Maternal Racial Differences and Socioeconomic Status in Preterm Infant Neurodevelopment, Feeding, and Growth

Abstract

Background:

Women from diverse socioeconomic status (SES) face a higher risk of preterm birth, increasing their infants’ vulnerability to neurodevelopmental and other health disorders; however, the predictive role of maternal ZIP code level SES in these outcomes remains underexplored.

Objectives:

To investigate the associations between maternal racial disparity, ZIP code-level SES, and infant breastfeeding, growth, and neurodevelopmental trajectories.

Methods:

In this cohort study, preterm infants were recruited from two Connecticut neonatal intensive care units (NICUs). Infant demographic data, feeding regimens, and growth during the NICU stay were documented. Neurodevelopmental outcomes were assessed using the NICU Neonatal Neurobehavioral Scale, the Bayley scale of infant and toddler development (3rd ed.), and the Brief Infant Toddler Social Emotional Assessment. To compare SES differences between infants born to Black and White mothers, both t-tests and Wilcoxon tests were conducted. We used XGBoost to analyze infant health outcomes and SHapley Additive exPlanations (SHAP) values to identify SES-related risk factors associated with feeding, growth during NICU stay, and neurodevelopmental outcomes up to 2 years of age.

Results:

In total, 181 preterm infants from eight ZIP code areas were included in the study. The majority of infants were born to mothers who were White and non-Hispanic. Compared with infants born to White mothers, those born to Black mothers had younger birth gestational age (GA), lower birth weights, shorter birth lengths, smaller head circumferences, and higher Score of Neonatal Acute Physiology with Perinatal Extension–II (SNAPPE–II), with all differences being statistically significant. Compared with White mothers, Black mothers were younger, single, and less educated. Black mothers also had lower median household incomes, larger average family sizes, and higher levels of poverty compared with White mothers. Based on SHAP values, the risk factors predicting infants’ feeding, growth, and neurodevelopment are ranked as follows: birth weight, birth GA, SNAPPE–II score, average family size, maternal age, median household income, poverty level, and school enrollment.

Discussion:

Maternal racial disparity and SES serve as predictors of feeding, growth, and neurodevelopmental outcomes in preterm infants. Understanding these associations can inform health care strategies for vulnerable preterm populations to improve long-term health outcomes.

Preterm birth, defined as delivery before 37 weeks of gestation, is 1.5 times more common among Black women compared with White women (March of Dimes, n.d.). Notably, Black preterm infants face significantly higher risks of morbidity and mortality compared with White infants (Howell et al., 2018). Furthermore, infants born at a younger gestational age (GA) are at increased risk for neurodevelopmental disorders (Zhao et al., 2022), with this risk being especially high among preterm infants born to Black women compared with other populations in the United States (Fraiman et al., 2022). However, the risk factors of developmental disorders for preterm infants born to Black women remain unclear.

Environmental factors, including the socioeconomic status (SES) of Black women, are associated with preterm birth and the well-being of their infants (Braveman et al., 2015). Specifically, maternal SES has been associated with a high incidence of motor, language, and cognitive disorders in preterm infants throughout early and later life; however, existing findings remain inconsistent (Der Nederlanden et al., 2023; Saurel-Cubizolles et al., 2020). ZIP code-derived SES may provide insights into the family environment, including factors such as average household income and family size, to which fetus/preterm infants are exposed before, during pregnancy, and after birth (Graham, 2016). Lower average household income, often resulting in maternal hardship such as food insecurity, lack of shelter, and lack of insurance coverage, has been negatively associated with infants’ health outcomes (Cordova-Ramos et al., 2023). In addition, family size has been linked to language development, although findings are inconsistent (Bergelson et al., 2023; Poudel et al., 2024). Given that preterm infant health outcomes cannot be solely determined by SES, it is essential to consider the combined effect of maternal racial and socioeconomic disparities on adverse infant health outcomes. Examining how SES influences the development of preterm infants born to Black women compared with those born to White women is critical for addressing health disparities. This understanding can inform targeted interventions aimed at mitigating these challenges early in life.

Our group previously investigated the association between early-life infant feeding regimens, such as mothers’ own milk (MOM), formula, and human donor milk, and infant neurodevelopmental outcomes (Zhao et al., 2025). We identified that a higher proportion of MOM was associated with lower infant stress levels at 36–38 postmenstrual age and positively linked to language and cognitive ability at 2 years of corrected age (CA; Zhao et al., 2025). Notably, these associations were observed primarily in females (Zhao et al., 2025). In addition, early-life growth trajectories have been associated with neurodevelopmental outcomes in preterm infants, particularly among those born small for GA (Ruys et al., 2019). However, the role of maternal SES shaping these associations remains underexplored.

We hypothesized that racially differentiated exposure to maternal adversity contributes to health disparities in infants, particularly those born preterm. This study aimed to identify risk factors based on ZIP code-derived SES to predict neurobehavioral outcomes of preterm infants at 36–38 weeks postmenstrual age, as well as neurodevelopmental outcomes at 1- and 2-year CA, which accounts for the degree of prematurity by adjusting for chronological age. In addition, we examined infant feeding and growth during the infant’s neonatal intensive care unit (NICU) stay and assessed racial differences in health outcomes among preterm infants.

METHODS

Study Design

We conducted a longitudinal study to investigate the associations of ZIP code-derived SES factors with neurodevelopment and growth outcomes in infants after birth; we followed them through their NICU hospitalization until their 1- and 2-year CA. A total of 216 preterm infants were recruited between 2017 and 2022 from two affiliated level III/IV NICU sites in Connecticut (Zhao et al., 2022). One NICU has 32 beds, and the other has 30 beds. These sites serve an ethnically and racially diverse population, with ~60% of preterm births among non-Hispanic White, 17% non-Hispanic Black, 21% Hispanic, and 2% from other racial or ethnic groups. Inclusion criteria were preterm infants who were (a) born between 28 0/7 and 32 0/7 weeks of GA, and (b) received consents from parents who were 18 years and above. Exclusion criteria were preterm infants who had (a) known congenital or chromosomal abnormalities; (b) severe periventricular/intraventricular hemorrhage (≥ grade III); (c) undergone surgery; and/or (d) exposure to illicit substances exposure during pregnancy. The study was reviewed and approved by the institutional review board (IRB) at each participating clinical site (Approval No. 16–001).

Clinical and Demographic Data

Infant sex, delivery mode, birth GA, birth weight, length, head circumference, and the Score for Neonatal Acute Physiology with Perinatal Extension–II (SNAPE–II), a validated biomarker of neonatal mortality and morbidity risk, were included in the study analysis (Berman et al., 2001). In addition, maternal age, race, ethnicity, marital status, and educational level were also included. Among these variables, infant birth weight, birth GA, and SNAPPE–II were considered as infant early-life adversity.

ZIP Code–Derived SES

Maternal ZIP code data were collected from the electronic health record and retrieved from Research Electronic Data Capture. The ZIP code–derived SES data used in this study were sourced from the 2022 American Community Survey (ACS) 5-year estimates (U.S. Census Bureau, 2024). The data were accessed through the ACS website, which provides comprehensive demographic, housing, and socioeconomic information at the ZIP code level. These data are imperative for understanding and analyzing community-level characteristics and disparities, allowing for detailed comparisons and analyses of various socioeconomic factors. We focused on 12 socioeconomic variables that are crucial for assessing community-level SES and its effect on the health outcomes under investigation. These variables include total households, school enrollment, employment, median household income, total housing units, total population, health insurance coverage, average family size, child population in households, residential stability (population 1 year and over), civilian employed population 16 years and over (occupation 16), and poverty. These specific indicators were selected based on their relevance to understanding socioeconomic disparities across different demographic groups.

Daily Feeding and the Proportion of MOM

During the NICU stay, research nurses collected data on infants’ daily feeding regimens, including the frequency and quantity of feedings with MOM, human donor milk, and/or formula. Using these records, we first calculated for each infant the total MOM feeding volume during the first 8 weeks. We then averaged these totals over the entire NICU stay to obtain each infant’s mean daily total intake (mL/day). This means daily total intake is summarized in the “Daily feeding total intake” column in Table 5. The proportion of MOM (daily MOM divided by daily feeding amount) were calculated and is summarized in the “Proportion of MOM” column in Table 5.

Table 5.

The risk factors of zip code derived SES and infants’ early life adversity predict feeding and growth outcomes in NICU

Variables	Daily feeding total intake	Proportion of MOM	weight z-score
Birth weight	0.318	0.115	0.185
Birth GA	0.071	0.17	0.134
SANPPEII	0.134	0.067	0.123
Sex	0.046	0.017	0.029
Race	0.058	0.067	0.022
Maternal age	0.213	0.222	0.046
Residence 1 year ago	0.006	0.005	0.002
Average family size	0.06	0.033	0.101
Median household income	0.048	0.109	0.086
Insurance coverage	0	0.001	0
Total households	0.031	0.029	0.024
Child population in household	0.014	0.017	0.009
Poverty	0.055	0.113	0.273
School enrollment	0.047	0.033	0.142
Occupation	0.016	0.003	0
Employment	0.018	0.007	0.001
Total housing units	0.015	0.009	0.007
Total population	0	0	0

Note. MOM, Mothers’ own milk. The values in these tables represent the mean absolute SHAP values derived from XGBoost models for each neurodevelopmental or behavioral outcome. SHAP values quantify the contribution of each predictor to the model’s output. For each outcome, a separate XGBoost model was fitted, and the average magnitude of each feature’s SHAP value was computed across all subjects. A higher value indicates that the corresponding variable had a greater average impact on the model prediction for that specific outcome. Values are rounded to three decimal places for clarity.

Growth Measurement

Infants’ daily or weekly growth parameters, including weight, length, and head circumference, were documented by research nurses from birth until 16 weeks of postnatal age. We focused on infants’ daily body weight, which was measured using an electronic scale (capacity = 20 kg, with an accuracy of ± 10 g). To ensure accuracy, the scale was calibrated every 2–3 months. Each weight measurement was repeated three times, and if the difference between measurements exceeded 30 g, a fourth measurement was taken. The three closest measurements were then used. Infant body weight measured at 2 years of CA was used to analyze the Weight z-score. The formula WZ = [(Weight (kg)/M)^L – 1] / (L × S) was used (Cole, 1990).

Neurobehavior and Development Measures

The NICU Neonatal Neurobehavioral Scale (NNNS) has demonstrated strong validity and reliability; it is increasingly recognized as the standard assessment for evaluating neonatal behavior across a wide range of at-risk populations (Lester et al., 2004). It consists of 115 items categorized into 13 summary scores that assess various domains, including habituation, attention, arousal, self-regulation, handling, quality of movement, excitability, lethargy, nonoptimal reflexes, asymmetric reflexes, hypertonicity, hypotonicity, and stress/abstinence (Lester et al., 2004). When infants reached 36–38 weeks of postmenstrual age (PMA), they were assessed using the NNNS by certificated research nurses. To align with our previous findings, we specifically focused on stress/abstinence (NSTRESS), quality of movement (NQMOVE), and arousal (NAROUSAL) subscales. Lower NSTRESS and NAROUSAL scores, along with higher NQMOVE score, indicate better neurodevelopmental outcomes.

The Bayley scale of infant and toddler development (3rd ed. [Bayley-III]; Bayley, 2006) was used to assess children’s cognitive, language, and motor skills. As a standardized, widely used tool, the Bayley–III is considered the gold standard for evaluating overall neurodevelopment in preterm infants between 1- and 3-year CA in follow-up studies (Velikos et al., 2015). Neonatologists administered Bayley–III assessments during routine neurodevelopmental follow-up visits at 1 and 2 years CA. Higher scores in each domain, or a higher total score across all domains, indicate better neurodevelopmental functioning.

The Brief Infant Toddler Social Emotional Assessment (BITSEA) is a standardized, validated, and reliable screening tool designed to assess social–emotional problems (31 items) and competencies (11 items) in young children aged 1 and 3 years (Kruizinga et al., 2012). The Problem scale consists of 31 items, and the Competence scale consists of 11 items. Parents completed these items at the 2-year CA follow-up to express concerns about their infants’ development. For each scale, responses were summed to generate a total score. The possible scores range from 0 to 62 for the Problem scale and 0 to 22 for the Competence scale. A higher score on the Problem scale or a lower score on the Competence scale indicates unfavorable developmental outcomes.

Statistical Analysis

The statistical analysis was conducted using R 4.2.0 (https://cran.r-project.org/bin/windows/base/old/4.0.2/). We applied t tests and Wilcoxon rank sum tests to compare groups and identify significant disparities between Black and White mothers using ZIP code-level data. In addition, we employed XGBoost to analyze feature importance and explore the relationship between socioeconomic factors and infant health outcomes, while adjusting for maternal and infant demographic variables (Chen & Carlos, 2016). This was followed by SHAP analysis to interpret the model results and highlight the most influential risk factors (Lundberg & Lee, 2017). These methods collectively provided a robust framework for understanding the socioeconomic disparities and their potential effect on health outcomes within our cohort.

RESULTS

Maternal and Infants’ Clinical and Demographic Characteristics

We included 181 infants whose mothers had complete ZIP code data, 26% of whom were Black (Table 1). On average, Black infants had a younger GA at birth (27.3 vs. 28.5 wk), lower birth weights (908.4 g vs. 1128.1 g), shorter birth lengths (34.8 cm vs. 37.1 cm), smaller head circumferences (24.3 cm vs. 25.8 cm), and higher SNAPPE–II score (31.2 vs. 21.0) compared with White infants, with all differences being significant (p < .001). In addition, compared with White mothers, Black mothers were younger (28.6 vs. 31.6 years; p < .001), and more likely to be unmarried (70.8% vs. 34.6%; p < .001; Table 2).

Table 1.

Infant demographic characteristics (N=181)

Variables	Black (n=47)	White (n=134)
	%	%	p-value
Gender
Female	26 (55.3)	49 (36.6)	0.04
Male	21 (44.7)	91 (63.4)
Delivery
C-section	37 (78.7)	91 (67.9)	0.22
Vaginal	10 (21.3)	43 (32.1)
PPROM
Yes	8 (17.0)	33 (24.6)
No	39 (83.0)	101 (75.4)	0.39
	Mean (SD)	Mean (SD)	p-value
Birth GA (week)	27.3 (2.4)	28.5 (2.4)	0.002
Birth weight (g)	908.4 (287.8)	1128.1 (339.0)	<0.001
Birth body length (cm)	34.8 (3.6)	37.1 (4.0)	<0.001
Birth HC (cm)	24.3 (2.4)	25.8 (2.6)	<0.001
SNAPPEII	31.2 (19.4)	21.0 (15.9)	<0.001

Note. PPROM, Preterm premature rupture of membranes; GA, gestational age; HC, head circumference; SNAPEII, Score for Neonatal Acute Physiology with Perinatal Extension-II

Table 2.

Maternal demographic characteristics (N=181)

Variables	Black (n=48)	White (n=133)
	%	%	p-value
Hispanic
No	41 (85.4)	98 (73.7)	0.15
Yes	7 (14.6)	35 (26.3)
Marital status
Married	12 (25.0)	83 (62.4)	<0.001
Other	2 (4.2)	4 (3.0)
Single	34 (70.8)	46 (34.6)
	Mean (SD)	Mean (SD)	p-value
Maternal age	28.6 (6.1)	31.6 (5.6)	0.002

Descriptive of Area Map of ZIP Code

In our study, participants’ ZIP codes spanned eight counties in Connecticut. The majority (63.4%) were from Hartford County, 8% were from New Haven County, and the remaining participants were evenly distributed across Fairfield, Litchfield, Middlesex, New London, Tolland, and Windham counties (Supplemental Digital Content [SDC] Figure 1, http://links.lww.com/NRES/A584). Five participants’ ZIP codes outside of Connecticut were excluded from the study.

Racially Differentiated ZIP Code–Derived SES

We compared the racially differentiated ZIP code–derived SES factors between infants born to Black women and those born to White women. Among all the SES factors analyzed, Black women had significantly lower median household income (p < .001), larger average family size, and higher poverty level (p < .001) compared with White women (Table 3).

Table 3.

Racial differentiated maternal social economic status

SES Variables	Black or AA (N=51)	White (N=140)	P
Total households	9990.0 [7602.0;12938.0]	9833.5 [4297.0;12980.0]	0.300
School enrollment	6975.0 [5462.0;8047.0]	6002.0 [2822.0;7864.0]	0.024
Employment	20255.0 [17926.0;25339.0]	19309.5 [10135.5;27809.0]	0.239
Median household income	59633.0 [42241.0;80426.0]	89274.5 [70648.5;114175.0]	<0.001
Total housing units	11391.0 [9051.0;14046.0]	10246.5 [4719.0;14300.0]	0.147
Total population	26077.0 [21977.0;30289.0]	23572.5 [12592.5;34405.0]	0.255
Insurance coverage	25963.0 [21624.0;30035.0]	23444.5 [12556.0;34007.0]	0.289
Average family size	3.1 [3.0;3.2]	3.0 [2.9;3.1]	0.010
Child population in household	7516.0 [6532.0;9077.0]	7129.0 [3391.0;9797.0]	0.174
Residence 1 year ago	25750.0 [21885.0;29924.0]	23181.5 [12421.0;33768.0]	0.255
Occupation	12748.0 [9359.0;15780.5]	11175.0 [5769.5;17587.0]	0.469
Poverty	11.8 [7.0;19.0]	5.7 [3.0;10.3]	<0.001

Note. SES, social economic status. Values in this table are presented as medians with interquartile ranges [25th percentile; 75th percentile].

The Risk Factors of ZIP Code–Derived SES and Infants’ Early-Life Adversity Predict Neurobehavioral Outcomes During NICU

The top key risk factors predicting quality of infant movement include poverty, birth GA, birth weight, SNAPPE–II score, average family size, and maternal age (Table 4 and SDC Figure 2, Supplemental Digital Content 1, http://links.lww.com/NRES/A584). For predicting infant stress responses, the key risk factors are birth weight, average family size, school enrollment, SNAPPE–II score, median household income, and birth GA (Table 4 and SDC Figure 2, Supplemental Digital Content 1, http://links.lww.com/NRES/A584). In addition, the SES factors that predict infant arousal levels, such as irritability, include maternal age, birth weight, total housing units, birth GA, median household income, and SNAPPE–II score (Table 4 and SDC Figure 2, Supplemental Digital Content 1, http://links.lww.com/NRES/A584). The risk factors commonly shared across all three neurobehavioral outcomes, quality of movement, stress and abstinence symptoms, and arousal level, are related to infant birth weight, birth GA, and SNAPPE–II.

Table 4.

The risk factors of zip code derived SES and infants’ early life adversity predict infant neurodevelopmental outcomes

Variables	NNNS			Bayley at 1st Year			Bayley at 2nd Year			BITSEA at 2nd Year
	NQMOVE	NSTRESS	NAROUSAL	Cognitive	Language	Motor	Cognitive	Language	Motor	Competence	Problem
Birth weight	0.15	0.265	0.153	0.093	0.304	0.177	0.301	0.323	0.293	0.274	0.165
Birth GA	0.151	0.071	0.095	0.059	0.103	0.052	0.098	0.1	0.06	0.092	0.101
SANPPEII	0.143	0.087	0.092	0.367	0.085	0.33	0.116	0.101	0.154	0.124	0.107
Sex	0.026	0.013	0.016	0.037	0.031	0.036	0.14	0.172	0.035	0.035	0.014
Race	0.018	0.011	0.01	0.015	0.032	0.025	0.05	0.107	0.023	0.083	0.018
Maternal age	0.092	0.067	0.215	0.052	0.168	0.062	0.048	0.049	0.052	0.09	0.058
Residence 1 year ago	0.011	0.01	0.004	0.019	0.02	0.004	0.008	0.003	0.013	0.01	0.016
Average family size	0.139	0.13	0.087	0.102	0.082	0.098	0.06	0.075	0.048	0.079	0.052
Median household income	0.057	0.072	0.095	0.046	0.028	0.066	0.111	0.111	0.038	0.065	0.145
Insurance coverage	0	0	0.002	0	0.001	0.003	0	0	0	0	0.001
Total households	0.054	0.033	0.077	0.048	0.047	0.057	0.03	0.023	0.06	0.068	0.056
Child population in household	0.05	0.035	0.016	0.156	0.046	0.069	0.034	0.062	0.134	0.005	0.013
Poverty	0.172	0.113	0.054	0.03	0.02	0.029	0.071	0.074	0.048	0.049	0.146
School enrollment	0.021	0.117	0.049	0.012	0.011	0.054	0.071	0.034	0.13	0.065	0.07
Occupation	0.004	0.019	0.004	0.004	0.013	0.002	0.028	0.002	0.004	0.001	0.039
Employment	0.002	0.003	0.008	0.003	0.002	0.002	0.001	0.001	0	0.001	0.002
Total housing units	0.006	0.042	0.136	0.04	0.016	0.022	0.006	0.003	0.011	0.007	0.01
Total population	0	0	0	0	0	0	0	0	0	0	0

Note. GA, gestational age; SNAPEII, Score for Neonatal Acute Physiology with Perinatal Extension-II; NQMOVE, NNNS subscale of quality of movement; NSTRESS, NNNS subscale of stress; NAROUSAL, NNNS subscale of arousal. The values in these tables represent the mean absolute SHAP values derived from XGBoost models for each neurodevelopmental or behavioral outcome. SHAP values quantify the contribution of each predictor to the model’s output. For each outcome, a separate XGBoost model was fitted, and the average magnitude of each feature’s SHAP value was computed across all subjects. A higher value indicates that the corresponding variable had a greater average impact on the model prediction for that specific outcome. Values are rounded to three decimal places for clarity.

ZIP Code–Derived SES and Infants’ Early-Life Adversity Predict Development at 1- and 2-Year CA

Neurodevelopmental outcomes—as assessed using Bayley–III scores at the 1-year CA follow-up—were predicted by SES factors. For cognitive development, the key SES risk factors are SNAPPE–II score, household child population, average family size, birth weight, birth GA, and maternal age (Table 4 and SDC Figure 3, Supplemental Digital Content 1, http://links.lww.com/NRES/A584). Language development was influenced by birth weight, maternal age, birth GA, SNAPPE–II score, average family size, and the total number of households (Table 4 and SDC Figure 3, Supplemental Digital Content 1, http://links.lww.com/NRES/A584). Motor development was associated with SNAPPE–II score, birth weight, average family size, household population of children, median household income, and maternal age (Table 4 and SDC Figure 3, Supplemental Digital Content 1, http://links.lww.com/NRES/A584). The risk factors commonly shared across all three neurodevelopmental outcomes (cognitive, language, and motor) were birth weight, SNAPPE–II score, maternal age, and average family size.

At 2-year CA follow-up, SES factors continued to predict neurodevelopment. For cognitive development, the most important SES risk factors were birth weight, infant sex, SNAPPE–II scores, median household income, birth GA, and school enrollment (Table 4 and SDC Figure 4, Supplemental Digital Content 1, http://links.lww.com/NRES/A584). Language development was influenced by birth weight, infant sex, median household income, race, SNAPPE–II score, and birth GA (Table 4 and SDC Figure 4, Supplemental Digital Content 1, http://links.lww.com/NRES/A584). Motor development was predicted by birth weight, SNAPPE–II score, child population in the household, school enrollment, total number of households, and birth GA (Table 4 and SDC Figure 4, Supplemental Digital Content 1, http://links.lww.com/NRES/A584). The risk factors commonly shared across all three neurodevelopmental outcomes at 2 years of CA were birth weight, birth GA, and SNAPPE–II score.

SES and early life adversity can also predict psychosocial problems assessed with the BITSEA at 2-year CA follow-up. For the Problem outcomes, the risk factors were birth weight, poverty, median household income, SNAPPE–II score, birth GA, and school enrollment (Table 4 and SDC Figure 5, Supplemental Digital Content 1, http://links.lww.com/NRES/A584). For the Competence outcomes, the risk factors were birth weight, SNAPPE–II score, birth GA, maternal age, race, and average family size (Table 4 and SDC Figure 5, Supplemental Digital Content 1, http://links.lww.com/NRES/A584). The common shared risk factors for both Problem outcomes and Competence outcomes were birth weight, SNAPPE–II score, and birth GA.

ZIP Code–Derived SES and Infants’ Early-Life Adversity Predict Feeding and Growth Outcomes

Infants’ daily feeding total intake was predicted by SES factors and infants’ early-life adversities, including birth weight, maternal age, SNAPPE–II score, poverty level, birth GA, and average family size (Table 5 and SDC Figure 6, Supplemental Digital Content 1, http://links.lww.com/NRES/A584). The proportion of MOM intake was predicted by maternal age, birth GA, birth weight, poverty level, median household income, and race (Table 5 and SDC Figure 6, Supplemental Digital Content 1, http://links.lww.com/NRES/A584). Common risk factors shared between daily total feeding intake and MOM proportion during the NICU stay included birth GA, birth weight, maternal age, and poverty. In addition, infants’ weight z-score was predicted by birth weight, SNAPPE–II scores, birth GA, maternal age, race, and average family size (Table 5 and SDC Figure 6, Supplemental Digital Content 1, http://links.lww.com/NRES/A584).

DISCUSSION

We found racial differences among mothers who delivered preterm infants, with Black mothers more likely to be younger, single, and have lower levels of education compared with White mothers. This is consistent with previous studies showing that Black mothers are generally younger, more likely to be single, and have historically attained lower levels of education (Braveman et al., 2015; Thoma et al., 2019). In 2022, the Centers for Disease Control and Prevention (CDC) reported that 69% of Black infants were born to unmarried mothers, compared with 27% of White infants (Michelle et al., 2024). In our study, more Black women (78%), compared with White women (68%), had cesarean deliveries, although the difference was not significant. The CDC report confirmed that Black women have a higher cesarean delivery rate compared with White women (Michelle et al., 2024). However, the smaller proportion of Black women (26%) in our sample compared with White women may have limited our ability to detect a significant difference.

Racial differences in SES status were also observed in our study. We found that Black mothers had lower median household incomes compared with White women, a finding that has also been reported in other studies (Schoen et al., 2007). Notably, even with the same level of education, Black mothers tend to earn less than White mothers (Schoen et al., 2007). In addition, we found that Black mothers tend to have larger average family sizes and higher levels of poverty compared with White mothers. Forty percent of Black children live in large families (three or more children), compared with 30% of White children, placing Black children at an increased risk of poverty (Curran, 2021). However, this association remains underexplored in the United States (Curran, 2021).

We identified ways that racial differences and SES influence the health outcomes of preterm infants. Infants born to Black mothers were more likely to be younger, have lower birth weight, shorter body length, smaller head circumference, and higher SNAPPE–II scores compared with those born to White mothers. The higher incidence of low birth weight of infants born to Black women has been observed in prior research; it has been attributed to factors such as maternal SES, lifestyle or behavioral differences, genetics, and experiences of racial discrimination (Mickelson et al., 2022). Interestingly, previous research has shown that this elevated risk of preterm birth and low birth weight is primarily observed in Black women, especially U.S.-born Black women (Morisaki et al., 2017). In our study, Black infants had an average SNAPE–II score of 31, compared with a score of 21 in White infants, indicating that Black infants have higher morbidity. Bansode et al. (2023) analyzed SNAPE–II scores among 292 newborns and identified a cutoff score of 29 for a high risk of mortality, whereas in a study of 255 neonates, Muktan et al. (2019) recommended a higher cutoff score of 38 for increased mortality. However, neither study provided race-specific cutoff scores.

Infants’ birth weight, GA, and SNAPE–II scores were found to contribute to neurobehavioral outcomes, including infants’ movement modulation, stress and abstinence symptoms, and overall arousal level during assessments at 36–38 weeks PMA. These findings align with findings in a previous study of 113 preterm infants, which showed that lower birth weight was associated with lower scores on the Neurobehavioral Assessment of Preterm Infants at 36 weeks PMA, as well as poorer neurodevelopmental outcomes at 1 and 2 years of age (Constantinou et al., 2005).

We also found that maternal age was predictive only for neurobehavioral outcomes at 1 year of age. Camerota’s study included both term and preterm infants and their mothers, with infants’ neurobehavioral outcomes linked to their mothers’ age at delivery (Camerota et al., 2023). DiLabio et al. (2021) did not find an association between mothers’ age and infants’ neurodevelopmental outcomes at 2 years in 2,652 Canadian infants who were born before 29 weeks GA. Camerota et al. (2023) also noted that older mothers tended to have more education and higher income, which influenced neurodevelopmental outcomes. However, Camerota et al. (2023) do not identify racial disparity in the association between maternal age and infant neurobehavioral outcomes.

Average family size was associated with quality of movement and stress levels at 36–38 weeks PMA, as well as cognitive, language, and motor development at 1 year CA. These findings are consistent with findings from earlier studies of children aged 2–3 years, which show that larger family size is associated with an increased risk of cognitive disorders (Symeonides et al., 2021). However, those studies did not find an association between family size and language or motor development, possibly because their participants were older and thus at a different developmental stage, influenced by family size (Symeonides et al., 2021). An interesting theory, the Resource Dilution Model, explains these associations by suggesting that increased family size may reduce parental resources available per child, especially for younger siblings, thereby affecting their neurodevelopmental outcomes (Downey, 2001). Downey’s Resource Dilution Model suggests that smaller family sizes may be associated with better financial resources, benefiting infants’ health development (Downey, 2001). While family size and associated income or resources are significant factors for infants’ neurodevelopmental outcomes, family structure and maternal education level may have a greater influence on infants’ language development (Poudel et al., 2024).

Our findings indicate that at age 2, infants’ neurodevelopment is influenced not only by birth GA, maternal age, and birth weight but also by maternal school enrollment and median household income. While increased maternal time with their infants positively supports infant development, maternal school enrollment may limit caregiving time, potentially affecting neurodevelopment outcomes. In one study, researchers found increased postnatal school enrollment among unmarried Black mothers, potentially limiting mother–infant interactions (Radey, 2017). Moreover, financial challenges often intensify for families with preterm infants (Lakshmanan et al., 2022). Growing up in a poverty-stricken environment can affect neurodevelopment, leading to behavioral problems and lower school achievement (Pollak & Wolfe, 2020). These effects may be linked to lower gray matter volumes in the frontal, temporal, parietal, and occipital lobes, as observed in a study of 486 subjects, which these associations were evident in children aged 2.5–6.5 and persisted into adolescence (Hair et al., 2022).

Overall, understanding the maternal ZIP-code-derived SES can help identify areas of social disadvantage, enabling policymakers to more equitably allocate maternal–child health resources such as housing assistance (Swope & Hernández, 2019) and access to prenatal care programs (Ladak et al., 2024). Nurse researchers and community stakeholders can also leverage these findings to advocate for legislation addressing upstream social determinants of health, such as paid maternity leave and minimum wage increases. In addition, nurse researchers can develop and implement tailored care plans for mother–infant dyads residing in high-risk areas.

Limitations

Our study had limited maternal data, including only maternal age, race and ethnicity, and ZIP code information. Future studies should incorporate comprehensive maternal medical histories, including body mass index, complications before and during the pregnancy, family history of preterm birth, substance use, chronic stress levels, and SES data (e.g., income, insurance, and education). In addition, the SES data in this study were derived from ZIP codes in only one major metropolitan area in the Northeast of the United States, limiting generalizability to broader U.S. populations. The low proportion of Black infants also introduces potential bias. Future studies should include larger, more diverse samples to better represent Black women and their infants. Lastly, as our study followed preterm infants only up to 2 years of age, longer follow-up is needed to assess the effect of these risk factors on later developmental outcomes, such as school performance.

CONCLUSION

Our findings highlight ZIP code–derived critical social determinants, such as poverty, larger family size, younger maternal age, and unmarried status, could predict nonoptimal neurodevelopmental outcomes among Black preterm infants. Future research should focus on developing and testing targeted interventions that support at-risk mothers and infants, particularly in socioeconomically disadvantaged populations.

Supplementary Material

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