Question:
You are given the full text of a scientific research paper.

    Summarize the paper's key **research findings** in the style of a policy brief. Your response should:
    - Focus on the major empirical or model-based results
    - Present findings that are policy-relevant and supported by the study
    - Use concise, non-technical language with essential technical terms as needed
    - Avoid quoting directly or summarizing minor details

    Write a short, clear paragraph appropriate for the “Scientific Research Findings” section of a policy brief.

    Scientific Research Paper:
    Differences in Determinants: Racialized Obstetric Care and
Increases in U.S. State Labor Induction Rates
Ryan K. Masters1,2, Andrea M. Tilstra2,3, Daniel H. Simon1,2, Kate Coleman-Minahan2,4
1University of Colorado Boulder, Boulder, CO, USA
2University of Colorado Population Center, Boulder, CO, USA
3Oxford University, Oxford, UK
4University of Colorado Anschutz Medical Campus, Aurora, CO, USA
Abstract
Induction of labor (IOL) rates in the United States have nearly tripled since 1990. We examine
official U.S. birth records to document increases in states’ IOL rates among pregnancies to Black,
Latina, and White women. We test if the increases are associated with changes in demographic
characteristics and risk factors among states’ racial-ethnic childbearing populations. Among
pregnancies to White women, increases in state IOL rates are strongly associated with changes
in risk factors among White childbearing populations. However, the rising IOL rates among
pregnancies to Black and Latina women are not due to changing factors in their own populations
but are instead driven by changing factors among states’ White childbearing populations. The
results suggest systemic racism may be shaping U.S. obstetric care whereby care is not “centered
at the margins” but is instead responsive to characteristics in states’ White populations.
Keywords
health care inequities; induction of labor; natality data; obstetric practices; structural racism
Racial-ethnic inequities in health care have been widely reported in the United States
whereby the care and treatment of White people is often prioritized more than that of
marginalized populations (Institute of Medicine [IOM] 2003). Evidence for discrimination
and unequal care in the United States has been documented in numerous settings (e.g., Daw
2015; Geiger 2003; Lewey and Choudhry 2014; Morris et al. 2010). Unequal treatment
in health care operates through multiple mechanisms, including policy creation and
enforcement (Krieger 2001, 2012), the organization of U.S. health care systems (Popescu et
al. 2010; Williams and Jackson 2005), medical training and culture (Cogburn 2019; Good
et al. 2003), and interpersonal interactions between patients and providers (Hoffman et al.
2016). Combined, evidence suggests that systemic racism creates and maintains health care
Corresponding Author: Ryan K. Masters, Department of Sociology, University of Colorado Boulder, Ketchum Hall 264, UCB 327,
Boulder, CO 80309-0327, USA., ryan.masters@colorado.edu.
SUPPLEMENTAL MATERIAL
Appendices A through L are available in the online version of the article.
HHS Public Access
Author manuscript
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Published in final edited form as:
J Health Soc Behav. 2023 June ; 64(2): 174–191. doi:10.1177/00221465231165284.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

systems that underserve and harm communities of color in the United States. That is, the
U.S. medical system has a history of centering care on the needs of dominant or majority
populations (i.e., White patients) rather than centering care “at the margins” or considering
the care needs of marginalized populations (Hardeman, Medina, and Kozhimannil 2016).
Failure to center U.S. obstetric care at the margins has likely produced unequal care and
discriminatory services in obstetric settings (Davis 2019, 2020; Liese et al. 2021; Logan et
al. 2022; Vedam et al. 2019). Indeed, “obstetric racism” (Davis 2019, 2020) is likely partly
responsible for high rates of poor maternal and neonatal health outcomes among U.S. Black
and Latina populations, such as elevated risks of maternal mortality and infant mortality
(Mathews, MacDorman, and Thoma 2015; Petersen et al. 2019).1 In her studies of obstetric
racism, Davis (2019, 2020) documents callous medical treatment of Black women during
pregnancy and highlights multiple instances in which they are disrespected and their birthing
preferences are discounted and ignored. The racialized experiences of people during prenatal
and obstetric care are documented further in an emerging body of literature that implicates
systemic and interpersonal racism as drivers of inadequate care for obstetric patients of color
(Chantarat, Van Riper, and Hardeman 2022; Janevic et al. 2020; Logan et al. 2022).
In the current study, we are primarily concerned with the rising use of obstetric interventions
among pregnancies to U.S. women and how systemic racism has likely shaped these trends.
Specifically, we consider racial-ethnic differences in the mechanisms driving the large
increases in rates of induction of labor (IOL) among U.S. pregnancies. From 1990 to 2017,
the average state IOL rate among singleton pregnancies to Black, Latina, and White women
increased from 12.5% to 34.4% (National Center for Health Statistics [NCHS] 2020). We
contend that the rising use of IOL in the United States provides a good case to illustrate
how obstetric care is not being centered at the margins. Although racial-ethnic differences in
IOL rates are small in the United States and the rising use of IOL has occurred among all
race-ethnic populations (Martin et al. 2017; Tilstra and Masters 2020), we examine how the
factors associated with increasing IOL rates differ for race-ethnic groups.
In the United States, birth attendants are afforded great discretion in decision-making,
guidelines for IOL are not well defined, and risk assessments of pregnancy and labor often
use highly subjective indications (American College of Obstetricians and Gynecologists
[ACOG] 2007, 2019a, 2019b; Marconi 2019). Combined, U.S. obstetric environments likely
allow implicit biases and obstetric racism to influence IOL decisions (Davis 2019; Liese
et al. 2021; Vedam et al. 2019). More broadly, systemic and cultural racism shape social
conditions and practices that generate racial inequities in health care access and heath
policies (Cogburn 2019). These racialized processes likely shape obstetric practices in the
United States. As a result, it is possible that rising IOL rates among pregnancies to White
women are partly responding to changes in the health and risk factors of this childbearing
population. By contrast, rising IOL rates among U.S. Black and Latina women may not
have occurred because of the changes or needs in these childbearing populations but, rather,
because of the standardization of U.S. obstetric care practices based on the changes and
1.For parsimony and consistency, we use “maternal” and “women” when referring to birthing people. However, the identities of
birthing people include all gender identities.
Masters et al.
Page 2
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

needs among White women. In short, obstetric racism and the failure to center U.S. obstetric
care at the margins likely has produced more “interventions without explanation” among
Black and Latina women than among White women (Davis 2019:569).
In this article, we first document increases in state IOL rates among pregnancies to
U.S. states’ Black, Latina, and White childbearing populations between 1990 and 2017.
The trends show similar monotonic increases in U.S. states’ IOL rates among all three
populations, although trends among White women exhibit some nonlinearity during the
2000s and 2010s. We then estimate how states’ IOL trends are affected by changes in
risk factors for “high-risk pregnancy” among states’ childbearing populations.2 Evidence
suggests that increases in state IOL rates among pregnancies to White women were likely
responding to changes in the demographic composition and changes in risk factors among
states’ White childbearing populations (e.g., increases in births to women with obesity and
increases in the prevalence of maternal hypertension and maternal diabetes). By contrast,
the increases in state IOL rates among pregnancies to Black and Latina women are not
associated with changes in demographics or changes in risk factors among states’ Black
and Latina childbearing populations. Instead, we find evidence to suggest that increases in
U.S. states’ IOL rates among Black and Latina women were strongly shaped by changes
in the demographics and risk factors of the states’ White childbearing populations. Taken
together, our findings suggest a clear example in which U.S. obstetric care is not being
centered at the margins (Hardeman et al. 2016) given that the rising IOL rates among all
three racial-ethnic groups appear to be responding only to changes in risk factors among
states’ White childbearing populations.
BACKGROUND
In the United States, White people, on average, have greater access to and receive higher
quality health care than people of color (IOM 2003). Inequities in care are extensively
documented and span many clinical settings and health conditions, including cardiovascular
care (Lewey and Choudhry 2014), diabetes treatment (Peek, Cargill, and Huang 2007),
kidney transplantations (Daw 2015; Malek et al. 2011), mental health services (Neighbors
et al. 2007), addiction treatment (Hansen, Parker, and Netherland 2020; Hansen and Skinner
2012), cancer screenings (Lansdorp-Vogelaar et al. 2012; Morris et al. 2010; Tehranifar et
al. 2009), and HIV/AIDS treatment (Bogart et al. 2010). The experience of unequal care
manifests across multiple settings both directly and indirectly related to the health care
system. The United States has a long and appalling history of racism in policy creation and
enforcement. Policies are often enacted to maintain existing power structures and prioritize
the dominant power group (Krieger 2001, 2012). Because the dominant power group in
the United States has been historically White, policies and policy enforcement disadvantage
and exclude people of color, often resulting in deleterious health consequences. Examples
include the War on Drugs, which affected how pain was recognized and treated for Black
patients; banks’ redlining policies; highway construction; mass incarceration; and other
2.“High-risk” pregnancy has no definition, but pregnancy risks are higher for women older than age 35, for women with preexisting
health conditions (e.g., high blood pressure, obesity, diabetes), for people who engage in certain health behaviors while pregnant (e.g.,
smoking cigarettes, using alcohol), and for multiple pregnancies (e.g., carrying twins or higher order multitudes; NICHD 2018).
Masters et al.
Page 3
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

segregationist measures that affect the neighborhoods and broader environments in which
people of color live and work (e.g., Bailey et al. 2017; Roberts 1999; Rothstein 2017).
Unequal care can also be a result of the organization of U.S. health care systems. Residential
segregation, unequal education and opportunities, and discriminatory hiring practices
have produced worse care and hindered adequate services in marginalized communities
(Guagliardo 2004; Hayanga et al. 2009; Odom Walker et al. 2010; White, Haas, and
Williams 2012). This, in turn, limits Black Americans’ access to high-quality medical
care (Popescu et al. 2010; Williams and Jackson 2005), including Black infants receiving
neonatal intensive care that is, on average, of poorer quality than care for White infants
(Horbar et al. 2019).
Health care education and culture is built on a history of racism that continues to inform
the training and guidance received in health care professional education. The history of
medicine is deeply rooted with horrific examples of racial exploitation and neglect (Feagin
and Bennefield 2014; Washington 2006), from fabricating biological differences by race to
experimentation and performing procedures on people of color without consent. This history
is compounded by the lack of training on implicit bias in medical education (Green et al.
2021; Holmes 2012; Nieblas-Bedolla et al. 2020). In addition to affecting the behaviors
and beliefs of medical professionals, this history has also seeped into the algorithms used
in health care systems to guide care decisions, which have been shown to exhibit racial
bias (Obermeyer et al. 2019). Cultural racism also more broadly reflects the ideology and
intent of health policy and practice wherein whiteness is often embedded and centered in
evaluations, metrics, and expectations of care (Cogburn 2019).
Finally, interpersonal racism, or racism experienced via interactions between individuals, is
a common form of medical racism that directly biases individual-level care. Two-thirds of
studies analyzed in a meta-analysis found evidence of interpersonal racism in the medical
setting (Paradies, Truong, and Priest 2014). This can manifest as implicit bias against Black
and Latina patients (Blair et al. 2013; Hoffman et al. 2016), and indeed, Black women
report more mistreatment and disrespect during childbirth than White women (Altman et al.
2019; Logan et al. 2022; McLemore et al. 2018; Slaughter-Acey et al. 2016; Vedam et al.
2019). Across all levels, the U.S. health care system directly and implicitly centers itself on
the needs of the White population, often resulting in the poor access and mistreatment of
patients from marginalized racial-ethnic groups. In response, scholars have called for health
care providers and researchers to “center at the margins—that is, to shift our viewpoint from
a majority group’s perspective to that of the marginalized group or groups” (Hardeman et
al. 2016:2114). Hardeman et al. (2016) encourage the health care community to redefine
“normal” and center the perspectives and needs of marginalized groups at the forefront of
care.
In U.S. obstetric care, examples of health care prioritizing the welfare and needs of
the White population include inequitable access to assisted reproductive technology for
Black women compared to White women (Ethics Committee of the American Society for
Reproductive Medicine 2015) and White infants receiving higher-quality care in neonatal
intensive care units than non-White infants (Profit et al. 2017; Sigurdson et al. 2019).
Masters et al.
Page 4
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

The failure to center obstetric care at the margins manifests as obstetric racism more
broadly, which Davis (2019:562) notes “includes, but is not limited to, critical lapses in
diagnosis; being neglectful, dismissive, or disrespectful; causing pain; and engaging in
medical abuse through coercion to perform procedures or performing procedures without
consent.” Although obstetric racism often manifests through interpersonal racism, existing
work also highlights how systemic and structural biases are deeply rooted in U.S. health
care systems, including policies, organization, and education. Indeed, Davis (2019:561) sees
“obstetric racism [as] an extension of racial stratification” and the result of “the historically
constituted stigmatization of Black women.” The coercive nature of obstetric racism is
particularly important to consider in the context of U.S. childbirth. Childbirth in the United
States presents a unique health care interaction that necessitates the balance of (1) the health
and risks for the pregnant person and the fetus with (2) the pregnant person’s preferences
and (3) the providers’ preferences and decisions. In many cases, these risks and preferences
may compete during pregnancy care and labor. This strain can be exacerbated by the
great amount of power and flexibility in obstetric decision-making by health care providers
(ACOG 2007) and the highly subjective risk criteria across many birth procedures, including
labor inductions (Marconi 2019).
The use of induction of labor, or “the initiation of uterine contractions before the
spontaneous onset of labor by medical and/or surgical means for the purpose of delivery,”
has steadily increased among U.S. pregnancies since the 1990s (Ventura et al. 1999:92).
In 2015, nearly one-quarter of all U.S. births were induced (Martin et al. 2017), up from
just 10% in 1990 (Osterman and Martin 2014). Labor induction is an important obstetric
intervention for minimizing risks to maternal and fetal health, and increases in IOL have
also come on the heels of efforts to reduce cesarean deliveries (Nicholson et al. 2004, 2009a,
2009b; Nicholson, Yeager, and Macones 2007). Indeed, scholars and practitioners use the
term “preventative labor induction” when considering how elective IOL might be used to
reduce risks associated with pregnancy and childbirth (Caughey 2009; Caughey et al. 2009;
Grobman et al. 2018; Nicholson et al. 2008). However, IOL is often overused in the United
States, as evidenced by research suggesting that two-thirds of the increase in IOL during
the 1990s was a result of “nonmedically indicated” inductions (Ramsey, Ramin, and Ramin
2000), and gestational distributions of U.S. births have been dramatically changed by the
increasing use of IOL at select gestations (Tilstra and Masters 2020).
In the United States, IOL rates do not substantially differ across race-ethnic childbearing
populations, and the increasing trends in IOL have occurred in all race-ethnic groups in
similar ways (Martin et al. 2017). Although IOL rates among pregnancies to U.S. Black,
Latina, and White childbearing populations are similar, we suspect that the reasons for
the high and rising use of IOL are different for White, Black, and Latina women in the
United States. For instance, inductions among pregnancies to Latina and Black women
may be more likely to occur due to nonmedically indicated reasons than inductions
among pregnancies to White women. Multilevel racism in the U.S. health care system
has possibly contributed to the perception that U.S. pregnancies among Black and Latina
women are more likely to be “high risk” than pregnancies among White women. The
deeply ingrained racist perceptions of risk in the U.S. health care system may affect IOL
decisions, where providers intervene in pregnancies to Black and Latina women to reduce
Masters et al.
Page 5
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

harm from these perceived risks. Also, higher rates of interventions without consent during
Black and Latina pregnancies reflect the callous and egregious care that patients of color
receive during childbirth (Logan et al. 2022). Indeed, for women of color in the United
States, “neglect, lack of information, dismissiveness, disrespect, and interventions without
explanation [emphasis added], permeate maternal care and coalesce into obstetric racism”
(Davis 2019:569). These forms of obstetric racism have likely shaped the rising use of IOL
in the United States in significantly racialized ways.
DATA AND METHODS
We examined trends in U.S. states’ IOL rates using the National Vital Statistics Systems
(NVSS) restricted birth data for years 1990 through 2017 (NCHS 2020).3 To reduce the
confounding effects of multiparous women and multiple pregnancies on risk for obstetric
interventions (Denona et al. 2020; Donahue et al. 2010), we restricted the analytic samples
to include only singleton first births among non-Hispanic White, non-Hispanic Black,
and Hispanic women (henceforth, White, Black, and Latina). The data are composed of
41,126,037 singleton first births: 26,446,616 to White women, 6,252,741 to Black women,
and 8,426,680 to Latina women. We aggregated the data at the state level by mother’s
race-ethnicity to create separate analytic samples for births among states’ White, Black,
and Latina childbearing populations (for the creation of the analytic samples, see Figure
S1 in the appendix in the online version of the article). Due to small counts of births,
we omitted Idaho, Maine, Montana, North Dakota, South Dakota, Vermont, and Wyoming
from the analytic sample for births to Black women, and we omitted Maine, Vermont, and
West Virginia from the analytic sample for births to Latina women. The analytic sample
for births to White women is composed of all 50 states plus the District of Columbia (DC)
across 28 years (1,428 state-years), and the analytic samples for births to Black and Latina
women were limited to 43 states plus DC (1,232 state-years) and 47 states plus DC (1,344
state-years), respectively.4
Measures
We calculated state-level time-varying measures of obstetric interventions, maternal
demographics, and risk factors for “high-risk pregnancy” among states’ Black, Latina,
and White childbearing populations. Our outcome measure was the proportion of births
in state i in year j in which labor was induced, where i =  Alabama, …, Wyoming and
j = 1990, …, 2017.5 Induction of labor is a characteristic of delivery that is comparable across
the 1989 U.S. Standard Certificate of Live Birth and the 2003 U.S. Standard Certificate of
Live Birth (Martin et al. 2007), and IOL coding in the NVSS is also comparable across
U.S. states.6 As a control variable, we also created a measure of the cesarean delivery rate
3.Data analyzed are Restricted-Use Vital Statistics Birth Data, which can be accessed via application: https://www.cdc.gov/nchs/nvss/
nvss-restricted-data.htm.
4.Results from models fitted to pregnancies among White and Latina women in reduced analytic samples composed of the same 44
states are nondifferent from results in the article and are available in Appendix J in the online version of the article.
5.For details about imputed values, see Appendix E in the online version of the article.
6.Records of IOL in Wisconsin during the 1990s and early 2000s were prone to error. We adjusted IOL rates in Wisconsin for all years
1990 through 2002. See Appendix D in the online version of the article.
Masters et al.
Page 6
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

among first-birth singletons born to states’ Black, Latina, and White women, calculated as
the proportion of singleton first births in state i delivered by cesarean in year j.
Several factors determine whether a pregnancy is considered high risk (Holness 2018;
National Institute of Child Health and Human Development [NICHD] 2018). Risk of
pregnancy complications is higher for women older than age 35, and pregnancies can also be
affected by several health behaviors such as smoking cigarettes, drinking alcohol, and using
other substances. Maternal health problems such as high blood pressure, obesity, diabetes,
thyroid disease, infections, and heart or blood disorders can also influence pregnancy risks
(NICHD 2018). Here, we included several state-level measures of maternal demographics
as the proportion of births in state i in year j to women younger than 20 years (i.e.,
proportion of births to teenagers), age 35 years and older (i.e., proportion of births to
women of advanced maternal age [AMA]),7 married at time of birth, born in the United
States, with an educational level less than high school degree, and with an educational level
at or above a bachelor’s degree. Direct measures of risk factors for high-risk pregnancy
included the proportion of births in state i in year j to women who used tobacco products
at any time during pregnancy, experienced high gestational weight gain (i.e., 40 pounds or
more), had diabetes (gestational or pre-pregnancy), and had hypertension (gestational or pre-
pregnancy). Additional state-level proxies for high-risk pregnancies included the proportion
of births in state i in year j delivered preterm (<37 weeks of gestation) and delivered late
term or postterm (≥41 weeks of gestation).8
State-Level Growth Curve Models
We modeled U.S. states’ IOL rates between 1990 and 2017 separately for Black, Latina,
and White women. We fit generalized linear mixed models to estimate the year-specific
state-level rates as outcomes of a fixed effect linear slope, a random intercept, a random
slope, and state-specific residual variance:
Y ij = π0i + π1i( Y earij −1990) /5 + εij
π0i = γ00 + μ0i
π1i = γ10 + μ1i .
(1)
Assuming μ0i
μ1i~
N([ 0
0] ,[ σ0
2 σ01
σ10 σ1
2])  and εij ∼N( 0, σε
2)  where Y ij is the IOL rate for state i in year
j, where i =  Alabama, …, Wyoming and j = 1990, …, 2017; γ00 is the average IOL rate among
U.S. states in year 1990; µ0i is the estimated deviation from γ00 for state I in 1990; γ10
is the average five-year change in IOL rate between 1990 and 2017; µ1i is the estimated
deviation from γ10 for state i; and εij is the Level 1 residual variance for state i in year j. We
divided the slope by 5 for interpretation reasons (i.e., the estimated coefficient indicates the
7.Advanced maternal age itself designates a pregnancy as “high risk,” but we include it as a measure of states’ childbearing
“demographics” alongside maternal age less than 20 years.
8.We included measures of states’ economic indicators and inequality such as the Gini index, child poverty rate, unemployment rate,
housing price index (in 1990 dollars), and rates of Supplemental Nutrition Assistance Program (SNAP) benefits. Results from models
that these controls are in Appendix I in the online version of the article.
Masters et al.
Page 7
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

expected change in states’ IOL rates over a five-year time span), and increasing the slope
size improves estimates of the variance component of µ1i, σ1
2 (Singer and Willett 2003).
We contrasted estimates from the random slope model in Equation 1 with estimates from
an unconditional means model to illustrate (a) the amount of variation in IOL rates that
exists within states and between states; (b) the amount of within-states variation, σε
2, that is
accounted for by a linear approximation of a state-specific slope, π1i; and (c) the amount
of between-states variation in the IOL rate in 1990, σ0
2, and the amount of between-states
variation in the change of IOL rate between 1990 and 2017, σ1
2. Results from these models
indicate the extent to which increases in IOL rates are similar or different across U.S. states
and the extent to which the increases are similar or different for racial-ethnic populations.
State-Level Fixed Effect Panel Regression
We then fitted trends in IOL rates among pregnancies to U.S. states’ Black, Latina, and
White women using state-level fixed effects panel regressions:
Y ij = β0 + γ2State2 + … + γnStaten
+δ19911991 + … + δ20172017 + μij,
(2)
where Y ij is the IOL rate among births to Black, White, or Latina women in state i in
time j; β0 is the IOL rate in the referent state in 1990; γn are the coefficients associated
with the binary state regressors, Staten; δj are the coefficients associated with the binary
time regressors, 1991, … , 2017; and µij is the error term. These “within-estimator” models
control for all time-invariant characteristics of U.S. states while also accounting for yearly
trends in IOL rates shared across states (Halaby 2004). The models are well suited for
identifying average trends in U.S. states’ IOL rates while controlling for time-invariant state
characteristics and for estimating how states’ time-varying characteristics are associated
with changes in states’ IOL rates.
We refitted the models to include time-varying indicators of the demographic profiles
and risk factors for high-risk pregnancy among states’ racial-ethnic-specific childbearing
populations:
Y ij = β0 + γ2State2 + … + ρnStaten + δ19911991
+… + δ20172017 + βteen%teenij + βAMA%AMAij
+βmarried%marriedij + βUSborn%USbornij
+β < HS% < HSij + βBA%BAij + βtobacco%tobaccoij
+βdiabetes%diabetesij + βℎypertension%ℎypertensionij
+βweigℎt%weigℎtij + βcesarean%cesareanij
+βpreterm%pretermij + βposttern%posttermij + μij,
(3)
where βteen is the coefficient associated with the percent of births in state i to Black,
Latina, or White women occurring among teenagers in year j, %teenij; βAMA is the coefficient
Masters et al.
Page 8
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

associated with the percent of births among women age 35 years or older, %AMAij; βmarried
is the coefficient associated with the percent of births among married women, %marriedij;
βUSborn is the coefficient associated with the percent of births among U.S.-born women,
%USbornij; β < HS is the coefficient associated with the percent of births among women with
less than high school education, % < HSij; βBA is the coefficient associated with the percent
of births among women with a college degree, %BAij; βtobacco is the coefficient associated
with the percent of births among women who used tobacco while pregnant, %tobaccoij;
βdiabetes is the coefficient associated with the percent of births among women with diabetes,
%diabetesij; βℎypertension is the coefficient associated with the percent of births among women
with hypertension, %ℎypertensionij; βweigℎt is the coefficient associated with the percent of
births among women with gestational weight gain greater than 40 pounds, %weigℎtij; βcesarean
is the coefficient associated with the percent of births delivered cesarean, %cesareanij; βpreterm
is the coefficient associated with the percent of births delivered at gestational week <37,
%pretermij; and βpostterm is the coefficient associated with the percent of births delivered after
gestational week 41, %posttermij.
We then refitted the models to U.S. states’ IOL rates among births to Black, Latina, and
White women to include time-varying indicators of the demographic profiles and risk factors
of the states’ other race-ethnic childbearing populations. For example, Equation 4 regresses
U.S. states’ IOL rates among births to Black women on changes in the demographics and
maternal risk factors of the states’ White childbearing populations:
Y ij = β0 + γ2State2 + … + γnStaten + δ19911991
+… + δ20172017 + βW teen%W teenij
+βW AMA%W AMAij + βW married%W marriedij
+βW USborn%W USbornij + βW < HS%W < HSij
+βW BA%W BAij + βW tobacco%W tobaccoij
+βW diabetes%W diabetesij
+βW ℎypertension%W ℎypertensionij
+βW weigℎt%W weigℎtij + βW cesarean%W cesareanij
+βW preterm%W pretermij
+βW posttern%W posttermij + μij .
(4)
After estimating Equations 2 through 4, we plotted the model-based expected IOL rates
across each year j holding all other covariates at their 1990 mean levels using the margins
module in Stata 17. We contrasted the average trends in U.S. states’ IOL rates among
Black, Latina, and White women estimated from Equation 2 (i.e., the observed rates) with
the adjusted trends estimated from Equation 3 to examine the extent to which changes in
the demographics and risk factors among states’ Black, Latina, and White childbearing
populations are associated with the observed trends in states’ IOL rates among these
populations. Then, we contrasted the average trends in U.S. states’ IOL rates estimated
from Equation 2 with the adjusted trends estimated from Equation 4 to examine the extent
to which changes in the demographics and risk factors among states’ other race-ethnic
Masters et al.
Page 9
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

childbearing populations are associated with the changes in IOL rates among births to states’
Black, Latina, and White childbearing populations.9
RESULTS
Table 1 contains state-level descriptive statistics of the Black, Latina, and White analytic
samples in 1990, 2004, and 2017. We present the mean state IOL rates among singleton
first births across these years and the demographic profiles and risk factors for high-risk
pregnancy among U.S. states’ Black, Latina, and White childbearing populations.
Average state IOL rates among births to U.S. Black women increased from about 11% in
1990 to 23% in 2004 and to 33% in 2017. Similar increases in IOL are observed among
singleton first births born to U.S. Latina (10%, 21%, 31%) and White women (14%, 28%,
36%). Across this time, we also see substantial changes in the demographic profiles of
states’ childbearing populations. Most noteworthy is the increasingly older age distributions
of the states’ childbearing populations, with large reductions in the proportion of births
to teens (e.g., 43%–17% among Black women) and concomitant increases among women
with AMA (e.g., 5%–10% among White women). We also see sizable decreases in the
proportion of births to women with education levels less than high school (e.g., 37%–23%
among Latina women), which are offset by large proportionate increases in births to women
with college degrees (e.g., 9%–20% among Black women and 24%–44% among White
women). The proportion of births to women who are married decreased substantially in the
Latina (60%–40%) and White (76%–66%) childbearing populations. Finally, the proportion
of births among immigrants increased in the Black childbearing population (7%–21%), and
the proportion of births among immigrants in the Latina childbearing population increased
between 1990 and 2004 (47%–62%) and then decreased from 2004 to 2017 (62%–40%).
Changes in risk factors for high-risk pregnancy are also observed in all three childbearing
populations. The proportion of births to women who used tobacco while pregnant dropped
(e.g., 10%–4% among Black women, 18%–8% among White women), and the proportion of
singleton first births born in late-term or postterm gestations also declined (e.g., 23%–15%
among Black women, 28%–18% among Latina women). Yet across this same time, in all
three populations, we see increasing rates of gestational diabetes (2% in 1990 to 5%–7% in
2017), hypertension (4%–5% in 1990 to 8%–13% in 2017), and high gestational weight gain
(e.g., 24%–28% among Black women). Among the White childbearing population, we also
see a small increase in the proportion of births born at premature gestations (8% to 10% to
9%).10
To see the full year-over-year changes in U.S. states’ IOL rates among births to Black,
Latina, and White women, we plot IOL rates in each state in Figure 1 (gray lines) and
indicate the yearly mean rate among all states (black lines).
9.For results from several sensitivity analyses, such as combining Equation 3 and Equation 4 and using three-year lagged effects of
predictors, see Appendix K in the online version of the article.
10.Correlations between characteristics and risk factors among states’ White, Latina, and Black populations in 1990, 2004, and 2017
are in Appendix L in the online version of the article.
Masters et al.
Page 10
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

In 1990, the mean IOL rate among states’ singleton first births born to Black, Latina, and
White women were 10.8%, 9.6%, and 13.5%, respectively. The rates varied considerably
across states. For example, among pregnancies to Black women, IOL rates ranged from
4.9% in Mississippi to 21.9% in Kentucky, and among White women, rates ranged from
8.0% in California to 21.5% in Oregon (for each state’s IOL rate among Black, Latina, and
White women in 1990, 2004, and 2017, Appendix B in the online version of the article).
Yet IOL rates increased among all states’ childbearing populations between 1990 and 2017.
The trends exhibit similar monotonic increases in all states’ IOL rates among the three
populations, although trends among White women exhibit some nonlinearity during the
2000s and 2010s. In 2017, the average IOL rate among U.S. states’ Black, Latina, and White
women were 33.4%, 31.0%, and 35.9%, respectively. As was the case in 1990, state-based
variation in these 2017 rates is high, ranging from 19.3% among Black and Latina women in
California to 54.0% among White women in West Virginia.
Table 2 presents results from growth curve models fitted to U.S. states’ IOL rates among
singleton first births born to Black, Latina, and White women for all years 1990 through
2017. Results from the unconditional means model (Panel A) suggest that only 14%
(Latina), 17% (Black), and 23% (White) of variation in U.S. IOL rates between 1990
and 2017 occurred between states ( i . e ., σ0
2(
σε
2 + σ0
2)) . Thus, 77% (White) to 86% (Latina)
of variation in U.S. IOL rates occurred within states over time ( i . e ., σε
2(
σε
2 + σ0
2)) , reflecting
the large increases in IOL rates observed in Figure 1. Results from the random slope
model (Panel B) indicate that 77% (Black), 78% (Latina), and 79% (White) of the within-
states variation is accounted for by a linear approximation of changes in states’ IOL rates (
i . e .,( σεRSM
2
−σεUMM
2)
σεUMM
2)
. Indeed, when linear ordinary least squares models are fitted
separately to each state’s IOL rates in these childbearing populations, the median R2 is about
.85 (Appendix C in the online version of the article). Results also indicate that estimates
of the slope do not substantively differ for Black (.039), Latina (.034), and White women
(.036), suggesting that states’ IOL rates increased among these childbearing populations in
similar ways (i.e., about 3.4%–3.9% every five years). Taken together, the findings from
the generalized mixed models suggest near-linear increases in U.S. states’ IOL rates among
White, Black, and Latina childbearing populations that occurred across U.S. states in similar
ways. The similarity in these trends is evident by the very small slope variances in Table
1 (σ1
2) and is also observed in Figure 2, which plots predicted Bayes estimates of IOL rates
among states’ Black, Latina, and White childbearing populations.
In Figure 3, we plot the mean state IOL rates estimated from Equations 2 through 4 to
contrast the observed mean IOL rates among states’ Black, Latina, and White women (i.e.,
“Observed”) with the estimated mean IOL rates among states’ Black, Latina, and White
women while controlling for changes in these populations’ demographics and risk factors
(i.e., “Control Own Characteristics”) and the estimated mean IOL rates among states’ Black,
Latina, and White women while controlling for the demographics and risk factors of the
states’ other race-ethnic childbearing populations. For the latter, we plot mean IOL rates
for Black and Latina women while controlling for the demographics and risk factors of
states’ White childbearing populations (i.e., “Control White Characteristics”), and we plot
Masters et al.
Page 11
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

mean IOL rates for White women while controlling for the demographics and risk factors
of states’ Black childbearing populations (for detailed results, see Appendices F–H in the
online version of the article).
Results from Equation 3 indicate that U.S. states’ IOL rates among pregnancies to Black
women are associated with both the demographic profiles and risk factors of states’ Black
childbearing populations (Appendix F in the online version of the article). Yet, as shown
in Figure 3 (Panel A), changes in these demographics and risk factors between 1990 and
2017 do not account for the upward trend in states’ IOL rates among pregnancies to Black
women. We see that the mean IOL rate indicated by the “Control Own Characteristics”
line (dashed black) is nondifferent from the mean IOL rate indicated by “Observed” line
(solid black). Thus, the increases in states’ IOL rates among pregnancies to Black women
are estimated to occur even while controlling for changes in demographic characteristics
and risk factors among states’ Black childbearing populations. Results from “Equation
(4)-White” (Appendix F in the online version of the article) indicate that changes in the
demographics and risk factors of states’ White childbearing populations are statistically
and substantively associated with states’ IOL rates among pregnancies to Black women.
Furthermore, changes in risk factors among states’ White childbearing populations account
for much of the rising IOL rates among states’ Black childbearing populations. As shown
in Figure 3 (Panel A), the rise in the mean IOL rate indicated by the “Control White
Characteristics” line (solid gray) is much lower than the upward trend of the “Observed”
line. This suggests that increases in IOL among states’ Black women would have been much
smaller if the demographic and risk factors of states’ White childbearing populations had not
changed between 1990 and 2017.
We find similar results from models fitted to states’ IOL rates among Latina women.
In Figure 3 (Panel B), there are no significant differences between the “Observed” line
(solid black) and “Control Own Characteristics” line (dashed black) during the time period
1990 to 2005, but the “Control Own Characteristics” line is significantly lower than the
“Observed” rates for the time period 2005 to 2017. Although the differences are not
substantively large, these findings suggest that a small fraction of the rising IOL rates
among states’ Latina women are associated with changes in the demographics and risk
factors of the states’ Latina childbearing populations. Yet states’ IOL rates among Latina
women are also statistically and substantively associated with changes in demographic
characteristics and risk factors among states’ White childbearing populations (“Equation
(4)-White,” Appendix G in the online version of the article). As shown in Figure 3 (Panel
B), the “Control White Characteristics” line (solid gray) indicates that the average IOL
rate among pregnancies to Latina women would not have substantively increased between
1990 and 2017 if states’ White childbearing populations had not experienced changes in
their demographic composition or maternal risk factors. Thus, the rising trends in states’
IOL rates among pregnancies to Latina women are largely explained by changes in the
demographic composition and risk factors of states’ White childbearing populations.
Results from Equation 2 (Appendix H in the online version of the article) suggest
that U.S. states’ IOL rates among pregnancies to White women are associated with
both the demographic profiles and risk factors in states’ White childbearing populations.
Masters et al.
Page 12
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

Furthermore, we find evidence suggesting that changes in these demographic characteristics
and risk factors of states’ White childbearing populations are strongly associated with the
rising IOL rates among pregnancies to states’ White women between 1990 and 2017.
This is clearly shown in Figure 3 (Panel C), which suggests that states’ IOL rates among
pregnancies to White women (“Observed” line, solid black) would not have increased if
the demographic profiles and risk factors of states’ White childbearing populations had
remained at 1990 levels (“Control Own Characteristics” line, dashed black). Also shown
in Figure 3 is the lack of associations between states’ IOL trends among pregnancies to
White women and changes in demographics and risk factors of states’ Black childbearing
populations (“Control Black Characteristics” line, solid gray). We see that the average IOL
rate among states’ White childbearing populations is estimated to have increased between
1990 and 2017 even if the demographic characteristics and maternal risk factors of states’
Black childbearing populations had not changed during this time.
Together, results presented in Figure 3 indicate that U.S. states’ rising IOL rates among
pregnancies to Black, Latina, and White women are strongly associated with changes in the
demographic profiles and risk factors in states’ White childbearing populations. By contrast,
states’ rising IOL rates among Black and Latina women are not strongly associated with
changes in the demographic characteristics or risk factors in these populations. Nor are
changes in the demographic characteristics or risk factors in Black and Latina childbearing
populations associated with the rising state IOL rates among pregnancies to White women.
Thus, the increasing use of IOL in the United States appears to be a national-level
phenomenon occurring in all states and among pregnancies to Black, Latina, and White
women in similar ways and strongly responding to changes in the demographic profiles and
risk factors in White childbearing populations.
DISCUSSION
Recent changes in U.S. obstetric practices provide a good case for identifying processes
that might generate racial-ethnic inequities in U.S. health care more broadly. In many
respects, obstetric care in the United States reflects the same policies, hospital systems,
medical trainings and cultures, and interpersonal racism that often shape medical care of
marginalized populations (Cogburn 2019; IOM 2003). In other respects, challenges unique
to obstetric practice might amplify the racialized care for women of color in the United
States. The guidelines for risk management of pregnancies and risk assessment for labor
complications can be unclear, and in these environments, “neglect, lack of information,
dismissiveness, disrespect, and interventions without explanation” can shape obstetric care
for U.S. women of color (Davis 2019:569). Indeed, at the individual level, Davis (2019)
and others have documented many examples and forms of “obstetric racism” in maternal,
prenatal, and labor care, including increased likelihood of receiving care without consent
(Janevic et al. 2020; Logan et al. 2022). In this study, we sought to examine how obstetric
racism and systemic racism more broadly may have shaped use of IOL at the population
level in the United States.
We first documented trends in U.S. states’ IOL rates among singleton first-birth pregnancies
to Black, Latina, and White women. Results show that mean rates of IOL among states’
Masters et al.
Page 13
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

Black, Latina, and White women (combined) nearly tripled between 1990 and 2017 (i.e.,
increased from 12.5% to 34.4%) and that the increases in IOL among these populations
occurred across all states in similar ways. At first glance, the racial-ethnic similarities in
(1) high IOL rates (i.e., 31%–36% in 2017), (2) the rising use of IOL (i.e., increases of
3.4%–3.9% per five years), and (3) the ubiquity of these trends throughout the United States
appear to provide evidence against obstetric racism shaping the increased use of IOL in the
United States. How can one implicate racism in a health outcome if that outcome does not
substantively differ across racial-ethnic groups? Thus, we hypothesized that the mechanisms
underlying the rising trends in IOL likely differ for U.S. racial-ethnic groups and devised
a simple test. If IOL has been increasingly used among U.S. pregnancies to reduce risk of
adverse birth and maternal health outcomes (Nicholson et al. 2009a), then the rising rates of
IOL among U.S. pregnancies should be associated with changes in demographic profiles and
risk factors for high-risk pregnancy.
The evidence here suggests that this has, indeed, been the case for pregnancies among
states’ White childbearing populations. Among pregnancies to White women, low maternal
education, gestational tobacco use, maternal diabetes, maternal hypertension, and high
gestational weight gain were all estimated to be positively associated with risk of IOL.
Changes in these and other risk factors among states’ White childbearing populations
accounted for all the increase in states’ IOL rates among pregnancies to White women.
By contrast, we did not find this to be the case for IOL use among pregnancies to Black or
Latina women. The increases in states’ IOL rates among pregnancies to Black and Latina
women were not explained by changes in these populations’ demographic profiles or risk
factors. Rather, the rising use of IOL among states’ Black and Latina women is strongly
associated with the changes in demographics and risk factors of states’ White childbearing
populations. Thus, it appears that changes in U.S. states’ IOL use are associated only with
changes in the characteristics of states’ White childbearing populations.
Taken together, evidence here suggests that the increasing use of IOL in the United States
has not been centered on the needs of the Black and Latina childbearing populations
but, instead, is likely responding to the changing needs and/or preferences of the White
childbearing population. Although we did not identify the underlying reasons for these
associations, we offer two potential explanations for future research to consider. One
possible explanation is the way in which standards of obstetric care are created for the
majority or “normal” pregnant person (i.e., White women) and then applied to all patients.
The processes underlying this explanation reflect health care providers’ desires and efforts
to reduce perceived risks associated with pregnancy and childbirth. That is, obstetric
interventions such as IOL can be used to minimize the risk of adverse birth outcomes and
adverse maternal outcomes from high-risk pregnancies (Nicholson et al. 2004, 2007, 2009a,
2009b). In their efforts to minimize risk among the majority White populations, hospitals,
clinics, and care providers in the United States may be shifting their practices to identify
“high-risk” pregnancies and minimize poor outcomes of these pregnancies via IOL. Indeed,
since 1990, the gestational age distribution of births in the United States has shifted from
IOLs primarily occurring at later gestational ages (40 weeks or more) to occurring more
frequently at earlier gestational ages (weeks 37–39; Tilstra and Masters 2020).
Masters et al.
Page 14
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

Evidence presented here suggests that this new normative practice of using obstetric
interventions at earlier gestations likely arose in response to the (perceived) needs of White
women but that the normative practice may have come to shape care for all U.S. populations
in similar ways. Thus, standards of pregnancy care are created and adjusted to meet the
needs of the majority or “normal patient” (i.e., White women alone) and then applied to all
racial-ethnic groups, regardless of patients’ needs and preferences (Hardeman et al. 2016).
Providers may also care more about minimizing harm and preventing adverse outcomes
among White patients because the lives and children of White women are implicitly more
valued in the United States (Harris and Wolfe 2014). In either case, obstetric racism has
resulted in prioritizing care for White women.
A second possible explanation is that racist “risk perception” in U.S. clinical and obstetric
practices have differentially affected the use of IOL for managing pregnancy and childbirth
among U.S. racial-ethnic populations. Here, pregnancies among Black and Latina women
may be perceived and diagnosed as being riskier and less healthy than pregnancies to
White women. Public attention to maternal and infant morbidity and mortality among U.S.
communities of color, health care training and education, and implicit biases might influence
providers’ perceptions of the health and needs of patients from these communities (Feagin
and Bennefield 2014; Horbar et al. 2019; Washington 2006). Thus, providers may perceive,
assess, and treat patients based on their skin color and/or on racist perceptions of their
familial background and communities instead of their individual risk factors for carrying a
healthy pregnancy to full term or engaging in person-centered care and listening to their
needs and preferences, as providers are more likely to do with White patients (Altman et al.
2019; Logan et al. 2022; McLemore et al. 2018; Slaughter-Acey et al. 2016; Vedam et al.
2019).
Additionally, misperceptions about pain sensitivity might influence providers’ reception to
Black and Latina women’s desires and preferences in obstetric care settings. Foundational
practices in obstetrics and gynecology were strongly influenced by the racist care of J.
Marion Sims, who developed surgical procedures by operating without consent and without
anesthesia on enslaved Black women (Washington 2006). Sims’s experiments contributed
to the persistent false belief that Black and White patients have biological differences in
pain perception (Hoffman et al. 2016). Indeed, the expansive and pervasive history of
unequal health care in the United States has fostered an environment ripe for obstetric
racism. Thus, our findings might implicate racism in the assessment of patients’ needs and
in clinical decision-making, which are consistent with a large body of evidence identifying
racial-ethnic inequities in delivery of medical care in the United States (IOM 2003).
Findings here provide complementary, structural-level evidence that align with individual-
level studies of obstetric racism (Davis 2019, 2020; Janevic et al. 2020; Logan et al. 2022).
We demonstrate that the pattern of adjusting obstetric care to meet the changing composition
and risk factors of the White population exists at the state level, thus showing that the
patterns of obstetric racism persist at a population level in the United States. This contributes
to an ever-growing body of literature implicating the pervasiveness of racism across U.S.
institutions. Examples of institutions outside health care not centering at the margins include
how systemic racism infiltrates the education system through segregation and standardizing
Masters et al.
Page 15
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

White children as the norm in testing (Knoester and Au 2017; McGee 2020; Vaught and
Castagno 2008) and how practices such as redlining and the behaviors of housing market
professionals discriminate against communities of color and contribute to racial segregation
by viewing White homeowners and tenants living arrangements and practices as “normal”
(Korver-Glenn 2021; Rothstein 2017). These and other institutions continue to engage in
and teach racist behaviors that perpetuate racial differences in several economic, social, and
health outcomes. Here, we provide further evidence that the field of obstetrics also likely
engages in behavior and practices that prioritizes the (perceived) needs and desires of the
White childbearing population.
This study has several limitations. First, data are composed at the state level and might
fail to measure how changes in risk factors and IOL move together at other spatial levels
within states (e.g., county or hospital). For example, hospitals that disproportionately serve
Black and Latina populations might be changing obstetric practices for reasons not observed
in these data but that are correlated with changes in demographics and risk factors among
states’ White childbearing populations. Second, the NVSS data contain a limited set of
measures for “high-risk” pregnancy. The rising use of IOL among states’ Black and Latina
women may be responding to changes in these childbearing populations that we are not
observing in these data. Yet our findings were robust to sensitivity analyses that controlled
for a number of possible economic-related confounders (see Notes 8 and 9). Third, we are
not measuring or directly observing “obstetric racism” or forms of systemic racism. Rather,
we are implicating differences in determinants of IOL trends as evidence for racism in the
use of IOL. This limitation, however, can be perceived as a strength of the study. One need
not document differences in outcomes by race-ethnicity or directly measure exposures to
racism to suggest that the processes generating those outcomes might be shaped by racial-
ethnic differences in the mechanisms. Indeed, in this case, if researchers had documented
only the racial-ethnic similarities in U.S. IOL rates and trends, they would have concluded
that there are no racial-ethnic inequities in IOL practices.
Although results from this study must be interpreted with these limitations in mind, our
findings are consistent with an extensive literature documenting health care inequity in the
United States and provide strong evidence that U.S. obstetric care has not been centered
on the needs of Black and Latina childbearing populations. Results here indicate that
rising use of IOL among pregnancies to Latina and Black women are likely responding
to characteristics in White childbearing populations, not characteristics in these populations
themselves. These findings provide population-level evidence to support claims of obstetric
racism documented in individual-level data and therefore provide an empirical foundation on
which researchers can study the underlying mechanisms for racial-ethnic inequities in U.S.
obstetric care.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
ACKNOWLEDGMENTS
The authors are grateful to the anonymous referees and the JHSB editors for their helpful comments.
Masters et al.
Page 16
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

FUNDING
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of
this article: This research was funded by a grant from the Eunice Kennedy Shriver Institute of Child Health Human
and Human Development (RHD099359A) and benefited from support provided to the University of Colorado
Population Center (Project 2P2CHD066613-06) from the Eunice Kennedy Shriver Institute of Child Health Human
and Human Development. The content is solely the responsibility of the authors and does not necessarily represent
the official views of the National Institute of Child Health and Human Development or the National Institutes.
Biographies
Kate Coleman-Minahan, PhD, RN, FNP-BC, is an assistant professor at the University
of Colorado College of Nursing and a research affiliate of the University of Colorado
Population Center. She is a nurse practitioner and social scientist who studies reproductive
health equity, including access to contraception and abortion among young people and
immigrants.
Ryan K. Masters is an associate professor of sociology at the University of Colorado
Boulder, a faculty associate of the University of Colorado Population Center, and a fellow of
the Institute of Behavioral Science.
Daniel H. Simon is a PhD candidate in the Department of Sociology at the University of
Colorado Boulder and a University of Colorado Population Center graduate research affiliate
in the Institute of Behavioral Science. He is a social demographer who studies the structural
determinants of suicide mortality and population health inequality.
Andrea M. Tilstra, PhD, is a postdoctoral researcher in the Leverhulme Centre for
Demographic Science, the Department of Sociology, and Nuffield College at the University
of Oxford. She is a social demographer who studies the population health consequences
of policy changes, institutional practices, and public health crises, revealing how these
structural factors may exacerbate or equalize existing health inequalities.
REFERENCES
Altman Molly R., Oseguera Talita, McLemore Monica R., Kantrowitz-Gordon Ira, Franck Linda S.,
and Lyndon Audrey. 2019. “Information and Power: Women of Color’s Experiences Interacting
with Health Care Providers in Pregnancy and Birth.” Social Science & Medicine 238. doi:10.1016/
j.socscimed.2019.112491.
American College of Obstetricians and Gynecologists. 2007. “Committee
Opinion Number 390: Ethical Decision-Making in Obstetrics and
Gynecology” https://www.acog.org/clinical/clinical-guidance/committee-opinion/articles/2007/12/
ethical-decision-making-in-obstetrics-and-gynecology.
American College of Obstetricians and Gynecologists. 2019a. “Committee Opinion Number 765:
Avoidance of Nonmedically Indicated Early Term Deliveries and Associated Neonatal Morbidities.”
Obstetrics & Gynecology 133(2):e156–63. [PubMed: 30681546]
American College of Obstetricians and Gynecologists. 2019b. “Committee Opinion Number
764: Medically Indicated Late-Preterm and Early Term Deliveries.” Obstetrics & Gynecology
133(2):e151–55. [PubMed: 30681545]
Bailey Zinzi D., Krieger Nancy, Agénor Madina, Graves Jasmine, Linos Natalia, and Bassett Mary
T.. 2017. “Structural Racism and Health Inequities in the USA: Evidence and Interventions.” The
Lancet 389(10077):1453–63.
Masters et al.
Page 17
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

Blair Irene V., Havranek Edward P., Price David W., Hanratty Rebecca, Fairclough Diane L., Farley
Tillman, Hirsh Holen K., and Steiner John F.. 2013. “Assessment of Biases against Latinos and
African Americans among Primary Care Providers and Community Members.” American Journal of
Public Health 103(1):92–98. [PubMed: 23153155]
Bogart Laura M., Wagner Glenn J., Galvan Frank H., and Klein David J.. 2010. “Longitudinal
Relationships between Antiretroviral Treatment Adherence and Discrimination due to HIV-
Serostatus, Race, and Sexual Orientation among African American Men with HIV.” Annals of
Behavioral Medicine 40(2):184–90. [PubMed: 20552416]
Caughey Aaron B. 2009. “Preventive Induction of Labor: Can Its Use Lower the Cesarean Delivery
Rate?” Journal of Women’s Health 18(11):1743–45. doi:10.1089/jwh.2009.1559.
Caughey Aaron B., Sundaram Vandana, Kaimal Anjali J., Gienger Allison, Cheng Yvonne W.,
McDonald Kathryn M., Shaffer Brian L., and Bravata Douglas M.. 2009. “Systematic Review:
Elective Induction of Labor versus Expectant Management of Pregnancy.” Annals of Internal
Medicine 151(4):252–63. [PubMed: 19687492]
Chantarat Tongtan, Van Riper David C., and Hardeman Rachel R.. 2022. “Multidimensional Structural
Racism Predicts Birth Outcomes for Black and White Minnesotans.” Health Services Research
57(3):448–57. [PubMed: 35468220]
Cogburn Courtney D. 2019. “Culture, Race, and Health: Implications for Racial Inequities and
Population Health.” The Milbank Quarterly 97(3):736–61. [PubMed: 31512293]
Davis Dána-Ain. 2019. “Obstetric Racism: The Racial Politics of Pregnancy, Labor, and Birthing.”
Medical Anthropology 38(7):560–73. [PubMed: 30521376]
Davis Dána-Ain. 2020. “Reproducing while Black: The Crisis of Black Maternal Health, Obstetric
Racism, and Assisted Reproductive Technology.” Reproductive Biomedicine & Society Online
11:56–64. [PubMed: 33305027]
Daw Jonathan. 2015. “Explaining the Persistence of Health Disparities: Social Stratification and
the Efficiency–Equity Trade-Off in the Kidney Transplantation System.” American Journal of
Sociology 120(6):1595–640.
Denona Branko, Foley Michael, Mahony Rhona, and Robson Michael. 2020. “Discrimination by
Parity Is a Prerequisite for Assessing Induction of Labour Outcome—Cross-Sectional Study.”
BMC Pregnancy Childbirth 20(1):709. doi:10.1186/s12884-020-03334-8. [PubMed: 33225906]
Donahue Sara M.A., Kleinman Ken P., Gillman Matthew W., and Oken Emily. 2010. “Trends in Birth
Weight and Gestational Length among Singleton Term Births in the United States: 1990–2005.”
Obstetrics & Gynecology 115(2 Part 1):357–64. [PubMed: 20093911]
Ethics Committee of the American Society for Reproductive Medicine. 2015. “Disparities in Access
to Effective Treatment for Infertility in the United States: An Ethics Committee Opinion.” Fertility
and Sterility 104(5):1104–10. [PubMed: 26364838]
Feagin Joe, and Bennefield Zinobia. 2014. “Systemic Racism and U.S. Health Care.” Social Science &
Medicine 103:7–14. [PubMed: 24507906]
Geiger H Jack. 2003. “Racial and Ethnic Disparities in Diagnosis and Treatment: A Review of
the Evidence and a Consideration of Causes.” Pp. 417–54 in Unequal Treatment: Confronting
Racial and Ethnic Disparities in Health Care, edited by Smedley BD, Stith AY, and Nelson AR.
Washington, DC: The National Academies Press.
Good Mary-Jo D., James Cara, Good Byron J., and Becker Anne E.. 2003. “The Culture of Medicine
and Racial, Ethnic, and Class Disparities in Healthcare.” Pp. 594–625 in Unequal Treatment:
Confronting Racial and Ethnic Disparities in Health Care, edited by Smedley BD, Stith AY, and
Nelson AR. Washington, DC: The National Academies Press.
Green Tiffany L., Zapata Jasmine Y., Brown Heidi W., and Hagiwara Nao. 2021. “Rethinking Bias
to Achieve Maternal Health Equity: Changing Organizations, Not Just Individuals.” Obstetrics &
Gynecology 137(5):935–40. [PubMed: 33831936]
Grobman William A., Rice Madeline M., Reddy Uma M., Tita Alan T.N., Silver Robert M.,
Mallett Gail, Hill Kim, et al. 2018. “Labor Induction versus Expectant Management for
Low-Risk Nulliparous Women.” New England Journal of Medicine 379(6):513–23. doi:10.1056/
NEJMoa1800566. [PubMed: 30089070]
Masters et al.
Page 18
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

Guagliardo Mark F. 2004. “Spatial Accessibility of Primary Care: Concepts, Methods, and
Challenges.” International Journal of Health Geographics 3(1):1–13. [PubMed: 14748927]
Halaby Charles N. 2004. “Panel Models in Sociological Research: Theory into Practice.” Annual
Review of Sociology 30:507–44.
Hansen Helena, Parker Caroline, and Netherland Jules. 2020. “Race as a Ghost Variable in (White)
Opioid Research.” Science, Technology, & Human Values 45(5):848–76.
Hansen Helena, and Skinner Mary E.. 2012. “From White Bullets to Black Markets and Greened
Medicine: The Neuroeconomics and Neuroracial Politics of Opioid Pharmaceuticals.” Annals of
Anthropological Practice 36(1):167–82.
Hardeman Rachel R., Medina Eduardo M., and Kozhimannil Katy B.. 2016. “Structural Racism and
Supporting Black Lives—The Role of Health Professionals.” New England Journal of Medicine
375(22):2113–15. [PubMed: 27732126]
Harris Lisa H., and Wolfe Taida. 2014. “Stratified Reproduction, Family Planning Care, and the
Double Edge of History.” Current Opinion in Obstetrics and Gynecology 26(6):539–44. [PubMed:
25379770]
Hayanga Awori J., Waljee Akbar K., Kaiser Heather E., Chang David C., and Morris Arden M..
2009. “Racial Clustering and Access to Colorectal Surgeons, Gastroenterologists, and Radiation
Oncologists by African Americans and Asian Americans in the United States: A County-Level
Data Analysis.” Archives of Surgery 144(6):532–35. [PubMed: 19528386]
Hoffman Kelly M., Trawalter Sophie, Axt Jordan R., and Oliver M. Norman. 2016. “Racial Bias in
Pain Assessment and Treatment Recommendations, and False Beliefs about Biological Differences
between Blacks and Whites.” Proceedings of the National Academy of Sciences 113(16):4296–
301.
Holmes Seth M. 2012. “The Clinical Gaze in the Practice of Migrant Health: Mexican Migrants in the
United States.” Social Science & Medicine 74(6):873–81. [PubMed: 21992736]
Holness Nola. 2018. “High-Risk Pregnancy.” Nursing Clinics of North America 53(2):241–51.
doi:10.1016/j.cnur.2018.01.010. [PubMed: 29779516]
Horbar Jeffrey D., Edwards Erika M., Greenberg Lucy T., Profit Jochen, Draper David, Helkey Daniel,
Lorch Scott A., et al. 2019. “Racial Segregation and Inequality in the Neonatal Intensive Care
Unit for Very Low-Birth-Weight and Very Preterm Infants.” JAMA Pediatrics 173(5):455–61.
[PubMed: 30907924]
Institute of Medicine. 2003. Unequal Treatment: Confronting Racial and Ethnic Disparities in Health
Care Washington, DC: The National Academies Press. doi:10.17226/12875.
Janevic Teresa, Piverger Naissa, Afzal Omara, and Howell Elizabeth A.. 2020. “‘Just because You
Have Ears Doesn’t Mean You Can Hear’—Perception of Racial-Ethnic Discrimination during
Childbirth.” Ethnicity & Disease 30(4):533–42. [PubMed: 32989353]
Knoester Matthew, and Au Wayne. 2017. “Standardized Testing and School Segregation: Like Tinder
for Fire?” Race Ethnicity and Education 20(1):1–14.
Korver-Glenn Elizabeth. 2021. Race Brokers: Housing Markets and Segregation in 21st Century Urban
America New York, NY: Oxford University Press.
Krieger Nancy. 2001. “Theories for Social Epidemiology in the 21st Century: An Ecosocial
Perspective.” International Journal of Epidemiology 30(4):668–77. [PubMed: 11511581]
Krieger Nancy. 2012. “Methods for the Scientific Study of Discrimination and Health: An Ecosocial
Approach.” American Journal of Public Health 102(5):936–44. [PubMed: 22420803]
Lansdorp-Vogelaar Iris, Kuntz Karen M., Knudsen Amy B., Van Ballegooijen Marjolein, Zauber
Anne G., and Jemal Ahmedin. 2012. “Contribution of Screening and Survival Differences to
Racial Disparities in Colorectal Cancer Rates.” Cancer Epidemiology and Prevention Biomarkers
21(5):728–36.
Lewey Jennifer, and Choudhry Niteesh K.. 2014. “The Current State of Ethnic and Racial Disparities
in Cardiovascular Care: Lessons from the Past and Opportunities for the Future.” Current
Cardiology Reports 16(10):530. doi:10.1007/s11886-014-0530-3. [PubMed: 25135343]
Liese Kylea L., Davis-Floyd Robbie, Stewart Karie, and Cheyney Melissa. 2021. “Obstetric
Iatrogenesis in the United States: The Spectrum of Unintentional Harm, Disrespect, Violence,
and Abuse.” Anthropology & Medicine 28(2):188–204. [PubMed: 34196238]
Masters et al.
Page 19
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

Logan Rachel G., McLemore Monica R., Julian Zoë, Stoll Kathrin, Malhotra Nisha, GVtM Steering
Council, and Saraswathi Vedam. 2022. “Coercion and Non-consent during Birth and Newborn
Care in the United States.” Birth 49(4):749–62. [PubMed: 35737547]
Malek Sayeed K., Keys Brandon J., Kumar Sanjaya, Milford Edgar, and Tullius Stefan G.. 2011.
“Racial and Ethnic Disparities in Kidney Transplantation.” Transplant International 24(5):419–24.
[PubMed: 21166727]
Marconi Anna M. 2019. “Recent Advances in the Induction of Labor.” F1000Research 8:F1000
Faculty Rev-1829. doi:10.12688/f1000research.17587.1.
Martin Joyce A., Hamilton Brady E., Osterman Michelle J., Driscoll Anne K., and Mathews TJ.
2017. “Births: Final Data for 2015.” NVSS 66(1). https://www.cdc.gov/nchs/data/nvsr/nvsr66/
nvsr66_01.pdf.
Martin Joyce A., Hamilton Brady E., Sutton Paul D., Ventura Stephanie J., Menacker Fay, Kirmeyer
Sharon, and Munson Martha L.. 2007. “Births: Final Data for 2005.” NVSS 56(6). https://
www.cdc.gov/nchs/data/nvsr/nvsr56/nvsr56_06.pdf.
Mathews TJ, MacDorman Marian F., and Thoma Marie E.. 2015. “Infant Mortality Statistics from
the 2013 Period Linked Birth/Infant Death Dataset.” NVSS 64(9). https://stacks.cdc.gov/view/cdc/
32752.
McGee Ebony Omotola. 2020. “Interrogating Structural Racism in STEM Higher Education.”
Educational Researcher 49(9):633–44.
McLemore Monica R., Altman Molly R., Cooper Norlissa, Williams Shanell, Rand Larry, and Franck
Linda. 2018. “Health Care Experiences of Pregnant, Birthing, and Postnatal Women of Color at
Risk for Preterm Birth.” Social Science & Medicine 201:127–35. [PubMed: 29494846]
Morris Arden M., Rhoads Kim F., Stain Stain C., and Birkmeyer John D.. 2010. “Understanding
Racial Disparities in Cancer Treatment and Outcomes.” Journal of the American College of
Surgeons 211(1):105–13. [PubMed: 20610256]
National Center for Health Statistics. 2020. [Natality Birth Files, 1990–2017]
National Institute of Child Health and Human Development. 2018. “High-Risk Pregnancy” https://
www.nichd.nih.gov/health/topics/high-risk.
Neighbors Harold W., Caldwell Cleopatra, Williams David R., Nesse Randolph, Taylor Robert J.,
Bullard Kai M., Torres Myriam, and Jackson James S.. 2007. “Race, Ethnicity, and the Use of
Services for Mental Disorders: Results from the National Survey of American Life.” Archives of
General Psychiatry 64(4):485–94. [PubMed: 17404125]
Nicholson James M., Caughey Aaron B., Stenson Morghan H., Cronholm Peter F., Kellar Lisa, Bennett
Ian, Margo Katie, and Straton Joseph B.. 2009a. “The Active Management of Risk in Multiparous
Pregnancy at Term: Association between a Higher Preventive Labor Induction Rate and Improved
Birth Outcomes.” American Journal of Obstetrics and Gynecology 200(3):250.e1–13.
Nicholson James M., Cronholm Peter, Kellar Lisa C., Stenson Morghan H., and Macones George
A.. 2009b. “The Association between Increased Use of Labor Induction and Reduced Rate of
Cesarean Delivery.” Journal of Women’s Health 18(11): 1747–58.
Nicholson James M., Kellar Lisa C., Cronholm Peter F., and Macones George A.. 2004.
“Active Management of Risk in Pregnancy at Term in an Urban Population: An Association
between a Higher Induction of Labor Rate and a Lower Cesarean Delivery Rate.” American
Journal of Obstetrics and Gynecology 191(5):1516–28. doi:10.1016/j.ajog.2004.07.002. [PubMed:
15547519]
Nicholson James M., Parry Samuel, Caughey Aaron B., Rosen Sarah, Keen Allison, and Macones
George A.. 2008. “The Impact of the Active Management of Risk in Pregnancy at Term on
Birth Outcomes: A Randomized Clinical Trial.” American Journal of Obstetrics and Gynecology
198(5):511.e1–e15.
Nicholson James M., Yeager David L., and Macones George. 2007. “A Preventive Approach to
Obstetric Care in a Rural Hospital: Association between Higher Rates of Preventive Labor
Induction and Lower Rates of Cesarean Delivery.” Annals of Family Medicine 5(4):310–9.
doi:10.1370/afm.706. [PubMed: 17664497]
Masters et al.
Page 20
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

Nieblas-Bedolla Edwin, Christophers Briana, Nkinsi Naomi T., Schumann Paul D., and Stein Elizabeth
2020. “Changing How Race Is Portrayed in Medical Education: Recommendations from Medical
Students.” Academic Medicine 95(12):1802–806. [PubMed: 32379145]
Obermeyer Ziad, Powers Brian, Vogeli Christine, and Mullainathan Sendhil. 2019. “Dissecting Racial
Bias in an Algorithm Used to Manage the Health of Populations.” Science 366(6464):447–53.
[PubMed: 31649194]
Odom Walker, Gery Ryan Kara, Ramey Robin, Nunez Felix L., Beltran Robert, Splawn Robert G., and
Brown Arleen F.. 2010. “Recruiting and Retaining Primary Care Physicians in Urban Underserved
Communities: The Importance of Having a Mission to Serve.” American Journal of Public Health
100(11):2168–75. [PubMed: 20935263]
Osterman Michelle J., and Martin Joyce A.. 2014. “Recent Declines in Induction of Labor by
Gestational Age” Centers for Disease Control and Prevention, National Center for Health Statistics
National Vital Statistics Report. https://stacks.cdc.gov/view/cdc/23533.
Paradies Yin, Truong Mandy, and Priest Naomi. 2014. “A Systematic Review of the Extent and
Measurement of Healthcare Provider Racism.” Journal of General Internal Medicine 29(2):364–
87. [PubMed: 24002624]
Peek Monica E., Cargill Algernon, and Huang Elbert S.. 2007. “Diabetes Health Disparities.” Medical
Care Research and Review 64(5 Suppl.):101S–56S. [PubMed: 17881626]
Petersen Emily E., Davis Nicole L., Goodman David, Cox Shanna, Syverson Carla, Seed Kristi,
Shapiro-Mendoza Carrie, Callaghan William M., and Barfield W. 2019. “Racial-Ethnic Disparities
in Pregnancy-Related Deaths—United States, 2007–2016.” Morbidity and Mortality Weekly
Report 68(35):762–65. [PubMed: 31487273]
Popescu Iona, Nallamothu Brahmajee K., Vaughan-Sarrazin Mary S., and Cram Peter. 2010. “Racial
Differences in Admissions to High-Quality Hospitals for Coronary Heart Disease.” Archives of
Internal Medicine 170(14):1209–15. [PubMed: 20660838]
Profit Jochen, Gould Jeffrey B., Bennett Mihoko, Goldstein Benjamin A., Draper David, Phibbs
Ciaran S., and Lee Henry C.. 2017. “Racial-Ethnic Disparity in NICU Quality of Care Delivery.”
Pediatrics 140(3):e20170918. doi:10.1542/peds.2017-0918. [PubMed: 28847984]
Ramsey Patrick S., Ramin Kirk D., and Ramin Susan M.. 2000. “Labor Induction.” Current Opinion in
Obstetrics and Gynecology 12(6):463–73. [PubMed: 11128407]
Roberts Dorothy E. 1999. Killing the Black Body: Race, Reproduction, and the Meaning of Liberty
New York, NY: Vintage.
Rothstein Richard. 2017. The Color of Law: A Forgotten History of How Our Government Segregated
America New York, NY: Liveright Publishing Corporation.
Sigurdson Krista, Mitchell Briana, Liu Jessica, Morton Christine, Gould Jeffrey B., Lee Henry
C., Nicole Capdarest-Arest, and Jochen Profit. 2019. “Racial-Ethnic Disparities in Neonatal
Intensive Care: A Systematic Review.” Pediatrics 144(2):e20183114. doi:10.1542/peds.2018-3114.
[PubMed: 31358664]
Singer Judith D., and Willett John B.. 2003. Applied Longitudinal Data Analysis: Modeling Change
and Event Occurrence New York, NY: Oxford University Press.
Slaughter-Acey Jaime C., Sealy-Jefferson Shawnita, Helmkamp Laura, Caldwell Cleopatra H., Osypuk
Theresa L., Platt Robert W., Straughen Jennifer K., Dailey-Okezie Rhonda K., Abeysekara Purni,
and Misra Dawn P.. 2016. “Racism in the Form of Microaggressions and the Risk of Preterm Birth
among Black Women.” Annals of Epidemiology 26(1):7–13. [PubMed: 26549132]
Tehranifar Parisa, Neugut Alfred I., Phelan Jo C., Link Bruce G., Liao Yuyan, Desai Manisha, and
Beth Terry Mary. 2009. “Medical Advances and Racial-Ethnic Disparities in Cancer Survival.”
Cancer Epidemiology and Prevention Biomarkers 18(10):2701–708.
Tilstra Andrea M., and Masters Ryan K.. 2020. “Worth the Weight? Recent Trends in Obstetric
Practices, Gestational Age, and Birth Weight in the United States.” Demography 57(1):99–121.
[PubMed: 31997231]
Vaught Sabrina E., and Castagno Angelina E.. 2008. “‘I Don’t Think I’m a Racist’: Critical Race
Theory, Teacher Attitudes, and Structural Racism.” Race Ethnicity and Education 11(2):95–113.
Vedam Saraswathi, Stoll Kathrin, Taiwo Tanya K., Rubashkin Nicholas, Cheyney Melissa, Strauss
Nan, McLemore Monica, et al. 2019. “The Giving Voice to Mothers Study: Inequity and
Masters et al.
Page 21
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

Mistreatment during Pregnancy and Childbirth in the United States.” Reproductive Health
16(1):77. doi:10.1186/s12978-019-0729-2. [PubMed: 31182118]
Ventura Stephanie J., Martin Joyce A., Curtin Sally M., and Mathews TJ. 1999. “Births: Final Data for
1997.” NVSS 47(18). https://stacks.cdc.gov/view/cdc/84459.
Washington Harriet A. 2006. Medical Apartheid: The Dark History of Medical Experimentation on
Black Americans from Colonial Times to the Present New York, NY: Doubleday Books.
White Kellee, Haas Jennifer S., and Williams David R.. 2012. “Elucidating the Role of Place in
Health Care Disparities: The Example of Racial-Ethnic Residential Segregation.” Health Services
Research 47(3 Pt. 2):1278–99. [PubMed: 22515933]
Williams David R., and Braboy Jackson Pamela. 2005. “Social Sources of Racial Disparities in
Health.” Health Affairs 24(2):325–34. [PubMed: 15757915]
Masters et al.
Page 22
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

Figure 1.
U.S. States’ Labor Induction Rates among Pregnancies to Black Women, Latina Women,
and White Women, 1990 to 2017.
Source: National Vital Statistics System restricted natality data, 1990 to 2017.
Masters et al.
Page 23
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

Figure 2.
Predicted Bayes Estimates of U.S. States’ Labor Induction Rates among Pregnancies to
Black Women, Latina Women, and White Women, 1990 to 2017.
Source: National Vital Statistics System restricted natality data, 1990 to 2017.
Masters et al.
Page 24
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

Figure 3.
Estimates of Mean Labor Induction Rates among U.S. States’ Black, Latina, and White
Childbearing Populations, 1990 to 2017.
Source: National Vital Statistics System restricted natality data, 1990 to 2017.
Note: “Observed” lines are mean induction of labor (IOL) rates estimated from Equation
2, the “Control Own Characteristics” lines are mean IOL rates estimated from Equation 3
while holding constant states’ 1990 levels of demographic characteristics and risk factors,
“Control White Characteristics” lines are mean IOL rates estimated from Equation 4 while
holding constant states’ 1990 levels of demographic characteristics and risk factors among
White childbearing populations, and “Control Black Characteristics” lines are mean IOL
rates estimated from Equation 4 while holding constant states’ 1990 levels of demographic
characteristics and risk factors among Black childbearing populations.
Masters et al.
Page 25
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript

Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript
Masters et al.
Page 26
Table 1.
Descriptive Statistics of Analytic Samples of Singleton First Births among U.S. States’ Black, Latina, and White Childbearing Populations in 1990, 2004,
and 2017.
Black
Latina
White
1990
2004
2017
1990
2004
2017
1990
2004
2017
Characteristics of labor/delivery
 Labor induction
.10
.23
.33
.10
.21
.31
.14
.28
.36
 Cesarean delivery
.23
.30
.32
.24
.25
.25
.24
.28
.28
Maternal demographics
 Teen maternal age
.43
.35
.17
.32
.31
.21
.20
.16
.08
 Advanced maternal age
.02
.05
.07
.02
.04
.06
.05
.09
.10
 Education <high school
.31
.25
.13
.37
.44
.23
.15
.12
.06
 Education BA+
.09
.14
.20
.11
.11
.17
.24
.37
.44
 Married
.28
.25
.29
.60
.42
.40
.76
.67
.66
 U.S.-born
.93
.85
.79
.53
.38
.60
.96
.95
.94
Pregnancy risk factors
 Gestational tobacco use
.10
.07
.04
.09
.04
.03
.18
.13
.08
 Diabetes
.02
.03
.05
.02
.03
.07
.02
.03
.06
 Hypertension
.05
.07
.13
.04
.05
.08
.05
.07
.11
 Weight gain 40+ lbs.
.24
.29
.28
.24
.26
.25
.27
.34
.30
 Preterm gestation
.16
.15
.14
.11
.11
.10
.08
.10
.09
 Postterm gestation
.23
.17
.15
.28
.20
.18
.31
.20
.19
States
44
44
44
48
48
48
51
51
51
Source: National Vital Statistics System restricted natality data, 1990 to 2017.
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.

Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript
Masters et al.
Page 27
Table 2.
Results from Unconditional Growth Curve Models Fitted to States’ Labor Induction Rates among Pregnancies to Black, Latina, and White Childbearing
Populations, 1990–2017.
Black
Latina
White
b
SE
b
SE
b
SE
Panel A: Unconditional means model
 Intercept
.227
.006
.208
.004
.265
.006
 Variance components
  σ0
2
.0012
.0003
.0007
.0002
.0015
.0003
  σε
2
.0060
.0002
.0044
.0002
.0050
.0002
Panel B: Random slope model, unstructured error-covariance matrix
 5-year time
.039
.002
.034
.001
.036
.002
 Intercept
.121
.007
.116
.005
.168
.005
 Variance components
  σ1
2
.0001
.0000
.0001
.0000
.0002
.0000
  σ0
2
.0021
.0005
.0011
.0003
.0013
.0003
  σ01
−.0003
.0001
−.0002
.0001
−.0002
.0001
  σε
2
.0014
.0001
.0010
.0000
.0010
.0000
N states
44
48
51
State-years
1,232
1,344
1,428
Source: National Vital Statistics System restricted natality data, 1990 to 2017.
Note: b indicates estimated coefficient, and SE indicates standard error.
J Health Soc Behav. Author manuscript; available in PMC 2024 February 07.

    Scientific Research Findings:

Answer:
• Between 1990 and 2017, increases in IOL among pregnancies to White women are explained by changes in risk factors among White childbearing populations.
• Between 1990 and 2017, increases in IOL among pregnancies to Black women are not explained by changes in risk factors among their own populations; rather, these increases are largely explained by changes in risk factors among White childbearing populations.
• Together, changes in the demographic characteristics and risk factors of White childbearing populations are driving the rising use of IOL in the United States for all racial-ethnic populations.