Neighborhood Deprivation and Timely Entry into Prenatal Care,Risk Screening,and Receipt of Care Management Services Among High-Risk Pregnant Medicaid Enrollees in North Carolina

Abstract

Background:

Hypertensive disorders of pregnancy disproportionately impact Black women, contributing to higher morbidity and mortality. Neighborhood deprivation provides insights, as Black families often face hindered access to health care. This study examines the link between neighborhood deprivation and access to prenatal services for high-risk pregnant individuals in North Carolina’s Medicaid-managed Pregnancy Medicaid Homes (PMH).

Methods:

Dataset included Medicaid claims, birth certificate, risk screening, care management, and American Community Survey data from January 2017 to February 2020. We used multivariate logistic regression models to estimate the relationship between timely prenatal care entry, risk screening, and face-to-face care management and Indices of Concentration of the Extremes for race and income, categorized into quintiles from 1 (most deprived) to 5 (most privileged). Analysis included 53,000 pregnant individuals with ≥1 preeclampsia risk factor.

Results:

We found that those from the most deprived neighborhoods had an 11.1 (standard error [SE]: 0.023; 0.0000), 4.1 (SE: 0.014; 0.000), and 8.0 (SE: 0.013; 0.000) percentage points lower likelihood of timely prenatal care entry, risk screening, and care management, respectively, compared with those from the most privileged neighborhoods. Conversely, non-Hispanic Black (NHB) individuals in the least privileged neighborhoods demonstrated a higher likelihood of timely prenatal care entry and care management compared with their non-Hispanic White peers.

Conclusions:

This study reveals that neighborhood deprivation limits access to prenatal care, risk screening, and care management. Although NHB pregnant individuals have slightly better access, likely due to initiatives such as the PMH, deprivation remains a significant barrier. Addressing these disparities requires equity-focused interventions to ensure that all pregnant individuals receive timely prenatal services.

Keywords

hypertensive disorders of pregnancy preeclampsia maternal morbidity Index of Concentration of the Extremes neighborhood deprivation racial segregation

Background

Stark disparities¹ exist in hypertensive disorders of pregnancy (HDoPs) in the United States,^2–5 particularly among low-income, non-Hispanic Black (NHB) birthing people, who account for 24.2% of births.⁵ Studies have shown that Black birthing individuals have a 21–31 percentage point higher probability of developing HDoPs, greater than any other racial/ethnic group, even after adjusting for microlevel factors such as comorbidities, socioeconomic status, and education.^4–8 Until recently, it was common to assume race as a risk factor for HDoPs, particularly preeclampsia, an assumption premised on “inherent racial biological differences.”^9,10 Now, it is more widely understood that racism has biological effects, and significant research has shown that Black birthing people’s experiences of and exposures to systemic racism drive disparities in HDoPs.^11–18 One of the most salient and long-standing manifestations of place-based structural racism in the United States is residential segregation,^19,20 which, despite being illegal since the passage of the Civil Rights Act of 1964, remains a key context in the lives of Black and non-Black people of color in the United States.^21,22 Research has shown that racialized residential segregation, especially when compounded with concentrated poverty, underlies Black residents’ disproportionate exposures to chronic and psychosocial stressors,^19,20 police brutality,^23,24 and fewer opportunities, limiting opportunities for upward social mobility and negatively impacting timely entry into prenatal care.^15,25,26 This evidence reinforces a recent conceptual framework describing how segregation concentrates disinvestment in neighborhoods, creating feedback loops of poor infrastructure, limited access to health care providers, inadequate transportation, environmental hazards, and psychosocial stress during pregnancy.²⁷ Although existing studies explore the effects of residential segregation on maternal and neonatal health, less focus has been placed on its impact on prenatal service accessibility.

Several state and federal initiatives aim to enhance timely entry into prenatal care and access to services, improving maternal and newborn outcomes.^28–34 For instance, North Carolina’s Baby Love program increased the likelihood of first-trimester prenatal care by 4.8 percentage points.³² However, studies highlight stark racial disparities; one study found Black–White differences in timely care entry (74.81% vs. 89.63%).³⁵ These studies often focus on individual socioeconomic status, such as income and education, and rarely consider the role of residential segregation in shaping these factors. Delayed entry hampers risk assessment and early identification of high-risk pregnancies for preeclampsia. According to the American College of Obstetrics and Gynecology and the US Preventive Services Task Force, early screening for preeclampsia risk factors is vital for timely interventions (e.g., monitoring, low-dose aspirin, care management) that may reduce morbidity and mortality.^36,37 Evidence suggests that segregation-related stressors and resource gaps persist beyond initial entry, potentially reducing adherence to recommended screenings and continuity of in-person management for high-risk pregnancies.^38,39 Theoretical models of implicit bias further explain how provider attitudes and stereotypes can shape clinical decision-making during risk screening and care management, leading to underestimation of symptoms, delayed interventions, and diminished trust among Black patients.⁴⁰ A 2022 systematic review and meta-analysis involving 24 studies found that self-reported racial discrimination was significantly associated with increased odds of preterm birth (OR 1.40, 95% CI 1.17–1.68) and low birth weight.⁴¹ However, the link between residential segregation and care processes, such as risk screening and face-to-face care management, remains less understood. This gap underscores the need for research that examines structural determinants that influence access to care across the pregnancy continuum.

This study aims to examine the association of residential segregation with timely entry into prenatal care, subsequent risk screening, and care management among Medicaid enrollees at a high risk of developing preeclampsia. The potential benefits of this research are significant, as it could lead to improved maternal health care for high-risk populations. Hereafter, we use the term neighborhood deprivation for residential segregation, which is consistent with the literature.^17,19,42,43 We pose the following hypotheses: Hypothesis 1:

High-risk pregnant individuals living in the most deprived neighborhoods will have a lower likelihood of timely entry into prenatal care, risk screening, and receipt of care management compared with those living in the most privileged neighborhoods.

Hypothesis 2:

Black birthing individuals living in the most deprived neighborhoods would have a lower likelihood of timely entry into prenatal care, risk screening, and receipt of care management compared with White birthing individuals in the most deprived neighborhoods.

Methods

Setting

In collaboration with the Community Care of North Carolina (CCNC), the North Carolina Medicaid program launched a voluntary Pregnancy Medical Home (PMH) program in 2011 to improve the quality of care for pregnant Medicaid beneficiaries.⁴⁴ Under this program, pregnant individuals and providers complete a standardized risk screening at the first prenatal visit, were given a risk score called the Maternal–Infant Compatibility Score, and were referred to the Care Management for High-Risk Pregnancies program at the local health departments if the score was ≥200, and those with a score of >500 received more intensive care management.⁴⁵ The care manager provided additional touch points and referred for higher levels of care and social support when needed (e.g., transportation, housing). By 2020, 95% of providers were participating in the PMH program. On July 1, 2021, North Carolina Medicaid transitioned from a single-payer fee-for-service model to a capitated model through five prepaid health plans, replacing the PMH program with a mandatory Pregnancy Management Program. This program maintained universal risk screening and care management services through local health departments.⁴⁶

Data Sources

This study uses North Carolina Medicaid claims data linked with birth certificate and CCNC’s pregnancy risk screen and care management data from January 1, 2017, to February 28, 2020, approved for reuse⁴⁵ by CCNC and the NC Department of Health, and was made available by the Cecil G. Sheps Center at UNC Chapel Hill. We also linked the US Census Bureau’s 5-year American Community Survey (ACS) estimates (2016–2020), which are publicly available. The pregnancy risk screen comprises 30 items covering obstetric (e.g., gestational hypertension), medical (e.g., autoimmune disorders), and psychosocial factors (e.g., food insecurity), which are partly self-reported and assessed clinically.⁴⁴ Medicaid claims, birth certificate, pregnancy risk screen, and care management data were linked using the birth key, resulting in a 97% match rate. The ACS is a comprehensive nationwide survey that provides communities with social, economic, housing, and demographic data,⁴⁷ offering a representative sample of the noninstitutionalized US population. It was linked to the analytic file using the maternal county of residence.

The sample included 53,000 individuals aged 15–45, identified as NHB or non-Hispanic White (NHW), who had ≥1 preeclampsia risk factor. Participants had their last menstrual period between December 15, 2016, and March 31, 2019, to capture their prenatal and postpartum periods, and had prenatal Medicaid coverage for ≥6 months. Individuals with emergency Medicaid were excluded because of the limited window of observability. Table 1 lists the preeclampsia risk factors used in this study. Figure 1 illustrates sample selection and distribution.

FIG. 1.

Sample selection criteria and distribution. ICE = Index of Concentration of the Extremes.

Table 1.

Preeclampsia Risk Factors Identified by the US Preventive Task Force and the American College of Obstetrics and Gynecology

Risk factors of preeclampsia
Pregnant individuals at high risk of preeclampsia with one or more of the following risk factors: History of preeclampsia, especially when accompanied by an adverse outcome^a Multifetal gestation^b Chronic hypertension^b Pregestational type 1 or 2 diabetes^b Kidney disease^b Autoimmune disease (i.e., systemic lupus erythematous, antiphospholipid syndrome)^b Combinations of multiple moderate-risk factors
Pregnant individuals with more than one of several moderate risk factors: Nulliparity^b Obesity (i.e., body mass index >30)^b Family history of preeclampsia (i.e., mother or sister) Black race (as a proxy for underlying racism)^c Lower income^d Age 35 years or older^b Personal history factors (e.g., low birth weight or small for gestational age, previous adverse pregnancy outcome, >10-year pregnancy interval) In vitro fertilization

Risk factors of preeclampsia

Pregnant individuals at high risk of preeclampsia with one or more of the following risk factors:

History of preeclampsia, especially when accompanied by an adverse outcome^a

Multifetal gestation^b

Chronic hypertension^b

Pregestational type 1 or 2 diabetes^b

Kidney disease^b

Autoimmune disease (i.e., systemic lupus erythematous, antiphospholipid syndrome)^b

Combinations of multiple moderate-risk factors

Pregnant individuals with more than one of several moderate risk factors:

Nulliparity^b

Obesity (i.e., body mass index >30)^b

Family history of preeclampsia (i.e., mother or sister)

Black race (as a proxy for underlying racism)^c

Lower income^d

Age 35 years or older^b

Personal history factors (e.g., low birth weight or small for gestational age, previous adverse pregnancy outcome, >10-year pregnancy interval)

In vitro fertilization

Variables included in the analysis. Other variables were either unavailable or had high levels of missingness.

These variables were available for only those who had completed risk screening and hence were included only in the statistical models for face-to-face care management.

Race was not included as a risk factor; instead, it was included as a separate variable in the models.

The income variable was not available. However, the study included only Medicaid enrollees who were under 138% of the federal poverty level, hence a homogenous group.

Dependent and Independent Variables

The outcomes include timely entry into prenatal care, risk screener completion, and face-to-face care management services. All outcome variables were coded as binary. Timely entry was defined as starting prenatal care at or before 13 weeks of gestation, consistent with the literature and clinical guidelines.^34,35,48

The Index of Concentration of the Extremes (ICE) is a parsimonious measure of area-level segregation^49–51 that captures spatial and social polarization by quantifying extremes of deprived and privileged social groups and thus proxies area-level racialized and economic segregation,^49,52–55 and has been commonly examined for the association with maternal and infant outcomes,^{42,43,52–54,56} including HDoPs.^16–18 We used the US Census ACS 5-year estimates (2016–2020) to generate three ICE measures for county-level distribution of racial segregation and economic deprivation: ICE_race, ICE_income, and ICE_race+income. The ICE measures were computed using the following formula:^{51,52,16,58,59}

I C E_{i} = (A_{i} - P_{i}) / T_{i}

where A_i represents the number of individuals belonging to the privileged extreme, while P_i indicates the number of individuals who belong to the deprived extreme in the ith county. T_i denotes the total population of the ith county. All ICE variables were coded as continuous, ranging from −1 to 1, with −1 corresponding to complete deprivation and 1 to complete privilege. For ICE_race, the privileged group was defined as NHW, and the deprived group as NHB individuals at the county level. For ICE_income, the income-deprived group consisted of individuals who earned less than $25,000 annually, while the privileged group included those with an income of $100,000 or more at the county level. For ICE_race+income, the deprived group being NHB people with <$25,000 earnings annually, while the privileged group being NHW people who made ≥$100,000 at the county level. Next, all ICE scores were categorized into five quintiles based on sample distributions of these measures: quintile 1 (least privileged) to quintile 5 (most privileged).^50,52 A detailed description of how each ICE variable was coded and its spatial distribution in North Carolina is available in Supplementary File 1. We chose these three ICE variables to examine the association of racial segregation, economic segregation, and their combined association with access to prenatal services. Racial segregation operates through mechanisms, such as resource deprivation, environmental exposure, and institutional exclusion, that systematically disadvantage communities of color, reinforcing long-standing racial inequities in health, education, and economic opportunity.⁵⁹ Economic segregation operates through mechanisms such as unequal resource allocation, educational disparities, and labor market isolation, which systematically limit opportunities for low-income communities.⁶⁰ When racial segregation overlaps with economic disadvantage, it amplifies exposure to structural barriers and systemic neglect, leading to worse health and social outcomes than either form of segregation alone.^50,58,59

Covariates

We included several covariates in the analysis based on preeclampsia risk factors (Table 1), other conditions complicating pregnancy (e.g., gestational diabetes), demographic factors (e.g., race and ethnicity), and Medicaid coverage (e.g., number of months of Medicaid coverage) (Table 2). The ICD-10 for diagnoses, as determined and identified using Medicaid claims data, is available in Supplementary File 2.

Table 2.

Participant Characteristics by Race/Ethnicity for the Study Sample from 2017 to 2019

	Total(n = 53,000)	Non-Hispanic White(n = 25,976)	Non-Hispanic Black(n = 27,024)	p Value
ICE^a_race				<0.001
1	24.58% (13,027)	12.4% (3221)	36.29% (9,806)
2	14.93% (7,912)	7.77% (2,017)	21.81% (5,895)
3	18.94% (10,039)	16.42% (4,264)	21.37% (5,775)
4	20.92% (11,086)	26.79% (6,958)	15.28% (4,128)
5	20.63% (10,936)	36.63% (9,516)	5.25% (1,420)
ICE_income				<0.001
1	21.70% (11,502)	22.54% (5,854)	20.90% (5,648)
2	22.43% (11,889)	25.05% (6,508)	19.91% (5,381)
3	24.22% (12,835)	23.85% (6,194)	24.57% (6,641)
4	15.10% (8,002)	18.48% (4,801)	11.85% (3,201)
5	16.55% (8,772)	10.08% (2,619)	22.77% (6,153)
ICE_race+income				<0.001
1	26.09% (13,828)	12.03% (3,125)	39.61% (10,703)
2	13.45% (7,130)	8.17% (2,123)	18.53% (5,007)
3	19.06% (10,101)	16.57% (4,305)	21.45% (5,796)
4	20.81% (11,027)	26.67% (6,929)	15.16% (4,098)
5	20.59% (10,914)	36.55% (9,494)	5.25% (1,420)
Timely entry into care				<0.001
Yes	62.95% (34,425)	70.13% (20,178)	59.98% (16,209)
No	32.95% (16,402)	22.32% (5,798)	35.59% (9,618)
Missing	4.10% (2,173)	3.76% (976)	4.42% (1,197)
Risk screening				0.060
Yes	78.02% (41,353)	77.68% (20,178)	78.26% (21,175)
No	21.98% (11,649)	22.32% (5,798)	21.64% (5,849)
Receipt of face-to-face care management^a				<0.001
Yes	60.84% (17,371)	56.42% (7,961)	64.07% (9,410)
No	39.16% (11,180)	42.58% (5,903)	35.93% (5,277)
Number of prenatal visits	12.10 (4.63)	12.48 (4.42)	11.73 (4.80)	<0.001
Maternal–Infant Compatibility Score				<0.001
<200	46.13% (24,449)	46.63% (12,112)	45.65% (12,337)
≥200 & <500	19.65% (10,413)	18.34% (4,765)	20.09% (5,648)
≥500 & <700	2.57% (1,361)	2.16% (561)	2.96% (800)
≥700	31.65% (16,777)	32.87% (8,538)	30.49% (8,239)
Preeclampsia risk factors (count)	1.46 (0.74)	1.42 (0.70)	1.50 (0.78)	<0.001
Preeclampsia risk factors
Advanced maternal age >35 years	11.80% (6,254)	12.56% (3,262)	11.07% (2,097)	<0.001
Chronic hypertension	10.64% (5,639)	7.98% (2,073)	13.2% (3,566)	<0.001
Prepregnancy type 1 diabetes	0.65% (344)	0.73% (188)	0.58% (156)	0.036
Prepregnancy type 2 diabetes	3.70% (1,958)	2.98% (774)	4.38% (1,184)	<0.001
Kidney disorders^b	9.73% (5,155)	8.38% (2,177)	11.02% (2,978)	<0.001
Nulliparity	48.23% (25,564)	50.92% (13,228)	45.65% (12,336)	<0.001
Body mass index >30	53.71% (28,464)	50.26% (13,055)	57.02% (15,409)	<0.001
Multifetal gestation	3.98% (2,107)	3.33% (886)	4.60% (1,241)	<0.001
Systemic lupus erythematosus	0.35% (187)	0.27% (71)	0.43% (116)	<0.002
Obstetric history of preeclampsia	3.32% (1,760)	3.27% (848)	3.38% (912)	0.15
Health conditions complicating index pregnancy
Gestational diabetes	7.77% (4,114)	7.82% (2,032)	7.70% (2,082)	0.61
Gestational hypertension	9.85% (5,639)	8.32% (2,161)	11.31% (3,057)	<0.001
Smoking	22.04% (11,683)	26.95% (7,000)	17.33% (4,683)	<0.001
Substance use	11.79% (6,251)	14.06% (3,652)	9.62% (2,599)	<0.001
Health-related social needs
Unstable housing	1.87% (478)	2.57% (313)	1.21% (128)	<0.001
Food insecurity	4.11% (1,050)	4.24% (517)	4.29% (455)	<0.001
Other characteristics
Maternal education				<0.001
Less than high school	13.49% (7,150)	14.00% (3,637)	13.00% (3,513)
High School	67.03% (35,529)	66.47% (17,266)	67.58% (18,263)
More than high school	19.20% (10,177)	19.17% (4,980)	19.23% (5,197)
Rural residence^c	41.06% (21,761)	49.28% (12,801)	33.16% (8,960)	<0.001
Type of Medicaid				<0.001
Medicaid for pregnant women	47.93% (25,403)	51.17% (13,291)	44.82% (12,112)
Medicaid for adults of families with children	42.68% (24,213)	43.88% (11,398)	47.42% (12,815)
Other Medicaid with full pregnancy benefits	6.39% (3,384)	4.96% (1,287)	7.76% (2,097)
Months of Medicaid (prenatal)	8.14 (1.25)	8.06 (1.25)	8.23 (1.24)	<0.001

Data are presented as mean (standard deviation) for continuous measures and % (n) for categorical measures.

If the Maternal–Infant Compatibility Score was more than 200, women were referred for care management.

Kidney disorders include chronic nephritic syndrome, chronic renal failure, and other chronic kidney diseases.

Maternal county of residence as rural or urban based on rural–urban commuting area codes.⁶⁷

ICE = Index of Concentration of the Extremes.

Analysis

First, we computed the distributions of all variables and tested differences across NHW and NHB using the chi-square test for categorical and t-test for continuous variables. We then used separate logistic regression models to estimate the association of ICE variables with timely entry into prenatal care, risk screen completion, and receipt of face-to-face care management services. Each ICE variable was modeled separately due to collinearity. To evaluate the relationship between ICE variables and risk screening, we included timely entry into prenatal care as a covariate in the models because of its association with risk screening and care management. To assess the association between ICE variables and receipt of face-to-face care management, we restricted the sample to those who completed risk screening.

We conducted postestimation tests (Link test) to assess model specification. All models showed nonsignificant results when prediction was squared, indicating that additional interactions or squared terms may not improve fit. Theory and literature suggest that all covariates could cause heteroskedasticity. For example, differences in timely entry, risk screening, or management between the urban and rural populations can lead to variances and unequal scatter of residuals. The likelihood ratio tests were significant for each model, confirming that the included variables contributed to heteroskedasticity. We compared the fit of the heteroskedastic probit model with the logit and probit models. Detailed model comparisons are in Supplementary File 3. We present results from heteroskedasticity-adjusted models and include year-fixed effects to account for unobserved, time-specific factors that affect outcomes across years. To estimate differential effects by race, we examined whether there are statistically significant differences in the average marginal effects of ICE indices on timely prenatal care, risk screening, and management between NHB and NHW high-risk pregnant individuals. We computed average marginal effects of NHW and NHB from the main models and then tested the null hypothesis $H_{0} : A M E_{NHB} = A M E_{NHW}$ in the same quintile using the delta method. All analyses were conducted using Stata 17.0. The study was approved by the Institutional Review Board at the University of North Carolina at Chapel Hill (#23-2983).

Results

Our sample comprised 53,000 pregnant individuals: 27,024 NHB and 25,976 NHW. NHB individuals had lower rates of timely prenatal care (59.98% vs. 70.13%) but slightly higher risk screening completion (78.68% vs. 77.68%). Among those who had completed risk screening, NHB individuals received more face-to-face care management (64.07% vs. 57.42%) compared with NHW. (Table 2). Table 3 presents participant characteristics further stratified by completion of risk screening. Individuals who completed risk screening had more preeclampsia risk factors (NHW mean 1.53 vs. 1.32; NHB 1.63 vs. 1.38) and a higher prevalence of prepregnancy type 1/2 diabetes, kidney disorders, and gestational hypertension but were less often primiparous and less often had BMI >30; they also had longer prenatal member months, consistent with greater continuity in coverage and care. Furthermore, substance use and smoking were more common among those who completed risk screening. Additional stratification of participant characteristics by ICE variables is available in Supplementary File 4.

Table 3.

Participant Characteristics by Race/Ethnicity, Further Stratified by Completion of Risk Study for the Study Sample from 2017 to 2019

	Non-Hispanic White				Non-Hispanic Black
	Totaln = 25,976	Risk screen not completedn = 5,798	Risk screen completedn = 20,178	p Value	Totaln = 27,024	Risk screen not completedn = 5,849	Risk screen completedn = 21,175	p Value
ICE_race				<0.001				<0.001
1	12.22% (3,174)	12.14% (704)	12.25% (2,471)		34.73% (9,385)	37.43% (2,189)	33.98% (7,195)
2	7.77% (2,017)	9.45% (548)	7.28% (1,469)		22.43% (6,061)	25.00% (1,462)	21.72% (4,599)
3	16.93% (4,398)	19.98% (1,158)	16.06% (3,240)		21.77% (5,883)	21.99% (1,286)	21.71% (4,597)
4	36.33% (9,437)	25.81% (1,496)	27.02% (5,452)		15.84% (4,281)	12.40% (725)	16.79% (3,555)
5	36.33% (9,437)	32.43% (1,880)	37.45% (7,557)		5.23% (1,412)	3.50% (205)	5.70% (1,207)
ICE_income				<0.001				<0.001
1	22.48% (5,839)	19.10% (1,107)	23.45% (4,732)		19.54% (5,280)	17.20% (1,006)	20.18% (4,274)
2	24.49% (6,362)	25.12% (1,456)	24.31% (4,905)		19.30% (5,216)	17.83% (1,043)	19.71% (4,174)
3	24.01% (6,237)	23.90% (1,386)	24.04% (4,851)		25.41% (6,867)	24.21% (1,416)	25.73% (5,447)
4	18.4% (4,780)	16.80% (974)	18.86% (3,806)		12.67% (3,424)	10.71% (626)	13.21% (2,797)
5	10.61% (2,756)	14.99% (869)	9.35% (1,887)		23.08% (6,237)	29.59% (1,731)	21.28% (4,506)
ICE_race+income				<0.001				<0.001
1	11.92% (3,096)	13.07% (758)	11.59% (2,339)		38.50% (10,404)	44.98% (2,631)	36.71% (7,773)
2	8.12% (2,110)	8.51% (493)	8.01% (1,616)		18.71% (5,056)	17.50% (1,024)	19.05% (4,033)
3	17.03% (4,424)	20.86% (1,209)	15.93% (3,214)		21.84% (5,902)	21.96% (1,284)	21.81% (4,617)
4	26.64% (6920)	25.71% (1,491)	26.91% (5,429)		15.73% (4,251)	12.05% (705)	16.75% (3,546)
5	36.28% (9,424)	32.03% (1,857)	37.50% (7,567)		5.23% (1,255)	3.60% (211)	5.67% (1,201)
Timely entry into care				<0.001				<0.001
No	28.03% (7.281)	25.41% (1,473)	28.78% (5,807)		35.92% (9,707)	32.59% (1,906)	36.84% (7,801)
Yes	67.78% (17,607)	68.90% (3,995)	67.46% (13,612)		59.44% (16,063)	61.90% (3,620)	58.76% (12,442)
Missing	4.20% (1,090)	5.68% (329)	3.77% (761)		4.65% (1,255)	5.60% (327)	4.38% (927)
Received face-to-face care management				<0.001				<0.001
No	60.41% (15,694)	95.97% (5,564)	50.19% (10,127)		54.45% (14,715)	91.30% (5,340)	44.28% (9,375)
Yes	39.59% (10,284)	4.03% (234)	49.81% (10,050)		45.55% (12,309)	8.72% (510)	55.72% (11,799)
Number of prenatal visits	12.51 (4.78)	12.44 (5.04)	12.53 (4.70)	0.36	12.07 (5.18)	12.15 (5.50)	12.04 (5.07)
Maternal–Infant Compatibility Score^a
<200	—	—	46.63% (12,113)		—	—	45.65% (9,666)
≥200 & <500	—	—	18.34% (4,764)		—	—	20.09% (4,254)
≥500 & <700	—	—	2.16% (561)		—	—	2.96% (627)
≥700	—	—	32.87% (8,538)		—	—	30.49% (6,456)
Number of preeclampsia risk factors	1.42 (0.64)	1.32 (0.52)	1.53 (0.68)	<0.001	1.50 (0.63)	1.38 (0.57)	1.63 (0.65)	<0.001
Preeclampsia risk factors
Maternal age >35 years	12.56% (3,263)	8.72% (506)	13.66% (2,757)	<0.001	11.07% (2,992)	6.63% (388)	12.30% (2,605)	<0.001
Chronic hypertension	20.24% (5,258)	19.95% (1,157)	20.33% (4,102)	0.66	29.83% (8,061)	29.58% (1,730)	29.90% (6,331)	0.57
Prepregnancy type 1 diabetes	0.73% (188)	0.35% (20)	0.83% (168)	<0.001	0.58% (156)	0.34% (20)	0.65% (137)	<0.001
Prepregnancy type 2 diabetes	7.52% (1,953)	7.60% (441)	7.50% (1,512)	<0.001	10.52% (2,843)	10.86% (635)	10.43% (2,207)	<0.001
Kidney disorders^b	8.38% (2,177)	5.60% (325)	9.18% (1,853)	<0.03	11.02% (2,978)	8.48% (496)	11.72% (2,482)	<0.001
Nulliparity	57.72% (14,993)	67.32% (3,903)	54.96% (11,090)	<0.001	50.90% (13,755)	57.29% (3,351)	49.14% (10,405)	<0.001
Prepregnancy BMI >30	81.00% (21,041)	84.83% (4,915)	79.90% (16,122)	<0.001	84.01% (11,703)	87.69% (5,129)	82.9% (17,574)	<0.001
Multifetal gestation	7.26% (1,886)	8.19% (481)	6.97% (1,406)	0.039	8.65% (2,338)	10.89% (637)	8.04% (1,701)	<0.001
Systemic lupus erythematosus	0.27% (71)	0.11% (6)	0.32% (65)		0.43% (116)	0.14% (8)	0.51% (108)	<0.001
Obstetric history of preeclampsia^a			3.27% (848)				3.38% (918)
Health conditions complicating index pregnancy
Gestational diabetes	12.68% (3,294)	12.61% (731)	12.7% (2,563)	0.90	13.21% (3,570)	12.21% (714)	13.49% (2,857)	<0.001
Gestational hypertension	14.34% (3,725)	11.52% (668)	15.15% (3,057)	<0.001	18.70% (5,053)	17.44% (1,020)	19.05% (4,034)	<0.001
Substance use	13.8% (3,585)	8.58% (497)	15.30% (3,087)	<0.001	9.98% (2,698)	7.20% (421)	10.75% (2,276)	<0.001
Smoking	25.57% (6,642)	17.02% (987)	28.03% (5,655)	<0.001	17.61% (4,759)	12.60% (737)	19.01% (4,025)	<0.001
Health-related social needs
Housing instability^a	—	—	1.58% (318)		—	—	3.42% (723)
Food insecurity^a	—	—	5.63% (1,136)		—	—	5.60% (1,186)
Other Characteristics
Maternal education				<0.001				<0.001
Less than high school	14.00% (3,637)	8.06% (467)	15.71% (3,170)		13.00% (3,513)	9.16% (536)	14.06% (2,977)
High school	66.47% (17,266)	62.50% (3,624)	67.61% (13,642)		67.58% (18,263)	61.70% (3,609	69.21% (14,655)
More than high school	19.17% (4,980)	30.25% (1754)	15.99% (3,226)		19.23% (5,197)	29.19% (1,707)	16.47% (3,488)
Rural residence	49.17% (12,772)	46.80% (2,713)	49.85% (10,059)	0.007	32.38% (8,750)	30.50% (1,784)	32.90% (6.967)	0.005
Medicaid type								<0.001
Medicaid for pregnant women	54.52% (14,162)	64.99% (3,768)	51.51% (10,393)		48.25% (13,039)	56.97% (3,332)	45.84% (9,707)
Medicaid for adults of families with children	41.01% (10,653)	32.33% (1,874)	43.51% (8,778)		44.96% (12,150)	38.78% (2,268)	46.67% (9,882)
Other Medicaid with full pregnancy benefits	4.46% (1,160)	3.31% (192)	4.80% (969)		6.78% (1,833)	4.63% (271)	7.37% (1,561)
Number of months of Medicaid coverage during pregnancy	6.95 (2.36)	5.67 (2.781)	7.39 (2.02)	<0.001	7.14 (2.33)	6.07 (2.833)	7.48 (2.03)	<0.001

Data are presented as mean (SD) for continuous measures, and % (N) for categorical measures.

These variables were only available for those who completed risk screening; hence, the obstetric history of preeclampsia was not included as a risk factor in the statistical models.

Kidney disorders include chronic nephritic syndrome, other chronic kidney diseases, and acute and chronic renal failure.

ICE, Index of Concentration of the Extremes.

We hypothesized that high-risk pregnant individuals in the most deprived neighborhoods, based on race and income, would have a lower likelihood of timely entry into prenatal care, risk screening, and care management compared with those in the most privileged neighborhoods. We found a significantly lower likelihood of timely entry, risk screening, and care management among individuals in deprived neighborhoods, assessed by race (ICE_race) and combined race and income (ICE_income+race). However, a positive association was found between ICE_income and timely entry into prenatal care and risk screening, indicating that individuals in economically deprived neighborhoods were more likely to enter prenatal care during the first trimester and complete risk screening.

Timely Entry into Prenatal Care

The adjusted multivariate logistic regression model revealed that pregnant individuals in the most deprived neighborhoods based on race (ICE_race) were 11.1 percentage points less likely to enter prenatal care during the first trimester (standard deviation [SE]: 0.8; p value: 0.000) than those in the most privileged neighborhoods, all else constant (Table 4). This trend persisted in the second (8.5 percentage points, SE: 0.8; p value: 0.000), third (9.0 percentage points, SE: 0.7; p value: 0.000), and fourth (8.5 percentage points, SE: 2.4; p value: 0.000) ICE_race quintiles. We found that those in the most deprived neighborhoods based on combined race and income (ICE_income+race) had a 9.5 percentage point (SE: 0.7; p value: 0.000) lower likelihood of entering prenatal care in the first trimester compared with those in the privileged neighborhoods, all else constant. Similar patterns were observed for the second (11.2 percentage points, SE: 0.8; p value: 0.000), third (8.9 percentage points, SE: 0.8; p value: 0.000), and fourth (8.6 percentage points, SE: 0.7; p value: 0.000) quintiles, all else constant. No statistically significant differences were found in the average marginal effect of ICE_race or ICE_income+race on timely access by race/ethnicity (Table 5). Our estimates showed a significant positive average marginal effect of ICE_income on timely prenatal care, with pregnant individuals living in the most deprived neighborhoods based on income having a 5.4 percentage point (SE: 2.4; p value: 0.002) higher likelihood of entering prenatal care during the first trimester compared with those in the most privileged neighborhoods, all else constant. We also found a statistically significant difference in the average marginal effect of ICE_income on timely entry into care by race; NHW individuals in deprived counties had a lower likelihood of entering prenatal care (6.8 percentage points; SE: 1.4) compared with NHB individuals (4.3 percentage points; SE: 1.4).

Table 4.

Average Marginal Effect of Index of Concentration of the Extremes-Race, Index of Concentration of the Extremes-Income, and Index of Concentration of the Extremes-Race plus Income on Timely Entry into Prenatal Care, Risk Screening, and Receipt of Care Management Services

	Timely entry into care(n = 50,425)^a		Risk screening(n = 47,731)^b		Receipt of face-to-face care management(n = 26,094)^c
	Coefficient (SE); p Value	ME (SE); p Value	Coefficient (SE); p Value	ME (SE); p Value	Coefficient (SE); p Value	ME (SE); p Value
ICE_race
1	−1.780(−2.202, −1.358)0.000	−0.111(−0.110, −0.100)0.000	−0.122(−0.120, −0.090)0.002	−0.041(−0.050, −0.030)0.005	−0.421(−0.650, −0.190)0.040	−0.080(−0.09, −0.07)0.000
2	−1.54(−1.930, −1.150)0.000	−0.086(−0.100, −0.080)0.000	−0.594(−0.710, −0.470)0.006	−0.051(−0.062, −0.041)0.000	−0.684(−0.864, −0.511)0.000	−0.127(−0.14, −0.12)0.000
3	−1.740(−2.14, −1.34)0.000	−0.090(−0.10, −0.08)0.000	−0.630(−0.831, −0.432)0.003	−0.050(−0.060, −0.040)0.000	0.169(−0.014, 0.038)0.419	0.007(−0.003, 0.017)0.464
4	−1.36(−1.36, −0.97)0.000	−0.085(−0.09, −0.08)0.000	−0.513(−0.765, −0.263)0.037	−0.001(−0.060, −0.040)0.988	0.063(−0.064, 0.190)0.624	0.038(−0.052,0.128)0.008
5	-ref-	-ref-	-ref-	-ref-	-ref-	-ref-
ICE_income
1	0.010(0.00, 0.02)0.003	0.055(0.043, 0.067)0.000	0.237(0.167, 0.307)0.001	0.077(0.070, 0.084)0.000	0.335(0.125, 0.545)0.111	0.032(−0.019, 0.083)0.523
2	0.013(0.01, 0.02)0.001	0.079(0.070, 0.088)0.000	0.231(0.170, 0.292)0.000	0.058(0.052, 0.064)0.000	0.228(0.005, 0.451)0.307	0.014(−0.039, 0.067)0.797
3	0.013(0.01, 0.02)0.036	0.054(0.046, 0.062)0.000	0.312(0.251, 0.373)0.000	0.057(0.051, 0.063)0.001	0.262(0.146, 0.378)0.023	0.063(0.015, 0.111)0.191
4	0.022(0.018,0.026)0.05	0.094(0.086, 0.102)0.000	0.336(0.401,0.271)0.000	0.087(0.080, 0.094)0.000	0.237(0.149, 0.325)0.007	0.082(0.042, 0.122)0.051
5	-ref-	-ref-	-ref-	-ref-	-ref-	-ref-
ICE_race+income
1	−1.381(−1.761, −1.02)0.000	−0.095(−0.102, −0.088)0.000	−0.237(−0.278, −0.196)0.000	−0.060(−0.066, −0.054)0.000	−0.520(−0.622, −0.418)0.000	−0.071(−0.081, −0.061)0.000
2	−1.840(−2.271, −1.410)0.000	−0.112(−0.120, −0.104)0.000	−0.109(−0.141, −0.077)0.001	−0.020(−0.027, −0.013)0.003	−0.359(−0.474, −0.244)0.002	−0.083(−0.183, −0.073)0.000
3	−1.641(−2.042, −1.241)0.000	−0.089(−0.097, −0.081)0.000	−0.229(−0.367, −0.091)0.018	−0.051(−0.057, −0.045)0.000	0.029(−0.148, 0.206)0.867	0.008(−0.001, 0.017)0.361
4	−1.302(−1.263, −0.912)0.000	−0.086(−0.092, −0.080)0.000	−0.052(−0.103, −0.001)0.315	0.001(−0.004, 0.006)0.849	0.231(0.136, 0.326)0.015	0.042(0.034, 0.050)0.000
5	-ref-	-ref-	-ref-	-ref-	-ref-	-ref-

Results are presented as marginal effects (confidence interval); p value. The Index of Concentration of the Extremes (ICE) variables are in quintiles, ranging from 1 to 5, with 1 indicating the least privileged and 5 the most privileged. The reference categories are the most privileged (i.e., the ICE quintile 5).

Missing observations of timely entry into care were dropped from the analysis.

Health-related social needs are not included in timely entry into care and risk screening models because of perfect prediction.

Conditional on completing risk screening.

Table 5.

Differential Effects of Index of Concentration of the Extremes on Timely Entry into Prenatal Care, Risk Screening, and Receipt of Face-to-Face Care Management by Race (non-Hispanic Black vs. non-Hispanic White): Average Marginal Effects and Wald Tests

	Timely entry into care(n = 50,425)		Risk screening(n = 47,731)		Receipt of face-to-face care management(n = 26,094)
	NHW	NHB	NHW	NHB	NHW	NHB
ICE_race
1	−0.108 (−0.117, −0.099)	−0.112 (−0.119, −0.105)	−0.040 (−0.047, −0.035)	−0.041 (−0.047, −0.035)	−0.097 (−0.124, −0.070)	−0.047*** (−0.483, −0.034)
2	−0.087 (−0.095, −0.079)	−0.084 (−0.093, −0.075)	−0.051 (−0.058, −0.044)	−0.051 (−0.058, −0.044)	−0.135 (−0.116, 0.154)	−0.108 (−0.135, −0.081)
3	−0.090 (−0.097, −0.083)	−0.090 (−0.099, −0.081)	−0.047 (−0.052, −0.040)	−0.046 (−0.052, −0.040	0.013 (−0.107, 0.025)	0.002 (−0.008, 0.012)
4	−0.088 (−0.094, −0.082)	−0.083 (−0.091, −0.075)	−0.001 (−0.004, 0.006)	0.001 (−0.004, 0.006)	0.039 (0.029, 0.049)	0.038 (0.028, 0.048)
5	-ref-	-ref-	-ref-	-ref-	-ref-	-ref-
ICE_income
1	0.068 (0.054, 0.082)	0.044*** (0.034, 0.054)	0.078 (0.071, 0.085)	0.076 (0.070, 0.082)	0.054 (−0.015, 0.123)	0.140 (0.105, 0.175)
2	0.092 (0.081, 0.103)	0.067*** (0.058, 0.076)	0.057 (0.051, 0.063)	0.057 (0.051, 0.063)	0.032 (−0.040, 0.104)	−0.002 (−0.039, 0.035)
3	0.062 (0.053, 0.071)	0.046* (0.039, 0.053)	0.057 (0.051, 0.063)	0.058 (0.052, 0.064)	0.081 (0.018, 0.144)	0.048 (−0.001, 0.097)
4	0.107 (0.098, 0.116)	0.082*** (0.074, 0.090)	0.087 (0.080, 0.094)	0.086 (0.080, 0.092)	0.097 (0.047, 0.147)	0.069 (0.037, 0.101)
5	-ref-	-ref-	-ref-	-ref-	-ref-	-ref-
ICE_race+income
1	−0.098 (−0.105, −0.091)	−0.093 (−0.101, −0.085)	−0.059 (−0.065, −0.053)	−0.060 (−0.066, −0.054)	−0.108 (−0.124, −0.092)	−0.044*** (−0.054, −0.034)
2	−0.114 (−0.123, −0.105)	−0.113 (−0.123, −0.103)	−0.020 (−0.026, −0.014)	−0.020 (−0.027, −0.013)	−0.906 (−0.917, −0.895)	−0.0772 (−0.088, −0.066)
3	−0.089 (−0.097, −0.081)	−0.090 (−0.099, −0.081)	−0.051 (−0.057, −0.045)	−0.050 (−0.056, −0.044)	0.009 (0.000, 0.018)	0.008 (−0.001, 0.017)
4	−0.088 (−0.094, −0.082)	−0.084 (−0.092, −0.076)	−0.001 (−0.004, 0.006)	−0.002 (−0.007, 0.003)	0.033 (0.024, 0.042)	0.047** (0.039, 0.055)
5	-ref-	-ref-	-ref-	-ref-	-ref-	-ref-

*p < 0.05, **p < 0.01, ***p < 0.001. Significant p values indicate statistically significant Wald tests.

The Index of Concentration of the Extremes (ICE) variables are in quintiles, ranging from 1 to 5, with 1 indicating the least privileged and 5 the most privileged. The reference categories are the most privileged (i.e., the ICE quintile 5).

Completion of Risk Screening

The adjusted logistic regression indicated that pregnant individuals in the most deprived counties, based on race (ICE_race), had a 4.1 percentage points lower likelihood of completing risk screening (SE: 0.6; p value: 0.000) than those in the most privileged counties, all else being constant (Table 4). The effect remained consistent for the second and third quintiles, while the fourth was not statistically significant. For ICE_income+race, individuals in the most deprived neighborhoods showed a 6.0 percentage point lower likelihood of completing risk screening (SE: 0.6; p value: 0.000) compared with those in privileged neighborhoods. The effect for ICE_income+race decreased for the second and third quintiles. Similar to timely entry into care, ICE_income had a statistically significant positive effect on risk screening, with individuals in the most deprived income neighborhoods having a 7.7 percentage point higher likelihood of completing screening (SE: 0.7; p value: 0.000) than those in privileged neighborhoods, consistent across the second, third, and fourth quintiles. We found no statistically significant difference in the average marginal effect of ICE_race, ICE_income+race, and ICE_income on risk screening completion by race/ethnicity (Table 5).

Receipt of Face-to-Face Care Management

Among those who completed risk screening, pregnant individuals in the most deprived counties based on race had an 8.0 percentage point (SE: 1.1; p value: 0.000) lower likelihood of receiving face-to-face care management compared with those in the most privileged neighborhoods. The average marginal effect of ICE_income+race indicated that pregnant individuals in the most deprived counties had a 7.1 percentage point (SE: 1.0; p value: 0.000) lower likelihood, while those in the second most deprived counties had an 8.3 percentage point lower likelihood (SE: 0.9; p value: 0.000) of receiving face-to-face care compared with their counterparts in the most privileged counties (Table 4). There is a significant difference in average marginal effects of ICE_race and ICE_income+race on receipt of face-to-face care management by race/ethnicity, with NHW individuals in the least privileged neighborhoods having a lower likelihood of receiving face-to-face care management than NHB individuals (ICE_race: −9.7; SE: 2.7 vs. −4.7; SE: 1.3; ICE_income+race: −10.8; SE: 1.6 vs. −4.4; SE: 1.0) in the least privileged neighborhoods. Furthermore, NHB individuals in the fourth quintile had a higher likelihood of receiving face-to-face care management services compared with NHW individuals in the same quintile (0.047; SE: 0.011 versus 0.033; SE: 0.09). No statistically significant effect of ICE_income on receiving face-to-face care management was found (Table 5). Average marginal effects of covariates on prenatal care entry, completion of risk screening, and receipt of care management services are available in Supplementary File 3.

Discussion

Our findings highlight the association of neighborhood deprivation, particularly ICE_race and ICE_race+income, with timely prenatal care entry, risk screening completion, and face-to-face care management. The association with ICE_race+income weakened, showing that race-based neighborhood deprivation (ICE_race) is the strongest predictor of these services. Pregnant individuals in the most racially and economically deprived neighborhoods were less likely to access essential prenatal services than those in privileged areas. These disparities likely stem from structural barriers such as limited health care access, transportation issues, financial hardship, and systemic racism. Conversely, income-based neighborhood deprivation (ICE_income) was associated with a slightly higher likelihood of timely care entry and risk screening in economically deprived neighborhoods, likely due to the Medicaid-managed PMH program, a public health initiative aimed at improving access and screening rates among low-income individuals.⁶¹ Next, we discuss these findings in detail.

Our study also showed a negative association of neighborhood deprivation based on race (ICE_race) and race and income combined (ICE_race+income) on timely access to prenatal care, risk screening, and receiving face-to-face care management with pregnant individuals in the most racially and economically deprived counties with a lower likelihood of accessing these resources compared with those living in the most racially and economically privileged counties. The negative association highlights the added challenges faced by individuals living in these communities. Our results align with existing evidence that racially marginalized populations often encounter significant barriers to accessing first-trimester prenatal care, including geographic isolation, financial hardship, limited availability of care, and distrust in the health care system,^{15,55,56,64,65} as individuals fear mistreatment or worry that their concerns will not be taken seriously.^62,65 These factors may contribute to delayed or foregone care and may undermine well-intentioned efforts to improve maternal health outcomes, such as HDoPs. Targeted strategies could include community-based care models that expand identification and outreach in underserved neighborhoods and risk screening through interventions such as mobile health clinics and community health workers. In addition, health care systems must continue to strengthen patient-centered models that build trust and validate the lived experiences of Black patients in racially and economically deprived areas. Future studies can focus on designing and testing targeted community-based interventions to close racial gaps in health care access and improve maternal health equity.

Our study showed that neighborhood deprivation based on income (ICE_income) was associated with a slightly higher likelihood of timely entry into care and risk screening in the most economically deprived neighborhoods. This may be attributed to the Medicaid-managed PMH program⁶¹ based on several factors. Medicaid eligibility is primarily based on income, so interventions target economic barriers such as access to providers, transportation, or scheduling. Improvements may be more evident among income groups due to alignment with financial constraints. However, it is crucial to note that pregnant individuals in economically privileged neighborhoods have lower access to face-to-face care management services, suggesting that economic deprivation alone does not fully explain racial inequities in prenatal care access. Prior research shows that racial discrimination and implicit bias persist across socioeconomic strata, influencing clinical decision-making and patient experiences even among higher income Black women.^40,66 These patterns underscore that factors, such as provider bias, differential treatment, and systemic racism, may operate independently of neighborhood privilege and may contribute to disparities in risk screening and care management uptake. These structural inequities may not be fully addressed by the PMH, which aims to enhance service delivery but not address inequities from structural racism. Future studies may focus on disentangling the effects of structural racism from socioeconomic disadvantage and ensuring that interventions address racial inequities beyond neighborhood deprivation, as well as exploring policy levers for racial equity-centered program design.

Our study further highlights that upon entry into care, NHB individuals demonstrate a greater likelihood of receiving face-to-face care management compared with NHW individuals, particularly in neighborhoods marked by racial (ICE_race) and racial-economic deprivation (ICE_race+income). This pattern may result from several intersecting factors. NHB individuals residing in these areas exhibit greater clinical and social needs, prompting more intensive care management efforts. In addition, public health initiatives, such as PMHs, may prioritize NHB communities for targeted interventions to reduce disparities, thereby enhancing access to in-person care management services. These findings suggest that efforts to mitigate inequities may be more actively implemented in racially and economically marginalized communities once access is established.

This study has several limitations. First, selection bias may be present because timely entry into care, risk screening, and receipt of face-to-face care management services are not randomly distributed. Our data are drawn from the PMH program, which limits the ability to compare associations among Medicaid enrollees who are not part of the program. It is possible that PMH participation mitigates the association between ICE variables and outcomes; without this program, these associations may be stronger. Future research could apply methods such as propensity score matching, instrument variable, or synthetic control approaches to better address this bias.

Second, omitted variable bias is possible; unmeasured factors such as patient preferences may correlate with covariates such as maternal education, potentially biasing estimates toward the null. Third, neighborhood-level analysis was limited to the county level due to data constraints and sample size limitations, which may not capture within-county variability. However, given that the PMH program operates at the county level, these findings remain relevant for informing county-level interventions. Future studies should explore census-track-level analysis to assess whether these findings persist at finer geographic scales. Fourth, Black individuals may receive higher risk scores within the PMH program due to higher prevalence among them, which could explain their greater likelihood of receiving care management. We addressed this by including fixed effects of overall risk burden in the models using a count variable. Finally, generalizability is limited, as the analysis includes only Medicaid beneficiaries receiving care through PMHs in North Carolina. Future studies should consider broader populations, including those with other insurance types and care models. Despite these limitations, this study leverages a robust, state-level dataset to examine the impact of neighborhood deprivation on prenatal care access, offering valuable insights into Medicaid policy in North Carolina.

Conclusion

In conclusion, this study demonstrates that neighborhood deprivation based on race and income is significantly associated with reduced access to timely prenatal care, risk screening, and face-to-face care management. While income-based deprivation alone was linked to slightly improved access, likely due to targeted programs such as the PMH, racial deprivation continues to pose substantial barriers. Addressing these disparities will require equity-focused interventions and targeted outreach to ensure that all pregnant individuals receive timely and appropriate prenatal care.

Authors’ Contributions

M.A.: Conceptualization (lead), investigation (lead), formal analysis (lead), and writing—original draft (lead). C.M.S.: Conceptualization (supporting), supervision (supporting), investigation (supporting), formal analysis (supporting), and writing—review and editing (supporting). M.K.M.: Conceptualization (supporting), writing—review and editing (supporting), and writing—review and editing (equal). L.K.: Data curation (supporting) and writing—review and editing (supporting). M.E.D.: Formal analysis (supporting) and writing—original draft (supporting). V.A.L.: Conceptualization (supporting), formal analysis (supporting), and writing—original draft (supporting). A.M.P.: Conceptualization (supporting), supervision (lead), investigation (supporting), formal analysis (supporting), and writing—review and editing (supporting).

Footnotes

Acknowledgments

The authors extend their sincere gratitude to the Community Care of North Carolina and the North Carolina Department of Health and Human Services for allowing the reuse of the dataset and the Cecil G. Sheps Center for Health Services Research for providing access to the dataset.

Author Disclosure Statement

The authors declare no conflict of interest.

Funding Information

This article is part of doctoral dissertation and did not have any funding support.

Supplemental Material

Abbreviations

References

Howell

. Reducing disparities in severe maternal morbidity and mortality. Clin Obstet Gynecol, 2018; 61(2):387–399; doi: 10.1097/GRF.0000000000000349

Holdt Somer

, Sinkey

, Bryant

. Epidemiology of racial/ethnic disparities in severe maternal morbidity and mortality. Semin Perinatol, 2017; 41(5):258–265; doi: 10.1053/j.semperi.2017.04.001

Petersen

, Davis

, Goodman

, et al. Vital signs: Pregnancy-related deaths, United States, 2011–2015, and strategies for prevention, 13 States, 2013–2017. MMWR Morb Mortal Wkly Rep, 2019; 68(18):423–429; doi: 10.15585/mmwr.mm6818e1

Gyamfi-Bannerman

, Pandita

, Wright

, et al. 434 Racial disparities in preeclampsia outcomes at delivery. AJOG, 2019; doi: 10.1016/j.ajog.2018.11.455

Suresh

, Amegashie

, Patel

, et al. Racial disparities in diagnosis, management, and outcomes in preeclampsia. Curr Hypertens Rep, 2022; 24(4):87–93; doi: 10.1007/S11906-022-01172-X

Boakye

, Sharma

, Ogunwole

, et al. Relationship of preeclampsia with maternal place of birth and duration of residence among non-Hispanic Black women in the United States. Circ Cardiovasc Qual Outcomes, 2021; 14(2):E007546; doi: 10.1161/CIRCOUTCOMES.120.007546

Adegoke

, Pinder

, Ndiwane

, et al. Inequities in adverse maternal and perinatal outcomes: The effect of maternal race and nativity. Matern Child Health J, 2022; 26(4):823–833; doi: 10.1007/S10995-021-03225-0

Creanga

, Bateman

, Kuklina

, et al. Racial and ethnic disparities in severe maternal morbidity: A multistate analysis, 2008-2010. Am J Obstet Gynecol, 2014; 210(5):435.e1–435.e8; doi: 10.1016/j.ajog.2013.11.039

Johnson

, Louis

. Does race or ethnicity play a role in the origin, pathophysiology, and outcomes of preeclampsia? An expert review of the literature. Am J Obstet Gynecol, 2022; 226(2S):S876–S885; doi: 10.1016/j.ajog.2020.07.038

10.

Savage

, Panofsky

. The self-fulfilling process of clinical race correction: The case of eighth joint National Committee recommendations. Health Equity, 2023; 7(1):793–802; doi: 10.1089/heq.2023.0064

11.

Cartus

, Jarlenski

, Himes

, et al. Adverse cardiovascular events following severe maternal morbidity. Am J Epidemiol, 2022; 191(1):126–136; doi: 10.1093/AJE/KWAB208

12.

Baiden

, Parry

, Nerenberg

, et al. Connecting the dots: Structural racism, intersectionality, and cardiovascular health outcomes for African, Caribbean, and Black Mothers. Health Equity, 2022; 6(1):402–405; doi: 10.1089/HEQ.2021.0077

13.

Giscombé

, Lobel

. Explaining disproportionately high rates of adverse birth outcomes among African Americans: The impact of stress, racism, and related factors in pregnancy. Psychol Bull, 2005; 131(5):662–683; doi: 10.1037/0033-2909.131.5.662

14.

Wallace

, Mendola

, Liu

, et al. Joint effects of structural racism and income inequality on small-for-gestational-age birth. Am J Public Health, 2015; 105(8):1681–1688; doi: 10.2105/AJPH.2015.302613

15.

Hailu

, Maddali

, Snowden

, et al. Structural racism and adverse maternal health outcomes: A systematic review. Health Place, 2022; 78:102923; doi: 10.1016/J.HEALTHPLACE.2022.102923

16.

Francis

, Pearl

, Colen

, et al. Racial and economic segregation over the life course and incident hypertensive disorders of pregnancy among Black women in California. Am J Epidemiol, 2024; 193(2):277–284; doi: 10.1093/aje/kwad192

17.

Scott

, Hauspurg

, Muldoon

, et al. Neighborhood deprivation, perceived stress, and pregnancy-related hypertension phenotypes a decade following pregnancy. Am J Hypertens, 2024; 37(3):220–229; doi: 10.1093/ajh/hpad090

18.

Mayne

, Yellayi

, Pool

, et al. Racial residential segregation and hypertensive disorder of pregnancy among women in Chicago: Analysis of electronic health record data. Am J Hypertens, 2018; 31(11):1221–1227; doi: 10.1093/ajh/hpy112

19.

Vos

, Posthumus

, Bonsel

, et al. Deprived neighborhoods and adverse perinatal outcome: A systematic review and meta-analysis. Acta Obstet Gynecol Scand, 2014; 93(8):727–740; doi: 10.1111/AOGS.12430

20.

Collins

, Wambach

, David

, et al. Women’s lifelong exposure to neighborhood poverty and low birth weight: A population-based study. Matern Child Health J, 2009; 13(3):326–333; doi: 10.1007/s10995-008-0354-0

21.

Candipan

, Phillips

, Sampson

, et al. From residence to movement: The nature of racial segregation in everyday urban mobility. Urban Stud, 2021; 58(15):3095–3117; doi: 10.1177/0042098020978965

22.

Crowell

, Fossett

. Racial and ethnic residential segregation across the United States: New approaches to understanding trends and patterns. The Springer Series on Demographic Methods and Population Analysis. Springer International Publishing: Cham; 2023.; doi: 10.1007/978-3-031-38371-7

23.

Chegwin

, Teitler

, Muchomba

, et al. Racialized police use of force and birth outcomes. Soc Sci Med, 2023; 321:115767; doi: 10.1016/J.SOCSCIMED.2023.115767

24.

Hardeman

, Chantarat

, Karbeah

. Police exposure as a determinant of structural racism: An exploration of the association between preterm birth in neighborhoods with high police exposure. Health Serv Res, 2020; 55(S1):50; doi: 10.1111/1475-6773.13396

25.

Liu

, Fiorentini

, Bailey

, et al. Structural racism and severe maternal morbidity in New York State. Clin Med Insights Womens Health, 2019; 12:1179562X19854778; doi: 10.1177/1179562X19854778

26.

Kelly-Taylor

, Badon

, Dyer

, et al. Racial residential segregation and mental health during pregnancy. JAMA Health Forum, 2024; 5(10):e243669; doi: 10.1001/jamahealthforum.2024.3669

27.

Headen

. Structural racism, geographies of opportunity, and maternal health inequities: A dynamic conceptual framework. J Racial Ethn Health Disparities, 2025; doi: 10.1007/s40615-025-02345-5

28.

Nurse-family partnership (NFP)® | Home visiting evidence of effectiveness. US Department of Health and Human Sciences. Office of Planning, Research and Evaluation. n.d. Available from: https://homvee.acf.hhs.gov/models/nurse-family-partnership-nfpr [Last accessed: March 11, 2025].

29.

Applegate

, Gee

, Martin

. Improving maternal and infant health outcomes in medicaid and the children’s health insurance program. Obstet Gynecol, 2014; 124(1):143–149; doi: 10.1097/AOG.0000000000000320

30.

Centre for Medicare and Medicaid Services. Improving access to maternal health care in rural communities. An Issue Brief, 2019.

31.

Maternal, infant, and early childhood home visiting (MIECHV) program | MCHB. Health Resources and Services Administration. Maternal Child Health. n.d. Available from: https://mchb.hrsa.gov/programs-impact/programs/home-visiting/maternal-infant-early-childhood-home-visiting-miechv-program [Last accessed: March 16, 2023].

32.

Hillemeier

, Domino

, Wells

, et al. Does maternity care coordination influence perinatal health care utilization? Evidence from North Carolina. Health Serv Res, 2018; 53(4):2368–2383; doi: 10.1111/1475-6773.12742

33.

Greene

, Tran-Smith

, Moua

. Beyond common outcomes: Client’s perspectives on the benefits of prenatal care coordination. medRxiv, 2022; doi: 10.1101/2022.04.12.222733312022.04.12.22273331;

34.

Janevic

, Weber

, Howell

, et al. Analysis of state medicaid expansion and access to timely prenatal care among women who were immigrant vs US born. JAMA Netw Open, 2022; 5(10):e2239264; doi: 10.1001/jamanetworkopen.2022.39264

35.

Green

. Unpacking racial/ethnic disparities in prenatal care use: The role of individual-, household-, and area-level characteristics. J Womens Health (Larchmt), 2018; 27(9):1124–1134; doi: 10.1089/jwh.2017.6807

36.

Gestational hypertension and preeclampsia: ACOG practice bulletin summary, number 222. American College of Obstetrics and Gynecology, 2020; 135(6):1492–1495; doi: 10.1097/AOG.0000000000003892

37.

Bibbins-Domingo

, Grossman

, Curry

, et al.; US Preventive Services Task Force. Screening for Preeclampsia: US Preventive Services Task Force recommendation statement. JAMA, 2017; 317(16):1661–1667; doi: 10.1001/jama.2017.3439

38.

Walsh

, Dove-Medows

. Experiences of blame among pregnant Black women during prenatal care. J Racial Ethn Health Disparities, 2025; doi: 10.1007/s40615-025-02392-y

39.

Stegman

, Lucarelli-Baldwin

, Ural

. Disparities in high risk prenatal care adherence along racial and ethnic lines. Front Glob Womens Health, 2023; 4:1151362; doi: 10.3389/fgwh.2023.1151362

40.

Saluja

, Bryant

. How implicit bias contributes to racial disparities in maternal morbidity and mortality in the United States. J Womens Health (Larchmt), 2021; 30(2):270–273; doi: 10.1089/jwh.2020.8874

41.

Van Daalen

, Kaiser

, Kebede

, et al. Racial discrimination and adverse pregnancy outcomes: A systematic review and meta-analysis. BMJ Glob Health, 2022; 7(8):e009227; doi: 10.1136/bmjgh-2022-009227

42.

Janevic

, Zeitlin

, Egorova

, et al. Neighborhood racial and economic polarization, hospital of delivery, and severe maternal morbidity: An examination of whether racial and economic neighborhood polarization is associated with severe maternal morbidity rates and whether the delivery hospital partially explains the association. Health Aff (Millwood), 2020; 39(5):768–776; doi: 10.1377/hlthaff.2019.00735

43.

Eick

, Cushing

, Goin

, et al. Neighborhood conditions and birth outcomes: Understanding the role of perceived and extrinsic measures of neighborhood quality. Environ Epidemiol, 2022; 6(5):e224; doi: 10.1097/EE9.0000000000000224

44.

Community Care of North Carolina. Pregnancy medical home | Community Care of North Carolina. n.d. Available from: https://www.communitycarenc.org/population-management/pregnancy-home [Last accessed: December 8, 2022].

45.

Mallampati

, Jackson

, Menard

. The association between care management and neonatal outcomes: The role of a Medicaid-managed pregnancy medical home in North Carolina. Am J Obstet Gynecol, 2022; 226(6):848.e1–848.e9; doi: 10.1016/j.ajog.2022.03.018

46.

Pregnancy medical home transitioned to pregnancy management program | NC Medicaid. Department of Health Benefits. 2021. Available from: https://medicaid.ncdhhs.gov/blog/2021/10/19/pregnancy-medical-home-transitioned-pregnancy-management-program [Last accessed: October 3, 2023].

47.

Bureau UC. American Community survey information guide. n.d. Available from: https://www.census.gov/programs-surveys/acs/about/information-guide.html [Last accessed: January 27, 2025].

48.

Braveman

, Marchi

, Egerter

, et al. Barriers to timely prenatal care among women with insurance: The importance of prepregnancy factors. Obstet Gynecol, 2000; 95(6 Pt 1):874–880; doi: 10.1016/s0029-7844(00)00780-8

49.

Bailey

, Krieger

, Agénor

, et al. Structural racism and health inequities in the USA: Evidence and interventions. Lancet, 2017; 389(10077):1453–1463; doi: 10.1016/S0140-6736(17)30569-X

50.

Krieger

, Waterman

, Spasojevic

, et al. Public health monitoring of privilege and deprivation with the index of concentration at the extremes. Am J Public Health, 2016; 106(2):256–263; doi: 10.2105/AJPH.2015.302955

51.

Massey

, White

, Phua

V-C

. The dimensions of segregation revisited. Sociol Methods Res, 1996; 25(2):172–206; doi: 10.1177/0049124196025002002

52.

Chambers

, Baer

, McLemore

, et al. 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; 96(2):159–170; doi: 10.1007/s11524-018-0272-4

53.

Dyer

, Chambers

, Crear-Perry

, et al. The index of concentration at the extremes (ICE) and pregnancy-associated mortality in Louisiana, 2016-2017. Matern Child Health J, 2022; 26(4):814–822; doi: 10.1007/s10995-021-03189-1

54.

Mari

, Yang

, Boland

, et al. Assessing racial residential segregation as a risk factor for severe maternal morbidity. Ann Epidemiol, 2023; 83:23–29; doi: 10.1016/j.annepidem.2023.04.018

55.

Sonderlund

, Charifson

, Schoenthaler

, et al. Racialized economic segregation and health outcomes: A systematic review of studies that use the index of concentration at the extremes for race, income, and their interaction. PLoS One, 2022; 17(1):e0262962; doi: 10.1371/journal.pone.0262962

56.

Jeffers

, Berger

, Marea

, et al. Investigating the impact of structural racism on black birthing people—associations between racialized economic segregation, incarceration inequality, and severe maternal morbidity. Soc Sci Med, 2023; 317:115622; doi: 10.1016/j.socscimed.2022.115622

57.

Feldman

, Waterman

, Coull

, et al. Spatial social polarisation: Using the index of concentration at the extremes jointly for income and race/ethnicity to analyse risk of hypertension. J Epidemiol Community Health, 2015; 69(12):1199–1207; doi: 10.1136/jech-2015-205728

58.

Krieger

, Kim

, Feldman

, et al. Using the index of concentration at the extremes at multiple geographical levels to monitor health inequities in an era of growing spatial social polarization: Massachusetts, USA (2010–2014). Int J Epidemiol, 2018; 47(3):788–819; doi: 10.1093/ije/dyy004

59.

Chetty

, Hendren

, Kline

, et al. Where is the land of opportunity? The geography of intergenerational mobility in the United States. 2014; doi: 10.3386/w19843

60.

Albert J, Anyanwu U, Borders K, et al. Pregnancy medical home program care pathway management of hypertensive disorders in pregnancy. Community Care of North Carolina. 2019.

61.

Boyd

, Mehra

, Thomas

, et al. Features and impact of trust-based relationships between community health workers and low-resource perinatal women with chronic health conditions. Matern Child Health J, 2021; 25(12):1866–1874; doi: 10.1007/S10995-021-03242-Z

62.

Wang

, Phillips

, Small

, et al. Urban mobility and neighborhood isolation in America’s 50 largest cities. Proc Natl Acad Sci U S A, 2018; 115(30):7735–7740; doi: 10.1073/pnas.1802537115

63.

Williams

, Collins

. Racial residential segregation: A fundamental cause of racial disparities in health. Public Health Rep, 2001; 116(5):404–416.

64.

Musa

, Schulz

, Harris

, et al. Trust in the health care system and the use of preventive health services by older Black and White adults. Am J Public Health, 2009; 99(7):1293–1299; doi: 10.2105/ajph.2007.123927

65.

Montalmant

, Ettinger

. The racial disparities in maternal mortality and impact of structural racism and implicit racial bias on pregnant Black women: A review of the literature. J Racial Ethn Health Disparities, 2024; 11(6):3658–3677; doi: 10.1007/s40615-023-01816-x

66.

USDA ERS – Rural-urban commuting area codes | Economic Research Service. n.d. Available from: https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes [Last accessed: February 11, 2025].

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