Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2026 Mar 31.
Published in final edited form as: Demography. 2025 Dec 1;62(6):1821–1834. doi: 10.1215/00703370-12325838

Distributions of Adverse Childbearing Experiences across Racial and Ethnic Groups: A Research Note

Mieke Beth Thomeer 1, Courtney Williams 2
PMCID: PMC13034855  NIHMSID: NIHMS2157429  PMID: 41268751

Abstract

Adverse childbearing experiences, such as preterm births and NICU stays, are especially prevalent among Black and Hispanic pregnant people. In this research note, we provide a novel way of considering racial and ethnic patterns around adverse childbearing experiences by analyzing the 1979 National Longitudinal Survey of Youth (NLSY79; N=3,637). We use Latent Class Analysis to identify four specific classes of adverse childbearing experiences that are unequally distributed within and across racial and ethnic groups. These four classes—Minimal Complications, High Childbearing Complications, Complex Gestation, and Increased Medicalized Interventions—represent unique types of reproductive health outcomes and interactions within the reproductive health care system. Distributions across these classes reveal what racial and ethnic groups are most at risk for multiple pregnancy and gestational complications (e.g., late pregnancy losses, closely spaced births), highly medicalized childbearing experiences (e.g., c-sections, NICU stays), or a broad constellation of adverse childbearing-related outcomes. Our research note draws attention to how specific childbearing experiences cluster together, reflecting broader racial and ethnic structures and potentially mattering for future health and well-being outcomes.

Keywords: Childbearing, Reproductive Life Course, Racial and Ethnic Disparities, Latent Class Analysis

Introduction

Adverse reproductive outcomes—such as prematurity, low birthweight, and closely spaced births—are relatively common among pregnant people and their families (CDC 2022a; March of Dimes 2021). These types of childbearing circumstances might require extra adjustments for families, impact future fertility-related decisions, increase stress levels, and shape health trajectories (Lamb 2002; Provenzi et al. 2016). Labeled as adverse because the outcomes are often physically, emotionally, or economically deleterious, the distributions and consequences of these experiences are sometimes undergirded by racism, sexism, and other forms of structural oppressions (Chae et al. 2018; Davis 2019). Further, these adverse childbearing outcomes likely cluster in unique configurations which potentially have compounding implications for life trajectories and well-being (Johnson et al. 2018; Thomeer et al. 2022a). Thus, in this research note, we use a holistic reproductive life course-oriented approach to understand distributions of adverse childbearing outcomes—in particular racial and ethnic patterns around adverse childbearing experiences—and provide a novel way of considering adverse childbearing experiences. We suggest that our approach is a way to advance a holistic conceptualization of stressors and reproduction and that looking at adverse childbearing experiences in isolation may underestimate the extent of racial and ethnic inequities around childbearing.

Our approach is further motivated by cumulative disadvantage (Dannefer 2003) and stress universe (Wheaton 1994) perspectives which understand individual hardships—including adverse reproductive outcomes—as occurring across the life course amid a matrix of stressors. Our conceptual framework (see Figure 1) shows how heightened levels of exposure to intersectional oppression—including stressors, weathering, resource disadvantages, and discrimination—drive greater risks of adverse childbearing experiences for racially and ethnically minoritized pregnant people (Averbach et al. 2023; Geronimus 1992; Scott et al. 2019). For instance, Non-Hispanic Black1 and Hispanic pregnant people have a higher risk for preterm birth and low birthweight births compared to their non-Hispanic White2 counterparts, which manifests via biased medical treatment, limited health care access, and cumulative exposure to stressors and discrimination (Chae et al. 2018; Chambers et al. 2023; McLemore et al. 2018). Identifying clusters of adverse childbearing experiences and their distributions across racial and ethnic groups using a reproductive life course framework provides insight into how intersectional oppressions impact health.

Figure 1:

Figure 1:

Open in a new tab

Conceptual Framework Linking Structural and Interpersonal Sources of Racism and Sexism to Adverse Childbearing Experiences

Our research note focuses on seven specific reproductive outcomes traditionally recognized as disadvantageous, shown in Figure 1. Health scholarship consistently shows both preterm and low birthweight births heighten chances for acute and chronic morbidities among birthing people (Behrman and Butler 2007). Closely spaced births are highly correlated with these adverse birth outcomes and can also have deleterious health implications (Congdon et al. 2022; Lonhart et al. 2019). NICU stays constitute an adverse childbearing experience, as they typically include separation of birthing people from their newborns, medical uncertainty, and health and economic complications (Alkozei et al. 2014). Caesarean sections (hereafter, c-sections), unwanted pregnancies resulting in live births, and late pregnancy losses are three types of reproductive outcomes that are less frequently studied in terms of their maternal health consequences, but each can introduce novel challenges into the reproductive life course. For example, c-sections increase risk of maternal morbidity when unplanned or in emergency situations (Leonard et al. 2019). Additionally, some studies indicate unwanted pregnancies are associated with greater risk for detrimental outcomes in specific circumstances through multiple complex mechanisms, including being unaware of the pregnancy and thus not seeking early prenatal care (Kost and Lindberg 2015; Mark and Cowan 2022). Finally, late pregnancy loss is associated with a high risk for emotional hardships, such as depression and low self-esteem (CDC 2022b; Evans et al. 2023).

Considering how these adverse childbearing experiences cluster may allow us to identify processes undergirding these inequities or develop interventions to address them. We advocate for considering multiple childbearing outcomes cumulatively to advance understanding of the reproductive life course and inequities across that life course, asking: (1) How do adverse childbearing experiences cluster within the population? and (2) How are adverse childbearing experiences distributed across racial and ethnic groups? We use Latent Class Analysis (LCA), which is well-suited for this work because it builds upon prior reproductive life course scholarship (Johnson et al. 2018; Thomeer et al. 2022a) and allows us to simultaneously account for multiple childbearing experiences within the same model while stratifying by race and ethnicity.

Data and Methods

Data

To develop our model of adverse childbearing experiences, we analyze data from the 1979 National Longitudinal Survey of Youth (NLSY79) (Rothstein, Carr and Cooksey 2018). This dataset includes comprehensive childbearing variables at multiple time points from 1979 to 2020, allowing for detailed consideration of the reproductive life course. Our analytic sample includes the 3,637 respondents who had at least one pregnancy. NLSY79 respondents report dates of all pregnancies and births and information about pregnancy losses, birthweights, and other variables relevant to our study.

Measures

Adverse Childbearing Experiences.

Childbearing measures include pregnancy and childbearing outcomes that occurred before the survey start in 1979 and during the study period (1979–2020). We construct seven dichotomous measures: low birthweight (2500 grams or less), preterm birth (gestation less than 37 weeks), late pregnancy loss (4–9 months gestation), NICU stay (hospital stay by newborn for 7 days or more), closely spaced births (births within 18 months, excluding multiple births), c-section, and unwanted birth (pregnancy categorized as unwanted by respondent that resulted in live birth). Respondents are categorized as having each experience if occurred at least once.

Race and ethnicity.

Race and ethnicity are non-Hispanic White, non-Hispanic Black, and Hispanic. We drop the 81 people who did not identify with these three groups due to statistical power issues.

Covariates.

We adjust for respondent’s birth year, number of births, year of first birth, year of last birth, whether unmarried when had first birth, respondent’s educational attainment by midlife (<12 years of education, 12 years, 13–15 years, 16 or more years), whether respondent reported health limitations in 1979, and parents’ educational attainment (no parents with ≥12 years of education, at least one parent with ≥12 years). These covariates are included as each is associated with an increased risk of having adverse childbearing experiences (Nabukera et al., 2021; Thomeer et al. 2022b).

Estimation

Well-suited for our holistic conceptualization of reproductive experiences, we conduct LCA to uncover and describe contextual patterns and complex intersections among covarying measures (Muthén 2002). LCA is a person-centered approach that presupposes responses to a set of observed variables (e.g., adverse childbearing experiences) are indicative of an underlying latent variable with a finite number of classes (Collins and Lanza 2010). LCA allows for identification of homogeneous subpopulations (e.g., people with similar childbearing experiences) within a larger population (e.g., pregnant people) (Vermunt and Magidson 2004). We identified classes (including optimum number of classes, between 1 and 10) based on model fit statistics (see Table 1) alongside theory (Jung and Wickrama 2008). Entropy >0.80 indicates sufficient separation between classes (Clark and Muthén 2009), supporting our 2-, 3-, and 4-class models. Graphing Akaike information criterion, Bayesian information criterion (BIC), and sample size-adjusted BIC shows the point of “diminishing returns” is at the 4-class models. The Vuong-Lo-Mendell-Rubin likelihood ratio test and parametric bootstrapped likelihood ratio test provide a p-value comparing a k-1 class model to a k class model and support our 2-, 3-, and 4-class models. We suggest that these fit statistics—alongside class meanings, parsimony, and size—collectively support the 4-class model.

Table 1:

Fit Statistics (NLSY79; N=3,637)

Number of Classes Entropy AIC1 BIC2 SSA-BIC3 VLMR LRT (p)4 PBLRT (p)5
1 22703.52 22746.91 22724.67
2 0.85 21370.42 21463.40 21415.74 <.001 <.001
3 0.92 21301.02 21443.59 21370.51 <.001 <.001
4 0.82 21245.16 21437.33 21338.82 <.001 <.001
5 0.78 21243.36 21485.12 21361.20 0.571 0.188
6 0.76 21239.47 21530.82 21381.48 0.720 0.500
7 0.74 21242.79 21583.73 21408.97 0.110 0.667
8 0.70 21254.27 21644.80 21444.62 0.929 1.000
9 0.65 21260.84 21700.96 21475.36 0.358 0.667
10 0.62 21270.37 21760.09 21509.06 0.357 1.000
Open in a new tab
1

Akaike information criterion;

2

Bayesian information criterion;

3

Sample-adjusted Bayesian information criterion;

4

Vuong-Lo-Mendell-Rubin likelihood ratio test;

5

Parametric Bootstrapped likelihood ratio test

Using the 4-class model, we estimate the probabilistic assignment of each respondent to each class, using latent class posterior distributions to create the most likely class variable. Table 2 shows item-response probabilities for each adverse childbearing experience variable. After calculating descriptive statistics stratified by race and ethnicity, we fit multinomial logistic regressions to estimate how race and ethnicity are associated with the most likely class variable, adjusting for covariates. Analyses were conducted in Stata, and models adjust for the complex structure of the data. We use multiple imputation with chained equations (m=20) to handle missing data.

Table 2:

Item Response Probability and Means/Standard Deviations for Adverse Childbearing Experiences Indicators Used in Latent Class Analysis and Expected Sample Size (NLSY79; N=3,637)

Minimal Complications High Childbearing Complications Complex Gestation Increased Medicalized Interventions
Expected n 2,900 348 190 199
Expected % 79.74% 9.57% 5.22% 5.47%
Low birthweight 0.05 0.96 0.25 0.00
Preterm birth 0.04 0.76 0.09 0.14
Late pregnancy loss 0.10 0.14 0.18 0.11
Unwanted pregnancy 0.17 0.31 0.56 0.31
NICU stay 0.00 0.67 0.21 1.00
Closely spaced birth 0.12 0.29 0.64 0.16
c-section 0.29 0.48 0.12 0.65
Open in a new tab

Results

Table 3 shows descriptive statistics for the total analytic sample and by race and ethnicity. About 16 percent of the total sample had low birthweight babies, 12 percent preterm births, 11 percent late pregnancy loses, 22 percent unwanted pregnancies, 14 percent NICU stays, 18 percent closely spaced births, and 31 percent c-sections. Black respondents had a higher prevalence of each experience relative to White respondents (p<.05) except for c-sections. Hispanic respondents had a higher prevalence of all experiences compared to White respondents except for preterm births, as well as lower prevalences of low birthweight, unwanted pregnancy, and NICU stays but higher prevalence of c-sections compared to Black respondents (p<.05).

Table 3:

Descriptive Statistics: Percentages for Total Sample and by Race and Ethnicity (NLSY79; N=3,637)

Total By Race and Ethnicity
White Black Hispanic
Percentage 100.00 47.68 31.45 20.87
Adverse Childbearing Variables
Low birthweight 15.72 10.94B 23.20A 15.25AB
Preterm birth 12.21 11.08B 14.08A 11.94
Late pregnancy loss 11.25 9.40B 13.29A 12.38A
Unwanted pregnancy 22.39 14.19B 35.26A 21.87AB
NICU stay 13.76 9.54B 19.89A 14.09AB
Closely spaced birth 18.23 13.90B 22.20A 22.13A
c-section 31.13 30.05 30.02 35.25AB
Adverse Childbearing Classes
Minimal Complications 79.74 86.04B 70.80A 78.79AB
High Childbearing Complications 9.57 7.04B 13.72A 9.09B
Complex Gestation 5.22 2.83B 8.57A 5.67AB
Increased Medicalized Interventions 5.47 4.09B 6.91A 6.46A
Covariates
Birth year* 1960.49
(0.04)		 1960.43
(0.05)		 1960.55
(0.06)		 1960.52
(0.08)
Number of births* 2.40
(0.02)		 2.23B
(0.03)		 2.51A
(0.04)		 2.64AB
(0.05)
Year of first birth* 1984.79
(0.11)		 1986.31B
(0.15)		 1982.93A
(0.18)		 1984.14AB
(0.22)
Year of last birth* 1990.35
(0.11)		 1990.92B
(0.15)		 1989.17A
(0.19)		 1990.81B
(0.24)
Whether unmarried when had first birth 39.79 19.88B 71.61A 37.33AB
Respondent’s educational attainment:
 Less than 12 years		 11.27 6.52B 12.33A 20.55AB
 12 years 43.02 44.23 42.83 40.58
 13–15 years 26.73 23.07B 31.64A 27.67A
 16+ years 18.97 26.18B 13.20A 11.20A
Had health limitations at baseline (1979) 4.99 5.74 5.10 3.14AB
Parental educational attainment:
 No parents with 12 or more years of education		 37.31 19.31B 46.93A 65.12AB
 At least one parent with 12 or more years of education 62.69 80.69B 53.07A 34.88AB
Open in a new tab

p<.05:

A

Compared to White,

B

Compared to Black;

*

Mean and Standard Deviations

We named the four latent classes based on their defining characteristics. We discuss these classes (Table 2), as well as how likely membership in each childbearing class is distributed within the sample and across racial and ethnic groups (Table 3). All differences discussed are significant at p<.05 level. About 80 percent of respondents in the total analytic sample comprise the Minimal Complications class, a class characterized by relatively low proportions of each adverse childbearing outcome—specifically no NICU stays, only 4 percent with preterm births, and 5 percent with low birthweight births. This class is the largest in the analytic sample, including the largest within each racial and ethnic group. We do still identify statistically significant differences when stratified by racial and ethnic groups. About 71 percent of Black respondents are in the Minimal Complications class, which is significantly less than White (86%) and Hispanic (79%) respondents. The difference between White and Hispanic respondents is also statistically significant.

We recognize the remaining three classes as the “adverse childbearing outcomes” classes since they each contain higher proportions of each adverse childbearing outcome, although the proportions of these outcomes are distributed differently. First, the High Childbearing Complications class represents about 10 percent of the sample and is characterized by relatively high proportions of each adverse childbearing outcome, including low birthweight births (0.96), preterm births (0.76), NICU stays (0.67), and c-sections (0.48). Only 7 percent of White respondents are represented in this class, which is a significantly lower percentage than the 14 percent of Black respondents but a statistically similar percentage as the 9 percent of Hispanic respondents.

Next, 5 percent of all respondents are in the Complex Gestation class. This class is characterized by relatively high proportions of adverse childbearing outcomes related to gestation, such as unwanted pregnancies (0.56), closely spaced births (0.64), and the highest proportion of late pregnancy losses (0.18), but relatively low proportions of experiences related to birth outcomes or medical care experiences (e.g., preterm birth: 0.09, NICU stays: 0.21). Again, we found significant differences across racial and ethnic groups. Only 3 percent of White respondents are within this class, fewer than the 9 percent of Black respondents and 6 percent of Hispanic respondents. The difference between Black and Hispanic respondents is also statistically significant.

Finally, the Increased Medical Interventions class also represents about 5 percent of the sample and is characterized by low proportions of many of the adverse childbearing outcomes (e.g., no respondents in this class have low birthweight birth), but high proportions of two variables associated with the medical care experience. Namely, all respondents in this class had at least one birth with a NICU stay (1.00), and this class represented the highest proportion of c-sections (0.65). Black (7%) and Hispanic (6%) respondents have more representation in the Increased Medical Interventions class relative to White respondents (4%) but are statistically similar to each other.

Relative risk ratios (RRRs) from regression models adjusting for key covariates are in Table 4. Relative risk ratios are exponentiated coefficients from multinomial logistic models, similar to how odds ratios are exponentiated coefficients from binary logit models. These adjusted regression models examine the competing risks of probabilistic membership in the Minimal Complications class (base outcome) compared to the other three classes—High Childbearing Complications, Complex Gestation, and Increased Medicalized Interventions. All significant differences discussed are at p<.05 level. In adjusted models, compared to White respondents, Black respondents have greater relative risks of belonging to all three of the adverse childbearing classes (High Childbearing Complications, Complex Gestation, and Increased Medicalized Interventions) than the Minimal Complications class (RRR=1.70, 1.77, and 1.60, respectively). Black respondents also have greater relative risks compared to Hispanic respondents of being in the High Childbearing Complications class and the Complex Gestation class than the Minimal Complications class (RRR not shown). Both patterns are similar to what was seen in the bivariate descriptive statistics (Table 3). Yet in contrast to the descriptive statistics from Table 3, in the adjusted regression models, there are no longer any statistically significant differences between White and Hispanic respondents in their relative risks of belonging to the adverse childbearing classes.

Table 4:

Relative Risk Ratios (RRR) with Standard Errors from Adjusted Multinomial Logistic Regression Models Estimating Adverse Childbearing Class Membership by Race and Ethnicity (NLSY79)

High Childbearing Complications versus Minimal Complications Complex Gestation versus Minimal Complications Increased Medicalized Interventions versus Minimal Complications
Race and Ethnicity
White (Reference)
Black 1.70**
(0.26)		 1.77*
(0.40)		 1.60*
(0.31)
Hispanic 0.97
(0.17)		 1.07
(0.28)		 1.36
(0.29)
Covariates
Birth year 0.96
(0.03)		 1.05
(0.05)		 1.01
(0.04)
Number of births 1.75***
(0.11)		 3.02***
(0.25)		 1.16
(0.10)
Year of first birth 1.03
(0.02)		 1.05
(0.03)		 0.95*
(0.02)
Year of last birth 1.00
(0.01)		 0.89***
(0.02)		 1.02
(0.02)
Whether unmarried when had first birth 1.73***
(0.24)		 2.02**
(0.42)		 1.24
(0.22)
Respondent’s educational attainment:
 Less than 12 years (Reference)
 12 years 0.85
(0.16)		 0.60*
(0.14)		 0.70
(0.15)
 13–15 years 0.72
(0.15)		 0.75
(0.19)		 0.48**
(0.12)
 16+ years 0.62
(0.16)		 0.58
(0.22)		 0.45*
(0.14)
Had health limitations at baseline (1979) 0.90
(0.27)		 1.46
(0.48)		 1.62
(0.46)
At least one parent with 12 or more years of education 0.90
(0.12)		 1.13
(0.22)		 1.07
(0.18)
Open in a new tab

N=3,637;

***

p<.001,

**

p<.01,

*

p<.05

Discussion

This research note provides a framework, method, and empirical evidence for understanding how adverse reproductive experiences cluster and are distributed across racial and ethnic groups among midlife US pregnant people. Our framework, considering adverse childbearing outcomes as collective experiences which unfold across the reproductive life course, is a novel approach towards documenting racial and ethnic disparities in childbearing and, by extension, their long-term consequences for pregnant people and their families. We highlight our key findings and their broader implications.

First, recognizing common combinations of adverse childbearing experiences—as we do through our LCA on seven childbearing variables—reveals previously unacknowledged patterns and contexts. By offering a descriptive rather than mechanistic understanding, LCA allows us to deconstruct notions of monolithic childbearing profiles. We find that, altogether, about 20 percent of respondents belong within one of the classes we categorized as adverse childbearing experiences (i.e., High Childbearing Complications, Complex Gestation, Increased Medicalized Interventions). Only half of the respondents in these three classes, however, occupy a class (i.e., High Childbearing Complications) with adverse childbearing complications that are typically documented as correlating with one another (e.g., preterm birth and low birthweight; Bediako et al. 2015; Kramer and Hogue 2009). Our findings reveal a more heterogenous distribution of adverse events, pushing against any assumption that these outcomes are only correlated with each other in predictable ways.

In identifying four unique classes of childbearing experiences, we emphasize two classes—Complex Gestation and Increased Medical Interventions—which introduce a nuanced understanding of reproductive experiences. The Complex Gestation class involves relatively high rates of events related to the embodiment of pregnancy, including late pregnancy loss, unwanted pregnancies, and closely spaced births, yet low rates of adverse birthing and postpartum events. Previous research indicates that these specific gestational experiences are often stigmatized (Kelley and Trinidad 2012; Pollock et al. 2021), which may deter individuals from seeking support (e.g., therapy in case of pregnancy loss). This stigma may be especially prevalent for Black pregnant people, who are more likely to be blamed for these childbearing events within existing dominant narratives (Moseson et al. 2019; Scott et al. 2019).

In contrast, the Increased Medicalized Interventions class is composed of birthing people with relatively high rates of NICU stays and c-sections despite low rates of adverse experiences related to the embodiment of pregnancy or newborn health characteristics (i.e., low birthweight, prematurity). In other words, this group has incredibly high likelihoods of using, or needing, high-tech clinical care, but using these services likely arises from complications that are separate from preterm birth, low birthweight, or other adverse pregnancy-specific experiences. The Increased Medicalized Interventions class may uniquely have reliable access to complex medicalized care but also may experience overmedicalized childbirth, possibly increasing stressors and deleterious health outcomes (Ranjbar et al. 2019). Additionally, c-sections, NICU stays, and other high-tech birthing procedures increase the cost of childbirth and potential medical debt (Hsia et al. 2014; Negrini et al. 2021).

Recognizing these four classes is a useful way to contextualize individual childbearing experiences and develop appropriate interventions that adapt to distinct reproductive profiles. For example, a pregnant person may disclose details regarding a late pregnancy loss to their clinician but the acute implications of that loss likely differ depending on whether the individual had multiple adverse childbearing experiences (e.g., NICU stay, c-section, preterm birth), other adverse childbearing experiences related to gestation (e.g., unwanted pregnancy, closely spaced birth), or no other adverse childbearing experiences. Further, building upon cumulative disadvantage and the stress universe frameworks (Dannefer 2003; Wheaton 1994), the long-term consequences of adverse childbearing experiences may vary depending on whether, and how, grouped with other childbearing experiences. In other words, individual reproductive outcomes provide limited details on broader childbearing profiles. We encourage scholars to recognize holistic childbearing profiles which can provide insight into long-term well-being above and beyond using individual reproductive events.

Second, in identifying important racial and ethnic variation in adverse childbearing class membership, we point to the need to contextualize individual adverse childbearing experiences within other childbearing experiences to provide insight into broader racialized dynamics that likely shape disparities in holistic childbearing profiles. We do so both descriptively and in models adjusting for covariates associated with higher risks of adverse childbearing experiences, namely age, parity, socioeconomic status, and relational context (Nabukera et al. 2021; Thomeer et al. 2022b). Our descriptive results indicate that White respondents have significantly lower rates of belonging to each of the three adverse childbearing classes than Black and Hispanic respondents. Adjusting for covariates accounts for the differences between White and Hispanic respondents, but the disparities between Black and White respondents—as well as Black and Hispanic respondents—remain. We focus below on the inequity experienced by Black pregnant people as these are more robust in our models, but we suggest future research continue to interrogate adverse childbearing experiences of Hispanic pregnant people—especially considering differences within and across groups based on nativity, generation, and immigration status.

Our conclusions are in line with a significant body of research that has identified unique risks experienced by Black people during pregnancy and birth (Bediako et al. 2015; Manuck 2019; Su et al. 2021). We expand on these risks in two ways: first, by recognizing the importance of examining these risks across the reproductive life course, and second, by examining how these different childbearing experiences cluster across and within domains to identify unique classifications of reproductive disparities (e.g., related to gestation, around medicalized experiences during birth).

Considering the importance of a life course perspective for racial reproductive disparities, we suggest that if multiple disadvantageous, stressful, and stigmatized events occur throughout the reproductive life course more so for Black pregnant people than White—and to a lesser extent Hispanic—people, Black people may experience heightened degradation, or intersectional stressors tied to racism and sexism (Erving et al. 2021; see Figure 1). That we find that Black pregnant people being disproportionately represented in all three adverse childbearing classes demonstrates that the negative consequences of this degradation are not concentrated within one aspect of the obstetric experience or an isolated reproductive event. Rather degradation might shape Black pregnant people’s exposure to adverse outcomes throughout gestation, birth, and beyond, in line with a reproductive life course approach.

This reproductive life course approach is relevant for any of the three adverse childbearing classes but is especially pertinent for addressing challenges experienced by Black birthing people in the High Childbearing Complications class who arguably face the most concentrated adversity around reproduction. For this group, obstetric professionals and policy makers must remain attuned to the unique cumulative reproductive stress universe faced by this group. Altogether, our findings align with and extend prior research in ways that are not surprising, by broadening the scope of racial inequities to encompass multiple events, we emphasize the notable accumulation of adverse events across the reproductive life course. Prior studies which typically only consider racial and ethnic disparities around individual adverse childbearing outcomes (e.g., preterm birth, late pregnancy loss; Bediako et al. 2015; Manuck 2019) likely underestimate the full scope of the stress of childbearing for Black birthing people.

We also identify different domains of racial and ethnic reproductive disparities. Almost one third (30%) of Black pregnant people are in one of the adverse childbearing classes compared to only 14 percent of White birthing people, yet there are diverse distributions within this 30 percent. Namely, 14 percent of Black birthing people are in the High Childbearing Complications class, 9 percent are in the Complex Gestation class, and 7 percent in the Increased Medicalized Interventions class. Recognizing this diversity through identifying specific clusters of adverse childbearing experiences draws attention to the limitations of making inferential claims from individual childbearing outcomes. For example, a high prevalence of NICU stays is seen within two different classes: Increased Medicalized Interventions and High Childbearing Complications. If the goal is to understand and reduce racial NICU disparities (or other specific childbearing outcomes), the likely causes and recommended interventions may depend on whether that outcome is situated within the context of Increased Medicalized Interventions or High Childbearing Complications. Rather than using a “one-size-fits-all” approach, we urge scholars and practitioners to place specific reproductive events within the context of the additional adverse childbearing outcomes experienced by the birthing person which can provide needed insight into the reforms required to improve the lives of Black birthing people and their families.

Our findings should be considered alongside several limitations. First, there are limitations with our childbearing outcome measures as they tend to lack important details (e.g., NLSY79 does not distinguish between elective and emergency c-sections; questions regarding newborn’s hospital stay does not specify degree of care required). Second, we intentionally focus on outcomes “ever occurring” in the reproductive life course. Future research should consider timing (i.e. whether events are dispersed across pregnancies/births or clustered together in the same pregnancy/birth) and mechanisms behind different distributions of adverse childbearing experiences, with the goal of more precise and directional analyses. Researchers could also employ qualitative approaches to further elucidate the nuanced experiences of individuals from our childbearing classes. Third, we are limited in our measurement of race and ethnicity, which only considers broad categories and overlooks experiences of people outside of these categories (e.g., Asian or Indigenous people), heterogeneity within these categories (e.g., Mexican American people, immigration status), and people who belong to more than one of these categories (e.g., multiracial, Afro-Latine). We also cannot incorporate structural racism measures within our analysis. Future studies should include more multifaceted and critical conceptualizations of race, ethnicity, and racism into understandings of how adverse childbearing experiences are distributed within the population.

Notwithstanding these limitations, we propose that our conceptual model for recognizing the cumulative adverse childbearing experience, rather than just considering individual childbearing outcomes, is a novel and necessary way to document racial and ethnic inequities around reproduction, pregnancy, and birth. Our LCA approach is one way to test this model, but we encourage researchers to use our conceptual model to develop and test other types of methods to move this framework forward. We also argue that our conceptual model would benefit from being placed in conversation with a reproductive justice lens. As a theoretical lens and praxis developed by a multi-ethnic coalition of women, reproductive justice acknowledges individual and systemic factors at play throughout the reproductive life course (Ross and Solinger 2017). This approach looks beyond inequity in one area of childbearing (e.g., contraception access, reducing maternal mortality, improving affordability of childcare) to holistically examine how best to support pregnant people and families. To foster equitable access to health services and mitigate exposure to adverse pregnancy and birth outcomes throughout the reproductive life span, reproductive justice advocates can utilize our findings to create culturally cognizant interventions that align with the core tenets of the critical framework. We suggest our model of assessing multiple adverse childbearing outcomes and diversity in outcome patterning is an important step in incorporating this comprehensive reproductive justice framework into demographic models of fertility and childbearing processes, illuminating persistent racial disparities within this area. To mitigate reproductive inequities, our findings should inform social policies aimed at assisting pregnant people and families throughout the reproductive life course.

Acknowledgements:

This work was partially supported by the National Institute on Aging (NIA; R01AG069251; Thomeer & Reczek, Principal investigators). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

1

Hereafter, Black

2

Hereafter, White

Contributor Information

Mieke Beth Thomeer, University of Alabama at Birmingham

Courtney Williams, The University of Texas at Austin

References

  1. Alkozei A, McMahon E, & Lahav A (2014). Stress levels and depressive symptoms in NICU mothers in the early postpartum period. The Journal of Maternal-Fetal & Neonatal Medicine, 27(17), 1738–1743. 10.3109/14767058.2014.942626 [DOI] [PubMed] [Google Scholar]
  2. Averbach S, Ha D, Meadows A, Brubaker L, & Gyamfi-Bannerman C (2023). Failure to progress: Structural racism in women’s healthcare. eClinicalMedicine, 57. 10.1016/j.eclinm.2023.101861 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bediako PT, BeLue R, & Hillemeier MM (2015). A comparison of birth outcomes among Black, Hispanic, and Black Hispanic women. Journal of Racial and Ethnic Health Disparities, 2(4), 573–582. 10.1007/s40615-015-0110-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Behrman RE, & Butler AS (Eds.). (2007). Preterm birth: Causes, consequences, and prevention. National Academies Press. http://www.ncbi.nlm.nih.gov/books/NBK11362/. Accessed 4 November 2023 [PubMed] [Google Scholar]
  5. Centers for Disease Control and Prevention. (2022a). Preterm birth | Maternal and infant health | Reproductive health | CDC. Reproductive Health. https://www.cdc.gov/reproductivehealth/maternalinfanthealth/pretermbirth.htm. Accessed 11 October 2023 [Google Scholar]
  6. Centers for Disease Control and Prevention. (2022b). What is stillbirth? | CDC. What Is Stillbirth? https://www.cdc.gov/ncbddd/stillbirth/facts.html. Accessed 1 June 2023
  7. Chae DH, Clouston S, Martz CD, Hatzenbuehler ML, Cooper HLF, Turpin R, et al. (2018). Area racism and birth outcomes among Blacks in the United States. Social Science & Medicine, 199, 49–55. 10.1016/j.socscimed.2017.04.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Chambers BD, Fontenot J, McKenzie-Sampson S, Blebu BE, Edwards BN, Hutchings N, Karasek D, Coleman-Phox K, Curry VC, & Kuppermann M (2023). “‘It was just one moment that I felt like I was being judged’: Pregnant and postpartum Black women’s experiences of personal and group-based racism during the COVID-19 pandemic. Social Science & Medicine, 322, 115813. 10.1016/j.socscimed.2023.115813 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Clark SL, & Muthén B (2009). Relating Latent Class Analysis results to variables not included in the analysis. StatModel. https://www.statmodel.com/download/relatinglca.pdf [Google Scholar]
  10. Collins LM, & Lanza ST (2009). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences (Vol. 718). Wiley. [Google Scholar]
  11. Congdon JL, Baer RJ, Arcara J, Feuer SK, Gómez AM, Karasek D, et al. (2022). Interpregnancy interval and birth outcomes: A propensity matching study in the California population. Maternal and Child Health Journal, 26(5), 1115–1125. 10.1007/s10995-022-03388-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Dannefer D (2003). Cumulative advantage/disadvantage and the life course: Cross-fertilizing age and social science theory. The Journals of Gerontology Series B, 58(6), S327–S337. [DOI] [PubMed] [Google Scholar]
  13. Davis Dána-Ain. (2019). Reproductive injustice: Racism, pregnancy, and premature birth. New York: New York University Press. [Google Scholar]
  14. Erving CL, Patterson EJ, & Boone J (2021). Black women’s mental health matters: Theoretical perspectives and future directions. Sociology Compass, 15(9), e12919. 10.1111/soc4.12919 [DOI] [Google Scholar]
  15. Evans NM, Hsu Y-L, Kabasele CM, Kirkland C, Pantuso D, & Hicks S (2023). A qualitative exploration of stressors: Voices of African American women who have experienced each type of fetal/infant loss: Miscarriage, stillbirth, and infant mortality. Journal of Black Psychology, 49(2), 236–263. 10.1177/00957984221127833 [DOI] [Google Scholar]
  16. Geronimus AT (1992). The weathering hypothesis and the health of African-American women and infants: Evidence and speculations. Ethnicity & Disease, 207–221. [PubMed] [Google Scholar]
  17. Hsia RY, Akosa Antwi Y, & Weber E (2014). Analysis of variation in charges and prices paid for vaginal and caesarean section births: A cross-sectional study. BMJ Open, 4(1), e004017. 10.1136/bmjopen-2013-004017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Johnson KM, Greil AL, Shreffler KM, & McQuillan J (2018). Fertility and infertility: Toward an integrative research agenda. Population Research and Policy Review, 37(5), 641–666. 10.1007/s11113-018-9476-2 [DOI] [Google Scholar]
  19. Jung T, & Wickrama KA (2008). An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass, 2(1), 302–317. 10.1111/j.1751-9004.2007.00054.x [DOI] [Google Scholar]
  20. Kelley MC, & Trinidad SB (2012). Silent loss and the clinical encounter: Parents’ and physicians’ experiences of stillbirth–a qualitative analysis. BMC Pregnancy and Childbirth, 12(1), 137. 10.1186/1471-2393-12-137 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Kost K, & Lindberg L (2015). Pregnancy intentions, maternal behaviors, and infant health: Investigating relationships with new measures and propensity score analysis. Demography, 52(1), 83–111. 10.1007/s13524-014-0359-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Kramer MR, & Hogue CR (2009). What causes racial disparities in very preterm birth? A biosocial perspective. Epidemiologic Reviews, 31(1), 84–98. 10.1093/ajerev/mxp003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Lamb EH (2002). The impact of previous perinatal loss on subsequent pregnancy and parenting. The Journal of Perinatal Education, 11(2), 33–40. 10.1624/105812402X88696 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Leonard SA, Main EK, & Carmichael SL (2019). The contribution of maternal characteristics and cesarean delivery to an increasing trend of severe maternal morbidity. BMC Pregnancy and Childbirth, 19(1), 16. 10.1186/s12884-018-2169-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Lonhart JA, Mayo JA, Padula AM, Wise PH, Stevenson DK, & Shaw GM (2019). Short interpregnancy interval as a risk factor for preterm birth in non-Hispanic Black and White women in California. Journal of Perinatology, 39(9), 1175–1181. 10.1038/s41372-019-0402-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Manuck TA (2017). Racial and ethnic differences in preterm birth: A complex, multifactorial problem. Seminars in Perinatology, 41(8), 511–518. 10.1053/j.semperi.2017.08.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. March of Dimes. (2021). Low birthweight. Health Topic https://www.marchofdimes.org/find-support/topics/birth/low-birthweight. Accessed 11 October 2023
  28. Mark NDE, & Cowan SK (2022). Do pregnancy intentions matter? A research note revisiting relationships among pregnancy, birth, and maternal outcomes. Demography, 59(1), 37–49. 10.1215/00703370-9710311 [DOI] [PubMed] [Google Scholar]
  29. McLemore MR, Altman MR, Cooper N, Williams S, Rand L, & Franck L (2018). Health care experiences of pregnant, birthing and postnatal women of color at risk for preterm birth. Social Science & Medicine, 201, 127–135. 10.1016/j.socscimed.2018.02.013 [DOI] [PubMed] [Google Scholar]
  30. Moseson H, Filippa S, Baum SE, Gerdts C, & Grossman D (2019). Reducing underreporting of stigmatized pregnancy outcomes: Results from a mixed-methods study of self-managed abortion in Texas using the list-experiment method. BMC Women’s Health, 19(1), 113. 10.1186/s12905-019-0812-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Muthén BO (2002). Beyond SEM: General Latent Variable Modeling. Behaviormetrika, 29, 81–117. 10.2333/bhmk.29.81 [DOI] [Google Scholar]
  32. Nabukera SK, Wingate MS, Owen J, Salihu HM, Swaminathan S, Alexander GR, & Kirby RS (2009). Racial disparities in perinatal outcomes and pregnancy spacing among women delaying initiation of childbearing. Maternal and Child Health Journal, 13(1), 81–89. 10.1007/s10995-008-0330-8 [DOI] [PubMed] [Google Scholar]
  33. Negrini R, da Silva Ferreira RD, & Guimarães DZ (2021). Value-based care in obstetrics: Comparison between vaginal birth and caesarean section. BMC Pregnancy and Childbirth, 21, 333. 10.1186/s12884-021-03798-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Pollock DD, Pearson DE, Cooper DM, Ziaian APT, Foord C, & Warland APJ (2021). Breaking the silence: Determining prevalence and understanding stillbirth stigma. Midwifery, 93, 102884. 10.1016/j.midw.2020.102884 [DOI] [PubMed] [Google Scholar]
  35. Provenzi L, Barello S, Fumagalli M, Graffigna G, Sirgiovanni I, Savarese M, & Montirosso R (2016). A comparison of maternal and paternal experiences of becoming parents of a very preterm infant. Journal of Obstetric, Gynecologic & Neonatal Nursing, 45(4), 528–541. 10.1016/j.jogn.2016.04.004 [DOI] [PubMed] [Google Scholar]
  36. Ranjbar F, Gharacheh M, & Vedadhir A (2019). Overmedicalization of pregnancy and childbirth. International Journal of Women’s Health and Reproduction Sciences, 7(3), 419–420. [Google Scholar]
  37. Ross L, & Solinger R (2017). Reproductive justice: An introduction. University of California Press. 10.1525/9780520963207 [DOI] [Google Scholar]
  38. Rothstein DS, Carr D, & Cooksey E (2019). Cohort profile: The National Longitudinal Survey of Youth 1979 (NLSY79). International Journal of Epidemiology, 48(1), 22–22e. 10.1093/ije/dyy133 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Scott KA, Britton L, & McLemore MR (2019). The ethics of perinatal care for Black women: Dismantling the structural racism in “mother blame” narratives. Journal of Perinatal & Neonatal Nursing, 33(2), 108–115. 10.1097/JPN.0000000000000394 [DOI] [PubMed] [Google Scholar]
  40. Su D, Samson K, Hanson C, Anderson Berry AL, Li Y, Shi L, & Zhang D (2021). Racial and ethnic disparities in birth Outcomes: A decomposition analysis of contributing factors. Preventive Medicine Reports, 23, 101456. 10.1016/j.pmedr.2021.101456 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Thomeer MB, Reczek R, & Stacey L (2022a). Childbearing biographies as a method to examine diversity and clustering of childbearing experiences: A research brief. Population Research and Policy Review, 41, 1405–1415. 10.1007/s11113-022-09699-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Thomeer MB, Reczek R, & Ross C (2022b). Childbearing biographies and midlife women’s health. Journal of aging and health, 34(6–8), 870–882. 10.1177/08982643211070136 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Vermunt JK, & Magidson J (2002). Latent class cluster analysis. In Hagenaars JA & McCutcheon AL (Eds.), Applied Latent Class Analysis (pp. 89–106). Cambridge University Press. [Google Scholar]
  44. Wheaton Blair. 1994. “Sampling the Stress Universe.” in Stress and Mental Health: Contemporary Issues and Prospects for Future, edited by Avison W and Gotlib I. Plenum Press. [Google Scholar]

RESOURCES