Social disadvantage and the black-white disparity in spontaneous preterm delivery among California births
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Socialdisadvanges.pdf
RESEARCH ARTICLE
Social disadvantage and the black-white
disparity in spontaneous preterm delivery
among California births
Suzan L. Carmichael 1*, Peiyi Kan1, Amy M. Padula2, David H. Rehkopf3, John W. Oehlert1,
Jonathan A. Mayo 1 , Ann M. Weber
1 , Paul H. Wise
1 , Gary M. Shaw
1 , David K. Stevenson
1
1 Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of
Medicine, Stanford, CA, United States of America, 2 Division of Maternal-Fetal Medicine, University of
California San Francisco School of Medicine, San Francisco, CA, United States of America, 3 Division of
General Medical Disciplines, Department of Medicine, Stanford University School of Medicine, Stanford, CA,
United States of America
Abstract
We examined the contribution of social disadvantage to the black-white disparity in preterm
birth. Analyses included linked vital and hospital discharge records from 127,358 black and
615,721 white singleton California births from 2007–11. Odds ratios (OR) were estimated by
4 logistic regression models for 2 outcomes: early (<32 wks) and moderate (32–36 wks) spontaneous preterm birth (ePTB, mPTB), stratified by 2 race-ethnicity groups (blacks and
whites). We then conducted a potential impact analysis. The OR for less than high school
education (vs. college degree) was 1.8 (95% confidence interval 1.6, 2.1) for ePTB among
whites but smaller for the other 3 outcome groups (ORs 1.3–1.4). For all 4 groups, higher
census tract poverty was associated with increased odds (ORs 1.03–1.05 per 9% change in
poverty). Associations were less noteworthy for the other variables (payer, and tract percent
black and Gini index of income inequality). Setting 3 factors (education, poverty, payer) to
‘favorable’ values was associated with lower predicted probability of ePTB (25% lower
among blacks, 31% among whites) but a 9% higher disparity, compared to probabilities
based on observed values; for mPTB, respective percentages were 28% and 13% lower
probability, and 17% lower disparity. Results suggest that social determinants contribute to
preterm delivery and its disparities, and that future studies should focus on ePTB and more
specific factors related to social circumstances.
Introduction
Preterm delivery (i.e., delivery at <37 weeks gestation) affects approximately 11% of U.S.-born
infants and is one of the most common causes of infant morbidity and mortality [1]. Babies
born to black mothers have a prevalence of preterm delivery that is twice that of infants born
to white mothers. One potential contributor to this disparity is social disadvantage, which is
much more prevalent among blacks than whites and associated with higher risk of preterm
delivery. Its actual contribution to the disparity is unclear. Several studies report that after
PLOS ONE | https://doi.org/10.1371/journal.pone.0182862 August 11, 2017 1 / 12
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OPEN ACCESS
Citation: Carmichael SL, Kan P, Padula AM,
Rehkopf DH, Oehlert JW, Mayo JA, et al. (2017)
Social disadvantage and the black-white disparity
in spontaneous preterm delivery among California
births. PLoS ONE 12(8): e0182862. https://doi.org/
10.1371/journal.pone.0182862
Editor: Abigail Fraser, University of Bristol, UNITED
KINGDOM
Received: April 27, 2017
Accepted: July 25, 2017
Published: August 11, 2017
Copyright: © 2017 Carmichael et al. This is an open access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The data are publicly
available from the Office of Statewide Health
Planning and Development (OSHPD). The data are
not available for replication because specific
approvals from OSHPD and the California
Committee for the Protection of Human Subjects
have to be obtained in order to access them.
Requests for data may be sent to: Healthcare
Information Resource Center 400 R Street, Suite
250 Sacramento, CA 95811-6213 Tel: (916) 326-
3802 [email protected].
adjustment for markers of social disadvantage, the disparity changes minimally [2–4]. Others
suggest the disparity is greatest among the most socially advantaged women [2, 3, 5]. A fundamental challenge to understanding the contribution of social disadvantage to this
disparity is that social disadvantage is not a singular construct. Markers related to education
and income are most commonly examined; others are potentially of importance but less fre-
quently examined, such as segregation and inequality [6–9]. Social disadvantage may incur
higher risk through many pathways, such as reduced access to care, worse nutrition, increased
stress, higher-risk reproductive patterns (e.g., teen birth, high parity) and higher prevalence of
conditions such as obesity, diabetes and hypertension. Studies vary in how they deal with these
potential pathways; many adjust for variables along the pathways, but this approach may result
in over-adjustment and thus an underestimate of the contribution of social disadvantage to
disparities. Another challenge is structural confounding, unless very large populations are
available for study [5]. In addition, preterm deliveries are usually examined as a single group,
despite evidence of etiologic heterogeneity based on timing of delivery and events that insti-
gated delivery, e.g., how early the delivery was and whether it was spontaneous or induced [6].
Sample sizes are often not large enough and/or clinical data are not available to enable these
distinctions.
Our objective was to investigate the contribution of multiple individual- and residential
area- level factors related to social disadvantage to the black-white disparity in preterm delivery
among a large population of California births, which represent one in eight of all U.S. births.
We addressed the presence of effect modification among individual- and area- level factors, as
well as whether their associations with preterm delivery differed for blacks versus whites. We
separately examined early (i.e., <32 weeks) and moderate (i.e., 32–36 week) preterm deliveries
and focused on those that were spontaneous (i.e., preceded by spontaneous onset of labor or
rupture of membranes). We focused on spontaneous preterm deliveries because their etiology
may be distinct from those that are medically indicated, and they comprise the vast majority of
preterm deliveries in this dataset (86% of early preterm deliveries and 72% of moderate pre-
term deliveries).
In addition to examining risks, we conducted a potential impact analysis to consider how
different the disparity might be if social factors were more equal between blacks and whites.
That is, we used a substitution estimator approach to gauge the potential impact of a counter-
factual change in social disadvantage on the prevalence of spontaneous preterm delivery and
the black-white disparity [10, 11].
Methods
The study population included 822,414 singleton infants born to non-Hispanic black and
white mothers in California from 2007–2011 (referred to hereafter as ‘black’ and ‘white’
infants) and whose birth certificates were successfully linked to their delivery hospital dis-
charge records by the Office of Statewide Health Planning and Development (>98% of births).
Individual-level markers of social disadvantage included maternal education (less than high
school, equal to high school, some college, college degree) and payer for the delivery hospitali-
zation (Medi-Cal, private, uninsured, other).
To create census tract variables, we geocoded maternal residential addresses at delivery,
which we obtained from electronic birth certificates, and then assigned one of California’s
>8,000 census tracts (using PROC GEOCODE, SAS 9.4, U.S. Census 2015 TIGER/Line R
Sha-
pefiles, up to 4 iterations). Geocoding was successful for 782,861 subjects (95.2%).
We examined three census tract-level markers of social disadvantage, which we derived
from 2007–2011 American Community Survey files: poverty as measured by percent of the
Black-white disparity in preterm delivery
PLOS ONE | https://doi.org/10.1371/journal.pone.0182862 August 11, 2017 2 / 12
Funding: This work was supported in part by the
March of Dimes Prematurity Research Center at
Stanford (MOD PR625253) and the Stanford Child
Health Research Institute. The funders had no role
in study design, data collection and analysis,
decision to publish, or preparation of the
manuscript.
Competing interests: The authors have declared
that no competing interests exist.
tract population with household income below the poverty level; percent of the tract popula-
tion that was black, as a basic measure of segregation; and the Gini index of income inequality,
a measure of census tract income distribution calculated by the Census Bureau (0 reflects a
completely proportional distribution of income, 1 reflects one person having all the income).
In addition to poverty, we created an index that incorporated eight census tract variables rep-
resenting multiple aspects of socioeconomic level (poverty, occupation, employment, educa-
tion, and housing) following previously described methods [12]. Its correlation with tract
poverty was high (r = 0.86). We therefore included poverty rather than the index in our main
models [13–15].
Gestational age was based on best obstetric estimate from birth certificates. We excluded
11,160 infants with gestational age that was missing or outside 20–41 weeks and then 15,041
with any other missing variables, leaving 756,660 births (625,778 white, 130,882 black) avail-
able for analysis, with no missing data on covariates. The outcome of interest was early (20–31
weeks) or moderate (32–36 weeks) spontaneous preterm delivery, i.e., those preceded by pre-
term premature rupture of membranes (ICD-9-CM code 658.1 or birth certificate complica-
tion of labor/delivery code 10), premature labor (ICD-9-CM code 644), or use of tocolytics
(birth certificate complication/procedure of pregnancy code 28). Other preterm deliveries
were induced or delivered by cesarean section without a code for spontaneous onset of labor
(medically indicated). These types of variables have been shown to have good validity in
administrative hospital discharge data [16].
We used logistic regression to estimate odds ratios (OR) and 95% confidence intervals (CI)
for early and moderate spontaneous preterm delivery. The reference group was term infants
delivered at 37–41 weeks. Non-spontaneous preterm deliveries were excluded (10,057 whites,
3,524 blacks). Initial models included maternal black-white race-ethnicity, education, payer,
and census tract poverty, percent black, and Gini index (tract-level variables were specified as
continuous). We restricted the model to these variables given our objective of examining the
total contribution of social disadvantage to preterm delivery and the premise that social disad-
vantage leads to preterm delivery via many pathways. These pathways include maternal repro-
ductive patterns, which to some extent drive the demographics of women who deliver, and
thus we did not adjust for such factors (e.g., age, parity). We tested the interaction of individual
and census tract socioeconomic level (i.e., maternal education and tract poverty) and of black-
white race-ethnicity with maternal education and the three tract-level variables (poverty, Gini
index, percent black) (i.e., 5 interactions in total, per model), inputting one interaction (as a
product term) at a time into each baseline model. For early preterm delivery, all five interaction
terms had P<0.10. For moderate preterm delivery, only education by poverty and black-white
race-ethnicity by poverty had P<0.10. Based on the multiple significant interactions with black-
white race-ethnicity, we ran further models separately for blacks and whites. Within these strati-
fied models, the interaction of education by poverty was not significant for early preterm deliv-
ery for blacks or whites (P>0.10) and was therefore not included in subsequent models. We
used conventional logistic regression due to its relative simplicity, its amenability to our initial
tests of interaction, and minimal concern about independence of observations since there are
>8,000 census tracts in California.
We then conducted a potential impact analysis to consider what the prevalence of sponta-
neous preterm delivery and the black-white disparity might be in the hypothetical situation of
a similar level of social disadvantage among blacks and whites. To do this, we followed substi-
tution estimator methods described by Ahern et al. to estimate the unobserved counterfactual
probability of preterm delivery at specific imputed levels of education, payer status and poverty
[10, 11]. We did not estimate impact for percent black or the Gini index because most confi-
dence intervals for these variables included 1.0.
Black-white disparity in preterm delivery
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First, we estimated predicted probabilities of spontaneous preterm delivery for each individ- ual, for each specified scenario. We did this by using coefficients from our final logistic regres- sion models to estimate the predicted log odds (plox) for each individual at different values of
the specified variables, and the following equation to estimate the predicted probability:
PProbx = 1/(1+exp(-1 x plox)) where x refers to the value(s) of the variable(s) we manipulated. As our baseline comparator, we estimated the expected probability of preterm delivery after
inputting each mother’s observed values of all variables. We then estimated counterfactual probabilities after substituting values for the predictor variables to reflect each following sce-
nario, for all subjects: 1) input education to correspond to each of its four levels (with age-spe-
cific exceptions described below); 2) input payer to each of its four levels; 3) input census tract
poverty to range from 2% to 42%, to reflect its observed range (the 1 percentile values for pov-
erty were 2% among blacks and 1% among whites; the respective 99 percentile values were
57% and 42%); 4) input all 3 variables as favorable (i.e., education as college degree, payer as
private, poverty as 2%); 5) input all 3 variables as unfavorable (i.e., education as less than high
school, payer as Medi-Cal, poverty as 42%). We chose Medi-Cal as the value for the unfavor-
able scenario because eligibility is income-based, and it is much more common than uninsured
payer.
Given that educational potential varies by age, we made the following exceptions in assign-
ing imputed education values. For women <18 years old, the maximum substituted value was
‘less than high school’; for women 18 years old, it was ‘equal to high school;’ and for women
19–21 years old, it was ‘some college’. For example, for women <18 years old, if the intended
substituted value was high school education, and her observed value was less than high school,
we retained her observed value.
Second, we estimated the overall predicted probability (PProb) of spontaneous preterm delivery among all black and white women, and the black-white disparity (i.e., the ratio of the
PProb for blacks and whites), for each counterfactual scenario; as well as the percent change in
the PProb and disparity, relative to those based on observed values, for each scenario. For each
PProb, percent change in PProb, black-white disparity, and percent change in disparity, we
estimated confidence intervals using a nonparametric bootstrap [10]. This study is approved
by the California Committee for the Protection of Human Subjects and the Stanford Univer-
sity Institutional Review Board.
Results
The prevalence of preterm delivery among study subjects was 10.2% among blacks and 6.3%
among whites (Table 1). The prevalence of early spontaneous preterm delivery was 1.8%
among blacks (n = 2,390) and 0.6% among whites (n = 4,019), giving an unadjusted prevalence
ratio of 3.0 (95% CI 2.8, 3.1). The prevalence of moderate spontaneous preterm delivery was
5.7% among blacks (n = 7,489) and 4.1% (n = 25,388) among whites, giving an unadjusted
prevalence ratio of 1.5 (95% CI 1.4, 1.5). A total of 2.7% of blacks (n = 3,524) and 1.6% of
whites (n = 10,057) had preterm deliveries that were medically indicated or of unknown sub-
type. Black mothers were more likely than white mothers to have less than high school educa-
tion (17% vs. 6%) and less likely to have a college degree or higher (14% vs. 44%), and they
were more likely to have Medi-Cal (55% vs. 23%) and less likely to have private insurance
(37% vs. 72%) (Table 1). The median percent of the census tract population living below the
poverty level was 19% for blacks and 9% for whites; the median percent tract population that
was black was 14% for blacks and 2% for whites.
Table 2 provides results for multivariable logistic regression models for early and moderate
spontaneous preterm delivery. Among black women, education less than a college degree was
Black-white disparity in preterm delivery
PLOS ONE | https://doi.org/10.1371/journal.pone.0182862 August 11, 2017 4 / 12
associated with 23–32% increased odds of preterm delivery. Among white women, education
was associated with 43–83% increased odds of early preterm delivery, increasing monotoni-
cally with lower education; associations with moderate preterm delivery were more modest
(17 to 37% increase). Relative to private insurance, being uninsured was associated with
increased odds of early (OR 3.07 for blacks, 95% CI 2.55, 3.69, and 3.73 for whites, 95% CI
3.18, 4.37) and moderate preterm delivery (OR 2.10 for blacks, 95% CI 1.85, 2.39, and 2.09 for
whites, 95% CI 1.92, 2.28), but only 2% of black women and 1% of white women were unin-
sured. ORs for Medi-Cal and other insurance were smaller, ranging from 0.78 to 1.17. For all
four groups, higher census tract poverty was associated with increased odds (3–5% increased
odds per 9% change in poverty). For census tract percent black, the ORs per 6% higher percent
of blacks ranged from 1.00 to 1.03 across the four models. For the Gini index, the ORs per
0.1-unit change ranged from 0.99 to 1.01.
Table 3 provides results examining the predicted probability (PProb) of early and moderate
spontaneous preterm delivery and the black-white disparity, based on observed and counter-
factual (substituted) values of education, payer and census tract poverty. When we counterfac-
tually set education to college degree, the PProb of early preterm delivery was 11.9% lower
among blacks and 18.3% lower among whites, and the black-white disparity was 7.8% higher,
as compared to values obtained when incorporating observed values of education (as well as
Table 1. Prevalence of preterm delivery and descriptors of singleton infants born to non-hispanic black and white mothers in California, 2007–
2011.
Infants born to black mothers
(n = 130,882)
Infants born to white mothers
(n = 625,778)
Prevalence of preterm delivery (per 100 births): Percent Percent
Early spontaneous preterm delivery (20–31 weeks) 1.8 0.6
Moderate spontaneous preterm delivery (32–36 weeks) 5.7 4.1
Preterm delivery that was medically indicated or unknown
subtype
2.7 1.6
Total prevalence 10.2 6.3
Individual-level descriptors:
Maternal age at delivery
<20 years 13.6 4.3 20–35 years 76.0 77.9
>35 years 10.3 17.8 Maternal nulliparity 41.7 44.9
Maternal education
Less than high school 16.7 6.1
Equal to high school 34.5 21.7
Some college 34.9 28.6
College degree or higher 13.9 43.6
Payment for delivery
Medi-Cal 55.0 23.3
Private 36.6 72.0
Uninsured 2.2 1.2
Other 6.2 3.5
Census tract-level descriptors:* Median (25th-75th percentile) Median (25th-75th percentile)
Percent of population with income below poverty level 18.6 (10.6–28.6) 9.2 (5.3–15.7)
Gini index of income inequality 0.42 (0.38–0.48) 0.44 (0.38–0.69)
Percent of population that is black 13.9 (6.2–25.8) 2.3 (0.8–5.7)
https://doi.org/10.1371/journal.pone.0182862.t001
Black-white disparity in preterm delivery
PLOS ONE | https://doi.org/10.1371/journal.pone.0182862 August 11, 2017 5 / 12
all the other variables). In contrast, when we set education equal to high school, the respective
probabilities were 6.3% and 41.8% higher and the disparity was 25.1% lower as compared to
estimates based observed education. Setting everyone to private insurance was associated with
an 8.7% increase in the disparity in early preterm delivery, whereas setting everyone to Medi-
Cal was associated with a 12.3% decrease; for moderate preterm delivery, the respective per-
centages were 3.6% and 1.3% decreases in the disparity. Setting payer to uninsured was associ-
ated with much larger increases in the PProb (86.1–237.5%), and reductions in the black-white
disparity (12.6% for early and 4.9% for moderate preterm). Changing poverty was associated
with modest changes in the disparity. Setting all three factors to ‘favorable’ values was associ-
ated with substantially lower PProb of early preterm delivery (24.6% lower among blacks,
30.7% among whites) but a 8.8% higher disparity, as compared to the PProb for observed val-
ues of the three factors. Setting all three to ‘unfavorable’ values was associated with higher
probability of early preterm delivery (11.2% higher among blacks, 75.2% among whites) and a
36.6% lower disparity. For moderate preterm delivery, setting all three factors to favorable or
unfavorable values was associated with a lower disparity (16.8% and 11.5%, respectively).
Confidence intervals for all of the point estimates except one in Table 3 excluded the null
value, and they tended to be very narrow (most upper and lower limits deviated less than +/-
0.10 from their respective point estimates).
Discussion
In this study of California births, the risk of early spontaneous preterm delivery was 3-fold
higher among black than white infants. The risk for moderate spontaneous preterm delivery
Table 2. Association of markers of social disadvantage with odds of spontaneous early (<32 weeks) and moderate (32–36 weeks) preterm delivery, relative to term delivery (37–41 weeks).
a
Adjusted odds ratio (95% CI) for early preterm
delivery
Adjusted odds ratio (95% CI) for moderate
preterm delivery
Blacks Whites Blacks Whites
Maternal education
Less than high school 1.29 (1.09, 1.52) 1.83 (1.61, 2.08) 1.32 (1.20, 1.45) 1.37 (1.30, 1.45)
Equal to high school 1.23 (1.06, 1.42) 1.46 (1.34, 1.60) 1.20 (1.10, 1.31) 1.14 (1.10, 1.18)
Some college 1.24 (1.08, 1.43) 1.43 (1.32, 1.54) 1.23 (1.13, 1.34) 1.17 (1.13, 1.20)
College degree or higher reference reference reference reference
Payment for Delivery
Medi-Cal 0.94 (0.86, 1.04) 1.17 (1.08, 1.27) 1.07 (1.01, 1.13) 1.04 (1.01, 1.08)
Private reference reference reference reference
Uninsured 3.07 (2.55, 3.69) 3.73 (3.18, 4.37) 2.10 (1.85, 2.39) 2.09 (1.92, 2.28)
Other 0.78 (0.65, 0.95) 1.04 (0.88, 1.23) 1.07 (0.96, 1.19) 1.03 (0.96, 1.10)
Census tract poverty: OR per 1% change
and 9% change b
1.005 (1.002, 1.008) 1.05
(1.02, 1.08)
1.004 (1.001, 1.008) 1.04
(1.01, 1.07)
1.005 (1.003, 1.007) 1.05
(1.03, 1.06)
1.003 (1.001, 1.004) 1.03
(1.01, 1.04)
Census tract Gini index of income inequality:
OR per 0.1-unit change
1.01 0.99, 1.03) 0.99 (0.98, 1.00) 0.99 (0.98, 1.01) 1.00 (0.99, 1.00)
Census tract percent black: OR per 1%
change and 6% change b
1.002 (0.999, 1.004) 1.01
(1.00, 1.02)
1.006 (1.001, 1.010) 1.03
(1.00, 1.06)
1.000 (0.999, 1.001) 1.00
(0.99, 1.01)
1.000 (0.998, 1.002) 1.00
(0.99, 1.01)
a All variables were included in the models, which included 2,390 early preterm, 7,489 moderate preterm, and 117,479 term deliveries to black women and
4,019 early preterm, 25,388 moderate preterm, and 586,314 term deliveries to white women. b
In addition to ORs associated with a 1% change in census tract poverty and percent blacks, we present ORs for a 9% change for poverty and a 6% change
in percent blacks (the SDs for poverty and percent blacks were 9% and 6% for whites; they were 13% and 18% for blacks).
https://doi.org/10.1371/journal.pone.0182862.t002
Black-white disparity in preterm delivery
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e c o ll e g e
2 .1
2 .4
% 0 .8
1 1 .0
% 2 .7
-7 .7
% 6 .1
2 .2
% 4 .4
5 .7
% 1 .4
-3 .3
%
(2 .1
– 2 .1
) (2
.2 – 2 .6
) (0
.8 – 0 .8
) (1
0 .9
– 1 1 .0
) (2
.7 – 2 .7
) (-
7 .9
- -7
.6 )
(6 .1
– 6 .1
) (2
.1 -
2 .3
)
(4 .4
– 4 .4
) (5
.7 -
5 .7
)
(1 .4
– 1 .4
) (-
3 .4
- -3
.3 )
C o ll e g e
d e g re
e 1 .8
-1 1 .9
% 0 .6
-1 8 .3
% 3 .2
7 .8
% 5 .3
-1 1 .1
% 3 .9
-7 .2
% 1 .4
-4 .1
%
(1 .8
– 1 .8
) (-
1 2 .1
- -1
1 .8
) (0
.6 – 0 .6
) (-
1 8 .4
- -1
8 .3
) (3
.2 – 3 .2
) (7
.7 – 8 .0
) (5
.3 – 5 .4
) (-
1 1 .1
- -1
1 .0
) (3
.9 – 3 .9
) (-
7 .3
- -7
.2 )
(1 .4
– 1 .4
) (-
4 .2
- -4
.0 )
P a y e r
s ta
tu s :
M e d i- C
a l
1 .9
-5 .4
% 0 .7
7 .8
% 2 .6
-1 2 .3
% 6 .0
0 .1
% 4 .2
1 .5
% 1 .4
-1 .3
%
(1 .9
– 1 .9
) (-
5 .5
- -5
.4 )
(0 .7
– 0 .7
) (7
.7 – 7 .8
) (2
.6 – 2 .6
) (-
1 2 .3
- -1
2 .2
) (6
.0 – 6 .0
) (0
.1 – 0 .2
) (4
.2 – 4 .2
) (1
.4 – 1 .5
) (1
.4 – 1 .4
) (-
1 .4
- -1
.3 )
P ri v a te
2 .0
0 .1
% 0 .6
-7 .9
% 3 .2
8 .7
% 5 .7
-5 .9
% 4 .1
-2 .3
% 1 .4
-3 .6
%
(2 .0
– 2 .0
) (0
.1 – 0 .2
) (0
.6 – 0 .6
) (-
7 .9
- -7
.2 )
(3 .2
– 3 .2
) (8
.6 – 8 .7
) (5
.7 – 5 .7
) (-
5 .9
- -5
.8 )
(4 .1
– 4 .1
) (-
2 .3
- -2
.3 )
(1 .4
– 1 .4
) (-
3 .7
- -3
.6 )
U n in
s u re
d 5 .9
1 9 5 .0
% 2 .3
2 3 7 .5
% 2 .6
-1 2 .6
% 1 1 .2
8 6 .1
% 8 .1
9 5 .7
% 1 .4
-4 .9
%
(5 .9
– 5 .9
) (1
9 4 .9
– 1 9 5 .2
) (2
.3 – 2 .3
) (2
3 7 .3
– 2 3 7 .6
) (2
.6 – 2 .6
) (-
1 2 .6
- -1
2 .5
) (1
1 .2
– 1 1 .2
) (8
6 .0
– 8 6 .2
) (8
.1 – 8 .1
) (9
5 .7
– 9 5 .8
) (1
.4 – 1 .4
) (-
5 .0
- -4
.9 )
O th
e r
1 .6
-2 1 .1
% 0 .7
-4 .1
% 2 .4
-1 7 .8
% 6 .0
0 .2
% 4 .2
0 .4
% 1 .4
-0 .2
%
(1 .6
– 1 .6
) (-
2 1 .2
- -2
1 .1
) (0
.7 – 0 .7
) (-
4 .1
- -4
.0 )
(2 .4
– 2 .4
) (-
1 7 .8
- -1
7 .8
) (6
.2 – 6 .0
) (0
.2 – 0 .3
) (4
.2 – 4 .2
) (0
.4 -
0 .5
)
(1 .4
– 1 .4
) (-
0 .2
- -0
.1 )
C e n s u s
tr a c t p o v e rt
y :
b
2 %
1 .8
-9 .1
% 0 .7
-4 .5
% 2 .8
-4 .8
% 5 .5
-8 .6
% 4 .0
-2 .7
% 1 .4
-6 .1
%
(1 .8
– 1 .8
) (-
9 .2
- -8
.9 )
(0 .7
– 0 .7
) (-
4 .6
- -4
.4 )
(2 .8
– 2 .8
) (-
4 .9
- -4
.6 )
(5 .5
– 5 .5
) (-
8 .7
- -8
.5 )
(4 .0
– 4 .0
) (-
2 .8
- -2
.7 )
(1 .4
– 1 .4
) (-
6 .2
- -6
.0 )
4 2 %
2 .2
1 0 .6
% 0 .8
1 3 .2
% 2 .9
-2 .4
% 6 .6
9 .9
% 4 .5
8 .4
% 1 .5
1 .4
%
(2 .2
– 2 .2
) (1
0 .4
– 1 0 .7
) (0
.8 – 0 .8
) (1
3 .1
– 1 3 .3
) (2
.9 – 2 .9
) (-
2 .5
- -2
.2 )
(6 .6
– 6 .6
) (9
.8 – 1 0 .0
) (4
.5 – 4 .5
) (8
.4 – 8 .5
) (1
.5 – 1 .5
) (1
.3 – 1 .5
)
S u b s ti tu
te d
v a lu
e s
o f a ll
3 v a ri a b le
s :
c
A ll
fa v o ra
b le
1 .5
-2 4 .6
% 0 .5
-3 0 .7
% 3 .2
8 .8
% 4 .3
-2 7 .8
% 3 .6
-1 3 .2
% 1 .2
-1 6 .8
%
(1 .5
– 1 .5
) (-
2 4 .6
- -2
4 .6
) (0
.5 – 0 .5
) (-
3 0 .7
- -3
0 .7
) (3
.2 – 3 .2
) (8
.8 – 8 .8
) (4
.3 – 4 .3
) (-
2 7 .8
- -2
7 .7
) (3
.6 – 3 .6
) (-
1 3 .2
- -1
3 .2
) (1
.2 – 1 .2
) (-
1 6 .8
- -1
6 .8
)
A ll
u n fa
v o ra
b le
2 .2
1 1 .2
% 1 .2
7 5 .2
% 1 .9
-3 6 .6
% 7 .2
2 0 .0
% 5 .6
3 5 .6
% 1 .3
-1 1 .5
%
(2 .2
– 2 .2
) (1
1 .1
– 1 1 .2
) (1
.2 – 1 .2
) (7
5 .2
– 7 5 .2
) (1
.9 – 1 .9
) (-
3 6 .6
- -3
6 .6
) (7
.2 – 7 .2
) (2
0 .0
– 2 0 .0
) (5
.6 – 5 .6
) (3
5 .6
– 3 5 .6
) (1
.3 – 1 .3
) (-
1 1 .5
- -1
1 .5
)
a A
ll e s ti m
a te
s a re
d e ri v e d
fr o m
s e p a ra
te lo
g is
ti c
re g re
s s io
n m
o d e ls
fo r
b la
c k s
a n d
w h it e s
a n d
e a rl y
a n d
m o d e ra
te s p o n ta
n e o u s
p re
te rm
d e li v e ry
th a t in
c lu
d e d
m a te
rn a l e d u c a ti o n ,
p a y e r fo
r d e li v e ry
h o s p it a li z a ti o n , a n d
c e n s u s
tr a c t p o v e rt
y , p e rc
e n t b la
c k , a n d
G in
i in
d e x
(r e s u lt s
fr o m
th o s e
m o d e ls
a re
p re
s e n te
d in
T a b le
2 ).
F o r c o u n te
rf a c tu
a l e s ti m
a te
s , a ll
s u b je
c ts
w e re
s e t to
th e
s a m
e v a lu
e o f th
e s p e c if ie
d v a ri a b le
(s ),
w it h
s o m
e e x c e p ti o n s
fo r
e d u c a ti o n
to a c c o u n t fo
r m
a te
rn a l a g e
(s e e
M e th
o d s
fo r m
o re
d e ta
il ).
P e rc
e n t c h a n g e
re fl e c ts
p e rc
e n t c h a n g e
in p re
d ic
te d
p ro
b a b il it ie
s a n d
d is
p a ri ti e s
w h e n
u s in
g c o u n te
rf a c tu
a l (s
u b s ti tu
te d ) v e rs
u s
o b s e rv
e d
v a lu
e s
o f th
e v a ri a b le
s .
b P
o v e rt
y w
a s
s e t to
2 %
, w
h ic
h c o rr
e s p o n d s
to th
e 1
p e rc
e n ti le
v a lu
e fo
r b la
c k s
a n d
5 p e rc
e n ti le
fo r
w h it e s
o r 4 2 %
, w
h ic
h c o rr
e s p o n d s
to th
e 9 9 th
p e rc
e n ti le
fo r
w h it e s
a n d
th e
9 5
th fo
r
b la
c k s .
c A
ll fa
v o ra
b le
: s e t e d u c a ti o n
to c o ll e g e
d e g re
e , p a y e r to
p ri v a te
, a n d
p o v e rt
y to
2 %
. A
ll u n fa
v o ra
b le
: s e t e d u c a ti o n
to le
s s
th a n
h ig
h s c h o o l,
p a y e r to
M e d i- C
a l,
a n d
p o v e rt
y to
4 2 %
.
h tt
p s :/ /d
o i. o rg
/1 0 .1
3 7 1 /j o u rn
a l. p o n e .0
1 8 2 8 6 2 .t 0 0 3
Black-white disparity in preterm delivery
PLOS ONE | https://doi.org/10.1371/journal.pone.0182862 August 11, 2017 7 / 12
was 1.4-fold higher. With a few exceptions, the contribution of markers of social disadvantage
to odds of spontaneous preterm delivery among blacks and whites and the black-white dispar-
ity tended to be modest, as evidenced by logistic regression models and a potential impact
analysis.
Social disadvantage is much more prevalent among blacks than whites. In our study popu-
lation, 14% of black but 44% of white mothers had a college degree; 37% of black but 72% of
white mothers had private health insurance; and black mothers lived in census tracts with a
much higher prevalence of poverty. Many studies have investigated the extent to which these
types of variables may explain the higher prevalence of preterm delivery among blacks. Results
have been mixed but in general suggest that the disparity is not easily explained by them [3, 17,
18]. Our results concur, even with the inclusion of varied measures of social disadvantage and
more focused phenotypes. As an example, our potential impact analysis suggests that even if
we set multiple social disadvantage variables to ‘favorable’ values for everyone, the majority of
the variability in the black-white disparity is not explained. In fact, under this scenario, we esti-
mate that the disparity in early preterm delivery would actually increase by 8.8%, whereas the
disparity in moderate preterm delivery would decrease by 16.8%.
Given the stronger disparity for early than moderate preterm delivery and some differences
in results for early and moderate preterm delivery, we recommend that future studies differen-
tiate between these subgroups. A focus on early preterm delivery is particularly important,
given its stronger disparity, associated morbidity, and less frequent study. Although prior
research suggests that associations with some risk factors may be stronger for earlier than later
preterm deliveries and vary for spontaneous versus medically indicated births [18], most prior
studies examine all preterm deliveries together. As noted above, we focused on spontaneous
preterm deliveries because their etiology may be distinct and they comprise most preterm
deliveries in this dataset; future studies of medically indicated preterm delivery are needed. In
addition, most prior studies focus on indicators of socioeconomic level, whereas we also
included measures of segregation and inequality. These latter measures did not however con-
tribute substantially to risk. Some studies have suggested they contribute, but study designs
and settings have varied widely [2, 7, 9, 19–21]. However, each of the measures in our study is
relatively general, and more in-depth study would be informative. Further studies could
include more complex and multi-level measures of segregation and inequality [7, 8, 22] and
consider factors associated with social disadvantage that may have a more direct impact on
health risks such as health care access and quality, stress-associated conditions such as crime,
environmental exposures, and pre- existing maternal medical conditions. Studies of racism
against blacks would also likely be informative. We hope our results will serve as a springboard
for such analyses in the future.
We used results from logistic regression models, which emphasize individual-level esti-
mates, to conduct a potential impact analysis, which emphasizes population-level estimates.
We do not consider observed associations to be directly causal but rather consider the impact
analysis to be a thought experiment to gauge the potential contribution of social disadvantage
to population-level prevalence. Prior studies of preterm delivery have not typically explored
such estimates, but extensive justification exists for doing so, as long as results are interpreted
carefully [23–25]. Prior studies have used various approaches to estimate the extent to which
health outcomes and disparities may be attributable to social factors [26, 27]. We used a substi-
tution estimator approach [10, 11], which has the advantages of being based on individual-
level estimates, allowing incorporation of multiple covariates and interactions, and enabling
manipulation of multiple variables at a time. Given how prevalent social disadvantage is, espe-
cially among blacks, even modest associations have the potential to explain a substantial pro-
portion of risk and disparity.
Black-white disparity in preterm delivery
PLOS ONE | https://doi.org/10.1371/journal.pone.0182862 August 11, 2017 8 / 12
The extent to which the probability of preterm delivery and its black-white disparity
changed under different scenarios of the potential impact analysis varied, and not always in
‘favorable’ directions. Substitution of most of the study variables with a constant value resulted
in modest predicted change in the black-white disparity (<15% for most scenarios), and the
predicted change was usually a decrease in the disparity. Notably, substituting education as
college degree and payer as private (one at a time, or together, while also changing tract-level
poverty to low) resulted in a predicted increase in the disparity for early preterm birth, by 8–9% for each scenario. In addition, some changes were more dramatic; e.g., the probability of
early preterm delivery among whites was predicted to increase 75.2% after substituting ‘unfa-
vorable’ values for multiple variables, but only 11.2% among blacks. This variability in results
stems from a combination of the odds ratios and actual distribution of each variable, and how
different they were between blacks and whites. We chose extremes for illustration (e.g., every-
one living in tracts with <2% poverty, everyone having less than high school education) not
because we think they are feasible (or in some cases desirable) but rather to illustrate the maxi-
mum amount of change that could result, and in what direction, given the strength of the asso-
ciations estimated by the logistic regression models and varied distributions of the predictor
variables among blacks and whites. Results from the impact analysis, and in particular esti-
mates that are based on setting all variables to ‘favorable’ or ‘unfavorable,’ provide perspective
on the proportion of PTB and its black-white disparity that may be attributable to these types
of variables.
Strengths of our study include its population-based design, large sample size which enabled
separate analysis of early preterm deliveries, focus on spontaneous preterm deliveries, and abil-
ity to examine a variety of individual- and census tract-level variables. An important limitation
was the general nature of the studied markers of social disadvantage; however, it is important
to understand contributions of these types of variables, as well as more proximal factors. Sev-
eral assumptions could impact the validity of our results, including those related to identifiabil-
ity; although we do not believe our results to be directly causal, we do believe it is important to
discuss these assumptions [10]. With such a large sample size, violation of the positivity
assumption was not a major concern, but we were careful not to extrapolate beyond levels of
variables that were observed among blacks and whites (the positivity assumption refers to the
assumption of non-zero probability of observations across combined strata of variables of
interest). Temporality is straightforward, in that social disadvantage likely existed before preg-
nancy began. Residual confounding by social disadvantage certainly may still exist, given the
complexity and challenge of measuring it (and thus the assumption of exchangeability may be
violated) [28]. We were interested in the overall association with social disadvantage; accord-
ingly, we did not adjust for maternal demographic or health-related characteristics, under the
assumption that social disadvantage may have preceded them. This assumption may not be
completely valid; for example, although social disadvantage may affect a mother’s age at first
birth or parity decisions, her age and parity also affect her level of social (dis)advantage and
where she chooses to live. As a case in point, we did not adjust for maternal age at delivery
because we considered it to be a potential mediator of the association of interest; however, we
also ran models that included age, since it could also be conceptualized as a potential con-
founder. The ORs for education became modestly larger after adjustment for age (by 0.1–0.2
units for most of the ORs), and the ORs for the other variables in the models changed even less
(<0.01 units). This indicates that leaving age out of the models did not substantially influence
the overall message of our results. We do, however,encourage further studies that explicitly
focus on the potentially complex inter-relationships of social disadvantage and the sociodemo-
graphics of childbearing, and their potential joint impacts on disparities. Our ability to assess
the stability assumption (i.e., an individual’s exposure-outcome combination is not affect by
Black-white disparity in preterm delivery
PLOS ONE | https://doi.org/10.1371/journal.pone.0182862 August 11, 2017 9 / 12
that of others) is limited. Another limitation is that the generalizability of our results to women
with missing data, women having twins or higher order births, and Hispanic women (who
comprise almost half of all California births and warrant independent study) is uncertain. Cali-
fornia births represent 13% of all U.S. births, but generalizability beyond California births, for
example to populations where blacks comprise a larger percentage of all births, is uncertain.
We did not incorporate paternal-related variables such as race-ethnicity and education
because they were much more likely to be missing for blacks than whites (6% of whites and
22% of blacks were missing father’s education, and 4% of whites and 17% of blacks were miss-
ing paternal race-ethnicity).
In summary, this study found that the black-white disparity in spontaneous preterm deliv-
ery was much more marked for early than moderate preterm deliveries, suggesting that early
preterm delivery is a particularly important target for future research on understanding the
disparity. We also found that while several of the studied markers of social disadvantage did
contribute to the odds of preterm delivery, they tended to have modest potential impact on the
disparity, suggesting that future studies should examine more specific factors. Health dispari-
ties reflect group differences in health outcomes that are systematic and driven by factors that
are potentially remediable [29]; the challenge is to identify these factors, which we expect may
improve not just the black-white disparity but also the health of all infants.
Author Contributions
Conceptualization: Suzan L. Carmichael, Peiyi Kan, Amy M. Padula, David H. Rehkopf, John
W. Oehlert, Jonathan A. Mayo, Ann M. Weber, Paul H. Wise, Gary M. Shaw, David K.
Stevenson.
Data curation: Peiyi Kan, John W. Oehlert, Jonathan A. Mayo.
Formal analysis: Peiyi Kan, John W. Oehlert, Jonathan A. Mayo.
Funding acquisition: Paul H. Wise, Gary M. Shaw, David K. Stevenson.
Investigation: Suzan L. Carmichael, Peiyi Kan, Amy M. Padula, David H. Rehkopf, John W.
Oehlert, Jonathan A. Mayo, Ann M. Weber, Paul H. Wise, Gary M. Shaw, David K.
Stevenson.
Methodology: Suzan L. Carmichael, Peiyi Kan, Amy M. Padula, David H. Rehkopf, John W.
Oehlert, Jonathan A. Mayo, Ann M. Weber, Paul H. Wise, Gary M. Shaw, David K.
Stevenson.
Project administration: Suzan L. Carmichael.
Resources: Paul H. Wise, Gary M. Shaw, David K. Stevenson.
Software: Peiyi Kan, John W. Oehlert, Jonathan A. Mayo.
Supervision: Suzan L. Carmichael.
Validation: Peiyi Kan, John W. Oehlert, Jonathan A. Mayo.
Visualization: Suzan L. Carmichael, Peiyi Kan, Amy M. Padula, David H. Rehkopf, John W.
Oehlert, Jonathan A. Mayo, Ann M. Weber, Paul H. Wise, Gary M. Shaw, David K.
Stevenson.
Writing – original draft: Suzan L. Carmichael, Peiyi Kan, Amy M. Padula, David H. Rehkopf,
John W. Oehlert, Jonathan A. Mayo, Ann M. Weber, Paul H. Wise, Gary M. Shaw, David
K. Stevenson.
Black-white disparity in preterm delivery
PLOS ONE | https://doi.org/10.1371/journal.pone.0182862 August 11, 2017 10 / 12
Writing – review & editing: Suzan L. Carmichael, Peiyi Kan, Amy M. Padula, David H.
Rehkopf, John W. Oehlert, Jonathan A. Mayo, Ann M. Weber, Paul H. Wise, Gary M.
Shaw, David K. Stevenson.
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