The Impact of Increased Pre-Pregnant Adiposity on Birth Weight Indices BY Helen Anderson B.S.N., Curtin University, 1990 M.S.N., University of Texas, Houston, 1996 THESIS Submitted as partial fulfillment of the requirements for the degree of Doctor of Philosophy in Nursing Sciences in the Graduate College of the University of Illinois at Chicago 2011 Chicago, Illinois Defense Committee: Pamela HIll, Chair and Advisor Mary Ann Anderson Lauretta Quinn Shannon Zenk Chang Gi Park, University of Illinois at Chicago
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The Impact of Increased Pre-Pregnant Adiposity on Birth Weight Indices
BY
Helen Anderson B.S.N., Curtin University, 1990
M.S.N., University of Texas, Houston, 1996
THESIS
Submitted as partial fulfillment of the requirements for the degree of Doctor of Philosophy in Nursing Sciences
in the Graduate College of the University of Illinois at Chicago 2011
Chicago, Illinois
Defense Committee: Pamela HIll, Chair and Advisor Mary Ann Anderson Lauretta Quinn Shannon Zenk Chang Gi Park, University of Illinois at Chicago
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This thesis is dedicated to my parents, Peggy and Brian, for encouraging me to
explore, strive and achieve my dreams; my husband, Kim, for his ongoing support; my
daughter, Darcy, who makes every day wonderful; and Dr. Pamela Hill, for her patience
and constructive feedback.
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ACKNOWLEDGEMENTS
I would like to thank my thesis committee: Drs. Pamela Hill, Chang Park, Lauretta
Quinn, Mary Ann Anderson and Shannon Zenk, for their expertise and feedback as I
progressed through the research process.
I would also like to thank Elizabeth Stapleton and Janet Stifter at Saint Joseph
Hospital for their support and assistance in the extraction of the data.
HA
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TABLE OF CONTENTS
CHAPTER PAGE
I. INTRODUCTION.............................................................................................. 1 A. Background................................................................................................. 1 B. Statement of the Problem........................................................................... 5 C. Purpose of the Study .................................................................................. 6 D. Operational Definitions ............................................................................... 7 E. Significance of this Study............................................................................ 8 II. CONCEPTUAL FRAMEWORK AND RELATED LITERATURE ...................... 9 A. Conceptual Frameworks Used in Obesity Research .................................. 9 B. Theoretical Framework .............................................................................. 11 1. Ecological Model of Social and Maternal Influences on Fetal Growth 15 2. Strengths and Limitations.................................................................... 17 3. Concepts within the Study Conceptual Framework............................. 17 C. Review of the Literature............................................................................ 18 1. Search Process ................................................................................... 18 D. Outcome Variable: Infant Birth Weight ..................................................... 19 E. Exposure Variables................................................................................... 24 1. Maternal Biological Modifiable Characteristics.................................... 24 a. Body mass index............................................................................ 24 b. Pre-pregnant body mass index and birth weight............................ 24 c. Maternal pre-pregnant obesity and high birth weight..................... 25 d. Gestational weight gain.................................................................. 36 e. Maternal health status.................................................................... 39 2. Maternal Non-modifiable Biological Factors that Affect Fetal Growth and Birth Weight .................................................. 40 a. Maternal age .................................................................................. 40 b. Genetic makeup............................................................................. 41 c. Parity.............................................................................................. 41 3. Maternal Behaviors that Influence Infant Birth Weight ........................ 42 a. Maternal substance use................................................................. 42 4. Social Environment Influences on Birth Weight .................................. 43 a. Socioeconomic status .................................................................... 43 b. Race and ethnicity ......................................................................... 44 c. Education ....................................................................................... 46 d. Medicaid......................................................................................... 46 e. Family and support ........................................................................ 47 F. High Birth Weight...................................................................................... 48 1. Short-Term Complications Associated with High Birth Weight............ 48 2. Long-Term Complications Associated with High Birth Weight ............ 48 a. Childhood risks .............................................................................. 48 b. Childhood obesity and ethnicity ..................................................... 50
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TABLE OF CONTENTS (continued)
CHAPTER PAGE
c. Adult obesity .................................................................................. 50 G. Statistical Methods Used .......................................................................... 51 H. Summary .................................................................................................. 52 III. METHODS ..................................................................................................... 54 A. Source of Data.......................................................................................... 54 B. Sample...................................................................................................... 55 C. Research Design ...................................................................................... 56 D. Measures .................................................................................................. 56 1. Validity of the Data Set........................................................................ 56 E. Variable Definitions................................................................................... 57 1. Exposure Variable – Infant Birth Weight Index.................................... 57 2. Predictor Variables .............................................................................. 59 3. Maternal Variables .............................................................................. 59 a. Non-modifiable biological characteristics....................................... 61 b. Modifiable biological factors........................................................... 61 c. Behavioral factors .......................................................................... 62 d. Social environment factors............................................................. 62 F. Instrumentation ......................................................................................... 65 1. Original Data Collection Methods........................................................ 65 2. Reliability and Validity of Data............................................................. 66 a. Infant anthropometric data ............................................................. 67 b. Maternal anthropometric data ........................................................ 68 c. Gestational weight gain or loss ...................................................... 69 d. Gestational age.............................................................................. 69 G. Ethical Considerations .............................................................................. 70 1. Human Subjects .................................................................................. 70 H. Data .......................................................................................................... 70 1. Data Extraction Procedure .................................................................. 70 2. Assessment of Accuracy of the De-identified Data ............................. 71 3. Data Cleaning...................................................................................... 72 4. Missing Data........................................................................................ 74 I. Statistical Analysis .................................................................................... 75 1. Data Preparation for Regression......................................................... 75 2. Initial Statistical Analysis ..................................................................... 81 J. Research Questions ................................................................................. 81 1. Research Question 1........................................................................... 81 a. Exposure (predictor) variables ....................................................... 81 b. Outcome (criterion) variables......................................................... 82 c. Method of statistical analysis ......................................................... 82 2. Research Question 2........................................................................... 82 a. Exposure (predictor) variable......................................................... 82
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TABLE OF CONTENTS (continued)
CHAPTER PAGE
b. Outcome (criterion) variables......................................................... 82 c. Method of statistical analysis ......................................................... 82 IV. Results ........................................................................................................... 83 A. Characteristics of Subjects ....................................................................... 83 1. Maternal Characteristics...................................................................... 83 2. Infant Characteristics........................................................................... 92 B. Analyses Related To Each Research Question........................................ 97 1. Research Question 1........................................................................... 97 a. Research Question 1a ................................................................... 97 b. Research Question 1b ................................................................... 99 c. Research Question 1c ................................................................. 103 2. Research Question 2......................................................................... 109 C. Summary ................................................................................................ 112 V. Discussion.................................................................................................... 116 A. Modifiable Biological Predictors.............................................................. 118 1. Pre-Pregnant BMI.............................................................................. 118 2. Gestational Weight Gain ................................................................... 122 B. Non-Modifiable Biological Predictors ...................................................... 125 1. Maternal Factors ............................................................................... 125 a. Age............................................................................................... 125 b. Parity............................................................................................ 125 c. Height........................................................................................... 125 2. Infant Factors .................................................................................... 126 a. Gestational age............................................................................ 126 b. Gender ......................................................................................... 126 3. Maternal Behavioral Predictors ......................................................... 127 a. Smoking ....................................................................................... 127 4. Social Environment Predictors .......................................................... 128 a. Marital status................................................................................ 128 b. Support partner ............................................................................ 129 c. Medicaid....................................................................................... 129 C. Strengths and Limitations ....................................................................... 130 1. Data................................................................................................... 130 2. Birth Weight Indices and Statistical Methods .................................... 132 D. Summary ................................................................................................ 133 E. Conclusion .............................................................................................. 134 F. Implications............................................................................................. 135 1. Future Research................................................................................ 138
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TABLE OF CONTENTS (continued)
CHAPTER PAGE
CITED LITERATURE................................................................................... 139 VITA ............................................................................................................. 155
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LIST OF TABLES
TABLE PAGE
I. DESCRIPTIVE DETAILS OF STUDIES REVIEWED...............................22
II. RISK OF HIGH BIRTH WEIGHT BY
PRE-PREGNANT OBESITY: CLASSES I, II & III.....................................27
Linear F (9, 10544) = 172.19, p < .001; Quantile: (9, 10544, p < .001) t * p < .05 ppBMI: Pre-pregnant BMI, GWG: gestational weight gain, Cohabit: Cohabitation, Support: Support Partner
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The linear and quantile regression analyses both indicated that biological,
behavioral and social variables contributed to the explained variations in birth weight z-
scores. The effects of the significant variables were different: the biological predictors
demonstrated a positive impact, while the behavioral predictor smoking had a negative
influence on birth weight z-scores. The quantile regression showed that the biological
variables all had an increasing impact as the birth weight z-scores percentile increased.
Meanwhile, the behavioral variable smoking had a reduced impact as birth weight z-
scores increased. Quantile regression indicated progressive changes in the relationship
of the maternal predictor variables as birth weight z-scores levels increased.
Research Question 1c
Ponderal Index was analyzed using linear regression, followed by simultaneous-
quantile regression. Eight predictor variables were included in both regression models:
pregnant BMI, and gestational weight gain. This study was only able to identify an
explained variance of R2 = .127 for birth weight z-scores.
As the relationship between pre-pregnant BMI and birth weight z-scores may not
be linear, additional analysis was performed using pre-pregnant BMI squared; however,
this did not improve the level of explained variance. It is unclear why both previous
studies (Abrams & Laros, 1986; Frederick et al., 2008) were able to achieve a higher
explained variance from their regression models. The inclusion of the predictors known
to influence fetal growth (preterm birth, diabetes and pre-eclampsia) as used by
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Fredrick et al. (2008) may have also contributed to the higher level of explained
variance.
The ponderal index analysis also showed a positive regression coefficient of b =
.006) for pre-pregnant BMI. This equates to a small increase (.001) in ponderal index
per a one-unit increase in pre-pregnant BMI. Nohr et al. (2008) was the only other
study to examine ponderal index using pre-pregnant BMI. They also noted a reduced
“less pronounced” relationship in ponderal index compared to LGA.
To further examine the possibility of a non-linear relationship between birth
weight indices and pre-pregnant BMI, quantile regression was performed. Quantile
regression analysis of birth weight z-scores demonstrated a progressive increase in the
impact of pre-pregnant BMI, with an increasing regression coefficient from .022 to .056
as birth weight z-scores increased from the 10th to the 95th percentile (Table XXII). This
result suggests that pre-pregnant BMI has a greater impact on high birth weight z-
scores than low birth weight z-scores. There was a twofold increase in the impact of
pre-pregnant BMI on the higher birth weight z-score quantiles compared to the lowest
quantile, suggesting that mechanism changes as birth weight z-scores increase.
In the quantile regression of ponderal index, the pre-pregnant BMI regression
coefficient had a slight increase (1.33-fold) from .006 at the 10th percentile to .008 at the
95th percentile. At the lower (10th-30th) and mid (50th-70th) ponderal indices, the
regression coefficient decreased, followed in both cases by a marked increase in the
impact of pre-pregnant BMI. The quantile regression of ponderal index suggests that
the linear regression may underestimate the impact of pre-pregnant BMI. This may be
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due to the alterations in the mechanism of pre-pregnant BMI as the index changes. No
other studies have examined ponderal index using quantile regression.
To date, no other identified studies have performed quantile regression on pre-
pregnant BMI in this manner. The quantile regression analysis showed a lower impact
of pre-pregnant BMI at the lower percentiles and a higher impact at the higher
percentiles in both birth weight z-scores and ponderal index. This study suggests that
there is a change in the mechanism between pre-pregnant BMI and birth weight indices
as the index percentile increases. Pre-pregnant BMI is contributing more to high birth
weight infants than previously realized. As a modifiable and significant predictor of high
birth weight indices, more attention needs to be paid to pre-pregnant BMI prior to
conception, given the research suggesting that high birth weight contributes to
childhood obesity and associated morbidities.
Gestational Weight Gain
Gestational weight gain is a known contributor to birth weight and it has been the
primary focus in prenatal care to prevent low birth weight infants. However, this
strategy is based on the strong association between underweight and lean women and
low birth weight infants. High gestational weight gain is associated with higher birth
weight (Abrams & Laros, 1986; Cedergren, 2006; Dietz et al., 2009; Frederick et al.,
2008; Kabali & Werler, 2007; Magriples et al., 2009; Nohr et al., 2008). In this study,
gestational weight gain was shown to have a positive impact on the birth weight index.
The logistic regression of LGA showed a 9% increase in the risk of having a LGA infant
from a one-unit increase (kg) in gestational weight gain (OR 1.09, 95% CI [1.078,
1.106]). The linear regression model for birth weight z-score showed a one-unit
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increase in gestational weight gain would result in a 13-gram increase in birth weight.
The ponderal index linear model suggested that a one-unit increase in gestational
weight gain would result in a small increase (.001) in the index.
The impact of gestational weight gain is modified by pre-pregnant BMI. Pre-
pregnant BMI alters the association between gestational weight gain and birth weight.
Women with a low BMI have a strong association between gestational weight gain and
birth weight; the association weakens as pre-pregnant BMI increases (Abrams & Laros,
1986). Fredrick et al. (2008) found an interaction between pre-pregnant BMI and
gestational weight gain when birth weight was stratified to macrosomia, but not raw birth
weight. Two studies were unable to find an interaction between pre-pregnant BMI and
gestational weight gain (Magriples et al., 2009; Nohr et al., 2008). In this study, there
was an interaction between pre-pregnant BMI and gestational weight gain, present at
the lower birth weight z-scores (Figure 7). However, no interaction was identified in
LGA or ponderal index models.
In this study, gestational weight gain showed a lower regression coefficient than
pre-pregnant BMI in all of the birth weight indices except for the 10th and 20th birth
weight z-score percentile. The lower impact of gestational weight gain compared to pre-
pregnant BMI was also found in the quantile regression analyses. Additionally, the birth
weight z-score and ponderal index quantile regression models showed that pre-
pregnant BMI had a greater increase in impact as the birth weight percentile increased.
The impact of pre-pregnant BMI on birth weight z-score increased more than twofold
(2.5) compared to less than twofold (1.7) increased impact with gestational weight gain.
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However, gestational weight gain had a higher impact at the low birth weight z-scores
(Figure 7).
In the ponderal index quantile analysis, the impact of gestational weight gain
fluctuated as the percentile increased, with the highest impact at the 50th and 95th
percentile (Figure 8). There was a 1.33-fold overall increase from the 10th to the 90th
percentile. The impact of pre-pregnant BMI on the ponderal index percentiles also
showed some fluctuation; however, there was an overall upward trend, with a 1.5-fold
increase (Figure 7). Abrevaya (2001) performed quantile regression and found a
reducing impact of gestational weight gain as birth weight percentiles increased.
Previous studies (Cedergren, 2006; Frederick et al., 2008; Getahun et al., 2007;
Kabali & Werler, 2007; Magriples et al., 2009; Nohr et al., 2008; Rode et al., 2007) have
stratified both pre-pregnant BMI and gestational weight gain. Only two studies
(Frederick et al., 2008; Nohr et al., 2007) directly compared the impact of pre-pregnant
BMI and gestational weight gain on birth weight; consistent with this study, they found
pre-pregnant BMI to have a greater impact on birth weight indices than gestational
weight gain. The current management to avoid a high birth weight infant is to control
gestational weight gain. However, given the large proportion of reproductive aged
women who have a BMI > 25 kg/m2 and the known reduced association between
gestational weight gain and birth weight in these women, managing gestational weight
gain may only have a limited effect on preventing high birth weight infants. This
research, as well as past research by Fredrick et al. (2008) and Nohr et al. (2008),
suggest that more attention be paid to pre-pregnant BMI as a significant contributor to
high birth weight.
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Non-Modifiable Biological Predictors
Maternal Factors
Age. Maternal age was not identified as a significant predictor of LGA or
ponderal index. However, maternal age achieved significance with birth weight z-
scores in the linear and quantile regression, with the exception of the 10th percentile.
Age had a small impact on birth weight z-scores (b = .008) and showed a slight increase
in impact as birth weight z-scores increased (Table XXII). The linear regression
analysis appears to accurately reflect the impact of age on birth weight z-scores. This
subtle but significant finding of increased risk with increasing maternal age is consistent
with Jolly et al. (2003) and Cleary-Goldman et al. (2005).
Parity. Parity was found to be a strong predictor in all three birth weight indices.
Logistic regression identified a 17% increased risk of delivering a LGA infant from an
increase in parity (OR 1.168 [95% CI 1.08, 1.25]). Parity had a positive impact on birth
weight z-scores (b = .058). A one-unit increase in parity would contribute to a 26-gram
increase in birth weight. The positive impact of parity on birth weight identified in this
study is consistent with existing knowledge and previous studies (Cogswell & Yip, 1995;
Jolly et al., 2003). Quantile regression did not show any trends in regard to parity in
either birth weight z-scores or ponderal index, suggesting there was no change in the
mechanism across the birth weight index percentiles.
Height. Maternal height demonstrated a significant regression coefficient to birth
weight z-scores. Maternal height demonstrated a small but significant impact on birth
weight z-scores (b = .02). Logistic regression analysis indicated an increased risk for
LGA (OR 1.05 [95% CI 1.046, 1.07]) from maternal height. This is consistent with
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findings from Orskou et al. (2003), who found an increased risk for infants greater than
4,000 g with maternal height greater than 181 cm (referent 171-180 cm). Height was
not included in the ponderal index regression model, as it failed to demonstrate a
significant association in the bivariate analysis. The failure to demonstrate significance
in the ponderal index model was surprising, as maternal height is known to influence
infant length (Veena et al., 2009).
Infant Factors
Gestational age. Gestational age and gender were only included in the
prediction equation for ponderal index, as LGA and birth weight z-scores were both
adjusted for these factors when the indices were generated. Gestational age was a
significant predictor of the explained variance of ponderal index, with a regression
coefficient of .172. Although gestation is one of the biggest contributors to birth weight,
this study was performed on term infants (37-42 weeks' gestation) when the rate of fetal
growth has slowed. The impact of gestational age on ponderal index progressively
decreased until the 30th percentile, when it then fluctuated around the same point
across the remaining percentiles. It maintained significance at all centiles. It is unclear
why gestational age was a stronger predictor at the lower percentile; however, the
mechanisms that contribute to poor growth may predispose the fetus to increased
sensitivity to external factors.
Gender. Being female had a positive impact on ponderal index (b = .023) in the
linear regression model. The quantile regression model of ponderal index did not show
any trends as the percentiles increased. The female infants in this study had a higher
mean ponderal index than the males: 2.58 vs. 2.56, respectively. This result was
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unexpected, as existing knowledge demonstrates that male infants have a higher
ponderal index than female infants (Roje et al., 2004). Further analysis showed that a
greater proportion of female infants were LGA (6.6%) infants compared to the male
infants (6.4%), although this difference was not significant. It could be suggested that
inaccurate length assessment contributed to the unexpected finding of increased
ponderal index in female infants; however, the additional finding of more female LGA
infants than male LGA tends to discount this theory. Further assessment of the study
data may be necessary to examine why this result occurred.
Maternal Behavioral Predictors
Smoking. Smoking demonstrated a significant negative impact on birth weight
z-scores in both linear and quantile regression. However, it failed to achieve
significance in the logistic model of LGA. It was not included in the ponderal index
model, as smoking failed to demonstrate a significant relationship in the initial bivariate
analysis. Smoking had a marked impact on birth weight z-score analysis, with a
negative regression coefficient of b = -.26. In this study, smoking during pregnancy
would result in a 116-gram reduction in birth weight in a term infant. The quantile
regression indicated that smoking had an even greater impact on the lower percentiles.
The impact progressively reduced with each increasing percentile (Table XXII).
Smoking during pregnancy is known to significantly reduce birth weight, as well
as increase the risk for prematurity (Li, Windsor, Perkins, Goldenberg, & Lowe, 1993).
Smoking affects placental development and function (Zdravkovic, Genbacev, McMaster,
& Fisher, 2005) and has been shown to reduce fat mass (Lindsay, Thomas, & Catalano,
1997). Fat mass contributes to 2% of birth weight in term SGA infants compared to
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13% in AGA term infants (Petersen, Gotfredsen, & Knudsen, 1988). Infant birth weight
has a strong correlation to fat mass (R2 = .78, p < .00001) (Catalano, Thomas, Avallone,
& Amini, 1995). The greater impact of smoking on the lower birth weight z-scores may
be due to the severity of the impairment to placental function from smoking. The trend
of reduced impact of smoking as the birth weight z-scores increased may explain why
smoking was not identified as a significant contributor to LGA.
Social Environment Predictors
Marital status. Cohabitation failed to achieve significance in any of the linear
regression models of birth weight indices: LGA, z-score or ponderal index. This finding
was unexpected, as previous research had shown that living with a partner increased
the risk of high birth weight (Orskou et al., 2003; Surkan et al., 2004). However,
Fredrick et al. (2008) also failed to identify marital status as a significant predictor of
birth weight. The variable had a high response rate and a reasonable proportion of
responses, with 33% of the women stating they were single, while 66% were
cohabitating.
The quantile regression of ponderal index demonstrated cohabitation to be a
significant predictor of ponderal index at the 40th and 50th percentile. The regression
coefficient results in the quantile regression varied markedly (range -.0025 to .0162)
across the ponderal index percentiles. Other studies have shown that family structure
was significantly related to variations in birth weight (Ramsey et al., 1986). There was a
significant interaction between marital status and pre-pregnant BMI in birth weight z-
scores, which supports that marital status did have an impact. Feldman et al. (2000)
suggest that marital status is indirectly related to fetal growth through social support.
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Marital status as a single measure has limitations, as it fails to directly confirm partner
involvement or support. This limitation may have contributed to the unexpected results.
Support partner. Support was stratified as having partner or non-partner
support. Only birth weight z-score attained a significant regression coefficient with this
predictor. Having partner support contributed to a 10% increase (b = .101) in birth
weight z-score, which equates to a 45-gram weight increase in a term infant. Quantile
regression model of support for birth weight z-scores attained significance at all levels
except for the 90th percentile. The impact of support on birth weight z-scores fluctuated
as the percentiles increased.
Both LGA and ponderal index failed to achieve significance in relation to support
partner. The failure to achieve significance in the LGA logistic regression and at the
90th percentile in the birth weight z-score quantile regression suggests that support
partner does not contribute to high birth weight indices. Feldman et al. (2000) found
that a mother attains more benefit when she is supported by a husband or partner than
family. This variable had a higher level of 18.5% missing data; however, it was
impossible to tell whether missing data reflected the lack of a support person. To some
extent, support partner is related to marital status; however, the different results suggest
that they are measuring different concepts.
Medicaid. Medicaid was only included in the birth weight z-score regression
model, as it failed to demonstrate a significant association with other birth weight indices
in the initial bivariate analysis. Medicaid failed to demonstrate significance as a
predictor in the birth weight z-score linear regression. It did demonstrate significance as
a negative predictor in the quantile regression model but only at the 20th percentile,
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suggesting that receiving Medicaid reduced the risk of having a low birth weight infant at
the 20th percentile. Although not significant, Medicaid showed a negative regression
coefficient for the remaining birth weight z-scores up to the 80th percentile and a positive
predictive regression coefficient at the 90th and 95th percentile. It was surprising that
Medicaid did not demonstrate significance as a predictor of high birth weight indices.
Previous research has indicated that recipients of Medicaid were more likely to be
overweight (Chu et al., 2008), and research has shown an association between
increased pre-pregnant BMI and high birth weight. The failure of Medicaid to
demonstrate significance may be related to use of birth weight indices versus raw birth
weight or the limitations of the predictor as a marker for SES.
Strengths and Limitations
Data
The use of existing data was a study limitation. The researcher had no control
over variable definitions or the reliability of measurements. The original data were
collected for clinical care and not part of a previous research study; as such, data
definitions were not guided by a conceptual framework. However, the outcome
variables (birth weight indices) and the maternal biological predictors of age, parity,
height, pre-pregnant BMI, and gestational weight gain are recognized as valid
measures. The behavioral variable, smoking, is a term that accurately measured the
attribute, although this study did not consider the quantitative effects of smoking on fetal
growth.
The behavioral variables, marital status and support partner, are both simple
terms; however, the study variable may not have appropriately measured the construct,
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and as such they may have been inaccurate. Living with a partner does not necessarily
ensure that a woman has psychological or financial support that creates a positive living
environment. The presence of a partner in labor does not confirm that the pregnant
woman had positive and ongoing positive partner support during her pregnancy.
The social variable Medicaid was used as a marker for SES. Preliminary
analysis of Medicaid by ethnic racial groups supported that the groups known to have
lower SES (African American and Hispanic) had significantly higher usage of Medicaid,
suggesting that Medicaid was an accurate marker. The limitations of the marital status
and support partner concepts may have contributed to the failure of these variables to
contribute as predictors of the birth weight indices.
The data used to generate the primary outcome variable birth weight indices
were routine anthropometric measures. The nurses assessing infant anthropometrics
used standard procedures to ensure reliability of the measures. The data are used for
state reporting and birth certificate generation. Pre-pregnant BMI and gestational
weight gain were generated from self-reported maternal data or from prenatal care
records. While both of these methods potentially reduce the reliability of the data,
research indicates that maternal self-report height and pre-pregnant weight maintain
high reliability to direct measures r = .90 (Tomeo et al., 1999) and r = .99, (Herring et al.,
2008) respectively.
As the data set was extracted specifically for this research, random cross-checks
and illogical data were checked against paper records to confirm the accuracy of the
generated data set. The data extraction was performed in individual years; hospital
reporting records were used to confirm the accuracy of each year of the extraction.
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Descriptive statistics were performed on the anthropometric data in each of the 10
extracted data files. The 10 individual years of data files were not merged until these
checks were completed.
This study has limited generalizability as it was not obtained as a random sample
and the use of secondary data inhibited control over reliability and validity of the data.
However, the diversity of the subjects and large number of cases that were collected
over a period of years provided strength to this research project. The outcome
variables and the maternal anthropometric data were normally distributed. The lack of
behavioral variables and the conceptualization of the social variables were limitations.
The ecological model appeared to be an appropriate match for this project.
Birth Weight Indices and Statistical Methods
Large-for-gestational-age is frequently used as an outcome measure in birth
weight research; however, as a dichotomous variable, its use is limited to logistic
regression analysis. While logistic regression provides results that are easy to interpret
(odds ratio), it is only able to demonstrate whether a predictor achieves membership or
no membership to the outcome variable of interest.
The birth weight z-score model demonstrated the highest level of explained
variance of the models used in this research. The predictors used in this regression
model accounted for a lower level of the explained variance than achieved by others
(Abrams & Laros, 1986; Frederick et al., 2008); however, the results were consistent
with previous research. The quantile regression was able to demonstrate a change in
the relationship of the predictor variables at different levels of the birth weight z-score. It
showed a consistent increase in the impact of pre-pregnant BMI and gestational weight
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gain as birth weight z-scores increased. These results suggest more attention needs to
be placed on the influence on pre-pregnant BMI as a significant risk of high birth weight
indices.
The very low rate of explained variance attained with ponderal index suggests
that the predictors included in this model are not the primary contributors. This
limitation appears to have had a cascade effect on the regression model and its ability
to find significance from the predictor variables. Ponderal index is an interesting
measure that has a high correlation with infant adiposity (Wolfe et al., 1990); however, it
is not often used in birth weight research, maybe because researchers have yet to fully
identify the factors that contribute to it. It would be worthwhile to further investigate the
predictors that influence this index.
Summary
This study differed from other studies that have examined pre-pregnant BMI and
high birth risk in three ways: (1) pre-pregnant BMI was not stratified; (2) birth weight
indices were examined as both categorical and continuous data; and (3) birth weight
indices were additionally examined using quantile regression. This is the first study to
use quantile regression to examine birth weight indices in term singleton pregnancies of
women without diabetes. Quantile regression was selected as it was hoped it would
demonstrate changes in the relationship of the predictor variables at different levels of
the birth weight indices.
This study indicated that pre-pregnant BMI is a significant predictor of high birth
weight indices: LGA, z-scores and ponderal index. Quantile regression analysis
indicated that pre-pregnant BMI has a greater impact than gestational weight gain on
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birth weight z-scores above the 30th percentile. There was a more than twofold
increase in the association between pre-pregnant BMI and birth weight z-scores as birth
weight z-scores increased. The behavioral predictor smoking demonstrated a negative
impact, as expected. This impact increased in the lower birth weight z-score quantiles.
Marital status did not demonstrate the expected positive impact; however, support
partner did. Although the ponderal index model achieved significance, the low level of
explained variance obtained suggests that there are other predictors that contribute to
this measure. Quantile regression analysis of birth weight z-scores provided valuable
insight into the changes of influence of the predictors as the birth weight index centile
increased.
Conclusion
This study indicated that biological, behavioral, and social factors significantly
contribute to birth weight z-scores. Quantile analysis indicated that the impact of
biological predictors increased as birth weight z-scores increased suggesting the
mechanism changes as the percentile increases. The behavioral predictor smoking
demonstrated a reduced impact as birth weight z-scores increased. Only biological
predictors were found to be significant predictors of LGA and ponderal index. The
quantile analysis of ponderal index showed a trend of increasing impact from pre-
pregnant BMI. Pre-pregnant BMI has a greater impact on high birth weight z-scores
than gestational weight gain in term singleton pregnancies of women without diabetes.
Increases in pre-pregnant BMI have a greater impact on high birth weight indices.
This is the first study to use quantile regression to examine the impact of
biological, behavioral, and social predictors on high birth weight indices. Quantile
135
regression showed that the relationship of pre-pregnant BMI with birth weight z-scores
and ponderal index is not constant as birth weight indices increase. Linear regression
appears to underestimate the relationship of pre-pregnancy BMI to high birth weight
indices. Logistic regression failed to capture the influence of the behavioral and social
variables on high birth weight indices. Further studies are needed to examine the
increasing impact of pre-pregnant BMI and gestational weight gain across birth weight
index percentiles. It is important to understand the impact of pre-pregnant BMI on the
risk for high birth weight, as it may be contributing to childhood obesity and
comorbidities. More attention needs to be paid to maternal BMI prior to conception, as
its impact on fetal growth during the intrauterine period may have lifelong
consequences.
Implications
The clinical implications of this research are important. This study indicated that
pre-pregnant BMI significantly contributes to the risk of high birth weight indices and the
contribution is greatest in the higher birth weight percentiles. When these findings are
considered with emerging research that infants born in the highest quartile of the weight
for length z-score have the greatest risk for obesity (BMI ≥ 95th percentile) at age 3
years (Taveras et al., 2009), it suggests that maternal pre-pregnant BMI is contributing
to a cycle of obesity.
Until recently, childhood obesity was considered to be a result of postnatal social
environment over-nutrition. However, animal research (Shankar, Harrell, Liu, Gilchrist,
Ronis, & Badger, 2008) suggests that increased maternal adiposity during the
intrauterine period increases the risk for obesity in offspring. The influence of maternal
136
adiposity during pregnancy seems to be greater than the influences from the postnatal
environment that contribute to childhood obesity (Shankar et al., 2008). Armitage,
Poston, and Taylor (2008) proposed that increased maternal adiposity creates an
intrauterine environment that exposes the fetus to higher maternal glucose, free fatty
acids, and amino acids levels, which permanently alters the fetus’s metabolism and
neuroendocrine function and leads to increased adiposity in later life (childhood and
adulthood).
In the past 25 years, pre-pregnant BMI has been used as a tool to guide
gestational weight gain, the underlying rationale being the prevention of low birth weight
infants in underweight women. However, increases in pre-pregnant BMI moderate the
impact of gestational weight gain, reducing the correlation of gestational weight gain to
birth weight. In the United States today, more than 66% of reproductive-aged women
are overweight or obese. The intrauterine environment of a healthy woman with
increased adiposity who otherwise had an uneventful pregnancy appears to have long-
term implications on offspring health. Fetal growth is influenced by multiple maternal
and possibly social factors. This is a complex situation; however, we need to focus on
the factors that can be modified and prevent the potential alterations in fetal
development that may result in increased adiposity in later life.
Restricting gestational weight gain in women with increased pre-pregnant BMI
does not eliminate the risk of delivering an infant with a high birth weight index (Getahun
et al., 2007). The proportion of women who have excess gestational weight gain (which
increases the risk of a high birth weight infant) has increased, not decreased, in the last
25 years (Rasmussen & Yaktine, 2009). Research suggests that breastfeeding
137
significantly reduces the risk of childhood obesity (Mayer-Davis, Rifas-Shiman, Li, Hu,
Colditz, & Gillman, 2006; Weyermann, Rochenbacker, & Brenner, 2006). However,
breastfeeding fails to fully reduce the increased risk from maternal pre-pregnant obesity
(Mayer-Davis et al., 2006). Breastfeeding is a postnatal adjunct to ensuring a healthy
BMI percentile in childhood.
Nurses needs to act early and in an ongoing manner to inform young women of
the importance of an optimal BMI. Education needs to begin during the adolescent
years and continue in adulthood. Women need to be fully aware of the long-term risks
to potential offspring from increased pre-pregnant adiposity. Education needs to include
information on healthy nutrition, the importance of exercise and how the combination of
these factors can assist women to achieve and maintain an optimal BMI. Young women
should have a BMI assessment when visiting a health care provider, and if necessary
be provided with a referral to resources that can help them achieve or maintain a
healthy BMI.
Pregnancy is not a time for the implementation of weight loss, and by the
postnatal period, the potential alterations in the offspring’s physiological function have
already occurred. While healthy eating and appropriate gestational weight gain can
reduce the risk of having a high birth weight infant, pre-pregnant BMI has a greater
contribution to the risk of delivering an infant with a high birth weight index than
gestational weight gain. It is important to address pre-pregnant BMI prior to conception
and not just as a tool to guide gestational weight gain. As primary care providers,
nurses need to consistently discuss the importance of optimal BMI, not just for personal
health but also for the health of future offspring.
138
Future Research
This study highlighted the change in the mechanism of pre-pregnant BMI and
gestational weight gain as birth weight indices increase. It is important to further
investigate this phenomena using quantile regression. A prospective study should be
conducted using birth weight z-scores and quantile regression with expansion and
refinement of the biological, behavioral and social variables: waist hip measurement,
dietary habits, education, type of family support, family income, and access to health
care to enhance the model. Performing a prospective study would allow random
selection of subjects for inclusion. It would provide the opportunity to improve the
reliability and validity of the biological and social measures through validated
anthropometric measures and precise data definitions. Accurate assessment of
maternal biological and social variables could resolve some of the limitations that
occurred with this study.
Additionally, a more refined anthropometric assessment of the infant to include
skin-fold assessment as seen in the HAPO study (HAPO Study Cooperative Research
Group, 2009) so infant body composition could be included as an outcome variable.
The ultimate project would be to continue to follow the infants through childhood to
assess how birth weight indices correlate with BMI percentiles in childhood, as well as
assessing the influence of breastfeeding on childhood BMI percentiles in relation to birth
weight indices. Promoting and supporting healthy behaviors and lifestyle is an
important area where nursing can participate in both research and intervention.
139
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VITA
NAME Helen Anderson EDUCATION 2004 - 2011 University of Illinois at Chicago
Chicago, Illinois USA Doctorate of Philosophy in Nursing
1994 – 1996 University of Texas, Houston
Houston, Texas USA Master of Science in Nursing (Advance Practice - Women’s Health)
1988 – 1990 Curtin University
Perth, Western Australia, Australia Bachelor of Applied Science - Nursing
1985 -1986 Royal Women's Hospital
Melbourne, Victoria, Australia Midwifery Certificate
1979 – 1981 Preston & Northcote Community Hospital
Melbourne, Victoria, Australia Diploma of Nursing
DEGREES Master of Science 1996 University of Texas,
Houston, Texas, USA
Bachelor of Applied Science 1990 Curtin University, Perth, Western Australia, Australia
LICENSURE Registered Nurse State of Illinois and Texas Advance Practice Nurse State of Illinois and Texas CERTIFICATION Certified Nurse Midwife American Midwifery Certification Board
1999 to present Women’s Health Nurse Practitioner National Certification Corporation
1997 to present
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RESEARCH EXPERIENCE Co-Investigator 2005-2009 Intrauterine Environment in Women with PCOS Research Assistant 1996-1997 University Texas: School of Nursing ACADEMIC EXPERIENCE 2004 -2005 University of Illinois at Chicago
Maternal Child Health Clinical Instructor
1990-1991 Curtin University
School of Nursing Lecturer / Clinical Supervisor
PUBLICATIONS Anderson, H., Fogel, N., Grebe, S. K., Singh, R. J., Taylor, R. L., & Dunaif, A. (2010).
Infants of women with polycystic ovary syndrome have lower cord blood androstenedione and estradiol levels. Journal of Clinical Endocrinology and Metabolism, 95(5), 2180-2186.
PRESENTATIONS Anderson, H. (June 2009). Prospective Study of Birthweight and Cord Blood Hormone Levels in the Offspring of Women with Polycystic Ovary Syndrome (OR19). Oral presentation at Endocrine Society Annual Meeting Anderson, H. (March 2008). Androgen Levels at Birth in Offspring of Polycystic Ovary Syndrome Women. Northwestern University – Endocrinology Seminars. Anderson, H. (February 1995). Turning Point: Reducing Cesarean Section Rate. Houston Perinatal Nursing Symposium. GRANTS / AWARDS Endocrine Society (2009) - Exceptional Research
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PROFESSIONAL NURSING EXPERIENCE 2005 - 2007 Northwestern University – Division of Endocrinology
Research Nurse Practitioner
2004 - 2005 University of Illinois at Chicago - Maternal Child Nursing Graduate Assistant
2002 - 2003 Birth and Beyond -Singapore
Registered Midwife 2001 - 2001 Newton Medical Centre, London England
Midwife / Nurse Practitioner 1999 - 2000 Women’s Health Care Center of Houston, Texas
Midwife and Nurse Practitioner 1999 -1999 Nativiti Birthing Center – Houston, Texas
1996 -1997 MacGregor Medical Association, Houston, Texas
Nurse Practitioner 1996 -1996 University of Texas, Houston, Texas
Research Assistant 1993 -1996 Memorial Healthcare Systems, Houston, Texas
Nurse Manager Birthing Center 1991 -1992 Broome District Hospital, Broome, Western Australia
Clinical Nurse Specialist
1987 -1991 South Perth Community Hospital, Perth, Western Australia (WA) Staff Development Nurse / Midwife
1986 -1987 King Edward Memorial & Osborne Park Hospital, Perth, WA Registered Midwife 1985 -1986 Royal Women’s Hospital, Melbourne Victoria, Australia Student Midwife
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1992 - 1995 East and Central Gippsland Hospitals, Victoria, Australia. Registered Nurse 1979 -1981 Preston and Northcote Community Hospital, Melbourne, Victoria, Student Nurse