGA, low birth weight and preterm birth varied more between US-born Black versus White and immigrant Black participants than among the three typologies themselves.

Finally, to our knowledge, only one prior study has examined the effects of structural racism in racial groups beyond Black-White dyads. This study included birthing people of Mexican-origin and showed positive associations between higher levels of metropolitan-level segregation and risk of very preterm birth among both African American and Mexican-origin groups (42). This relationship was consistent across different measures of segregation for African Americans, but not for Mexican-origin birthing people, particularly after accounting for area-level poverty.

These prior studies illustrate many ways to measure structural racism (34,35,43–47). We chose to include measures that aligned with the domains relevant for reproductive health (7,47,48): the realms of employment, educational attainment, incarceration, residential segregation, housing access, medical insurance access, and life expectancy. Each of these indicators may independently affect birth outcomes; however, such experiences are unlikely to occur independently of one another (49). Guided by Ecosocial Theory(5,6), we anticipate that experiences of structural racism have mutually reinforcing feedback loops such that being affected in one domain is likely correlated with the probability of experiencing other domains, and previous work has shown a multidimensional measure is more predictive of health outcomes than single indices (50).

Our objective was to characterize the relationship between structural racism among Black and Latinx populations and adverse birth outcomes using multiple birth cohorts in the Environmental influences on Child Health Outcomes (ECHO) program. We aimed to study structural racism as an exposure mixture using quantile-based G-computation (51), which was specifically designed to address challenges in the analysis of multiple, correlated exposures by using the parameters of a marginal structural model to estimate the difference in outcome of a simultaneous one quantile increase in all exposure variables. This method addresses how the exposure mixture as a whole, rather than individual components, influences health outcomes.

We examined both absolute and relative measures, to characterize both the overall burden of disadvantage (which due to historical legacies of discrimination tends to be higher in racially minoritized communities) and relative disadvantage between Black and White and Latinx and White groups within specific geographies. We refer to the absolute measures as economic disadvantage, and the relative measures as structural racism. We hypothesize that exposure to economic disadvantage will be harmful for all groups, and that associations will be stronger among Black and Latinx populations. We also hypothesize exposure to higher structural racism using the relative measures will be associated with worse outcomes for Black and Latinx groups but not for White groups.

Data and Methods

Study Population

Our study uses data from the ECHO – Wide Cohort Study, a nationwide consortium of 69 cohorts across the US exploring how children’s social and physical environment impacts health. Participants were enrolled across different life stages, but most often during pregnancy or at birth (52). The institutional review boards of record for each cohort study site as well as for the Johns Hopkins Bloomberg School of Public Health approved all activities associated with this study.

For our analysis, we included all enrolled singleton ECHO infants who were born from 2010–2019 and for whom we had consent to use their US census tract-level information as well as data on birthweight and gestational age at birth (see Supplemental Figure 1 for a map of ECHO recruitment sites included and Supplemental Figure 2 for a flowchart of additional inclusion criteria).

Race/ethnicity

Participants self-reported their race/ethnicity in the survey questionnaires. Participants were classified as Black if they identified as Black and non-Latinx, Black and Latinx, or Black in combination with another race. Participants were classified as Latinx if they reported Latinx ethnicity regardless of race. Participants were identified as White if they reported White race only and non-Latinx ethnicity. Therefore, White, non-Latinx is a stand-alone group while the Black and Latinx groups have some overlap. Due to sample size limitations, Asian, American Indian, and Hawaiian or Pacific Islander participants were included in the overall analysis but not in the subgroup analyses.

Area-level measures of economic disadvantage and structural racism

Area-level variables chosen to represent seven domains of economic disadvantage and structural racism were operationalized as follows: employment as the annual county unemployment rate, educational attainment as the percent of the census tract population over the age of 25 with at least a bachelor’s degree, incarceration as the county jail rate per 100,000 people, residential economic segregation as the census tract population concentration of economic privilege and deprivation using the Index of Concentration at the Extremes (ICE), housing access as the percent of households in a census tract that spend more than 30% of their income on housing, medical insurance access as the percent of non-elderly adults (age 18–64) in a county who are Medicaid eligible (have income less than 138% of the Federal poverty level) but uninsured, and average census tract life expectancy from birth. Details on the data sources and construction of these area-level measures are provided in Supplemental Table 1. To improve interpretation of the mixture model, we reverse coded the educational attainment, ICE, and life expectancy variables so that higher quantiles represented increased levels of disadvantage for all variables.

We calculated Black-White and Hispanic/Latinx-White (hereinafter referred to as Latinx-White) ratios to create race/ethnicity-specific mixture measures. The ratio measures capture relative differences between White and Black or Latinx people living in the same areas, with the goal of characterizing racialized privilege and disadvantage. For the ICE racial and economic segregation measure, we used the absolute value to represent extremes of racialized economic segregation. This was based on research showing that living in a lower income, predominantly Black or Latinx neighborhood is associated with worse birth outcomes(53,54), while for Black or Latinx people, living in a wealthy White neighborhood can be stressful due to interpersonal discrimination (55); this stress may lead to adverse birth outcomes (56). Area-level indicators were assigned to participants by the address recorded closest to the date of conception, which was calculated by subtracting estimated gestational age at birth from the child’s date of birth. Additional details about the creation of these measures are provided in Supplemental Table 1.

Birth Outcomes

Our primary outcomes of interest were birthweight (in grams), sex-specific birthweight-for-gestational age (GA) z-scores, and gestational age (in weeks). All measures were derived from maternal and child medical records and parent-report. Z-scores were calculated using a 2017 US reference population (more details provided in supplemental material) (57). Gestational age was measured in completed weeks. To evaluate associations with clinical outcomes, we examined preterm birth (delivery before 37 weeks gestation) and small-for-gestational age (sex-specific birthweight-for-gestational age below the tenth percentile of the US reference population)(57).

Covariates

We selected individual-level covariates based on a directed acyclic graph and included variables associated with risk of adverse birth outcomes and residential neighborhood selection (Supplemental Figure 3). Covariates included birthing parent age at delivery, educational attainment (less than high school, high school or equivalent, associate’s degree, bachelor’s degree, graduate degree), and parity (number of prior pregnancies carried greater than 20 weeks). Infant sex was included as a covariate to improve precision. Birthing parent age, parity and infant sex were derived from maternal medical records, infant medical records, or were self-reported at baseline. Educational attainment was self-reported at baseline.

Statistical analysis

We first examined relationships of our exposure and outcome measures along with covariates using Spearman correlations. We then used quantile G-computation with linear regression to assess associations of economic disadvantage and structural racism with birth outcomes, using quartiles of area-level variables. We included a random intercept term to account for clustering at the census tract level. Quantile G-computation categorizes all exposure variables into quantiles, and then estimates the effects of simultaneously increasing all exposures by one quantile without requiring directional homogeneity (51). Parameters represent marginal effects that measure the difference in birth outcomes given a joint intervention on all area-level exposures while adjusting for individual-level covariates.

To evaluate the absolute associations between economic disadvantage and birth outcomes, we estimated a quantile G-computation linear regression model among all eligible newborns. We then stratified by participant race and ethnicity to evaluate how the effects of economic disadvantage on birth outcomes might differ across Black, Latinx, and White groups. We based cut-offs on quartile values in the overall population for all stratified analyses to allow for comparisons between race/ethnicity-specific quantile G-computation estimates (see Supplemental Table 2).

To evaluate the relative associations between structural racism and birthweight, birthweight-for-gestational age z-scores, and gestational age, we estimated the associations of the Black-White and Latinx-White ratios of the area-level measures with birth outcomes using quantile G-computation linear regression. For binary outcomes, we used quantile G-computation with logistic regression. We then stratified the analyses by participant race/ethnicity. Thus, for the measures of Black-White structural racism, we estimated effects for Black participants and White, non-Latinx participants separately. For measures of Latinx-White structural racism, we estimated effects for Latinx infants and non-Latinx White infants separately. The quantile values for these race-specific models were determined based on the subpopulations included in these sub-analyses (e.g., the Black-White structural racism models used quantiles based on Black and White participants only).

We used Multiple Imputation by Chained Equations (MICE) to address missing data in covariates (58). We ran 30 iterations of MICE, accounting for clustering by cohort by including cohort ID as a random intercept. We combined data using Rubin’s rules to calculate final mixture effects and variances for each parameter, and used the variances to calculate Wald-type 95% confidence intervals (59).

RESULTS

Our study sample included 15,650 infants born between 2010–2019 for whom we had linkable location information and neighborhood-level data (Table 1 and Supplemental Figure 2). On average, birthing people were 30.9 years of age at delivery and had one prior live birth (Table 1). About half of the study participants (48.0%) were non-Latinx White, 17.3% were Latinx, and 11.5% were Black. About 15.1% of participants reported having a high school degree or less, and most (89.6%) lived in metropolitan areas.

Table 1.

Individual- and area-level characteristics of infants in the United States ECHO cohort by race and ethnicity

	Black (N=1804)	Latinx (N=2703)	NL White (N=7517)	Overall (N=15650)

Birthweight in grams
Mean (SD)	3090 (579)	3320 (581)	3380 (576)	3310 (585)
Median [Min, Max]	3130 [460, 4760]	3340 [470, 5930]	3420 [465, 5690]	3350 [460, 5930]
Birthweight for gestational age z-score, sex-specific
Mean (SD)	−0.36 (1.06)	0.06 (1.08)	0.16 (1.06)	0.04 (1.08)
Median [Min, Max]	−0.37 [−5.09, 4.86]	0.04 [−3.77, 5.14]	0.14 [−6.58, 4.89]	0.02 [−6.58, 5.14]
Missing	<5	<5	<5	14 (0.1%)
Gestational Age at Birth
Mean (SD)	38.3 (2.27)	38.6 (2.05)	38.8 (1.97)	38.6 (2.05)
Median [Min, Max]	39.0 [23.0, 43.0]	39.0 [25.0, 43.0]	39.0 [24.0, 43.0]	39.0 [22.0, 43.0]
Missing	0 (0%)	<5	0 (0%)	<5
Preterm Birth Status
Full-term	1579 (87.5%)	2436 (90.1%)	6815 (90.7%)	14078 (90.0%)
Preterm	225 (12.5%)	267 (9.9%)	702 (9.3%)	1572 (10.0%)
Large for Gestational Age, sex-specific, single gestation only
No	1572 (87.1%)	2334 (86.3%)	6219 (82.7%)	13236 (84.6%)
Yes	103 (5.7%)	316 (11.7%)	985 (13.1%)	1794 (11.5%)
Missing	129 (7.2%)	53 (2.0%)	313 (4.2%)	620 (4.0%)
Small for Gestational Age, sex-specific, single gestation only
No	1374 (76.2%)	2392 (88.5%)	6625 (88.1%)	13506 (86.3%)
Yes	301 (16.7%)	258 (9.5%)	579 (7.7%)	1524 (9.7%)
Missing	129 (7.2%)	53 (2.0%)	313 (4.2%)	620 (4.0%)
Conception Year
2010	276 (15.3%)	189 (7.0%)	795 (10.6%)	1437 (9.2%)
2011	136 (7.5%)	190 (7.0%)	974 (13.0%)	1526 (9.8%)
2012	117 (6.5%)	223 (8.3%)	996 (13.3%)	1715 (11.0%)
2013	108 (6.0%)	333 (12.3%)	983 (13.1%)	1909 (12.2%)
2014	157 (8.7%)	327 (12.1%)	819 (10.9%)	1793 (11.5%)
2015	176 (9.8%)	358 (13.2%)	666 (8.9%)	1686 (10.8%)
2016	176 (9.8%)	334 (12.4%)	601 (8.0%)	1600 (10.2%)
2017	211 (11.7%)	334 (12.4%)	522 (6.9%)	1487 (9.5%)
2018	267 (14.8%)	245 (9.1%)	741 (9.9%)	1533 (9.8%)
2019	180 (10.0%)	170 (6.3%)	420 (5.6%)	964 (6.2%)
Child Sex
Female	898 (49.8%)	1338 (49.5%)	3647 (48.5%)	6986 (44.6%)
Male	906 (50.2%)	1365 (50.5%)	3870 (51.5%)	7265 (46.4%)
Missing	0 (0%)	0 (0%)	0 (0%)	1399 (8.9%)
Parity (>20 weeks)
Mean (SD)	1.38 (1.51)	1.10 (1.23)	0.940 (1.10)	1.03 (1.19)
Median [Min, Max]	1.00 [0, 11.0]	1.00 [0, 9.00]	1.00 [0, 9.00]	1.00 [0, 11.0]
Missing	296 (16.4%)	250 (9.2%)	481 (6.4%)	1322 (8.4%)
Maternal Age (years)
Mean (SD)	27.4 (5.57)	29.9 (5.74)	31.7 (4.85)	30.9 (5.41)
Median [Min, Max]	26.4 [16.8, 57.3]	30.1 [16.6, 51.6]	31.9 [16.5, 52.1]	31.2 [16.5, 57.3]
Missing	0 (0%)	0 (0%)	0 (0%)	1399 (8.9%)
Maternal Education
Less than High School	203 (11.3%)	337 (12.5%)	90 (1.2%)	782 (5.0%)
High School, GED	593 (32.9%)	407 (15.1%)	369 (4.9%)	1587 (10.1%)
Associate’s Degree	507 (28.1%)	765 (28.3%)	1021 (13.6%)	2808 (17.9%)
Bachelor’s Degree	172 (9.5%)	470 (17.4%)	2068 (27.5%)	3407 (21.8%)
Master or Professional Degree	98 (5.4%)	316 (11.7%)	2102 (28.0%)	3219 (20.6%)
Missing	231 (12.8%)	408 (15.1%)	1867 (24.8%)	3847 (24.6%)
% Census-Tract Housing Costs Burden
Mean (SD)	43.6 (10.6)	42.5 (11.8)	31.6 (9.20)	36.2 (11.2)
Median [Min, Max]	43.5 [10.9, 77.4]	41.9 [7.42, 83.2]	30.6 [7.56, 80.3]	35.3 [7.42, 83.2]
% Census-Tract Unemployed
Mean (SD)	13.4 (8.10)	8.65 (4.77)	5.52 (3.47)	7.40 (5.33)
Median [Min, Max]	11.4 [0.500, 57.2]	7.90 [0, 41.2]	4.80 [0, 41.3]	6.00 [0, 57.2]
County Jail Rate per 100,000 persons
Mean (SD)	366 (216)	273 (136)	285 (155)	287 (166)
Median [Min, Max]	332 [82.1, 1270]	249 [55.3, 981]	257 [39.3, 1760]	249 [39.3, 1760]
% Census-Tract over age 25 with at least a Bachelor’s Degree
Mean (SD)	23.4 (16.0)	30.1 (19.4)	42.3 (19.9)	37.5 (20.8)
Median [Min, Max]	19.3 [0, 88.1]	26.5 [0.600, 92.8]	40.4 [0.300, 96.1]	34.3 [0, 96.1]
Census-Tract Life Expectancy
Mean (SD)	75.5 (4.40)	79.7 (3.60)	80.9 (3.36)	80.0 (4.02)
Median [Min, Max]	75.7 [60.7, 88.8]	79.9 [61.1,93.3]	80.9 [64.4, 93.6]	80.5 [59.0, 96.1]
% Census-Tract Uninsured that are Medicaid eligible
Mean (SD)	19.9 (11.0)	21.3 (11.4)	21.2 (12.8)	21.4 (12.3)
Median [Min, Max]	17.5 [0.498, 46.3]	17.5 [0.333, 51.6]	22.6 [0, 49.8]	21.2 [0, 51.6]
Census-Tract ICE (measure of economic deprivation)
Mean (SD)	0.0031 (0.0279)	−0.0003 (0.0431)	0.0034 (0.0356)	0.0049 (0.0383)
Median [Min, Max]	0.0024 [−0.0924, 0.0719]	−0.0006 [−0.409, 0.0813]	0.0047 [−0.304, 0.122]	0.0093 [−0.409, 0.141]
Rural-Urban Continuum Codes
RUCC 1–3	1797 (99.6%)	2652 (98.1%)	6037 (80.3%)	14027 (89.6%)
RUCC 4–6	<10	<40	1075 (14.3%)	1176 (7.5%)
RUCC 7–9	<5	<20	405 (5.4%)	447 (2.9%)

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Note: There were 1,115 Asian, American Indian, Hawaiian or Pacific Islander infants who were included in the overall analysis but not in the subgroup analyses. RUCC codes 1–3 represent counties in metropolitan areas. RUCC codes 4–6 represent counties in nonmetropolitan areas that have either more than 20,000 residents or between 5,000 and 20,000 residents and are adjacent to a metro area. RUCC codes 7–9 represent areas with between 5,000 to 20,000 residents and are not adjacent to a metro area or have fewer than 5,000 residents.

Participants lived in census tracts where, on average, 36.2% of households experienced housing cost burden; 37.5% (SD:20.8) of people over 25 years of age had a bachelor’s degree or higher; average life expectancy was 80 years (SD: 4.0); and 21.4% (SD 12.3) of people who were Medicaid eligible were uninsured (Table 1). The average ICE measure of economic segregation was 0 (SD 0.04), indicating that the number of households living in the extremes (>80^th and <20^th percentiles) were similar (i.e., little difference in economic opportunity). The mean jail rate across counties where participants lived was 287 per 100,000 persons (SD: 166). Characteristics of 11,982 and 13,910 infants eligible for the Black-White and Latinx-White structural racism analyses, respectively, are found in Supplemental Tables 3 and 4.

When reviewing Spearman correlations among area-level variables (Table 2), we observed a low degree of correlation (<0.2) among measures of uninsured participants who were Medicaid eligible, the ICE measure of economic segregation, and the other area-level variables. High housing cost burden, unemployment rate, educational attainment, and life expectancy were more highly correlated with each other (0.34–0.59). Jail rate was moderately correlated with educational attainment (0.37) and life expectancy (0.34). See Supplemental Tables 5 and 6 for pairwise correlations among Black-White and Latinx-White area-level ratio measures.

Table 2.

Spearman correlation matrix for area-level study variables among all participants

	High housing cost burden	Unemployment	Educational attainment	Life Expectancy	Uninsured that are Medicaid eligible	ICE	Jail rate
High housing cost b urden	1	0.58	0.44	0.34	0.04	0.06	0.04
Unemployment	0.58	1	0.56	0.46	0.06	0.06	0.20
Educational attainment	0.44	0.56	1	0.59	−0.07	0.07	0.37
Life Expectancy	0.34	0.46	0.59	1	−0.01	0.04	0.34
Uninsured that are Medicaid eligible	0.04	0.06	−0.07	−0.01	1	−0.23	0.04
ICE	0.06	0.06	0.07	0.04	−0.23	1	0.03
Jail Rate	0.04	0.20	0.37	0.34	0.04	0.03	1

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Note: Unemployment, life expectancy, and ICE were reverse coded.

Overall economic disadvantage results

We observed an association of increasing overall economic disadvantage with lower infant birthweight [ $ψ$ : −25.65, 95% CI (−45.83, −5.48); Table 3 and Figure 1], where $ψ$ represents the average difference in birthweight in grams associated with increasing the quartile of each measure of economic disadvantage simultaneously. Race-and ethnicity-stratified results had wide confidence intervals and did not show consistent patterns. Similar trends were observed in the birthweight-for-gestational age z-score models (Table 4 and Figure 2), in which we observed an association between increasing economic disadvantage and lower birthweight-for-gestational age z-scores [ $ψ$ : −0.06, 95% CI (−0.11, −0.01); Table 4]. Contrary to our hypothesis, we observed a positive association between economic disadvantage and birthweight-for-gestational age z-scores among infants of Black parents [ $ψ$ : 0.17, 95% CI (0.01, 0.34)]. The association for infants of Latinx parents was also positive, although smaller in magnitude and with confidence intervals that crossed the null [ $ψ$ : 0.07, 95% CI (−0.08, 0.21)]. Higher economic disadvantage was associated with lower birthweight-for-gestational age z-scores for infants with non-Latinx White parents [ $ψ$ : −0.08, 95% CI (−0.15, −0.02)]. We did not observe any differences in gestational age associated with higher economic disadvantage, overall or for any of the stratified racial/ethnic groups (Table 5 and Figure 3). We did not find any associations between economic disadvantage and odds of preterm birth, overall or for any race/ethnic group (Supplemental Table 7); however, we did observe associations between overall economic disadvantage and odds of small-for-gestational age [ $ψ$ : 1.28, 95% CI (1.14, 1.43)] (Supplemental Table 8).

Table 3.

Quantile G-computation results for the associations of economic disadvantage and structural racism on birthweight (in grams).

		ψ	(95% CI)	Sample size	# cohorts	# census tracts
Overall economic disadvantage	All participants	−25.65	(−45.83, −5.48)	15,650	35	5,469
	Black participants	53.10	(−9.21, 115.40)	1,804	32	1,185
	Latinx participants	−12.46	(−59.69, 34.76)	2,913	35	1,717
	non-Latinx White participants	−1.69	(−27.99, 24.61)	7,517	33	3,045
Black/White structural racism	Black participants	−15.70	(−82.89, 51.48)	1,689	30	1,123
	non-Latinx White participants	57.47	(13.26, 101.67)	4,659	35	2,300
Latinx/White structural racism	Latinx participants	−80.83	(−143.42, −18.23)	2,801	35	1,654
	non-Latinx White participants	−62.23	(−100.17, −24.30)	6,292	33	2,772

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Note: All models adjusted for maternal education, parity, child sex, and maternal age. Models included a clustering term for census tract to account for potential clustering by level of analysis. The Black group includes both Latinx and non-Latinx infants, and the Latinx group includes infants of any race. Quartiles are consistent within each disadvantage measure to allow comparisons within subgroups. For example, estimates for the Overall economic disadvantage group are based on quartiles defined by all participants. Quartiles are based on Black and non-Latinx White participants for the Black/White structural racism measure, and quartiles are based on Latinx and non-Latinx White participants for the Latinx/White structural racism measure.

Figure 1: — Note: All models adjusted for maternal education, parity, child sex, and maternal age. Models included a clustering term for census tract to account for potential clustering by level of analysis. Quartiles are consistent within each disadvantage measure to allow comparisons within subgroups. For example, estimates for the Overall economic disadvantage group are based on quartiles defined by all participants. Quartiles are based on Black and non-Latinx White participants for the Black/White structural racism measure, and quartiles are based on Latinx and non-Latinx White participants for the Latinx/White structural racism measure.

Table 4.

Quantile G-computation results for the associations of economic disadvantage and structural racism on sex-specific birthweight for gestational age z-scores.

		ψ	(95% CI)	Sample size	# cohorts	# census tracts
Overall economic disadvantage	All participants	−0.06	(−0.11, −0.01)	15,636	35	5,464
	Black participants	0.17	(0.01, 0.34)	1,800	32	1,182
	Latinx participants	0.07	(−0.08, 0.21)	2,911	35	1,716
	non-Latinx White participants	−0.08	(−0.15, −0.02)	7,514	33	3,044
Black/White structural racism	Black participants	0.01	(−0.111, 0.13)	1,685	30	1,120
	non-Latinx White participants	0.01	(−0.07, 0.09)	4,657	33	2,300
Latinx/White structural racism	Latinx participants	−0.16	(−0.27, −0.04)	2,799	35	1,653
	non-Latinx White participants	−0.07	(−0.14, 0.01)	6,290	33	2,772

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Figure 2: — Note: All models adjusted for maternal education, parity, child sex, and maternal age. Models included a clustering term for census tract to account for potential clustering by level of analysis. Quartiles are consistent within each disadvantage measure to allow comparisons within subgroups. For example, estimates for the Overall economic disadvantage group are based on quartiles defined by all participants. Quartiles are based on Black and non-Latinx White participants for the Black/White structural racism measure, and quartiles are based on Latinx and non-Latinx White participants for the Latinx/White structural racism measure.

Table 5.

Quantile G-computation results for the associations of economic disadvantage and structural racism on gestational age at birth (in weeks).

		ψ	(95% CI)	Sample size	# cohorts	# census tracts
Overall economic disadvantage	All participants	−0.02	(−0.13, 0.09)	15,694	35	5,476
	Black participants	−0.15	(−0.46, 0.17)	1,810	32	1,187
	Latinx participants	−0.04	(−0.30, 0.22)	2,925	35	1724
	non-Latinx White participants	0.02	(−0.12, 0.15)	7,533	33	3,379
Black/White structural racism	Black participants	−0.13	(−0.41, 0.16)	1,695	30	1,125
	non-Latinx White participants	0.35	(0.19, 0.50)	4,667	33	2,301
Latinx/White structural racism	Latinx participants	−0.16	(−0.41, 0.09)	2813	35	1,661
	non-Latinx White participants	−0.18	(−0.31, −0.04)	6,306	33	2,774

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Figure 3: — Note: All models adjusted for maternal education, parity, child sex, and maternal age. Models included a clustering term for census tract to account for potential clustering by level of analysis. Quartiles are consistent within each disadvantage measure to allow comparisons within subgroups. For example, estimates for the Overall economic disadvantage group are based on quartiles defined by all participants. Quartiles are based on Black and non-Latinx White participants for the Black/White structural racism measure, and quartiles are based on Latinx and non-Latinx White participants for the Latinx/White structural racism measure.

Structural racism results

In the Latinx-White models, we observed an association between a group-specific quartile increase in structural racism measures and lower birthweight among Latinx parents [( $ψ$ : −80.83, 95% CI (−143.42, −18.23)]. We also observed an association of a simultaneous group-specific quartile increase in structural racism measures and birthweight among non-Latinx White parents [( $ψ$ : −62.23, 95% CI (−100.17, −24.30)]. In the Black-White structural racism mixture models, higher structural racism was associated with lower birthweight among Black parents, although the confidence intervals were wide and crossed the null [ $ψ$ : −15.70; 95% CI (−82.89, 51.48)]. Among non-Latinx White parents, higher Black-White structural racism was associated with higher birthweight [ $ψ$ : 57.47 95% CI (13.26, 101.67)] (Table 3 and Figure 1).

Latinx-White structural racism was negatively associated with birthweight-for-gestational age z-scores among infants of Latinx parents [ $ψ$ : −0.16, 95% CI (−0.27, −0.04)], with smaller associations among infants of non-Latinx White parents [ $ψ$ : −0.07, 95% CI (−0.14, 0.01; Table 4 and Figure 2)]. Group-specific quartile increases in Black-White structural racism were not associated with birthweight-for-gestational age z-scores among either Black infants [ $ψ$ : 0.01, 95% CI (−0.11, 0.13)] or White infants [ $ψ$ : 0.01, 95% CI (−0.07, 0.09)].

Latinx-White structural racism was associated with lower gestational age among infants of both Latinx parents [ $ψ$ : −0.16, 95% CI (−0.41, 0.09)] and non-Latinx White parents [ $ψ$ : −0.18, 95% CI (−0.31, −0.04)]. Black-White structural racism was associated with lower gestational age among infants of Black parents [ $ψ$ : −0.13, 95% CI (−0.41, 0.16)], but higher gestational age among infants of White parents [ $ψ$ : 0.35, 95% CI (0.19, 0.50)]. We did not find any associations between structural racism and odds of preterm birth, overall or for any race/ethnic group (Supplemental Table 7). We did observe associations between Latinx-White structural racism and small-for-gestational age for Latinx participants [ $ψ$ : 1.75, 95% CI (1.23, 2.49)] (Supplemental Table 8).

Discussion

Using a mixtures modeling approach, we examined the relationships between multiple indicators of area-level economic disadvantage and structural racism with perinatal health outcomes in a demographically diverse cohort of over 15,000 birthing people in the US. Our results indicated a consistently negative association between economic disadvantage and both birthweight and birthweight-for-gestational age z-scores. We observed stronger negative relationships between area-level structural racism measures and newborn birthweight and birthweight z-scores among Latinx participants compared with non-Latinx White participants. In Black-White racism models, effect estimates for birthweight were less precise but negative among Black participants and positive for White participants; results for birthweight-for-gestational age z-scores were null for both groups. These findings suggest that the effects of area-level measures of economic disadvantage and structural racism can differentially affect racial and ethnic groups in terms of fetal growth outcomes, with stronger negative associations among birthing people of color compared with their White counterparts.

Previous studies have taken different approaches to characterizing structural racism exposures, including deriving single area-level measures such as ICE (60), racial residential segregation (36,37,40,54,61), Black versus White employment probability (36), and gentrification (62), often using different geographic area units (e.g., census tract or county). A systematic review of structural racism and maternal morbidity and mortality found higher risk of adverse maternal health outcomes for people living in places with higher metrics for structural racism (63). This study included historical or current racial residential segregation, the ratio of Black and White female unemployment rates, the ratio of Black and White educational attainment, and the ratio of Black and White incarceration rates as indicators of structural racism. Other studies have developed multi-indicator scales to characterize area-level structural racism. For example, one study used confirmatory factor modeling that integrated indicators of education, housing, employment, criminal justice, and health care to characterize area level exposures to structural racism (22). Our study also used multiple indicators, but rather than summarizing into a single metric or several factors, we analyzed how increased exposure to all indicators simultaneously affected birth outcomes.

Our analysis has limitations. First, due to data availability we focused on primarily urban populations, as 90% of our participants were from counties in metropolitan areas. Other measures are likely needed to fully characterize forms of economic disadvantage and structural racism that affect other marginalized groups for which we did not have an adequate sample size, such as rural populations, Indigenous communities, and Asian American and Pacific Islander populations. Our sample size was smaller for Black participants compared to the White or Latinx groups, and we had few cohorts from the Southern US, both of which likely contributed to the lack of precision in our estimates for that group.

Several prior studies have highlighted the need to examine intersectional effect modifiers when studying the effects of racism on health (34,36,55). Data and sample size limitations did not allow us to examine heterogeneity of effects within the groups that we analyzed, for example variation between immigrant versus non-immigrant groups, between race and education, or among different populations within the Latinx diaspora. Our area-level metrics were only available at the county or census tract levels, even though smaller or larger units may better align with how communities define themselves or be more meaningful for policy assessment (35). In particular, while tract-level metrics may be best for characterizing overall neighborhood economic disadvantage, which can vary significantly within larger geographic units, tracts may not be well-suited for the ratio measures that compared Black to White and Latinx to White groups. Because of racial residential segregation, many neighborhoods do not have both Black and White, or Latinx and White people living near one another. Therefore, the tract-level ratio measures may underestimate the effects of structural racism because of the lack of both economic and racial integration at this geographic level.

Although our study focused on economic disadvantage and structural racism at the residential neighborhood level, it did not account for exposures to discrimination in other contexts, such as, workplace or health-care settings, or local, state, and federal policy jurisdictions. Indeed, racism in social security laws, voter suppression, credit markets, media, immigration policies, environmental regulation, as well as the criminal justice system (beyond our measure of the number of people incarcerated) are important features of the mutually reinforcing inequitable systems that, due to data limitations, could not be accounted for in our analysis (45). We did not include ECHO participants from Puerto Rico because most census tract-level variables used to derive our area-level measures were not available, a problematic and discriminatory data gap for a unique Latinx population that faces historical and contemporary forms of colonialism characterized by political disenfranchisement and socioeconomic marginalization (64). We also had to exclude participants with missing outcome or area-level variables, which may have introduced some selection bias.

Furthermore, the quantiles of exposures were based on the census tracts of participants included in the analysis and not the entire US. This ensured we had positivity to estimate our parameters, but the distribution of the exposures can not necessarily be generalized to a national population and may underestimate the range of exposures, especially since we had few cohorts from the Southern US. We wanted to evaluate individual-level mediators, including income and financial hardship, social support, stressful life events, and food insecurity, but these measures were not consistently available, precluding harmonization of these measures across multiple ECHO cohorts. Previous studies have highlighted the need to incorporate the chronicity, severity, and duration of discrimination to accurately capture experiences of interpersonal discrimination (3,4) as well as the importance of accounting for the timing of exposure over the life course rather than at a snapshot in time (34,43,45,65,66). Our analyses were limited to residential address information at one time point and so were unable to account for these important temporal elements of exposure. The quantile G-computation method we used was designed to leverage strong correlations between exposure variables; the area-level measures used in our study were not as consistently correlated as we expected, and this reduced the power of our analyses. Finally, we adjusted for the covariates available in ECHO, but there may be residual confounding due to our inability to control for the level of social and economic resources available for birthing people during childhood and early adulthood.

Our study has several notable strengths. To our knowledge, this is among the first studies to leverage nationwide ECHO data to evaluate the perinatal health impacts of economic disadvantage and structural racism among Latinx, Black, and White birthing people. The use of data from pooled ECHO sites allowed us to leverage a socioeconomically, and demographically diverse study population of over 15,000 birthing people from 35 cohorts spanning multiple geographic regions across the continental US and evaluate associations with census tract-level measures of structural racism and economic disadvantage. Second, we used quantile G-computation which although assumes linearity in associations, nonetheless allowed us to capture interactions between indicators which is a strength over approaches that use indices that sum over multiple indicators (30). Indeed, single measures of economic disadvantage and structural racism may not adequately capture real-life experiences and contexts, as these exposures are likely to co-occur in complex ways that may have joint or cumulative effects on health outcomes (22,35,43–45,47).

Conclusion

We found that economic disadvantage and structural racism were associated with lower birthweight and birthweight-for-gestational age z-scores, especially among Latinx birthing people. Our results indicate that co-occurring forms of structural inequity likely shape the origins and persistence of racialized disparities in perinatal outcomes. Our findings, along with prior research, can inform the development and implementation of socioeconomic policies that, for example, enhance equitable access to remunerative employment, quality health care, affordable housing, and educational opportunities, particularly for racially marginalized groups. Future studies should further elucidate the individual-level mediating effects and biological pathways related to these area-level indicators of structural inequality.

Supplementary Material

NIHMS2149144-supplement-Supplementary_Material.docx^{(591.8KB, docx)}

Group Authorship

NIHMS2149144-supplement-Group_Authorship.xlsx^{(60.4KB, xlsx)}

ECHO Acknowledgments

The authors wish to thank our ECHO Colleagues; the medical, nursing, and program staff; and the children and families participating in the ECHO cohort.

Funding Statement and Disclaimer

NIH Disclaimer:

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Research reported in this publication was supported by the Environmental influences on Child Health Outcomes (ECHO) Program, Office of the Director, National Institutes of Health, under Award Numbers U2COD023375 (Coordinating Center), U24OD023382 (Data Analysis Center), U24OD023319 with co-funding from the Office of Behavioral and Social Science Research (Measurement Core), U24OD035523 (Lab Core), ES0266542 (HHEAR), U24ES026539 (HHEAR Barbara O’Brien), U2CES026533 (HHEAR Lisa Peterson), U2CES026542 (HHEAR Patrick Parsons, Kannan Kurunthacalam), U2CES030859 (HHEAR Manish Arora), U2CES030857 (HHEAR Timothy R. Fennell, Susan J. Sumner, Xiuxia Du), U2CES026555 (HHEAR Susan L. Teitelbaum), U2CES026561 (HHEAR Robert O. Wright), U2CES030851 (HHEAR Heather M. Stapleton, P. Lee Ferguson), UG3/UH3OD023251 (Akram Alshawabkeh), UH3OD023320 and UG3OD035546 (Judy Aschner), UH3OD023332 (Clancy Blair, Leonardo Trasande), UG3/UH3OD023253 (Carlos Camargo), UG3/UH3OD023248 and UG3OD035526 (Dana Dabelea), UG3/UH3OD023313 (Daphne Koinis Mitchell), UH3OD023328 (Cristiane Duarte), UH3OD023318 (Anne Dunlop), UG3/UH3OD023279 (Amy Elliott), UG3/UH3OD023289 (Assiamira Ferrara), UG3/UH3OD023282 (James Gern), UH3OD023287 (Carrie Breton), UG3/UH3OD023365 (Irva Hertz-Picciotto), UG3/UH3OD023244 (Alison Hipwell), UG3/UH3OD023275 (Margaret Karagas), UH3OD023271 and UG3OD035528 (Catherine Karr), UH3OD023347 (Barry Lester), UG3/UH3OD023389 (Leslie Leve), UG3/UH3OD023344 (Debra MacKenzie), UH3OD023268 (Scott Weiss), UG3/UH3OD023288 (Cynthia McEvoy), UG3/UH3OD023342 (Kristen Lyall), UG3/UH3OD023349 (Thomas O’Connor), UH3OD023286 and UG3OD035533 (Emily Oken), UG3/UH3OD023348 (Mike O’Shea), UG3/UH3OD023285 (Jean Kerver), UG3/UH3OD023290 (Julie Herbstman), UG3/UH3OD023272 (Susan Schantz), UG3/UH3OD023249 (Joseph Stanford), UG3/UH3OD023305 (Leonardo Trasande), UG3/UH3OD023337 (Rosalind Wright), UG3OD035508 (Sheela Sathyanarayana), UG3OD035509 (Anne Marie Singh), UG3OD035513 and UG3OD035532 (Annemarie Stroustrup), UG3OD035516 and UG3OD035517 (Tina Hartert), UG3OD035518 (Jennifer Straughen), UG3OD035519 (Qi Zhao), UG3OD035521 (Katherine Rivera-Spoljaric), UG3OD035527 (Emily S Barrett), UG3OD035540 (Monique Marie Hedderson), UG3OD035543 (Kelly J Hunt), UG3OD035537 (Sunni L Mumford), UG3OD035529 (Hong-Ngoc Nguyen), UG3OD035542 (Hudson Santos), UG3OD035550 (Rebecca Schmidt), UG3OD035536 (Jonathan Slaughter), UG3OD035544 (Kristina Whitworth).

Role of Funder Statement

The sponsor, NIH, participated in the overall design and implementation of the ECHO Program, which was funded as a cooperative agreement between NIH and grant awardees. The sponsor approved the Steering Committee-developed ECHO protocol and its amendments including COVID-19 measures. The sponsor had no access to the central database, which was housed at the ECHO Data Analysis Center. Data management and site monitoring were performed by the ECHO Data Analysis Center and Coordinating Center. All analyses for scientific publication were performed by the study statistician, independently of the sponsor. The lead author wrote all drafts of the manuscript and made revisions based on co-authors and the ECHO Publication Committee (a subcommittee of the ECHO Operations Committee) feedback without input from the sponsor. The study sponsor did not review or approve the manuscript for submission to the journal.

Data Availability Statement

Select de-identified data from the ECHO Program are available through NICHD’s Data and Specimen Hub (DASH). Information on study data not available on DASH, such as some Indigenous datasets, can be found on the ECHO study DASH webpage.

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36.Chantarat T, Mentzer KM, Van Riper DC, Hardeman RR. Where are the labor markets?: Examining the association between structural racism in labor markets and infant birth weight. Health & Place. 2022. Mar 1;74:102742. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Chambers BD, Erausquin JT, Tanner AE, Nichols TR, Brown-Jeffy S. Testing the Association Between Traditional and Novel Indicators of County-Level Structural Racism and Birth Outcomes among Black and White Women. J Racial and Ethnic Health Disparities. 2018. Oct 1;5(5):966–77. [DOI] [PubMed] [Google Scholar]
38.Wallace ME, Mendola P, Liu D, Grantz KL. Joint Effects of Structural Racism and Income Inequality on Small-for-Gestational-Age Birth. Am J Public Health. 2015. Aug;105(8):1681–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
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43.Dean LT, Thorpe RJ. What Structural Racism Is (or Is Not) and How to Measure It: Clarity for Public Health and Medical Researchers. Am J Epidemiol. 2022. Jul 5;191(9):1521–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Neblett EW. Racism measurement and influences, variations on scientific racism, and a vision. Social Science & Medicine. 2023. Jan 1;316:115247. [DOI] [PubMed] [Google Scholar]
45.Groos M, Wallace M, Hardeman R, Theall KP. Measuring inequity: a systematic review of methods used to quantify structural racism. 2018;11(2). [Google Scholar]
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50.Chantarat T, Riper DCV, Hardeman RR. The intricacy of structural racism measurement: A pilot development of a latent-class multidimensional measure. eClinicalMedicine [Internet]. 2021. Oct 1 [cited 2024 Jan 3];40. Available from: https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(21)00372-2/fulltext [DOI] [PMC free article] [PubMed] [Google Scholar]
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52.Knapp EA, Kress AM, Parker CB, Page GP, McArthur K, Gachigi KK, et al. The Environmental Influences on Child Health Outcomes (ECHO)-Wide Cohort. Am J Epidemiol. 2023. Mar 24;192(8):1249–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
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56.Kothari CL, Paul R, Dormitorio B, Ospina F, James A, Lenz D, et al. The interplay of race, socioeconomic status and neighborhood residence upon birth outcomes in a high black infant mortality community. SSM - Population Health. 2016. Dec 1;2:859–67. [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Aris IM, Kleinman KP, Belfort MB, Kaimal A, Oken E. A 2017 US Reference for Singleton Birth Weight Percentiles Using Obstetric Estimates of Gestation. Pediatrics. 2019. Jul;144(1):e20190076. [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Glynn RJ, Laird NM, Rubin DB. Multiple Imputation in Mixture Models for Nonignorable Nonresponse with Follow-ups. Journal of the American Statistical Association. 1993. Sep 1;88(423):984–93. [PubMed] [Google Scholar]
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61.White K, Borrell LN. Racial/ethnic residential segregation: Framing the context of health risk and health disparities. Health & Place. 2011. Mar 1;17(2):438–48. [DOI] [PMC free article] [PubMed] [Google Scholar]
62.Huynh M, Maroko AR. Gentrification and Preterm Birth in New York City, 2008–2010. J Urban Health. 2014. Feb 1;91(1):211–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
63.Hailu EM, Maddali SR, Snowden JM, Carmichael SL, Mujahid MS. Structural racism and adverse maternal health outcomes: A systematic review. Health & Place. 2022. Nov 1;78:102923. [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Ramos JGP, Garriga-López A, Rodríguez-Díaz CE. How Is Colonialism a Sociostructural Determinant of Health in Puerto Rico? AMA Journal of Ethics. 2022. Apr 1;24(4):305–12. [DOI] [PubMed] [Google Scholar]
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66.Gaston SA, Jackson CL. Invited Commentary: The Need for Repeated Measures and Other Methodological Considerations When Investigating Discrimination as a Contributor to Health. American Journal of Epidemiology. 2022. Feb 19;191(3):379–83. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material

NIHMS2149144-supplement-Supplementary_Material.docx^{(591.8KB, docx)}

Group Authorship

NIHMS2149144-supplement-Group_Authorship.xlsx^{(60.4KB, xlsx)}

Data Availability Statement

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[R41] 41.Chantarat T, Van Riper DC, Hardeman RR. Multidimensional structural racism predicts birth outcomes for Black and White Minnesotans. Health Services Research. 2022;57(3):448–57. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R42] 42.Britton ML, Shin H. Metropolitan residential segregation and very preterm birth among African American and Mexican-origin women. Social Science & Medicine. 2013. Dec 1;98:37–45. [DOI] [PubMed] [Google Scholar]

[R43] 43.Dean LT, Thorpe RJ. What Structural Racism Is (or Is Not) and How to Measure It: Clarity for Public Health and Medical Researchers. Am J Epidemiol. 2022. Jul 5;191(9):1521–6. [DOI] [PMC free article] [PubMed] [Google Scholar]

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[R48] 48.LaFave SE, Bandeen-Roche K, Gee G, Thorpe RJ, Li Q, Crews D, et al. Quantifying Older Black Americans’ Exposure to Structural Racial Discrimination: How Can We Measure the Water In Which We Swim? J Urban Health. 2022. Oct 1;99(5):794–802. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R49] 49.Gee GC, Ford CL. Structural racism and health inequities. Du Bois Rev. 2011. Apr;8(1):115–32. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R50] 50.Chantarat T, Riper DCV, Hardeman RR. The intricacy of structural racism measurement: A pilot development of a latent-class multidimensional measure. eClinicalMedicine [Internet]. 2021. Oct 1 [cited 2024 Jan 3];40. Available from: https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(21)00372-2/fulltext [DOI] [PMC free article] [PubMed] [Google Scholar]

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[R52] 52.Knapp EA, Kress AM, Parker CB, Page GP, McArthur K, Gachigi KK, et al. The Environmental Influences on Child Health Outcomes (ECHO)-Wide Cohort. Am J Epidemiol. 2023. Mar 24;192(8):1249–63. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R53] 53.Ncube CN, Enquobahrie DA, Albert SM, Herrick AL, Burke JG. Association of neighborhood context with offspring risk of preterm birth and low birthweight: A systematic review and meta-analysis of population-based studies. Social Science & Medicine. 2016. Mar 1;153:156–64. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R54] 54.Mehra R, Boyd LM, Ickovics JR. Racial residential segregation and adverse birth outcomes: A systematic review and meta-analysis. Social Science & Medicine. 2017. Oct 1;191:237–50. [DOI] [PubMed] [Google Scholar]

[R55] 55.DeAngelis RT. Moving on Up? Neighborhood Status and Racism-Related Distress among Black Americans. Social Forces. 2022. Jun 1;100(4):1503–32. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R56] 56.Kothari CL, Paul R, Dormitorio B, Ospina F, James A, Lenz D, et al. The interplay of race, socioeconomic status and neighborhood residence upon birth outcomes in a high black infant mortality community. SSM - Population Health. 2016. Dec 1;2:859–67. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R57] 57.Aris IM, Kleinman KP, Belfort MB, Kaimal A, Oken E. A 2017 US Reference for Singleton Birth Weight Percentiles Using Obstetric Estimates of Gestation. Pediatrics. 2019. Jul;144(1):e20190076. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R58] 58.Glynn RJ, Laird NM, Rubin DB. Multiple Imputation in Mixture Models for Nonignorable Nonresponse with Follow-ups. Journal of the American Statistical Association. 1993. Sep 1;88(423):984–93. [PubMed] [Google Scholar]

[R59] 59.Marshall A, Altman DG, Holder RL, Royston P. Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines. BMC Medical Research Methodology. 2009. Jul 28;9(1):57. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R60] 60.Chambers BD, Baer RJ, McLemore MR, Jelliffe-Pawlowski LL. Using Index of Concentration at the Extremes as Indicators of Structural Racism to Evaluate the Association with Preterm Birth and Infant Mortality—California, 2011–2012. J Urban Health. 2019. Apr 1;96(2):159–70. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R61] 61.White K, Borrell LN. Racial/ethnic residential segregation: Framing the context of health risk and health disparities. Health & Place. 2011. Mar 1;17(2):438–48. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R62] 62.Huynh M, Maroko AR. Gentrification and Preterm Birth in New York City, 2008–2010. J Urban Health. 2014. Feb 1;91(1):211–20. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R63] 63.Hailu EM, Maddali SR, Snowden JM, Carmichael SL, Mujahid MS. Structural racism and adverse maternal health outcomes: A systematic review. Health & Place. 2022. Nov 1;78:102923. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R64] 64.Ramos JGP, Garriga-López A, Rodríguez-Díaz CE. How Is Colonialism a Sociostructural Determinant of Health in Puerto Rico? AMA Journal of Ethics. 2022. Apr 1;24(4):305–12. [DOI] [PubMed] [Google Scholar]

[R65] 65.Brown T, Homan P. Structural Racism and Health Stratification in the U.S.: Connecting Theory to Measurement. 2024. Jan 3 [cited 2024 Jan 3]; Available from: https://osf.io/3eacp [DOI] [PMC free article] [PubMed]

[R66] 66.Gaston SA, Jackson CL. Invited Commentary: The Need for Repeated Measures and Other Methodological Considerations When Investigating Discrimination as a Contributor to Health. American Journal of Epidemiology. 2022. Feb 19;191(3):379–83. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Applying mixtures methodology to analyze how exposure to structural racism and economic disadvantage affect perinatal health outcomes: An ECHO study

Dana E Goin

Ronel Ghidey

Holly Schuh

Lori Dean

Emily Barrett

Theresa M Bastain

Jessie Buckley

Nicole R Bush

Marie Camerota

Kecia N Carroll

Nicholas Cragoe

Lara J Cushing

Dana Dabelea

Anne L Dunlop

Stephanie Eick

Amy J Elliot

Tali Felson

Sarah Dee Geiger

Frank D Gilliland

Tamarra James-Todd

Linda G Kahn

Matt Kasman

Jordan Kuiper

Bennett Leventhal

Maristella Lucchini

Morgan Nelson

Gwendolyn Norman

Chaela Nutor

T Michael O’Shea

Amy M Padula

Susan Schantz

Mehta-Lee Shilpi

Benjamin Steiger

Tracey Woodruff

Rosalind J Wright

Rachel Morello-Frosch

Abstract

Introduction

Data and Methods

Study Population

Race/ethnicity

Area-level measures of economic disadvantage and structural racism

Birth Outcomes

Covariates

Statistical analysis

RESULTS

Table 1.

Table 2.

Overall economic disadvantage results

Table 3.

Figure 1: Forest plots of effects of economic disadvantage and structural racism on birthweight.

Table 4.

Figure 2: Forest plots of effects of economic disadvantage and structural racism on birthweight-for-gestational age z-scores.

Table 5.

Figure 3: Forest plots of effects of economic disadvantage and structural racism on gestational age in weeks at birth.

Structural racism results

Discussion

Conclusion

Supplementary Material

ECHO Acknowledgments

Funding Statement and Disclaimer

NIH Disclaimer:

Role of Funder Statement

Data Availability Statement

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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