ARTICLE
Perinatal Origins of First-Grade Academic Failure:
Role of Prematurity and Maternal Factors
AUTHORS: Bryan L. Williams, PhD,a,b Anne Lang Dunlop,
MD, MPH,a,b Michael Kramer, PhD,c Bridget V. Dever, PhD,d
Carol Hogue, PhD, MPH,c and Lucky Jain, MD, MBAb,e
aNell
Hodgson Woodruff School of Nursing, bSchool of Medicine,
of Epidemiology, Rollins School of Public Health, and
eEmory Children’s Center, Emory University, Atlanta, Georgia; and
dCollege of Education, Georgia State University, Atlanta, Georgia
cDepartment
KEY WORDS
prematurity, educational achievement, late prematurity, early
cognitive development
ABBREVIATIONS
AIC—Akaike information criterion
aOR—adjusted odds ratio
CI—confidence interval
CRCT—Criterion-Referenced Competency Test
ELA—English/language arts
EPT—extremely preterm (20–27 weeks)
LPT—late preterm (34–36 weeks)
MPT—moderately preterm (28–33 weeks)
SGA—small for gestational age
www.pediatrics.org/cgi/doi/10.1542/peds.2012-1408
doi:10.1542/peds.2012-1408
Accepted for publication Dec 5, 2012
Address correspondence to Bryan L. Williams, PhD, Nell Hodgson
Woodruff School of Nursing, Rollins School of Public Health,
Emory University, 1520 Clifton Rd NE, Atlanta, GA 30322. E-mail:
[email protected]
PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275).
Copyright © 2013 by the American Academy of Pediatrics
FINANCIAL DISCLOSURE: The authors have indicated they have
no financial relationships relevant to this article to disclose.
FUNDING: No external funding.
WHAT’S KNOWN ON THIS SUBJECT: Extreme prematurity is a wellestablished cause of cognitive and motor impairment. There is
some evidence that late prematurity and modifiable maternal
attributes may negatively influence scholastic achievement,
including standardized test performance.
WHAT THIS STUDY ADDS: We found that preterm birth
significantly increases risk of first-grade failure rate even when
the birth is just a few weeks before term gestation. Low maternal
education status compounds the effect of prematurity.
abstract
OBJECTIVE: We examined the relationships among gestational age at
birth, maternal characteristics, and standardized test performance in
Georgia first-grade students.
METHODS: Live births to Georgia-resident mothers aged 11 to 53 years
from 1998 through 2003 were deterministically linked with
standardized test results for first-grade attendees of Georgia public
schools from 2005 through 2009. Logistic models were used to
estimate the odds of failure of the 3 components of the first-grade
Criterion-Referenced Competency Test (CRCT).
RESULTS: The strongest risk factor for failure of each of the 3 components of the first-grade CRCT was level of maternal education. Child
race/ethnicity and maternal age at birth were also associated with
first-grade CRCT failure irrespective of the severity of preterm birth,
but these factors were more important among children born
moderately preterm than for those born on the margins of the
prematurity distribution. Adjusting for maternal and child
characteristics, there was an increased odds of failure of each
component of the CRCT for children born late preterm versus
term, including for math (adjusted odds ratio [aOR]: 1.17, 95%
confidence interval [CI]: 1.13–1.22), reading (aOR: 1.13, 95% CI:
1.08–1.18), and English/language arts, for which there was an
important interaction with being born small for gestational age
(aOR: 1.17, 95% CI: 1.07–1.29).
CONCLUSIONS: Preterm birth and low maternal education increase
children’s risk of failure of first-grade standardized tests. Promoting
women’s academic achievement and reduce rates of preterm birth
may be important to achieving gains in elementary school
performance. Pediatrics 2013;131:693–700
PEDIATRICS Volume 131, Number 4, April 2013
693
A decade after No Child Left Behind
education law was enacted in an attempt to improve schools, particularly
for the nation’s poor and minority
children, questions remain about the
inability of many schools to make adequate yearly progress. In 2010, ∼38%
of US public schools did not make adequate yearly progress; in Washington,
DC, the number not making adequate
yearly progress was 91%.1 Consequently, the stakes of standardized
testing are high for students, schools,
and parents alike. Parents often seek
guidance from their pediatrician concerning school readiness and developmental needs of their child.2 However,
there is a lack of research to support
pediatric practice in this area.
A child’s ability to succeed academically is influenced by innumerable social, economic, and biological factors.
Abundant evidence shows that preterm
birth, particularly ,33 weeks’ gestation, can adversely affect a child’s
neurocognitive development.3–6 Impaired neurologic development is associated with later poor academic
outcomes including grade retention,
poor test performance, and special
education referral.7,8 The impact of
preterm birth on cognitive development may be further exaggerated by
parental characteristics where there is
evidence of synergism among maternal socioeconomic status, preterm
birth, and academic performance.9 To
date, limited research has focused on
the academic difficulties of children
across the prematurity spectrum. Instead, most existing studies focus on
clinical measures of neurologic deficits among relatively small samples
of children born at extremely (EPT)
to moderately (MPT) preterm gestational ages. Although a great deal is
known about the acute neurologic
outcomes associated with preterm
birth before 33 weeks’ gestation, much
less is known about the subclinical
694
WILLIAMS et al
developmental and academic outcomes
associated with late preterm birth (LPT;
birth between 34 and 36-6/7 weeks).
been ignored. Our study sheds more
light on this area.
The developmental risks associated
with late preterm birth were once
thought to be minimal. However, studies
have demonstrated that even infants
who are at the margins of prematurity
suffer disproportionate rates of clinical
neurocognitive problems.3,10–12 These
LPT infants may be susceptible to earlyand long-term academic failure.11,13–15
Yet few, if any, studies have examined
the relative impact of LPT birth on
standardized test performance, the
most common measure of academic
performance and a principal determinant of grade retention in public
schools. In 2012, Lipkind et al examined
the relationship between prematurity
and standardized test performance in
New York City.16 They found a linear
association between gestational age
and test scores from 32 to 39 weeks’
gestation.16,17 In our study, we hypothesized that prematurity would significantly increase the risk of first-grade
test failure among our sample when
controlling for maternal (eg, age and
educational attainment) and child
characteristics (eg, gender and birth
weight for gestational age). We expected that maternal and child socioeconomic characteristics would moderate
the influence of prematurity on test
failure (Fig 1). Although socioeconomic
conditions are frequently blamed for
the “achievement gap” in educational
testing,17,18 the role of prematurity in
educational achievement has largely
METHODS
Data Sources
We constructed a retrospective cohort
by deterministically linking the birth
records of all singleton live births to
Georgia-resident mothers aged 11 to 53
years from 1998 to 2003 to the Georgia
State Department of Education standardized testing data for first-grade
attendees of Georgia public schools
from 2004 through 2009. Figure 2 delineates the construction of the cohort.
From the birth record, we ascertained
the gestational age, race, and birth
weight of the infants, as well as specific
maternal characteristic including maternal race/ethnicity, age at delivery,
and years of education. From the educational record, we ascertained the
child’s performance on all 3 first-grade
test components (mathematics, reading, English/language arts [ELA]) of the
Criterion-Referenced Competency Test
(CRCT). The CRCT is a set of standardized tests administered to students in
kindergarten through grade 8 at public
schools in Georgia. It is designed to
assess students’ mastery of the content outlined in the Georgia Performance Standards. CRCT performance
is a key indicator of student success.
Students in grades 3, 5, and 8 are required to pass the CRCT to be promoted to the next grade. A detailed
description of the CRCT properties (eg,
reliability) can be found on the web site
FIGURE 1
Hypothesized relationship between research variables.
ARTICLE
with female children as the referent
group. Child race was included as
non-Hispanic white (referent category), non-Hispanic African American,
and Hispanic of any race. The bivariate distributions of covariates by
gestational age categories were
assessed by using frequencies and x2
statistics.
Logistic Models
FIGURE 2
Flow diagram showing construction of retrospective birth cohort. The birth data were provided through
the Office of Health Indicators for Planning of the Georgia Department of Public Health, and the education
data were accessed through the Georgia Professional Standards Association. This study was reviewed
and approved by the Emory University Institutional Review Board. NH, non-Hispanic.
of the Georgia State Department of Education19 and in the extant literature.19–24
Once cleaned, the databases were
combined into one dataset using deterministic record linkages based on
unique ID’s created from the child’s
name and date of birth and mother’s
last name. Records that could not be
matched were compared with matched
records to assess systematic differences in maternal and child characteristics and were excluded from
subsequent analysis.
We included births based on the following criteria ascertained from the
birth certificate: (1) live-born singleton
infants with recorded birth weight
between 400 to 5000 g and gestational
age between 24 to 43 weeks; (2) absence of congenital anomalies and/or
chromosomal defects; (3) child race
categorized as non-Hispanic white,
non-Hispanic African American, or Hispanic of any race; and (4) births that
could be linked to student education
records.
Data Analysis
Three binary outcomes were considered: children were categorized as
failing versus passing on the math,
PEDIATRICS Volume 131, Number 4, April 2013
reading, and ELA components of the
first-grade CRCT. The primary exposure
of interest was the child’s gestational
age at birth categorized as EPT (20–27
weeks), MPT (28–33 weeks), LPT (34–36
weeks), term (37–41 weeks), and
postterm (42–43 weeks). A secondary
exposure of interest was a measure of
fetal growth restriction as indicated by
an infant being less than the 10th
percentile of weight for gestational age
using national growth curves25 (small
for gestational age [SGA]) versus
greater than or equal to the 10th percentile of weight for gestational age
(appropriate or large for gestational
age).
Several covariates were considered in
modeling as potential confounders of
the relationship between gestational
age and school performance. Maternal
age at birth was categorized as 10 to 14,
15 to 17, 18 to 19, 20 to 24, 25 to 29
(reference category), 30 to 34, 35 to 39,
and $40 years. Maternal highest
attained education at time of birth
was categorized as no high school diploma, high school diploma (or general
equivalency diploma), some college,
and $4-year college degree (referent
category). Child gender was included
Logistic regression was chosen as the
primary analysis because of the binary
outcome data (ie, “pass” vs “fail” on
each first-grade CRCT subject test).
Initial crude models regressed each
outcome variable (failure of math,
reading, and ELA components of the
CRCT) separately on each exposure and
covariate. Subsequent stepwise regression was conducted assessing change
in exposure-outcome association as
indication of potential confounding. All
possible 2-way interactions between
exposures and covariates were assessed, including possible 2-way interactions between gestational age
and growth restriction. Model fit was
assessed by using the change in 22log-likelihood and Akaike information
criterion [AIC] statistic. Because all
educational levels were not theoretically attainable for all maternal ages
(eg, teen moms are unlikely to have
completed college), models were repeated with restriction to mothers
$18 years and found to provide consistent results. All analyses were
conducted by using SAS 9.2 (Carey,
NC).
RESULTS
There were 628 115 eligible births from
1998 through 2003; this number represents the total number of singleton
live births to Georgia-resident mothers
who otherwise met the inclusion criteria. Of this cohort, 314 328 or 53%
were successfully linked to their firstgrade standardized test scores. There
695
TABLE 1 Description of Retrospective Birth Cohort
Characteristic
Total Births
EPT 20–27 wk
MPT 28–33 wk
LPT 34–36 wk
Term 37–40 wk
Postterm 41+ wk
Total
Maternal age at birth
10–14 y
15–17 y
18–19 y
20–24 y
25–29 y
30–34 y
35–39 y
40+ years
Maternal race/ethnicity
Non-Hispanic white
Non-Hispanic black
Hispanic
Maternal education at birth
No high school
High school degree or equivalent
1–3 y postsecondary education
4+ years postsecondary education
Child race/ethnicity
Non-Hispanic White
Non-Hispanic Black
Hispanic
Child gender
Male
Female
Child’s year of birth
1998
1999
2000
2001
2002
Child’s first-grade CRCT failure
Math
Reading
ELA
314 328
0.35
1.81
7.99
85.99
3.88
1157
16 658
30 379
91 089
83 496
60 133
26 726
4690
0.78
0.48
0.43
0.32
0.31
0.34
0.39
0.58
4.41
2.45
2.16
1.78
1.52
1.67
2.01
2.84
12.36
9.71
8.65
8.1
7.25
7.44
8.7
9.7
79.08
83.08
84.43
85.62
87.03
87.09
85.74
83.9
3.37
4.28
4.32
4.19
3.89
3.46
3.16
2.99
168 588
114 605
30 052
0.18
0.65
0.19
1.3
2.71
1.21
7.12
9.71
6.12
87.21
83.46
88.81
4.19
3.46
3.68
79 631
107 986
62 569
59 443
0.34
0.4
0.39
0.25
2.1
1.9
1.82
1.26
8.81
8.18
7.9
6.65
84.6
85.6
86.3
88.16
4.16
3.93
3.56
3.68
164 044
116 255
34 029
0.17
0.65
0.21
1.29
2.69
1.27
7.11
9.7
6.28
87.26
83.46
88.49
4.18
3.49
3.76
159 179
155 149
0.34
0.36
1.87
1.75
8.33
7.62
85.66
86.32
3.81
3.95
68 397
69 724
72 811
64 356
39 017
0.33
0.37
0.41
0.33
0.28
1.8
1.81
1.86
1.8
1.75
7.57
7.95
7.69
8.5
8.38
85.64
85.83
85.92
86.07
86.84
4.64
4.04
4.12
3.3
2.74
33.06
24.34
33.88
20.14
14.37
23.09
15.6
11.35
19.34
12.46
9.37
16.02
13.1
10.13
17
are 4 possible sources for loss to
follow-up in this cohort. First, outmigration from Georgia between
a child’s birth and entrance into first
grade is expected. According to interstate migration data from the US
Census and Internal Revenue Service,
there is an average out-migration rate
from Georgia of 2.5% to 3% per year.26,27
Therefore, an estimated 12.5% to 15%
of the original cohort may have moved
out of state before entering the first
grade, 23% per year.26,27 A second
source of loss is our reliance on standardized tests that are only mandated
for students enrolled in Georgia public
schools (traditional and charter). Based
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WILLIAMS et al
Percentage of Births in Gestational Age Categories
on educational and US Census data
sources, 8% to 9% of Georgia children
attend private schools, and an additional 2% to 5% are homeschooled or
otherwise not enrolled in public
schools.28–30 Therefore, 10% to 14% of
the original cohort may not be represented in the public school CRCT database. On the basis of educational and
US Census data sources, 8% to 9% of
Georgia children attend private schools,
and an additional 2% to 5% are homeschooled or otherwise not enrolled in
public schools.28–30 Third, a small proportion of children born in Georgia may
have died before entering the first
grade. Finally, the deterministic linkage
process used in relating individual birth
records to first-grade educational
records could fail to create true links
because of changes or errors in the
recording of identifying variables used
in linkage.
Loss to follow-up in this setting is particularly concerning if it is differential
with respect to key risk factors or
outcomes. Although we cannot assess
the educational outcomes for children
who were not linked, we can compare
the distribution of birth characteristics
for children who were linked to the total
birth cohort population. The linkage
rate is relatively stable within strata of
key variables, with some variation.
ARTICLE
TABLE 2 Odds of Failure of the Math Component of the First-Grade CRCT
Covariate
Gestational age category
20–28 wk
29–33 wk
34–36 wk
37–41 wk
42+ wk
Maternal age category
10–14 y
15–17 y
18–19 y
20–24 y
25–29 y
30–34 y
35–39 y
40+ y
Maternal education category
Less than high school
High school or GED
1-3 y post-secondary
4+ yrs post-secondary
Child race/ethnicity
Non-Hispanic white
Non-Hispanic black
Hispanic any race
Child gender
Female
Male
SGA vs AGA/LGA
Crude Estimate cOR and
95% CI
Adjusted Estimatea aOR
and 95% CI
cOR
95% CI
aOR
95% CI
3.45
1.79
1.30
1.00
1.05
3.04–3.92
1.67–1.91
1.25–1.35
Reference
1.00–1.11
2.96
1.49
1.17
1.00
1.07
2.59–3.38
1.39–1.59
1.13–1.22
Reference
1.01–1.13
2.57
1.96
1.73
1.52
1.00
0.75
0.78
0.98
2.24–2.95
1.88–2.05
1.66–1.79
1.48–1.56
Reference
0.72–0.77
0.74–0.81
0.89–1.08
0.83
0.77
0.91
1.04
1.00
0.94
0.96
1.12
0.72–0.95
0.73–0.81
0.88–0.95
1.00–1.06
Reference
0.91–0.98
0.91–1.01
1.02–1.24
7.97
4.91
2.77
1.00
7.59–8.36
4.68–5.16
2.53–2.92
Reference
6.35
3.84
2.22
1.00
6.03–6.70
3.65–4.03
2.10–2.34
Reference
1.00
3.14
2.82
Reference
3.07–3.22
2.72–2.91
1.00
2.59
1.74
Reference
2.52–2.65
1.67–1.80
1.00
1.27
Reference
1.25–1.30
1.00
1.31
1.34
Reference
1.28–1.33
1.30–1.38
AGA, appropriate for gestational age; cOR, crude odds ratio; LGA, large for gestational age.
a Logistic model containing gestational age, maternal age, maternal education, child race/ethnicity, child gender, and SGA
versus AGA/LGA as covariates without interaction terms (AIC = 217 280, pseudo-R2 = 11.84%).
Specifically, linkage was highest among
children whose mothers had a high
school degree or some college education (51%–60%) but was lower among
children whose mothers had $4 years
of college (44%). The linkage rate was
53% for children born term and 51%
for those born preterm. The linkage
rate was highest for children whose
mothers were non-Hispanic African
American (57%) but lower for nonHispanic white (52%) and Hispanic
(48%) mothers. Importantly, linkage
rates were consistent across the birth
years of interest.
The characteristics and distribution of
subjects by maternal and child characteristics are described in Table 1. As
shown, ,1% of the 314 328 matched
births were EPT, almost 2% were MPT,
PEDIATRICS Volume 131, Number 4, April 2013
∼8% were late preterm, the vast majority (∼86%) was term, and ∼4% were
postterm.
The results of our estimation of the
probability of failure of each component
of the first-grade CRCT are given in 3
separate models. For all models, maternal age at birth, maternal highest
attained education at time of birth, child
gender, and child race/ethnicity were
included as covariates.
Tables 2, 3, and 4 report results from
crude and adjusted logistic regression
models for the probability of a child
failing math (Table 2), reading (Table 3),
and ELA (Table 4). The results are
similar for each outcome, and only
results for math are detailed here.
The first column presents the independent crude associations between
gestational age and each covariate
with probability of failing. The second
column presents results from a multivariable adjusted model that includes
all maternal and child covariates except SGA. Finally, the third column
presents a fully adjusted model with all
covariates and SGA. For math and
reading (but not ELA), there were no
significant interactions between SGA
and preterm category. Overall there is
a clear and significant dose-response
association between gestational age
category and the probability of failing
the CRCT in first grade. Specifically risk
for failure is significantly increased in
each category including LPT and postterm compared with term (adjusted
odds ratio [aOR] for LPT compared with
term 1.17, 95% confidence interval [CI]
1.13–1.22). There is some attenuation
of the association between gestational
age category and outcome comparing
the adjusted to the crude model, suggesting that maternal and child characteristics confound this association
to some degree, but a robust independent association persists. Importantly, model fit as indicated by either
the AIC or the pseudo-R2 improves in
the fully adjusted (AIC = 217 280,
pseudo-R2 = 11.8%) compared with
the crude model (AIC = 236 812,
pseudo-R2 = 0.44%).
In both the crude and adjusted logistic
models, maternal age and education,
child race/ethnicity, gender, and SGA
status were significantly associated
with failure of each component of the
CRCT.
The relative influence of the model
covariates on the probability of test
failure varied considerably across the
gestational age categories (Fig 3). The
inclusion of the covariates in the logistic model reduced the probability
of failure of each component of the
CRCT among the moderately preterm
group substantially more so than it
did for the EPT and LPT groups. For
697
TABLE 3 Odds of Failure of the Reading Component of the First-Grade CRCT
Covariate
Gestational age category
20-28 wk
29-33 wk
34-36 wk
37-41 wks
42+ wk
Maternal age category
10–14 y
15–17 y
18–19 y
20–24 y
25–29 y
30–34 y
35–39 y
40+ y
Maternal education category
Less than high school
High school or GED
1-3 y postsecondary
4+ yrs postsecondary
Child race/ethnicity
Non-Hispanic white
Non-Hispanic African American
Hispanic any race
Child gender
Female
Male
SGA vs AGA/LGA
Crude Estimate
Adjusted Estimatea
cOR
95% CI
aOR
95% CI
3.12
1.64
1.24
1.00
1.08
2.71–3.59
1.52–1.77
1.19–1.29
Reference
1.02–1.15
2.82
1.40
1.13
1.00
1.10
2.44–3.27
1.29–1.51
1.08–1.18
Reference
1.03–1.17
2.50
1.93
1.69
1.51
1.00
0.73
0.75
0.97
2.14–2.91
1.84–2.03
1.62–1.76
1.46–1.56
Reference
0.70–0.76
0.71–0.81
0.87–1.08
0.85
0.78
0.91
1.04
1.00
0.92
0.95
1.13
0.78–0.99
0.74–0.82
0.87–0.95
1.00–1.07
Reference
0.88–0.96
0.90–1.01
1.01–1.26
9.11
5.29
2.87
1.00
8.60–9.66
4.99–5.61
2.70–3.07
Reference
7.11
4.19
2.35
1.00
6.68–7.58
3.94–4.45
2.20–2.51
Reference
1.00
2.75
3.23
Reference
2.68–2.83
3.11–3.35
1.00
2.25
1.97
Reference
2.19–2.32
1.89–2.05
1.00
1.77
Reference
1.73–1.82
1.00
1.84
1.33
Reference
1.79–1.88
1.29–1.38
AGA, appropriate for gestational age; cOR, crude odds ratio; GED, general equivalency diploma; LGA, large for gestational age.
a Logistic model containing gestational age, maternal age, maternal education, child race/ethnicity, child gender, and SGA
versus AGA/LGA as covariates without interaction terms (AIC = 178 704, pseudo-R2 = 11.49%).
example, the probability of failure of
the ELA component was reduced by
9.8% and 8.5% among the EPT and LPT
groups but by 14% among the moderately preterm group. The average
reduction in the aOR was 13%, 16%,
and 10% among the EPT, MPT, and LPT
groups, respectively.
DISCUSSION
We found that being born “preterm”
versus “term” increases a first-grade
child’s risk of failure of each of the 3
components of the CRCT when controlling for maternal age at birth,
maternal education, maternal race/
ethnicity, child race/ethnicity, child’s
gender, and year of birth. Increasing
severity of preterm birth was associated with increasing likelihood of
failure of all components of the CRCT
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WILLIAMS et al
in a significant dose-response pattern. Of note, even children born LPT
(34–36 weeks’ gestation) experienced
significantly increased risk of failure
for each of the 3 components of the
CRCT.
Of the factors considered in our model,
the strongest risk factor for failure of
each of the 3 components of the CRCT in
first grade was maternal education
with a consistent inverse exposureresponse relationship between level
of maternal education and risk of failure of all 3 components of the CRCT.
Importantly, we also found that child
race/ethnicity and maternal age at
birth are associated with first-grade
test performance irrespective of the
severity of preterm birth. However,
maternal (eg, age, education) and child
social characteristics seem to play
a greater role in test failure among
children born moderately preterm than
for those born on the margins of the
prematurity distribution (EPT and LPT).
There may be socioeconomic and sociocultural reasons for this finding.
Extreme premature infants who survive
to the first grade may have a more
nurturing environment as a group than
less-premature infants who are more
likely to survive irrespective of their
care. On the other side of prematurity,
LPT infants have more iatrogenic prematurity owing to elective cesarean
delivery, which may be more of an
upper-class phenomenon. Thus, the
infant gestational age groups do not
start out on a level playing field with
each other. The birth certificate variables are not nuanced enough to
pick this up, particularly lacking information on income and the interaction of socioeconomic status with
course of labor and delivery. Given
our lack of detailed information about
the trajectory of these children past
their birth, we can only speculate
about the potential reasons for these
observations.
This study also provides evidence that
preterm birth has long-term consequences even among those LPT infants
who were formerly thought to be at
negligible risk. This study adds to a
growing body of literature suggesting
that even LPT infants suffer disproportionate rates of clinical neurocognitive problems and are susceptible
to early and long-term academic
failure.3,10–15 Additional research is
needed to identify the specific pathways through which late prematurity
influences school performance. For
example, to what extent do clinical
interventions (eg, antenatal corticosteroid use) improve longer-term developmental outcomes by reducing the
incidence of neonatal morbidity (eg,
respiratory distress)? There is some
precedent in the literature in this area.
ARTICLE
TABLE 4 Odds of Failure of the ELA Component of the First-Grade CRCT
Covariate
Gestational age category
20–28 wk
29–33 wk
34–36 wk
37–41 wk
42+ wk
Maternal age category
10–14 y
15–17 y
18–19 y
20–24 y
25–29 y
30–34 y
35–39 y
40+ yr
Maternal education
Less than high school
High school or GED
1–3 y postsecondary
4+ yrs postsecondary
Child race/ethnicity
Non-Hispanic white
Non-Hispanic black
Hispanic any race
Child gender
Female
Male
Crude estimate
Adjusted estimate
SGA = 0a
Adjusted estimate
SGA = 1a
cOR
95% CI
cOR
95% CI
cOR
95% CI
2.70
1.59
1.23
1.0
1.07
2.38–3.07
1.49–1.69
1.22–1.30
Reference
1.02–1.12
2.45
1.35
1.15
1.00
1.09
2.14–2.80
1.26–1.45
1.11–1.20
Reference
1.03–1.15
5.43
1.61
1.17
1.00
0.98
2.73–10.8
1.34–1.94
1.07–1.29
Reference
0.85–1.14
2.38
1.95
1.74
1.51
1.00
0.73
0.74
0.86
2.09–2.71
1.87–2.03
1.68–1.79
1.47–1.55
ref
0.71–0.76
0.79–0.78
0.79–0.94
0.84
0.79
0.95
1.04
1.00
0.92
0.93
1.00
0.73–0.96
0.76–0.83
0.91–0.98
1.02–1.07
ref
0.89–0.95
0.89–0.97
0.92–1.10
8.41
4.81
2.70
1.00
8.06–8.77
4.61–5.02
2.57–2.83
Reference
6.76
3.95
2.8
1.0
6.45–7.09
3.78–4.13
2.17–2.39
Reference
1.00
2.35
2.99
Reference
2.30–2.40
2.90–3.08
1.00
1.93
1.83
Reference
1.89–1.98
1.77–1.89
1.00
1.71
Reference
1.68–1.74
1.00
1.79
Reference
1.75–1.82
AGA, appropriate for gestational age; cOR, crude odds ratio; GED, general equivalency diploma; LGA, large for gestational age.
a Logistic model containing gestational age, maternal age, maternal education, child race/ethnicity, child gender, and an
interaction term between gestational age and SGA versus LGA/AGA status (AIC = 251 324, pseudo = R2 = 12.97%).
FIGURE 3
Change in aOR of failure of CRCT components according to gestational age category: relative reduction in
the odds of test failure after the covariates (eg, maternal age, maternal education, child race/ethnicity,
and child gender) were added in to the logistic model.
PEDIATRICS Volume 131, Number 4, April 2013
Patrianakos-Hoobler et al found that
preterm survivors who experienced
respiratory distress also experienced
higher prevalence of neurologic impairment even 2 years after birth, with
23% of their sample labeled as
“delayed” at 2 years of age.8 They found
that developmental delays at age 2
years were highly predictive of the lack
of school readiness at first grade,
suggesting that neonatal and postneonatal morbidity may directly affect
school outcomes such as test performance. Consequently, it is reasonable
to suggest that reductions is neonatal
morbidity might be associated with
even longer-term developmental outcomes.
Our findings also appear to be similar to
that of Lipkind et al.16 Their study examined third-grade math and English
test scores in a single city, but the
magnitude of the relationship between
test performance and prematurity was
similar to that observed in our study.
Premature subjects in their study
scored from 4% to 10% lower on the
standardized than did their full-term
counterparts.16 Unlike the Lipkind
study, we examined first-grade test
performance. Test failure at this grade
level is inherently low. We expect that
we will be able to explain even more
variation in test failure when we are
able to analyze our second- and thirdgrade data. Approximately 19% of
third-grade students in Georgia failed
the math CRCT exam in 2011 as opposed to ∼13% of first graders.20
In the sixth grade, failure rates are
∼24%. Given our findings, it is reasonable to believe that prematurity could have an even longer and
more substantial impact on school
achievement as a child progresses
through the grade levels. This is important because stakes of state-level
assessments (eg, grade retention)
increase as a child moves up each
grade level, as do failure rates.
699
REFERENCES
1. Usher A. Update With 2009–10 Data and
Five-Year Trends: How Many Schools Have
Not Made Adequate Yearly Progress?
Washington, DC: Center on Education Policy; 2011
2. Combs-Orme T, Holden Nixon B, Herrod HG.
Anticipatory guidance and early child development: pediatrician advice, parent
behaviors, and unmet needs as reported
by parents from different backgrounds.
Clin Pediatr (Phila). 2011;50(8):729–737
3. Kiechl-Kohlendorfer U, Ralser E, Pupp
Peglow U, Reiter G, Trawöger R. Adverse
neurodevelopmental outcome in preterm
infants: risk factor profiles for different
gestational ages. Acta Paediatr. 2009;98(5):
792–796
4. Grunau RE, Whitfield MF, Petrie-Thomas J,
et al. Neonatal pain, parenting stress and
interaction, in relation to cognitive and
motor development at 8 and 18 months in
preterm infants. Pain. 2009;143(1–2):138–
146
5. Pallotto EK, Kilbride HW. Perinatal outcome
and later implications of intrauterine growth
restriction. Clin Obstet Gynecol. 2006;49(2):
257–269
6. Aylward GP. Neurodevelopmental outcomes
of infants born prematurely. J Dev Behav
Pediatr. 2005;26(6):427–440
7. Taylor HG, Klein N, Anselmo MG, Minich N,
Espy KA, Hack M. Learning problems in
kindergarten students with extremely
preterm birth. Arch Pediatr Adolesc Med.
2011;165(9):819–825
8. Patrianakos-Hoobler AI, Msall ME, Huo D,
Marks JD, Plesha-Troyke S, Schreiber MD.
Predicting school readiness from neurodevelopmental assessments at age 2 years
after respiratory distress syndrome in
infants born preterm. Dev Med Child Neurol. 2010;52(4):379–385
9. Gisselmann M, Koupil I, De Stavola BL. The
combined influence of parental education
and preterm birth on school performance.
J Epidemiol Community Health. 2011;65(9):
764–769
10. Luu TM, Ment LR, Schneider KC, Katz KH,
Allan WC, Vohr BR. Lasting effects of preterm birth and neonatal brain hemorrhage
700
WILLIAMS et al
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
at 12 years of age. Pediatrics. 2009;123(3):
1037–1044
McGowan JE, Alderdice FA, Holmes VA,
Johnston L. Early childhood development of
late-preterm infants: a systematic review.
Pediatrics. 2011;127(6):1111–1124
Picone S, Paolillo P. Neonatal outcomes in
a population of late-preterm infants. J
Matern Fetal Neonatal Med. 2010;23(suppl
3):116–120
Chyi LJ, Lee HC, Hintz SR, Gould JB, Sutcliffe
TL. School outcomes of late preterm infants: special needs and challenges for
infants born at 32 to 36 weeks gestation. J
Pediatr. 2008;153(1):25–31
Jain L. School outcome in late preterm
infants: a cause for concern. J Pediatr.
2008;153(1):5–6
MacKay DF, Smith GCS, Dobbie R, Pell JP.
Gestational age at delivery and special
educational need: retrospective cohort
study of 407,503 schoolchildren. PLoS Med.
2010;7(6):e1000289
Lipkind HS, Slopen ME, Pfeiffer MR, McVeigh
KH. School-age outcomes of late preterm
infants in New York City. Am J Obstet
Gynecol. 2012;206(3):222.e1–6
Yaffe D, Coley RJ, Pliskin R; Educational
Testing Service PIC. Addressing Achievement Gaps: Educational Testing in America:
State Assessments, Achievement Gaps, National Policy and Innovations (ETS Policy
Notes, Volume 17, no. 1). Princeton, NJ:
Educational Testing Service; 2009
Ladd HF. Education and poverty: confronting the evidence. J Policy Anal Manage.
2012;31(2):203–227
Georgia Department of Education. Curriculum, instruction, and assessment. 2012.
Available at: www.gadoe.org/ci_testing.aspx?
PageReq=CI_TESTING_CRCT. Accessed January
6, 2012
Georgia Department of Education. 2010–
2011 Report Card. 2012. Available at: www.
doe.k12.ga.us/ReportingFW.aspx?PageReq=
102&StateId=ALL&T=1&FY=2011. Accessed
January 6, 2012
Randall J, Cheong YF, Engelhard G. Using
explanatory item response theory modeling
22.
23.
24.
25.
26.
27.
28.
29.
30.
to investigate context effects of differential
item functioning for students with disabilities. Educ Psychol Meas. 2011;71(1):
129–147
Sanford EE. Examining the Impact of Complementary Assistance Learning on Student
CRCT Scores [doctoral dissertation]. Minneapolis, MN: Walden University; 2010. Available
at http://proquest.umi.com/pqdlink?Ver=1&
Exp=01-13-2018&FMT=7&DID=1999382311&
RQT=309&attempt=1&cfc=1. Retrieved January
14, 2013
Randall J, Engelhard G. Performance of
students with and without disabilities under modified conditions. J Special Educ.
2010;44(2):79–93
Randall J, Engelhard G Jr. Using confirmatory factor analysis and the Rasch model to
assess measurement invariance in a high
stakes reading assessment. Appl Meas
Educ. 2010;23(3):286–306
Oken E, Kleinman KP, Rich-Edwards J, Gillman
MW. A nearly continuous measure of birth
weight for gestational age using a United
States national reference. BMC Pediatr.
2003;3:6
Koerber K. Domestic migration flows for
states from the 2005 ACS. Presented at the
Population Association of America 2007
Annual Meeting, New York, NY
IRS. SOI Tax Stats - State-to-State Migration
Database Files. Available at: www.irs.gov/
uac/SOI-Tax-Stats—Free-Migration-DataDownloads
US Census Bureau. Census 2000 Summary
File 3. 2000. Available at http://www.census.
gov/census2000/sumfile3.html. Accessed
January 14, 2013
US Census Bureau. American Community Survey, 2005 Summary Tables. 2005.
Available at: www.census.gov/acs/www/
data_documentation/data_main/.
Council TGH. Public education (PreK–12).
The New Georgia Encyclopedia. 2009.
Georgia Humanities Council, in partnership
with the University of Georgia Press, the
University System of Georgia/GALILEO, and
the Office of the Governor. Available at:
http://www.georgiaencyclopedia.org. Accessed
September 1, 2012