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

III. HIGH BIRTH WEIGHT RISK BY

PRE-PREGNANT OBESITY CATEGORY ...............................................31

IV. HIGH BIRTH WEIGHT RISK BY

PRE-PREGNANT OVERWEIGHT CATEGORY ......................................34

V. INFANT BIRTH WEIGHT INDEX CONSTRUCTS ...................................58

VI. MATERNAL CONSTRUCTS ....................................................................60

VII. SOCIAL CONSTRUCTS...........................................................................64

VIII. BIVARIATE ANALYSIS OF CONTINUOUS INFANT BIRTH WEIGHT

INDICES TO CONTINUOUS PREDICTOR VARIABLES.........................76

IX. BIVARIATE ANALYSIS OF CONTINUOUS INFANT BIRTH WEIGHT

INDICES TO CATEGORICAL PREDICTOR VARIABLES .......................77

X. BIVARIATE ANALYSIS OF LARGE-FOR-GESTATIONAL-AGE TO

CONTINUOUS PREDICTOR VARIABLES ..............................................78

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LIST OF TABLES (continued)

TABLE PAGE

XI. BIVARIATE ANALYSIS OF LARGE-FOR-GESTATIONAL-AGE TO

CATEGORICAL PREDICTOR VARIABLES.............................................79

XII. MATERNAL CHARACTERISTICS ...........................................................87

XIII. MATERNAL BMI CATEGORIES .............................................................88

XIV. MATERNAL SOCIAL ENVIRONMENT CHARACTERISTICS .................89

XV. SOCIAL VARIABLES BY RACIAL GROUPS ..........................................91

XVI. YEARS OF EDUCATION BY RACIAL GROUPS.....................................92

XVII. INFANT CHARACTERISTICS..................................................................93

XVIII. INFANT ANTHROPOMETRIC CHARACTERISTICS...............................96

XIX. INFANT BIRTH WEIGHT PERCENTILE CATEGORIES .........................96

XX. LOGISTIC REGRESSION MODEL OF

LARGE-FOR-GESTATIONAL-AGE (LGA) ...............................................98

XXI. LINEAR REGRESSION MODEL OF BIRTH WEIGHT Z-SCORE..........100

XXII. REGRESSION COEFFICIENT FOR BIRTH WEIGHT Z-SCORES USING

LINEAR AND QUANTILE REGRESSION ..............................................102

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LIST OF TABLES (continued)

TABLE PAGE

XXIII. LINEAR REGRESSION MODEL OF PONDERAL INDEX .....................105

XXIV. REGRESSION COEFFICIENT FOR PONDERAL INDEX USING

LINEAR AND QUANTILE REGRESSION ..............................................108

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LIST OF FIGURES

FIGURE PAGE

1. Conceptual models for studying maternal influences on fetal growth...15

2. Distribution of gestational weight gain ..................................................84

3. Pre-pregnant BMI by Year ....................................................................85

4. Distribution of pre-pregnant BMI...........................................................86

5. Distribution of raw birth weight..............................................................94

6. Distribution of birth weight z-scores......................................................95

7. Linear and quantile regression coefficient of pre-pregnant BMI and gestational weight gain on birth weight z-score...........................110

8. Linear and quantile regression coefficient of pre-pregnant BMI and gestational weight gain on ponderal index ..................................112

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LIST OF ABBREVIATIONS

ADA American Diabetes Association

AGA Appropriate-for-Gestational-Age

ANOVA Analysis of variance

AOR Adjusted Odds Ratio

BMI Body Mass Index

BW Birth Weight

b Regression Coefficient

Cm Centimeters

CI Confidence Interval

DOHaD Developmental origins of health and disease

G Grams

GestAge Gestational Age (weeks)

GWG Gestational Weight Gain

HAPO Hyperglycemia and Adverse Pregnancy Outcomes

HELLP Hemolysis, Elevated Liver Enzyme Levels, and Low Platelet Count

HDL High Density Lipids

IT Information Technology

IUGR Intrauterine growth Retardation

LBW Low birth weight

LGA Large-for-gestational-age

Kg Kilograms

Kg/m2 Kilograms/Meter Squared

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LIST OF ABBREVIATIONS (continued)

Macro Macrosomia

NHLBI National Heart Lung and Blood Institute

NS Not Significant

OR Odds Ratio

ppBMI Pre-pregnant Body Mass Index

r Correlation Coefficient

R2 Adjusted R-square

RR Relative Risk

SD Standard Deviation

SES Socio-economic Status

SGA Small-for-gestational-age

TREC–IDEA Transdisciplinary research in energetic and cancer initiative –

Identifying determinants of eating and activity

UAE United Arab Emirates

UK United Kingdom

US(A) United States of America

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SUMMARY

A study was conducted to examine the maternal biological, behavioral and social

factors that influence the risk for high birth weight indices in term singleton infants of

non-diabetic women using secondary data analysis of data extracted from a perinatal

database at a Chicago hospital. Three birth weight indices were examined; large-for-

gestational-age (LGA), birth weight z-scores and ponderal index.

A retrospective sample for the study contained 14,397 linked mother infant pairs

from 10 years (2001-2010) of hospital deliveries. Multiple regression analyses using

logistic, linear and quantile methods were used to estimate the risk factors for high birth

weight indices. The predictor variables included biological (non-modifiable and

modifiable), behavioral and social factors. The non-modifiable biological factors were

age, parity and height. The modifiable biological factors were pre-pregnant body mass

index (BMI) and gestational weight gain. Smoking was the only behavioral factor.

Social factors included marital status, presence of a support partner and Medicaid

health care coverage during pregnancy.

An ecological model based on the developmental origins of health and disease

(DOHaD) was used as the conceptual framework. The dichotomous birth weight index

LGA was examined using logistic regression. The two remaining indices were both

examined using linear regression and then quantile regression.

Logistic regression demonstrated a positive relationship between LGA and the

biological factors parity, height, pre-pregnant BMI and gestational weight gain. An

increase in pre-pregnant BMI showed 11% increase of risk for a LGA infant. Linear

regression coefficients showed a relationship between birth weight z-scores and the

SUMMARY (continued)

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biological, behavioral and social variables. Two social factors did not achieve

significance in the birth weight z-score analysis: marital status and Medicaid.

Quantile analysis showed a positive increasing trend of the regression coefficient

for pre-pregnant BMI and gestational weight gain as the birth weight percentile

increased. Except for the lowest percentiles (10th and 20th), pre-pregnant BMI had a

higher regression coefficient than gestational weight gain. Increases in the regression

coefficient across the percentiles were seen in the remaining significant predictors: age

and height. Smoking had reducing regression coefficients as birth weight z-scores

percentiles increased. Neither parity nor support partner showed temporal trends

across birth weight z-score percentiles. Marital status was not identified as a significant

predictor; however, it was identified as an interaction term with pre-pregnant BMI.

The biological, behavioral and social factors only contributed to a low level of the

explained variance in the ponderal index regression model. The regression coefficient

progressively increased for pre-pregnant BMI as ponderal index percentiles increased.

Pre-pregnant BMI again showed a higher impact than gestational weight gain across all

of the ponderal index percentiles. Parity, gestational age and female gender had a

positive but fluctuating influence across ponderal index percentiles.

Results from this study indicate that there is a change in the mechanism of the

modifiable biological factors of pre-pregnant BMI and gestational weight gain as birth

weight indices increase. The impact and the increased change of pre-pregnant BMI

have a greater contribution to the risk of delivering an infant with a high birth weight

index than gestational weight gain. Combined with the research showing that infants

with a high birth weight index are at increased risk for childhood obesity and its

SUMMARY (continued)

xvi

comorbidities, the findings support that it is important for health care providers to

discuss and address BMI status well in advance of conception. Pre-pregnant BMI of

otherwise healthy women may influence long-term health of their offspring. Women

need to be fully aware of the long-term risks to potential offspring from increased pre-

pregnant adiposity.

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I. INTRODUCTION

Background

An alarming 60% of reproductive-aged women (20-39 years) in the United States

are either overweight as measured by body mass index (BMI) ≥ 25 kilogram/meter

squared (kg/m2) or obese (BMI ≥ 30 kg/m2) (2010). Significant variations in excess

adiposity among ethnic groups exist. More than three-quarters (78%) of young African

American women are overweight or obese, and the rate is only slightly less (70%)

among Mexican American women (Flegal et al., 2010). A positive correlation exists

between maternal pre-pregnant weight and infant birth weight. Women with an

increased BMI have heavier babies than leaner women (Abrams & Laros, 1986).

Historically, increased pre-pregnant BMI was considered a benefit, as it reduced the risk

of low birth weight infants and the associated morbidity. However, delivering a high

birth weight or macrosomic (> 4,000 grams) infant creates increased intrapartum risks

for the mother and infant (Boulet, Alexander, Salihu, & Pass, 2003). Current research

suggests that women who are overweight at the onset of pregnancy have an increased

risk-adjusted odds ratio (AOR) 1.54 99% CI [1.48, 1.60] of having a macrosomic infant

(Jolly, Sebire, Harris, Regan, & Robinson, 2003), and this risk further increases to AOR

2.58 95% CI [1.077, 6.2] when the woman is obese (Driul et al., 2008). Pre-pregnant

adiposity > 25 kg/m2 appears to increase the risk of a high birth weight infant; however,

fetal growth is influenced by numerous maternal factors.

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There is a reasonable body of literature examining the impact of pre-pregnant

weight on perinatal outcomes including infant birth weight. However, only limited

research has directly examined the relationship of maternal adiposity in non-diabetic

pregnancies and the risk of high birth weight adjusted for gestational age. The outcome

variable of this research was infant birth weight indices: centile values, z-scores and

ponderal index. The primary exposure variable was maternal adiposity.

Excess pre-pregnant adiposity increases the risk of perinatal complications.

Spontaneous abortions occur more frequently in women who are obese (AOR, 1.2 95%

CI [1.01, 1.46]) (Lashen, Fear, & Sturdee, 2004). Research suggests that the risks

increase as BMI levels increase; however, it is unclear what BMI level is optimal to

minimize the risks. Moreover, the risk of developing diabetes during pregnancy

increases as pre-pregnant adiposity levels increase. Overweight women (BMI 25-29.9

kg/m2) have a twofold increased risk (AOR 1.97 [1.77, 2.19]), while this rate nearly

doubles when women are obese (BMI ≥ 30 kg/m2) (AOR 3.76 [3.31,4.28]) (Torloni et al.,

2009). The risk for gestational hypertensive disorders also increases as pre-pregnant

BMI increases, with a twofold risk for obesity class I (BMI 30-34.9 kg/m2) to a threefold

risk for obesity class III (BMI ≥ 40 kg/m2), AOR 2.5 [2.1,3.0], 3.22 [2.6, 4.00],

respectively (Weiss et al., 2004). Neonatal complications include an increased risk of

congenital abnormalities (Leddy, Power, & Schulkin, 2008), neonatal intensive care

admissions, and neonatal hypoglycemia (Sebire et al., 2001). The risk of a neural tube

defect increases as maternal adiposity increases from pre-pregnant obesity class I to

obesity class III, AOR 1.7 [1.34, 2.15], 3.11 [1.75, 5.46], respectively (Rasmussen, Chu,

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Kim, Schmid, & Lau, 2008). Excessive pre-pregnant adiposity is largely a preventable

condition that, if left untreated, can create maternal and neonatal health problems.

Recent research suggests that maternal pre-pregnant adiposity may influence

the health status of the offspring throughout their lifetimes. Epidemiological studies

suggest that there is a U-shaped association between birth weight and long-term health

status; infants born at either end of the birth weight continuum are at increased risk for

short- and long-term sequelae (Kunz & King, 2007). Infants born large-for-gestational-

age (LGA) are at increased risk of developing childhood obesity and metabolic

syndrome, AOR 2.19 95% CI [1.25, 3.82] (Boney, Verma, Tucker, & Vohr, 2005).

Infants born to women who are overweight (BMI 25-29.9 kg/m2) at the onset of the

pregnancy are at increased risk (AOR 1.83 [1.66, 2.02]) of developing childhood obesity

(Hawkins, Cole, & Law, 2009). Studies also suggest that infants of overweight and

obese women have increased fat mass, not lean mass (Catalano, Thomas, Huston-

Presley, & Amini, 2003; Hull, Dinger, Knehans, Thompson, & Fields, 2008). In the last

25 years, the prevalence of excess adiposity has doubled and in some cases tripled

among children ages 6-19 years; 34% are at risk of being overweight (> 85th percentile

for age), and 17% are overweight (> 95th percentile for age) (Wang & Beydoun, 2007).

Childhood obesity not only impacts short-term health but also increases the likelihood of

obesity in adulthood (Reilly et al., 2003). A study of mother and daughter pairs found

that maternal pre-pregnant obesity (BMI > 29 kg/m2) resulted in a six-fold increased risk

of obesity in the daughters at age 18 years (Stuebe, Forman, & Michels, 2009). Excess

adiposity prior to and during pregnancy creates a plentiful intrauterine environment that

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not only increases fetal growth but also appears to influence long-term infant nutritional

and health status.

Research has identified an increased risk for macrosomia in overweight or obese

non-diabetic women (Driul et al., 2008; Kabali & Werler, 2007; Sewell, Huston-Presley,

Super, & Catalano, 2006; Usha Kiran, Hemmadi, Bethel, & Evans, 2005). Macrosomia

is a crude classification of high birth weight, using attained birth weight that fails to

account for factors that influence fetal growth. The point at which high birth weight

becomes macrosomia is arbitrary; the cut-off can be set at 4,000 grams (g), which

corresponds to the 90th percentile at 40 weeks (Jolly et al., 2003). However, the cut-off

can be set at either 4,500 g or 5,000 g; these higher birth weights are associated with

increased maternal and neonatal morbidity and mortality (Zhang, Decker, Platt, &

Kramer, 2008). Birth weight indices (centile values, z-scores, and ponderal index) are a

more refined assessment of fetal growth. Centile values were specifically developed to

adjust for the influence that gestational age has on birth weight (Battaglia & Lubchenco,

1967), as well as the influences of infant gender. A birth weight z-score is birth weight

expressed as a standard deviation unit from the mean birth weight, which has been

adjusted for gestational age and infant gender. Ponderal index is an indicator of infant

adiposity (nutritional status) (Gabbe, Niebyl, & Simpson, 2007). Birth weight and length

are used to create an index value that allows comparison of infants with different

lengths and weight measurements. These indices provide a more accurate assessment

of fetal growth and adiposity levels than raw birth weight or macrosomia.

Within the United States, there are ethnic and racial disparities in perinatal

outcomes (Janssen et al., 2007; Overpeck, Hediger, Zhang, Trumble, & Klebanoff,

5

1999; Ramos & Caughey, 2005); however, race or ethnicity is used as a marker to

reflect underlying socioeconomic inequalities and not biological variance. Limited data

exist regarding the influence of Mexican or Asian ethnicity on pre-pregnant BMI and the

influence on birth weight outcomes (National Research Council & Institute of Medicine,

2007). To date, no studies have examined whether socio-demographic factors

influence the risk for high birth weight indices in infants of non-diabetic women with a

singleton term pregnancy.

As the fetus develops within the intrauterine environment, it is influenced by

maternal well-being. Using the ecological paradigm, maternal well-being is influenced

by cultural, social, behavioral and biological factors. Therefore, the maternal

environment contributes to fetal development. While some biological factors such as

genetics, age and parity cannot be modified, there are biological, behavioral and social

factors that are modifiable. The goal of pre-conception and prenatal care is to assist

women to achieve an optimal health that will optimize maternal and fetal well-being

during pregnancy. Much of the research related to birth weight outcomes has focused

on the prevention of small-for-gestational-age (SGA) infants and the influence of

gestational weight gain. However, limited research has examined biological, behavioral

and social factors that influence fetal growth in healthy women.

Statement of the Problem

Excessive maternal adiposity involves nearly two-thirds of all pregnant women

(Flegal et al., 2010). These women are at increased risk for perinatal complications,

including excessive fetal growth and the increased risk of delivering a macrosomic

infant (Catalano, 2007; Surkan, Hsieh, Johansson, Dickman, & Cnattingius, 2004). To

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date, the primary prevention strategy to minimize the associated complications has

been to restrict gestational weight gain. However, this strategy is only partially effective

in reducing the risk of high birth weight infants in overweight and obese women (Jain,

Denk, Kruse, & Dandolu, 2007). There is increasing evidence that higher birth weight

leads to increased obesity and metabolic disturbances in children (Boney et al., 2005;

Parsons, Power, Logan, & Summerbell, 1999; Taveras et al., 2009) and adults

(Martorell, Stein, & Schroeder, 2001). It has been well established that diabetes in

pregnancy increases the risk of delivering a high birth weight infant (Ehrenberg, Mercer,

& Catalano, 2004; Metzger et al., 2008). However, the prevalence of excess adiposity

in reproductive aged women is four times greater than that of diabetes in pregnancy

(Flegal et al., 2010; Kim, Newton, & Knopp, 2002) and as such, excess pre-pregnant

adiposity is a greater contributor to the proportion of high birth weight infants than

women with diabetes (Ehrenberg et al., 2004). While it is clear that maternal pre-

pregnant BMI influences birth weight (Abrams & Laros, 1986), to date no study has

examined the risk of delivering a high birth weight infant using birth weight indices in

non-diabetic pregnancies. Nor has the interrelationship of socio-demographic factors

within these parameters been studied.

Purpose of the Study

The purposes of this study were to examine: (1) the maternal biological,

behavioral and social factors that influence the risk for high birth weight indices in term

(≥ 37 weeks’ gestation) singleton pregnancies of non-diabetic women; (2) whether

increasing pre-pregnant adiposity, as measured by BMI in term singleton pregnancies of

non-diabetic women, increases the risk of delivering an infant with a higher birth weight

7

index; and (3) whether socio-demographic factors influence the interrelationship of

maternal adiposity levels and high birth weight indices. The study was performed using

an existing data set, which contained 10 years (2001-2010) of linked maternal infant

records from a Chicago hospital.

Operational Definitions

Operational definitions for this study included pre-pregnant body mass index

(BMI) and birth weight indices. Body mass index is a non-invasive method to estimate

the percent of adipose mass generated from height and weight. Body mass index is

expressed as a numeric value (kilogram/meters squared [kg/m2]); excess adiposity is a

BMI ≥ 25 kg/m2. National Heart Lung and Blood Institute (NHLBI) (1998) stratification

criteria of BMI are: underweight < 18.5 kg/m2; ideal 18.5-24.9 kg/m2; overweight 25-29.9

kg/m2; obese class I 30-34.9 kg/m2; obese class II 35-39 kg/m2; and (extreme) obesity

class III ≥ 40 kg/m2. Body mass index was used as a continuous variable in this study.

Birth weight indices are population-specific standardized values that convert raw

birth weight into a centile value, z-score or ponderal value. Infant birth weight centiles

and birth weight z-scores were generated after adjusting for infant gender and

gestational age. Ponderal Index adjusts birth weight by birth length. Ponderal index is

equal to birth weight (grams) x 100 divided by the birth length cubed (cm3) (Davies,

1980). Birth weight centiles were stratified to a dichotomous variable at the 90th centile.

Greater than the 90th percentile is an accepted and well-used demarcation point for high

birth weight in both research and clinical practice. Large-for-gestational-age (LGA) was

used to describe the centile value greater than 90th percentile. U.S. growth tables by

8

Olsen, Groveman, Lawson, Clark, and Zemel (2010) were used to generate z-scores

and centile values.

Three levels of regression analysis were performed: logistic, linear and non-

parametric quantile regression (Koenker & Hallock, 2001). Logistic regression was

performed with the dichotomous outcome variable LGA. Linear and quantile regression

were performed with birth weight z-scores and ponderal index.

Significance of this Study

The findings from this study will enhance the knowledge of the risk for delivery of

an infant with a high birth weight index in non-diabetic women following a term singleton

pregnancy, as well as the relationship of socio-demographic factors and high birth

weight indices. Achieving and maintaining ideal maternal weight is a complex multi-

factorial issue that involves social, behavioral, and biological factors. Identifying the risk

factors will help health care providers and researchers to assist women to achieve and

maintain optimal nutritional status prior to conception as well as during and after

pregnancy.

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II. CONCEPTUAL FRAMEWORK AND RELATED LITERATURE

Infant birth weight is a common outcome variable in perinatal research, as it is an

important predictor of neonatal morbidity and mortality. The mechanisms that influence

fetal growth, and in turn infant birth weight, are vast. Extremes in birth weight,

particularly low birth weight, have received ongoing attention, as research suggests that

birth weight extremes may influence long-term health status (Barker, Eriksson, Forsen,

& Osmond, 2002; Boney et al., 2005; Oken & Gillman, 2003; Wang, Liang, Junfen, &

Lizhong, 2007). There is only limited research that has directly examined the

relationship of increased maternal adiposity in non-diabetic pregnancies and the risk of

high birth weight indices. Few research studies have included the interrelationship of

education, family structure (marital status) and support on the risk of high birth weight.

This chapter includes the proposed theoretical framework and a critique of the literature

related to this research topic. The outcome variable of this research was high infant

birth weight indices: centile values, z- scores, and ponderal index. The primary

exposure variable was maternal adiposity.

Conceptual Frameworks Used in Obesity Research

Three conceptual frameworks have been used to guide obesity research: (1)

Ecological Model of Human Development (Bronfenbrenner, 1979, 1994); (2)

Developmental Origins of Health and Disease (Gluckman & Hanson, 2006a; Gluckman,

Hanson, & Buklijas, 2010); and (3) Life Course Approach (Ben-Shlomo & Kuh, 2002;

Kuh, Ben-Shlomo, Lynch, Hallqvist, & Power, 2003). All three use the ecological

10

systems theoretical framework. The ecological systems framework proposes that the

interactions of the environment and the individual influence an individual’s development.

The ecological systems framework is a grand theory that has been used to develop a

mid-range ecological model of health theories. This model aims to demonstrate the

multidimensional, multilevel interaction of health. Additionally, it allows the practitioner

to view the individual’s disposition, resources, and characteristics that may influence

health (Grzywacz & Fuqua, 2000). The ecological perspective supports the holistic view

as seen in nursing. The ecological framework has a non-hierarchal structure consisting

of a series of interacting systems, with the individual centrally nested within the system.

The three ecological conceptual frameworks used in obesity research have

common elements; however, they vary in how they categorize and include the

components. The developmental origins of health and disease (DOHaD) model is a

biomedical conceptual framework; its current propositions are the result of 25 years of

epidemiological and animal research into the relationship of birth size and the

development of adult disease (Gluckman & Hanson, 2006b). The underlying

assumption is that the early life (fetal and early postnatal period) environment influences

developmental programming and in turn long-term health status (Gluckman & Hanson,

2006a). The life course approach is used in longitudinal research to track the bio-social

environment on health status and disease development (Ben-Shlomo & Kuh, 2002).

The ecological model of human development (Bronfenbrenner, 1979) was designed to

demonstrate the complex interactions of biopsychological development.

Bronfenbrenner proposed that human development is influenced by the

characteristics of the family and community environment (Bronfenbrenner, 1979) and in

11

turn this influences health status. “Human development takes place through processes

of progressively more complex reciprocal interaction between an active, evolving

biopsychological human organism and the persons, objects, symbols in its immediate

external environment” (Bronfenbrenner, 1994, p. 38). It has provided the underlying

theoretical framework in cross-sectional research to examine weight gain in pregnancy

(Rasmussen & Yaktine, 2009), childhood obesity (Christoffel, 2009; Davidson, Litwin,

Peleg, & Erlich, 2007; Hawkins et al., 2009; Lytle, 2009), and childhood growth

(Reifsnider, 1995). All of the above mentioned models recognize that the intensity and

length of exposure influence the impact on the individual’s development (outcome).

It has been suggested that ecological systems models are best suited to examine

the complex interaction of biological/physiologic, behavioral, and environmental

variables that both mediate and moderate an individual’s adiposity status (Egger &

Swinburn, 1997). Grzywacz and Fuqua (2000) suggest that refinements made by

Bronfenbrenner (1979) remove some of the ambiguity found in ecological models and,

as such, differentiate the linkage mechanisms among person, environment, and health.

Theoretical Framework

When selecting a theoretical framework for this project, the following

considerations were addressed. Does the theory help guide the research questions,

design and methodology? The theory should be able to support or refute the research

findings. Unfortunately, the vast majority of human research studies involving maternal

pre-pregnant adiposity and birth weight outcomes have not referred to a theoretical

framework. The DOHaD framework (Gluckman & Hanson, 2006b) has been used

extensively to examine the relationship between birth weight and long-term health

12

outcomes (Rasmussen, 2001); however, as a biomedical model it has predominantly

focused on biological/physiological components, with only limited attention to behavioral

components. The goal was to identify a theoretical framework that included biological

and behavioral aspects as they relate to maternal nutritional status and fetal outcomes

in a manner that is coherent with nursing.

Numerous ecological conceptual frameworks of health were examined during the

process of identifying a framework to support this research (Abu-Saad & Fraser, 2010;

Armitage, Taylor, & Poston, 2005; Bronfenbrenner, 1979; Davison & Birch, 2001; Egger

& Swinburn, 1997; Gluckman & Hanson, 2006a; Grzywacz & Fuqua, 2000; Lytle, 2009;

Rasmussen & Yaktine, 2009; Reifsnider, 1995; Reifsnider, Gallagher, & Forgione, 2005;

Reifsnider & Ritsema, 2008; Viswanathan et al., 2008). Although human development

was the overarching theme, the grouping of components varied considerably among the

conceptual frameworks, possibly demonstrating the flexibility of the ecological

framework. It appeared that the outcome variable (the individual in the center of the

model) influenced how the components were grouped and categorized. All the

variations appeared to be congruent with the ecological model and did not disrupt the

underlying theoretical perspective. The broad nature of the ecological framework

appeared to provide flexibility that would support numerous refinements while ensuring

the integrity of the theoretical proposition.

Four ecological models have been used to guide the investigation on the impact

of maternal nutritional status during pregnancy and birth outcomes (Abu-Saad & Fraser,

2010; Committee on Nutritional Status During Pregnancy and Lactation, 1990;

Rasmussen & Yaktine, 2009; Viswanathan et al., 2008). Three of the identified models

13

were used in an extensive review of the impact of gestational weight gain on birth

outcomes (Committee on Nutritional Status During Pregnancy and Lactation, 1990;

Rasmussen & Yaktine, 2009; Viswanathan et al., 2008). The models used by

Rasmussen and Yaktine (2009) and Viswanathan et al. (2008) were modifications of the

“nutrition during pregnancy” framework developed by the Institutes of Medicine

(Committee on Nutritional Status During Pregnancy and Lactation, 1990). Rasmussen

and Yaktine (2009) additionally acknowledged the ecological framework of human

development (Bronfenbrenner, 1979) and life-course epidemiology (Kuh & Ben-Shlomo,

1997) in the development of the ecological framework for GESTATIONAL WEIGHT

GAIN. Abu-Saad and Fraser (2010) reviewed the current research evidence related to

maternal nutrition and birth outcomes in developing their framework; however, they did

not reference any conceptual frameworks used during their development process.

The shared nature of pre-pregnant adiposity and GESTATIONAL WEIGHT GAIN

provided support that the components being considered in this project were appropriate.

However, the frameworks developed by both Rasmussen and Yaktine (2009) and

Viswanathan et al. (2008) were too broad and appeared to lack the needed refinement

for this study. The Transdisciplinary Research in Energetics and Cancer – Identifying

Determinants of Eating and Activity (TREC–IDEA) conceptual framework (Lytle, 2009)

provided a more refined approach; however, as it was developed to guide research into

childhood obesity, it lacked maternal biological components. The DOHaD model

(Gluckman & Hanson, 2006a) included maternal biological components and had

extensive research to support the framework; however, it had only limited use when

considering maternal behavioral factors. The social ecological model of health

14

(Grzywacz & Fuqua, 2000) provided a more generic framework. All of the previously

mentioned conceptual frameworks include biological, behavior and social components.

However, none were a complete match with the variables considered relevant to the

research topic.

The conceptual framework selected to support the examination of maternal

biological, behavior and social factors on fetal growth was theoretically guided by the

DOHaD (Gluckman & Hanson, 2006a). Maternal behavioral considerations were

additionally supported by ecology of human development (Bronfenbrenner, 1979). The

concepts included in the framework were identified through a review of the research

literature on maternal biological, behavior, and social factors that influence fetal growth.

The grouping of the components as illustrated in Figure 1 is a modification of the TREC-

IDEA framework (Lytle, 2009). This grouping was selected over the structure used in

the DOHaD model, as it clarifies the interaction (linkages) between the constructs. The

study framework is an ecological model that considers the biological, behavioral, and

social influences of the mother that influence fetal growth. It can be expanded to

include the community environment, as seen in the ecology of human development

framework (Bronfenbrenner, 1979), or further refined to include more detailed biological

components, as seen in the DOHaD framework (Gluckman & Hanson, 2006b). It has a

holistic bio-behavioral perspective that allows equal focus on both the person and

environment and as such is congruent with a nursing approach to the research topic.

15

Figure 1. Ecological model of social and maternal influences on fetal growth

Ecological Model of Maternal Influences on Fetal Growth

The ecological model places the fetus at the center of a series of nested

systems. It is based on the proposition that maternal biological/physiologic and

behavioral characteristics influence intrauterine environment and, therefore, fetal

growth. The social environment influences maternal behavioral and biological concepts.

The underlying proposition of the model is that fetal development occurs through a

complex reciprocal interaction with the mother; the form, power, and content of the

exchanges in utero influence fetal developmental plasticity (Gluckman, Hanson, Morton,

16

& Pinal, 2005). The framework aims to support the interactions of the social

environment, maternal characteristics, and risk factors on fetal growth. The ecological

perspective allows a multidimensional, multilevel, interactional view of the determinants

of health. Additionally, it allows the researcher to view the individual’s disposition,

resources, and characteristics that may influence health (Grzywacz & Fuqua, 2000). In

pregnancy, the individual (fetus) is totally dependent on the mother; therefore, the

researcher would be assessing maternal disposition, resources, and characteristics,

and not fetal. Changes within a system or interactions between systems may directly or

indirectly affect the fetus. Although the fetus is at the center of the framework, the

maternal characteristics were assessed.

Maternal characteristics were divided into biological and behavioral components.

Biological components were further separated into non-modifiable and modifiable. It

was proposed that non-modifiable biological and behavioral factors could influence

modifiable biological factors (Figure 1). For example, both age (via metabolic rate) and

substance use influence weight gain and subsequently BMI. Maternal health status was

treated as both non-modifiable and modifiable. Non-modifiable health characteristics

were classified as medical conditions that may include congenital anomalies as well as

persistent medical conditions that cannot be resolved with treatment (i.e., mitral valve

stenosis). Modifiable health conditions may include problems such as hypertension,

metabolic syndrome, and diabetes mellitus, as they are potentially modifiable with

treatment. All maternal characteristics were seen to be influential on fetal growth.

Social environment was perceived to be influential on maternal behavioral and

modifiable biological factors.

17

This framework is not an exhaustive list of all the components that may influence

fetal growth; rather, it is representative of the variables that were available for this

research study. The framework could be extended to include community and cultural

beliefs. Additionally, the framework could include a time continuum to show the

interrelationship of all of these factors through childhood.

Strengths and Limitations

Ecological models are considered holistic models that recognize the interactions

of an individual’s actions and social environment on health, while allowing an equal

focus on both the individual and the environment (Grzywacz & Fuqua, 2000).

Ecological models are often complex, and it can be difficult to tease out salient variables

related to health outcome. To address this issue, researchers using the ecological

framework appear to have only included the concepts directly relevant to their research

topic (Abu-Saad & Fraser, 2010; Davidson et al., 2007; Egger & Swinburn, 1997;

Gluckman & Hanson, 2006a; Grzywacz & Fuqua, 2000; Kuh & Ben-Shlomo, 1997;

Lytle, 2009; McMillen & Robinson, 2005; Rasmussen & Yaktine, 2009; Reifsnider &

Ritsema, 2008; Viswanathan et al., 2008).

Concepts within the Study Conceptual Framework

In this study, the outcome variable was infant birth weight indices. Infant birth

weight indices were generated from birth weight. Birth weight represents fetal growth

and development and is a biomarker for neonatal (short-term) and childhood (long-term)

well-being (morbidity and mortality); however, it is a gross measure of weight attained at

the end of pregnancy. Birth weight indices adjust birth weight for known factors that

influence fetal growth, allowing a refined comparison between infants. The exposure

18

(predictor) variable was maternal adiposity. Adiposity is the proportion of body fat (Mai,

Owl, & Kersting, 2005) and was operationalized as body mass index (BMI). Body mass

index is a simple method to estimate adiposity levels generated from an individual’s

height and weight.

Fetal growth is influenced by non-modifiable and modifiable factors that include

genetic predisposition as well as maternal and environmental factors. Maternal

variables were included as covariants. The covariates included: maternal age; parity;

health status (pre-existing medical conditions and gestational complications);

gestational weight gain; substance use (alcohol and tobacco use); and education level.

Social covariants included ethnicity, education, marital status, presence of a support

person and health insurance. Maternal ethnicity was a marker for socio-demographic

constructs that influence health (Lillie-Blanton & Laveist, 1996); these included

residential neighborhood, level of education attained, occupation, and income level.

Education, marital status, support and Medicaid coverage were the only socio-

demographic constructs available in the data set selected for this study.

Review of the Literature

Search Process

A literature search was performed using PubMed and the key terms: pregnancy;

maternal obesity; birth weight; body weight; and obesity. These broad terms yielded a

plethora of articles. The search terms were then combined in an attempt to limit total

volume and improve relevance. After the initial search, a separate search was

performed to identify articles that addressed ethnicity, socio-demographic factors and

pregnancy outcomes. No limitations were placed on dates or study design. Articles

19

were limited to the English language. Studies needed to be of singleton pregnancies

and include infants born at 37 weeks' or more gestation (term). Studies that were

primarily interested in the influence of diabetes on birth weight were not included in the

final review. Reference lists were used to obtain additional publications relevant to the

research topic. Twenty-nine publications reported birth weight in relation to maternal

pre-pregnant weight. The publication years were from 1968 to 2009. Only articles that

stratified and reported high birth weight were included, resulting in 24 publications that

spanned the years 2001 to 2009.

Outcome Variable: Infant Birth Weight

The exact mechanisms that influence infant birth weight are not clearly known.

Both intrinsic and extrinsic factors influence birth weight. Intrinsic factors are

considered to be non-modifiable, including: genetics; parity; maternal age; fetal gender

and congenital or non-modifiable medical conditions. Extrinsic factors are potentially

modifiable, including: maternal health status (pre-existing and gestational); pre-pregnant

BMI; nutritional intake; maternal gestational weight gain; smoking; and physical activity,

as well as social, cultural and environmental factors such as altitude of residence (Lain

& Catalano, 2006).

Gestational age is an important contributor to birth weight (Catalano, Drago, &

Amini, 1995; Wilcox & Skjaerven, 1992). Birth weight can be stratified by an absolute

weight or population-specific indices (centile values, z-scores, and ponderal index).

When defining high birth weight, there is no set cut-off point; however, the 90th

percentile is the most commonly used criterion. Macrosomia is the category used to

classify high birth weight when stratifying by absolute weight and the lowest accepted

20

cut-point is 4,000 grams (g). This cut-off point is said to represent the 90th percentile at

40 weeks' gestation (Jolly et al., 2003). However, some researchers and clinicians have

set the cut-off at 4,500 or 5,000 g based on the clinical impact of morbidity (Zhang et al.,

2008).

Birth weight centile values are created using population standards that adjust for

known influences on fetal growth, which include gestation, infant gender, birth order

(maternal parity) and ethnicity. There are three clinical centile value categories: small-

for-gestational-age (SGA), appropriate-for-gestational-age (AGA), and large-for-

gestational-age (LGA). Large-for-gestational-age is the category used to define high

birth weight infants. The most common cut-off point is the 90th percentile, but it can be

set at the 95th percentile or > 2 standard deviations (SD) above the mean. The ponderal

index is calculated from birth weight and infant length. It is considered the BMI

equivalent for infants, as it represents infant adiposity levels. It allows the comparison

of the long lean infant to the shorter but lighter infant. A birth weight z-score is birth

weight expressed as a standard deviation unit from the mean birth weight, which has

been adjusted for gestational age and infant gender. Birth weight indices are

considered a more refined assessment of high birth weight, as they allow adjustment of

raw birth weight using known intrinsic and extrinsic factors that influence fetal growth.

Three methods of high birth weight stratification were used in the studies

reviewed: macrosomia; centile values; and z-scores (Table I). Macrosomia was the

predominant method and was used in 17 studies (Abenhaim, Kinch, Morin, Benjamin, &

Usher, 2007; Bhattacharya, Campbell, & Liston, 2007; Burstein, Levy, Mazor, Wiznitzer,

& Sheiner, 2008; Driul et al., 2008; Frederick, Williams, Sales, Martin, & Killien, 2008;

21

Gilboa, Correa, & Alverson, 2008; Jolly et al., 2003; Kabali & Werler, 2007; Kumari,

2001; Lu et al., 2001; Orskou, Henriksen, Kesmodel, & Secher, 2003; Ramos &

Caughey, 2005; Rode et al., 2007; Sewell et al., 2006; Usha Kiran et al., 2005; Vesco et

al., 2009; Weiss et al., 2004) . Of the 17 studies, 12 studies used a 4,000 g cut-off

(Bhattacharya et al., 2007; Burstein et al., 2008; Driul et al., 2008; Frederick et al., 2008;

Jolly et al., 2003; Kabali & Werler, 2007; Kumari, 2001; Orskou et al., 2003; Rode et al.,

2007; Sewell et al., 2006; Usha Kiran et al., 2005; Vesco et al., 2009), while another

four studies used 4,500 g (Abenhaim et al., 2007; Gilboa et al., 2008; Lu & Halfon,

2003; Ramos & Caughey, 2005), and one study considered both 4,000 g and 4,500 g

(Weiss et al., 2004).

22 TABLE I

DESCRIPTIVE DETAILS OF STUDIES REVIEWED

Author and Year Design Country Sample size Stratification Method and Level

Abenhaim, 2007 Retrospective Canada 18,633 Macro > 4.5kg Bhattacharya, 2007 Retrospective Scotland 24,241 Macro > 4kg Burstein, 2008 Prospective Israel 376 Macro > 4kg Callaway, 2006 Retrospective Australia 11,252 BW z-score Cedergren, 2006 Retrospective Sweden 245,526 LGA > 2 SD Driul, 2008 Retrospective Italy 916 Macro > 4kg Frederick, 2008 Prospective USA 2,670 Macro > 4kg Getahun, 2007 Retrospective USA 146,227 LGA > 90th Gilboa, 2008 Retrospective USA 3,226 Macro > 4.5kg & LGA > 90th Jolly, 2003 Retrospective UK 350,311 Macro > 4kg & LGA > 90th Kabali, 2007 Retrospective USA & Canada 815 Macro > 4kg Kumari, 2001 Retrospective UAE 488 Macro > 4kg Lu, 2001 Retrospective USA 53,080 Macro > 4.5kg & LGA > 90th Magriples, 2009 Retrospective USA 841 LGA > 90th Nohr, 2008 Retrospective Denmark 60,892 LGA > 90th Orskou, 2003 Prospective Denmark 24,093 Macro > 4kg Ramos, 2005 Retrospective USA 22,658 Macro > 4.5kg Rode, 2007 Retrospective Denmark 2,248 Macro > 4kg Sebire, 2001 Retrospective UK 287,213 LGA > 90th Sewell, 2006 Retrospective USA 76 Macro > 4kg Surkan, 2004 Retrospective Sweden 861,608 LGA > 2 SD Usha Kiran, 2005 Retrospective UK 8,350 Macro > 4kg Vesco, 2009 Retrospective USA 12,146 Macro > 4kg Weiss, 2004 Retrospective USA 16,102 Macro > 4kg & > 4.5kg

23

Large-for-gestational-age was the criterion in nine studies (Cedergren, 2006;

Getahun, Ananth, Peltier, Salihu, & Scorza, 2007; Gilboa et al., 2008; Jolly et al., 2003;

Lu et al., 2001; Magriples, Kershaw, Rising, Westdahl, & Ickovics, 2009; Nohr et al.,

2008; Sebire et al., 2001; Surkan et al., 2004); seven studies used the 90th percentile as

the cut-off level (Getahun et al., 2007; Gilboa et al., 2008; Jolly et al., 2003; Lu & Halfon,

2003; Magriples et al., 2009; Nohr et al., 2008; Sebire et al., 2001), while two studies

set the cut-off at 2 SD > mean which equates to > 97th percentile (Cedergren, 2006;

Surkan et al., 2004). Three studies examined both macrosomia and LGA (Gilboa et al.,

2008; Jolly et al., 2003; Lu et al., 2001); one study chose to specifically determine

differences between the two criteria (Jolly et al., 2003). Z-scores were used in one

study only (Callaway, Prins, Chang, & McIntyre, 2006).

Fifteen studies focused on perinatal outcomes associated with increased

maternal pre-pregnant BMI (Abenhaim et al., 2007; Bhattacharya et al., 2007; Burstein

et al., 2008; Callaway et al., 2006; Cedergren, 2006; Driul et al., 2008; Kumari, 2001; Lu

et al., 2001; Magriples et al., 2009; Nohr et al., 2008; Ramos & Caughey, 2005; Sebire

et al., 2001; Usha Kiran et al., 2005; Vesco et al., 2009; Weiss et al., 2004). Seven

studies focused on birth weight (Frederick et al., 2008; Gilboa et al., 2008; Jolly et al.,

2003; Kabali & Werler, 2007; Orskou et al., 2003; Rode et al., 2007; Surkan et al.,

2004). Three studies examined the relationship of pre-pregnant BMI and gestational

weight gain on birth weight (Frederick et al., 2008; Kabali & Werler, 2007; Rode et al.,

2007), and one study focused on how changes in pre-pregnant BMI from first to second

pregnancy influenced birth weight (Getahun et al., 2007). One study examined the

impact of maternal adiposity on infant lean and fat mass (Sewell et al., 2006).

24

Exposure Variables

Maternal Biological Modifiable Characteristics

Body mass index. Body mass index is a formula to calculate percent adipose

mass. It is not a precise measure of adiposity but rather a relative estimation calculated

from weight and height. It has a high correlation with adipose mass (Willett, Dietz, &

Colditz, 1999), including in obese women prior to and during pregnancy (Sewell,

Huston-Presley, Amini, & Catalano, 2007). Research has shown that adiposity levels

influence long-term health status, especially the risk of cardiovascular disease (NHLBI,

1998). Body mass index categories have been developed based on the relative risks

for cardiovascular morbidity. With ongoing research, there have been minor

adjustments to the BMI categories. The current international and US BMI categories

are: Underweight < 18.5 kg/m2; Ideal Weight 18.5–24.9 kg/m2; Overweight 25–29.9

kg/m2; and Obese ≥ 30 kg/m2. The obese category has three subcategories: Obese

class I: 30–34.9 kg/m2; Obese class II: 35–39.9 kg/m2; and Obese (extreme) class III: ≥

40 kg/m2 (NHLBI, 1998). Although BMI categories are used in pregnancy to guide

gestational weight gain, the Institute of Medicine admitted that the stratification is

arbitrary, “as there has been no weight for height classification schemes that has been

validated against pregnancy outcomes” (Weiss et al., 2004, p. 1092).

Pre-pregnant body mass index and birth weight. There is an established

association between maternal pre-pregnant BMI and infant birth weight; heavier women

have infants with a higher mean birth weight than lean women (Abrams & Laros, 1986;

Eastman & Jackson, 1968; Peckham & Christianson, 1971). Traditionally, "being

heavier at the onset of pregnancy" was considered beneficial as it reduced the risk of

25

having a low birth weight (LBW) infant, which was at increased risk of morbidity and

mortality (Committee to study the prevention of low birth weight, 1985). However, more

recently, it has been noted that excess maternal adiposity (BMI > 25 kg/m2) may

contribute to high birth weight infants. High birth weight infants are also at increased

risk for morbidity (Kramer, 2003), although to a lesser extent than LBW infants.

Research suggests that high birth weight infants are at increased risk of

childhood (Wang, Liang, Junfen, & Lizhong, 2007; Whitaker, 2004) and adult obesity

(Stuebe et al., 2009), as well as the associated metabolic disorders such as diabetes

(Boney et al., 2005). Over the last several decades, data from both North America and

Europe have shown a progressive increase in median birth weight and high birth weight

infants that can be attributed to increasing maternal pre-pregnant weight (Ananth &

Wen, 2002; Surkan et al., 2004). Currently 59.5% of women between aged 20–39

years in the United States are overweight or obese (Flegal et al., 2010). It is important

to understand the impact of increased maternal adiposity on the risk for high birth

weight, as it may create an unhealthy cycle of obesity and associated comorbidities.

Maternal pre-pregnant obesity and high birth weight. Four studies

(Abenhaim et al., 2007; Callaway et al., 2006; Gilboa et al., 2008; Kumari, 2001)

examined pre-pregnant obesity class III (BMI > 40 kg/m2) (Table II). Gilboa and co-

workers (2008) found a sevenfold (AOR 7.43, 95% CI [1.92, 28.67]) increased risk for

macrosomia (> 4,500 g) and a fourfold (AOR 4.39, [2.01, 9.61]) increased risk for LGA

(> 90th percentile) with a pre-pregnant BMI of 47 kg/m2 (referent BMI 21 kg/m2).

However, Kumari (2001) only found a threefold increased risk (AOR 3.3, [2.0, 5.5]) for

macrosomia (> 4,000g) in the infants of women with pre-pregnancy class III obesity

26

compared to the non-obese group (BMI 22–28 kg/m2). Callaway, Prins, Chang and

McIntyre (2006) identified the highest birth weight z-score (0.4) in the obese class III

group compared to the ideal BMI category. Abenhaim, Kinch, Morin, Benjamin and

Usher (2007) identified an increased risk for macrosomia (> 4,500 g) in the infants of

women with obesity class III; however, it was not statistically significant, although there

was a significant increased risk in the combined obesity class I and II group (AOR 2.32,

[1.58, 3.41]). Three of the four studies that examined class III obesity found significantly

increased risks for high infant birth weight (Callaway et al., 2006; Gilboa et al., 2008;

Kumari, 2001). As expected, obesity class III had the highest risk for high birth weight

compared to the other BMI categories.

27

TABLE II

RISK OF HIGH BIRTH WEIGHT BY PRE-PREGNANT OBESITY: CLASSES I, II & III

Author Referent category

Obesity Class stratification Birth weight classification

Obese class I Obese class II Obese class III

Class I Class II Class III (95% CI) (95% CI) (95% CI)

Abenhaim 20–24.9 * 30–39.9* ≥ 40 * Macro > 4.5kg AOR 2.32 (1.58, 3.41)

NS OR 2.1 (0.64, 6.86)

Bhattacharya 19.8–24.9 * 30–34.9 * > 35 * Macro > 4 g AOR 1.9 (1.6, 2.2)

AOR 2.1 (1.3, 3.2)

Callaway > 20–25 * > 30–40 * > 40 * BW z-score p < .001 p < .001

Cedergren GWG > 16 30–34.9 * ≥ 35 * LGA > 2 SD AOR 2.24 (2.0,2.51)

AOR 1.54 (1.24, 1.9)

Gilboa 21* 35–40 * > 40 * Macro > 4.5kg AOR 3.23 (1.47, 7.09)

AOR 7.43 (1.92, 28.67)

“ 21* LGA > 90th AOR 2.32 (1.55, 3.45)

AOR 4.39 (2.01, 9.61)

Kumari < 40* > 40 * Macro > 4kg AOR 3.3 (2.1, 5.5)

Weiss < 30 * 30–34.9 * ≥ 35 * Macro > 4kg AOR 1.7 (1.4,2.0)

AOR 2.0 (1.5, 2.3)

“ < 30 * 30–34.9 * ≥ 35 * Macro > 4.5kg AOR 2.0 (1.4, 3.0)

AOR 2.4 (1.5, 3.8)

*BMI kg/m2; GWG: gestational weight gain – kilograms; Macro – macrosomia; BW – birth weight; LGA – large-for-gestational-age;

28

Five studies examined obesity class II (Abenhaim et al., 2007; Bhattacharya et

al., 2007; Callaway et al., 2006; Gilboa et al., 2008; Weiss et al., 2004), and all found an

increased risk for high birth weight. Weiss et al. (2004) examined the risk for

macrosomia at two levels (> 4,000 g & > 4,500 g) in maternal obesity classes I and II

compared to non-obese (< 30 kg/m2) counterparts. The obesity class II subjects had

the highest risk of delivering a macrosomic infant compared to the class I subjects at

both levels of macrosomia. However, it was unexpected to see a higher risk for

macrosomia at the 4,500 g level than the 4,000 g level in both obesity classes (Table II).

Bhattacharya, Campbell and Liston (2007) and Cedergren (2006) both compared

obesity class I and II to ideal pre-pregnant BMI counterparts. Bhattacharya et al. (2007)

found an increasing risk for macrosomia (> 4,000 g) with increasing BMI: obesity class I

(AOR 1.9, 95% CI [1.6, 2.2]) and obesity class II (AOR 2.1 [1.3, 3.2]). However,

Cedergren (2006) found a reduced risk for LGA (> 2 SD) as pre-pregnant BMI

increased: obesity class I (AOR 2.24 [2.0, 2.5]); obesity class II (AOR 1.54 [1.2,1.90]).

Gilboa et al. (2008) examined BMI as both a continuous and categorical variable. The

continuous data showed a monotonic increase in the risk for both macrosomia and LGA

for every two units that BMI increased above pre-pregnant BMI 30 kg/m2. The risk of

LGA (> 90th percentile) progressively increased from AOR 1.58 [1.12, 2.22] at BMI 31

kg/m2 to AOR 2.63 [1.66, 4.17] at BMI 39 m/kg2. The five studies that examined obesity

class II included macrosomia at both levels as well as LGA > 90th percentile and > 2 SD

(Table II) (Abenhaim et al., 2007; Bhattacharya et al., 2007; Cedergren, 2006; Gilboa et

al., 2008; Weiss et al., 2004). The strictest criteria, LGA > 2 SD, identified the lowest

level of increased risk (AOR 1.54 [1.2,1.90]); however, this was the only study

29

(Cedergren, 2006) in this subset that used gestational weight gain as the referent for

comparison. Among the remaining studies, all had similar AOR ranging from 2.0 to 3.23

with 95% CIs between 1.3 and 7.09.

Three studies examined obesity class I (Bhattacharya et al., 2007; Cedergren,

2006; Weiss et al., 2004) (Table II). All three studies found an increased risk for high

birth weight. Bhattacharya et al. (2007) and Weiss et al. (2004) both found an

increased risk of macrosomia (> 4,000 g) (AOR 1.9 95% CI [1.6, 2.2]) and (AOR 1.7

[1.4, 2.0], respectively); despite the fact that Bhattacharya et al. (2007) used 19.8–24.9

kg/m2 as the referent group while Weiss et al. used BMI < 30 kg/m2. All of the studies

that examined obesity class I and/or II found significant results, despite the variations in

the criteria for both high birth weight and referent BMI.

Fourteen studies examined obesity without class limitations (BMI ~ > 29 kg/m2)

(Burstein et al., 2008; Driul et al., 2008; Frederick et al., 2008; Getahun et al., 2007;

Gilboa et al., 2008; Jolly et al., 2003; Lu et al., 2001; Magriples et al., 2009; Nohr et al.,

2008; Ramos & Caughey, 2005; Rode et al., 2007; Sebire et al., 2001; Surkan et al.,

2004; Usha Kiran et al., 2005) (Table III). Ramos and Caughey (2005) found a

threefold increased risk for macrosomia (> 4,500 g) in obese White, Latino, and Asian

women compared to ideal BMI counterparts of the same ethnic subset; however, there

was a reduced risk for macrosomia in African American women. This finding was

unexpected, as the African American subset had the highest proportion (22%) of

women with a pre-pregnant BMI > 29 kg/m2. Only two studies (Magriples et al., 2009;

Rode et al., 2007) failed to find a significant increase in the risk for high birth weight with

BMI ~ > 29 kg/m2. Rode et al. (2007) examined macrosomia (> 4,000 g), while

30

Magriples, Kershaw, Rising, Westdahl and Ickovics (2009) examined LGA > 90th

percentile; however, in both cases, gestational weight gain was used as the primary

referent. Two other studies also used gestational weight gain as a referent and still

found a significant increased risk for high birth weight (Frederick et al., 2008; Nohr et al.,

2008).

31

TABLE III

HIGH BIRTH WEIGHT RISK BY PRE-PREGNANT OBESITY CATEGORY

Author & year Referent category * = referent Obesity category High birth weight

stratification High birth weight risk

GWG kg BMI kg/m2 BMI kg/m2 CI

Burstein, 2008 < 30* ≥ 30* Macro > 4kg 11.4 % p < .006 Driul, 2008 18.5–24.9 * ≥ 30* Macro > 4kg AOR 2.58 (1.077, 6.2) Ÿ Frederick, 2008 GWG * 19.8–26 * ≥ 29 Macro > 4kg ARR 1.65 (1.29, 2.11) Getahun, 2007 18.5–24.9* ≥ 30 LGA > 90th AOR 2.3 (2.2, 2.4) Ÿ Gilboa, 2008 18.5–24.9 * ≥ 30 Macro > 4.5kg AOR 2.07 (No CI given) “ “ 18.5–24.9 * ≥ 30 LGA > 90th AOR 1.96 (1.34, 2.88) Ÿ Jolly, 2003 20–24 * > 30 Macro > 4kg OR 1.97 (1.88, 2.06) † “ “ 20–24 * > 30 LGA > 90th OR 2.08 (1.99, 2.17) † Lu, 2001 < 29 * > 29 Macro > 4.5kg AOR 2.4 (1.7, 3.6) Ÿ “ “ < 29 * > 29 LGA > 90th AOR 2.2 (1.9, 2.4) Ÿ Magriples, 2009 GWG * 19.8–25.9 * > 30 LGA > 90th NS Nohr, 2008 GWG * 18.5–24.9 * ≥ 30 LGA > 90th AOR 2.9 (2.7, 3.2) Ÿ Ramos, 2005 19.8–25.9 * > 29 Macro > 4.5 kg AOR 3.04 (1.86, 4.98) Ÿ Rode, 2007 GWG * 19.8–26 > 29 Macro > 4kg NS Sebire, 2001 20–25 * > 30 LGA > 90th AOR 2.36 (2.23, 2.50) † Surkan, 2004 20–24.9 * ≥ 30 LGA > 2 SD AOR 3.28 (3.16, 3.41) Ÿ Usha Kiran, 2005 20–30 * > 30 Macro > 4kg AOR 2.1 (1.6,2.6) Ÿ

GWG – gestational weight gain - kilograms; Macro – macrosomia; LGA – large-for-gestational-age; ARR –

Adjusted risk ratio; Confidence Interval (CI), Ÿ 95%, † 99% C

32

In an interesting analysis, Getahun, Ananth, Peltier and Salihu (2007) examined

how changes in pre-pregnant BMI over two consecutive pregnancies influenced the risk

of LGA in the second pregnancy. Being obese in the second pregnancy had a greater

risk for LGA (> 90th percentile) regardless of BMI status in the first pregnancy.

Remaining obese in both pregnancies had a twofold risk (AOR 2.3, 95% CI [2.2, 2.4])

for LGA, although the highest risk occurred if the subject was underweight in the first

pregnancy and obese in the second pregnancy (AOR 3.0 [1.5, 5.8]).

The trend of increased risk for high birth weight continued among women who

had pre-pregnant obesity. The risk for high birth weight ranged from AOR 1.65 to 3.28

(Driul et al., 2008; Frederick et al., 2008; Getahun et al., 2007; Gilboa et al., 2008; Jolly

et al., 2003; Lu et al., 2001; Nohr et al., 2008; Ramos & Caughey, 2005; Sebire et al.,

2001; Surkan et al., 2004; Usha Kiran et al., 2005), with the 95% CI ranging from 1.07

to 6.2. Ten studies used ideal BMI as the referent (Driul et al., 2008; Frederick et al.,

2008; Getahun et al., 2007; Gilboa et al., 2008; Jolly et al., 2003; Magriples et al., 2009;

Nohr et al., 2008; Ramos & Caughey, 2005; Sebire et al., 2001; Surkan et al., 2004),

while two studies used BMI < 29 kg/m2 (Burstein et al., 2008; Lu et al., 2001); however,

this variation did not alter the associated risk identified.

Thirteen of the 18 studies that compared overweight pre-pregnant BMI (~25–30

kg/m2) to ideal pre-pregnant BMI (~18.5–24.9 kg/m2) found a significant increase for the

risk of high birth weight in the overweight group (Abenhaim et al., 2007; Bhattacharya et

al., 2007; Callaway et al., 2006; Cedergren, 2006; Getahun et al., 2007; Jolly et al.,

2003; Kabali & Werler, 2007; Nohr et al., 2008; Ramos & Caughey, 2005; Sebire et al.,

2001; Sewell et al., 2006; Surkan et al., 2004; Vesco et al., 2009) (Table IV). The trend

33

of increased risk ranged between AOR 1.4 and 2.6 and included both macrosomia and

LGA. Within the significant results, the 95% CI was narrow (1.23 to 2.8); however,

among the non-significant results, the range was wider (0.8 to 4.7). Two larger studies

were able to set the CI at 99% and continued to achieve significance (Jolly et al., 2003;

Sebire et al., 2001).

34

TABLE IV

HIGH BIRTH WEIGHT RISK BY PRE-PREGNANT OVERWEIGHT CATEGORY

Author Referent category * = referent Overweight category

High birth weight stratification High birth weight risk

GWG – kg BMI kg/m2 kg/m2 Abenhaim, 2007 20–24.9 * 25–29.9 Macro > 4.5kg AOR 1.66 (1.23,2.24) Ÿ Bhattacharya, 2007 19.8–24.9 * 25–29.9 Macro > 4kg AOR 1.4 (1.3,1.6) Ÿ Callaway, 2006 >20–25 * >25–30 BW z-scores p < .001 Cedergren, 2006 GWG * 20–24.9 25–29.9 LGA > 2 SD AOR 2.14 (2.01, 2.28) Ÿ Driul, 2008 18.5–24.9 * 25–29.9 Macro > 4kg NS 1.96 (0.95, 4.05) Ÿ Frederick, 2008 GWG * 19.8–26 * 26.1–29 Macro > 4kg NS ARR (0.84,1.57) Ÿ Getahun, 2007 18.5–24.9* 25–29.9 LGA > 90th AOR 1.7 (1.6,1.8) Ÿ Gilboa, 2008 18.5 – <25 * 25–29.9 Macro > 4.5kg NS 1.77 (0.88,3.55) Ÿ “ “ 18.5 – <25 * 25–29.9 LGA > 90th NS 1.33 (0.95,1.86) Ÿ Jolly, 2003 20–24 * 25–30 Macro > 4kg OR 1.54 (1.48,1.60) † “ “ 20–24 * 25–30 LGA > 90th OR 1.56 (1.5,1.62) † Kabali, 2007 GWG * 19.8–26 > 26 Macro > 4kg AOR 2.6 (1.2, 5.4) Ÿ Magriples, 2009 GWG * 19.8–25.9 * 26–30 LGA > 90th NS 2.16 (0.98 4.7) Ÿ Nohr, 2008 GWG * 18.5–24.9 * 25–29.9 LGA > 90th AOR 1.7 (1.6,1.8) Ÿ Ramos, 2005 19.8–25.9 * 26–29 Macro > 4.5kg 3% p < .05 Rode, 2007 GWG * 19.8–26 26.1– 29 Macro > 4kg NS 1.8 (0.8, 3.09) Ÿ Sebire, 2001 20–25 * 25 – <30 LGA > 90th AOR 1.57 (1.5, 1.64) † Sewell, 2006 < 25* ≥ 25 Macro > 4kg 14.4% p < .04 Surkan, 2004 20–24.9 * 25–29.9 LGA > 2 SD AOR 1.96 (1.90, 2.02) Ÿ Vesco, 2009 < 25 * ≥ 25 Macro > 4kg 20% p < .001

GWG – gestational weight gain (kilograms); Macro – macrosomia; LGA – large-for-gestational-age; Confidence Interval (CI), Ÿ 95%, † 99% CI

35

Three studies found a significant increase in the rate of high birth weight but did

not provide predicted odds (Ramos & Caughey, 2005; Sewell et al., 2006; Vesco et al.,

2009). Of the five studies that did not find a significant increase in the risk of high birth

weight, three studies (Frederick et al., 2008; Magriples et al., 2009; Rode et al., 2007)

used gestational weight gain as the referent. The remaining two studies (Driul et al.,

2008; Gilboa et al., 2008) both excluded subjects who were diabetic. However, six

studies that used the overweight category reported significance even after excluding or

adjusting for subjects who had diabetes (Callaway et al., 2006; Getahun et al., 2007;

Kabali & Werler, 2007; Ramos & Caughey, 2005; Sewell et al., 2006; Surkan et al.,

2004). Overweight is the lowest category of excess adiposity, and it had the lowest

range of risk for high birth weight infants and the highest proportion of non-significant

results.

The overall trend of the studies suggests that excess pre-pregnant adiposity

significantly increases the risk for high birth weight. The higher the level of adiposity

(obesity class II and III), the greater the increased risk. It is unclear why Abenhaim et

al. (2007) failed to find a significant increased risk in obesity class III but did so at the

lower obesity classes. Possibly the small sample of 104 subjects with obesity class III

among the 18,000 subjects contributed to this finding. There was no clear difference in

the predicted odds between the different methods of high birth weight stratification. The

variation in referent BMI category may have contributed to the failure to identify

differences between high birth weight stratification methods. It is unclear why some

researchers choose either overweight or obese BMI category as the referent; it is likely

that the use of ideal BMI may have increased the risk of high birth weight.

36

Gestational weight gain. Researchers have long been interested in the

influence gestational weight gain on birth weight (Eastman & Jackson, 1968; Peckham

& Christianson, 1971); however, the focus has predominantly been on the prevention of

LBW. Research suggests that the pattern of gestational weight gain over the

pregnancy, as well as total amount of gestational weight gain, influences infant birth

weight (Abrams & Selvin, 1995; Carmichael, Abrams, & Selvin, 1997). In 1990, the

Institute of Medicine issued a report on nutrition during pregnancy (Committee on

Nutritional Status During Pregnancy and Lactation, 1990) that recommended that

gestational weight gain be based on pre-pregnant BMI. This was the first time BMI

categories had been used in pregnancy guidelines. At the time, there was very limited

research that demonstrated the impact of gestational weight gain on infant birth weight

in obese women. Much of the recommendations for overweight and obese women

were based on the Abrams and Laros (1986) study that identified a poor correlation

between gestational weight gain and birth weight within these two groups. Optimal

gestational weight gain has not been established in either ideal weight or obese women

(Catalano, 2007). With the ongoing increase in the prevalence of obesity in

reproductive aged women (Flegal et al., 2010), there has been increased interest in the

impact of gestational weight gain in overweight and obese women.

Seven studies specifically examined how gestational weight gain influenced the

pre-pregnant BMI-birth weight relationship (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). Six of the seven studies used gestational weight gain as the referent

for analysis (Cedergren, 2006; Frederick et al., 2008; Kabali & Werler, 2007; Magriples

37

et al., 2009; Nohr et al., 2008; Rode et al., 2007), while the remaining study (Getahun et

al., 2007) adjusted for gestational weight gain during the analysis. Five of the seven

studies found an increased risk of high birth weight with increased pre-pregnant BMI

(Cedergren, 2006; Frederick et al., 2008; Getahun et al., 2007; Kabali & Werler, 2007;

Nohr et al., 2008). All the studies stratified gestational weight gain and then used a

criterion of gaining (less than, within, or above the range) in an attempt to identify a

relationship with infant birth weight. Two studies (Cedergren, 2006; Nohr et al., 2008)

used absolute values for gestational weight gain, while the remaining studies used

gestational weight gain ranges based on pre-pregnant BMI (Frederick et al., 2008;

Getahun et al., 2007; Kabali & Werler, 2007; Magriples et al., 2009; Rode et al., 2007).

Cedergren (2006); Frederick, Williams, Sales, Martin and Killien (2008); and

Nohr et al. (2008) all examined the influence of BMI and gestational weight gain on birth

weight separately, then used gestational weight gain as the referent. Frederick et al.

(2008) identified gestational weight gain as a modifier between BMI and birth weight.

Nohr et al. (2008) identified that pre-pregnant BMI was a bigger contributor to LGA than

gestational weight gain in both the overweight and obese groups. Frederick et al.

(2008) found that gestational weight gain did not significantly alter the association for

macrosomia in the overweight and obese groups; however, gestational weight gain did

alter the association in lean and ideal BMI groups. There was a significant interaction (p

< .01) between pre-pregnant BMI and gestational weight gain in relation to macrosomia

(≥ 4,000 g) but not raw birth weight. Fredrick et al. (2008) found the greatest risk for

macrosomia was in pre-pregnant overweight and obese women whose weight gain

exceeded the median value of 15.9 kg. However, Magriples et al. (2009) found that

38

although excess gestational weight gain was significantly associated with LGA, there

was no interaction effect between BMI and gestational weight gain. Excess gestational

weight gain significantly increased the risk for LGA in the overweight group but not in

the obese group. Cedergren (2006) found a significant increase in the risk for LGA with

excessive gestational weight gain (> 16 kg); however, the risk was lowest in obese and

overweight groups, respectively. Rode (2007) did not find an increased risk for LGA in

overweight and obese groups when controlling for gestational weight gain.

Comparison and identification of trends among the studies that considered

gestational weight gain and pre-pregnant BMI was challenging due to the variety of

analysis performed and reported. One theme that was present and consistent with past

findings (Abrams & Laros, 1986) was the reduced impact of gestational weight gain on

birth weight as pre-pregnant BMI increased. Gestational weight gain appears to be an

important covariant in the relationship between pre-pregnant BMI and high birth weight;

however, how gestational weight gain is handled during the analysis appears to strongly

influence the findings.

Gestational weight gain appears to mediate birth weight; however, increasing

pre-pregnant BMI moderates the effect of gestational weight gain on birth weight (Dietz,

Callaghan, & Sharma, 2009). Lean women with excess gestational weight gain have

the highest odds of delivering a LGA infant compared to overweight or obese women

AOR 2.5, 95% CI [2.0, 3.2]; 1.9 [1.7,2.1]; 1.6 [1.3,1.8], respectively (Dietz et al., 2009).

However, because increasing pre-pregnant BMI results in heavier infants, the

percentage of LGA was highest in the overweight (12.3%) and obese (13.8%)

categories, compared to 5.2% and 8.7% in the lean and ideal BMI categories,

39

respectively (Dietz et al., 2009). Di Cianni et al. (1996) found that pre-pregnant obesity

is more predictive of high birth weight than gestational weight gain. Separating the

influence of gestational weight gain and pre-pregnant BMI on birth weight is complex, as

both have an independent yet cumulative effect on high birth weight (Frederick et al.,

2008).

Maternal health status. Maternal health status, both prior to and during

pregnancy, can influence fetal growth and in turn infant birth weight. Pre-existing or

chronic hypertension is associated with poor fetal growth and SGA (Livingston, Maxwell,

& Sibai, 2003; Sibai, 2002). However, gestational hypertension and pre-eclampsia are

associated with both SGA and LGA (Xiong, Demianczuk, Buekens, & Saunders, 2000).

The increased risk for LGA is contrary to the common belief that both gestational

hypertension and pre-eclampsia are only associated with SGA. A more recent study

showed a strong association between increased pre-pregnant BMI and pre-eclampsia

(Yogev et al., 2010). It could be speculated that the increased pre-pregnant BMI has an

overriding influence on fetal growth. Increased maternal adiposity increases the risk for

gestational diabetes mellitus (GDM) (Chu et al., 2007). Diabetes Mellitus (DM) during

pregnancy is associated with increased fetal growth (Wong, Lee-Tannock, Amaraddio,

Chan, & McIntyre, 2006) and increased birth weight (HAPO Study Cooperative

Research Group, 2009).

Silva-Idos et al. (2005) found that women with pre-existing DM were more likely

to have a macrosomic or LBW infant compared to the general population; however, the

risk for macrosomia was greater than that of LBW. The level of glycemic control during

pregnancy appears to influence the incidence of high birth weight infants. Langer et al.

40

(2005) found that women who were untreated for GDM had a two- to fourfold increase

of delivering a high birth weight infant compared to non-diabetic women, while women

with GDM who received treatment had no significant increase in LGA.

The risk for macrosomia and/or LGA is greatest in obese women with GDM

(Ehrenberg et al., 2004; Ricart et al., 2005). Moses and Calvert (1995) examined

glucose levels in non-diabetic women and found that pre-pregnant BMI and maternal

age were both significant factors that increased the risk of a LGA infant; however,

glucose levels were not. Obesity is a risk factor for diabetes, and the combination of the

two increases the risk of a high birth weight infant. The Hyperglycemia and Adverse

Pregnancy Outcome (HAPO) study found that the population-attributable risk for a LGA

infant from obesity was far greater than pre-existing DM; obese women had 11 LGA

births in every 100 compared to 4 in every 100 for pre-existing DM (HAPO Study

Cooperative Research Group, 2009). Much of the past and current research has

focused on the factors that contribute to the risk for high birth weight infants in women

with diabetes. However, the prevalence of excess maternal adiposity is greater than

maternal diabetes. Excess maternal adiposity appears to be a greater risk factor for

delivery of a high birth weight infant.

Maternal Non-modifiable Biological Factors that Affect Fetal Growth and Birth

Weight

Maternal age. Maternal age has been shown to influence birth weight.

Adolescents have infants who weigh less than those of adult women (Buschman,

Foster, & Vickers, 2001; DuPlessis, Bell, & Richards, 1997). Data suggest that women

older than 40 years also have a significant risk for delivery of a LBW infant, although

41

women aged 35–39 years were at an increased risk for macrosomia compared to

women < 35 years of age (Cleary-Goldman et al., 2005). Jolly et al. (2003) identified an

increased risk of LGA in both 35–40 year olds and those > 40 years of age. However,

Oskou et al. (2003) used women > 35 years as the cut-off point and failed to find an

increased risk of macrosomia. The lower risk of macrosomia may be related to obstetric

intervention, as women > 35 years have a higher rate of fetal death in utero after 37

weeks (Reddy, Ko, & Willinger, 2006).

Genetic makeup. Research suggests that 31% of the variance in birth weight is

related to fetal genes, while maternal genes contribute 19%–22 % (Lunde, Melve,

Gjessing, Skjaerven, & Irgens, 2007). Paternal genes appear to have limited influence

on birth weight; however, their contribution appears to be related to infant length, in

particular femur length (Veena, Krishnaveni, Wills, Hill, & Fall, 2009). Maternal height

significantly contributes to infant length and head circumference as a combined

measure (truncal skeletal + head) (Veena et al., 2009). While more than 50% of the

variance in birth weight appears to be related to fetal and maternal genes, Lunde,

Melve, Gjessing, Skjaerven, and Irgens (2007) suggest that environmental factors

appear to contribute 9%–15% to birth weight variability. As genetic factors influence

birth length, consideration should be given to weight for length indices (ponderal index).

Parity. Maternal parity has an influence on mean birth weight; nulliparity is

significantly associated with birth weight (Frederick et al., 2008). First-born infants

consistently weigh less than subsequent offspring (Cogswell & Yip, 1995; Selvin &

Janerich, 1971), unless the first born was > 4,000 g (Wilcox, Chang, & Johnson, 1996).

The rate of high birth weight progressively increases with increasing parity (Cogswell,

42

Serdula, Hungerford, & Yip, 1995; Jolly et al., 2003). Catalano, Thomas, Avallone and

Amini (1995) found that parity with gestational age, pre-pregnant weight, and

gestational weight gain had a strong correlation with infant birth weight. Additionally,

parity contributed 17% of the variance in neonatal fat mass. Surkan, Hsieh, Johansson,

Dickman and Cnattingius (2004) found that increasing parity increased the risk of

macrosomia.

Maternal Behaviors that Influence Infant Birth Weight

Maternal substance use. Maternal substance usage during pregnancy is

associated with retarded fetal growth (Wilcox, 1993) rather than overgrowth. Maternal

smoking during pregnancy results in lower birth weight infants. Laml et al. (2000)

examined birth weight in smokers and non-smokers stratified by pre-pregnant BMI.

While birth weight was significantly increased with increasing pre-pregnant BMI, in each

maternal BMI category, the infants of the smokers were significantly smaller than the

infants of non-smokers. Boulet, Alexander, Salihu and Pass (2003) found that the rate

of maternal smoking significantly declined as macrosomia increased.

The influence of maternal alcohol consumption on fetal growth is less clear.

Mariscal et al. (2006) found that low alcohol consumption may reduce the risk of LBW,

while a higher level of consumption increased the risk of LBW. Brooke, Anderson,

Bland, Peacock and Stewart (1989) examined coffee and caffeine consumption on fetal

growth and found that birth weight was not significantly affected by coffee and caffeine.

Orskou, Henriksen, Kesmodel and Secher (2003) examined the maternal factors that

contribute to LGA and found an increase in the consumption of alcohol or caffeine

43

reduced the risk of LGA. Maternal substance use during pregnancy is not seen to be

associated with an increased risk of high birth weight; instead, it may reduce the risk.

Social Environment Influences on Birth Weight

Socioeconomic status. Socioeconomic status (SES) is a combined

assessment of social and economic constructs that influence standard of living, in

comparison to others. It includes but is not limited to education, occupation, income

(individual and household), and neighborhood resources. Disparities in SES have been

reported to contribute to variations in infant birth weight (Astone, Misra, & Lynch, 2007).

Fang, Madhavan and Alderman (1999) found that low community income increased the

incidence of LBW. Lower SES has been associated with LBW infants (Kramer, Seguin,

Lydon, & Goulet, 2000). Mean birth weight increases with higher maternal SES

(Laitinen, Power, & Jarvelin, 2001). Maternal BMI is inversely related to social class:

lower income women were more likely to be overweight than their higher income

counterparts (Laitinen et al., 2001).

The influence of SES on high birth weight is not clear. In a Canadian study, the

risk of delivering a high birth weight infant in non-smoking mothers varied by SES and

geographic regions (Dubois, Girard, & Tatone-Tokuda, 2007). In Quebec, women

within the highest two SES quintiles had the greatest risk (AOR 3.283, p < .0001) of a

delivering a high birth weight infant while in British Columbia women with the lowest two

SES quintiles had greatest risk (AOR 2.056, p < .0001) of delivering a high birth weight

infant. Socioeconomic status did not influence the risk of high birth weight in the

remaining three provinces in that study. Veena et al. (2009) found that maternal SES

score significantly contributed to neonatal fat mass but not infant length. Emanuel,

44

Kimpo and Moceri (2004) found that maternal anthropometric status (pre-pregnant

weight, maternal height and maternal birth weight) contributed more to the variance of

infant birth weight than any of the SES variables. Additionally, these results did not vary

by ethnic groups (Emanuel et al., 2004). Segregation and discrimination by

race/ethnicity has created disparities in SES; as such, race/ethnicity is often used as a

marker for SES disparities.

Race and ethnicity. Race and ethnicity are often used as markers in maternal-

child health outcomes; however, they are generally poorly defined and operationalized.

Race is a classification of people by phenotype, skin color being the primary

characteristic. Racial categories have assumed underlying genotypic distinctions;

however, scientific evidence demonstrates that race is not a biological marker

(Goodman, 2000). Ethnicity is a social construct that groups people by shared culture,

language or social inheritance (Ford & Harawa, 2010). Ethnic groups are

heterogeneous. They do not refer to national origins, as can be seen by the National

Institutes of Health (NIH) ethnic groups of Hispanic or non-Hispanic, either of which can

include people of European or African ancestry. Race and/or ethnicity are often used

as proxies for unmeasured SES variables, such as income status and residential

community.

Despite the inadequacies of race and ethnicity for stratification, there continues

to be use of these categories to assess maternal health and birth weight outcomes.

Studies have primarily examined LBW (Alexander et al., 2003; Colen, Geronimus,

Bound, & James, 2006; Madan et al., 2006; Ramos & Caughey, 2005; Shiono,

Klebanoff, Graubard, Berendes, & Rhoads, 1986; Shiono, Rauh, Park, Lederman, &

45

Zuskar, 1997; Vangen et al., 2002; Zambrana, Dunkel-Schetter, Collins, & Scrimshaw,

1999). Several researchers included SES variables (Colen et al., 2006; Madan et al.,

2006; Shiono et al., 1997; Vangen et al., 2002; Zambrana et al., 1999); however, most

studies failed to adequately operationalize race or ethnicity. Colen, Geronimus, Bound

and James (2006) performed an extensive longitudinal study that included three

generations to examine the influence of SES and family structure on the risk of LBW.

Ramos and Caughey (2005) examined the relationship between ethnicity and

increased pre-pregnant adiposity; however, education level was the only social variable

included. Pre-pregnant obesity increased the risk of having a macrosomic infant (>

4,500 g) in White, Latino and Asian women; however, obese African American women

had a reduced risk (AOR 0.33, 95% CI [0.04, 2.85]) of having a macrosomic infant

(Ramos & Caughey, 2005). Boulet et al. (2003) examined the risk factors for

macrosomia; the study included ethnicity as well as education level and marital status.

Both higher education and being married increased the risk for a macrosomic infant.

White and Native American women were more likely than African American or Latino

women to have a high birth weight infant (Alexander, Kogan, & Himes, 1999; Boulet et

al., 2003). Infants of White and Latino women were on average heavier and longer than

infants of African American women (Madan, Holland, Humbert, & Benitz, 2002; Thomas,

Peabody, Turnier, & Clark, 2000; Zhang & Bowes, 1995). A comparison of infants of

White and Asian women found that infants of White women were significantly heavier

(Janssen et al., 2007; Madan et al., 2002) and longer than infants of Asian women

(Madan et al., 2002), although Janssen et al. (2007) failed to find a significant difference

in length.

46

While biological variations are not the underlying mechanism for variations in

health outcomes among ethnic and racial groups, phenotype continues to be the prime

method for categorization. To fully understand why health inequalities exist between

groups, the underlying socio-demographic factors need to be studied in more detail.

These would include social, demographic and cultural factors such as educational

achievements, income, family structure, social support, cultural practices and

community resources that contribute to maternal and infant health.

Education. Years of education or degree obtained were rarely assessed in

studies related to high birth weight infants. Mothers with a higher education level were

more likely to have a macrosomic infant (Boulet et al., 2003). In addition, lower

education levels were found to reduce the risk of LGA, although the reference range

was set at > 10 years (Orskou et al., 2003). Two studies (Astone et al., 2007; Silva et

al., 2010) found a significant association between lower education levels and lower

mean birth weight. Maternal education, measured as not completing high school,

increased the risk for an SGA infant (Luo, Wilkins, & Kramer, 2006). Lower maternal

education was found to have a greater impact on the risk for adverse birth outcomes

than the risk associated with living in low neighborhood income (Luo et al., 2006).

Feldman, Dunkel-Schetter, Sandman and Wadhwa (2000) found that education levels

did not directly impact fetal growth; however, it indirectly influenced social support,

which impacts fetal growth.

Medicaid. Public insurance assistance for pregnant women was developed to

improve birth outcomes for low-income women (Susan Marquis & Long, 2002), primarily

to reduce the rate of preterm birth and low birth weight infants. Medicaid was included

47

in this study as a marker for low SES. No articles were found that described an

association between Medicaid insurance during pregnancy and high birth weight.

However, Chu, Kim and Bish (2008) found that the rate of pre-pregnant obesity was

higher in women whose insurance was funded by Medicaid. This is consistent with

research by Laitinen et al. (2001) who found that lower income women were more likely

to be overweight.

Family and support. Family structure, operationalized as marital status or

cohabitation, is often included in perinatal research studies. Living without a partner

reduced the risk of high birth weight (Orskou et al., 2003; Surkan et al., 2004). Family

and social support significantly predicted fetal growth (Feldman et al., 2000). Social

support accounted for 31% of the variance in birth weight when examining SGA

(Feldman et al., 2000). Family structure and function were significantly related to

variations in birth weight (Ramsey, Abell, & Baker, 1986). Page (2004) suggested that

traditional cultural practices (family support and community relationship) among

Mexican American immigrants contribute to the lower rates of LBW by limiting the effect

of low SES. Foreign-born mothers have lower rates of LBW despite living in a low-

income community (Collins & Shay, 1994; Fang et al., 1999).

While it appears that family structure and support influence fetal growth, the

structure of the positive factors varies between communities. Colen et al. (2006) found

that having a co-residential grandmother significantly reduced the risk of LBW in Black

women; however, this effect did not occur among White women. Being upwardly mobile

(in SES) reduced the risk for LBW infant in White women but not Black. The authors

noted that, as Black women became upwardly mobile, they were less likely to have a

48

co-residential grandmother or maintain the kin network (Colen et al., 2006). To date,

only limited research has examined if or how family structure or support impacts the risk

of high birth weight.

High Birth Weight

Short-Term Complications Associated with High Birth Weight

Within the United States, 8.9% of infants born are LGA (Donahue, Kleinman,

Gillman, & Oken, 2010), while as many as 11% of infants are macrosomic (> 4,000 g)

(Stotland, Hopkins, & Caughey, 2004). Neonatal intensive care admissions

progressively increase as macrosomia increases from 4,000 g to greater than 5,000 g

(Boulet et al., 2003). Macrosomic infants (> 4,000 g) have a higher incidence of

shoulder dystocia, brachial plexus injury (Nesbitt, Gilbert, & Herrchen, 1998), and birth

asphyxia (Gregory, Henry, Ramicone, Chan, & Platt, 1998). Macrosomic infants of non-

diabetic women were more likely to develop hypoglycemia and respiratory distress than

macrosomic infants of diabetic women, and the rate significantly increased as the level

of macrosomia increased (Das, Irigoyen, Patterson, Salvador, & Schutzman, 2009).

Macrosomia is associated with increased mortality in both the neonatal (< 28 days) and

post-neonatal (≥ 28 days) periods, and the rate increases as the level of macrosomia

increases (Boulet et al., 2003). High birth weight infants are at increased risk for

neonatal morbidity and mortality.

Long-Term Complications Associated with High Birth Weight

Childhood risks. Research suggests that high birth weight has long-term

consequences that influence both childhood and adult health (Boney et al., 2005;

Brophy et al., 2009; Catalano, Presley, Minium, & Hauguel-de Mouzon, 2009; Danielzik,

49

Czerwinski-Mast, Langnase, Dilba, & Muller, 2004; Gillman, Rifas-Shiman, Berkey,

Field, & Colditz, 2003; Laitinen et al., 2001; Nelson, Matthews, & Poston, 2010; Oken &

Gillman, 2003; Reilly et al., 2003; Sorensen et al., 1997; Vohr, McGarvey, & Tucker,

1999; Whitaker & Dietz, 1998). Each 1-kg increase in infant birth weight increases the

risk for both overweight and obesity in children aged 9–14 years (Gillman, Rifas-

Shiman, Berkey, Field, & Colditz, 2003). Dubois and Girard (2006) identified that

macrosomic infants (> 4,000 g) were at increased odds of being overweight at age 4.5

years. In addition, high birth weight was found to contribute to the risk of obesity at age

5 years (Brophy et al., 2009).

Children who had intrauterine exposure to maternal diabetes or obesity and were

LGA at birth were at increased risk for childhood obesity and metabolic syndrome (Vohr

et al., 1999). Large-for-gestational-age infants had a significantly higher hazard ratio for

metabolic syndrome in childhood (Boney et al., 2005). Children born in the upper

quartile of weight for length z-score had an increased risk for obesity (BMI ≥ 95th

percentile) at age 3 years (Taveras et al., 2009). Children (aged 3-10 years) who were

LGA had significantly elevated high-density lipids (HDL) and insulin levels, although

there was no difference in the childhood BMI levels between the AGA and LGA infants

(Evagelidou et al., 2006). However, infants born in the upper tertile of the birth weight

percentiles were at increased risk for obesity at age 2, 3, and 4 years, as well as having

significantly elevated HDL and insulin levels (Catalano et al., 2009). Animal studies of

the offspring of pregnant rodents with increased fat mass have shown increased

adiposity and abnormal metabolic profiles (Armitage et al., 2005). Silvermann, Rizzo,

Cho, and Metzger (1998) failed to find an association between macrosomia and

50

childhood obesity, although increased maternal pre-pregnant BMI was significantly

related to the risk of childhood obesity.

Childhood obesity and ethnicity. Currently in the United States, 11.3% of

children and adolescents are obese (Ogden, Carroll, & Flegal, 2008). The prevalence

of childhood obesity (≥ 97th percentile) also varies by ethnic/racial groups, with non-

Hispanic Black and Mexican American children and adolescents (ages 2-19 years)

having a significantly higher risk of being obese (Ogden et al., 2008). At age 5 years,

the risk for childhood obesity was greatest in children of Asian and African ethnic origin

compared to White (European) (Brophy et al., 2009). While other social and

environmental factors may contribute to this trend, it is important to identify whether the

modifiable factors of increased pre-pregnant adiposity and high birth weight are

contributing to the problem of childhood obesity.

Adult obesity. Some research suggests that children or adolescents who are

overweight or obese are at increased risk for obesity in adulthood (Laitinen et al., 2001;

Reilly et al., 2003). Rasmussen and Johansson (1998) identified a clear association

between ponderal index at birth and BMI at age 18 years. Stuebe, Forman and Michels

(2009) found that increased maternal pre-pregnant BMI was a significant contributing

factor to the risk of obesity in adolescent and adult offspring. A recently published study

found that maternal pre-pregnant BMI had the greatest influence on offspring adiposity

levels at age 30 years, independent of lifestyle factors (Reynolds, Osmond, Phillips, &

Godfrey, 2010). Although other environmental factors can influence the development of

obesity, high birth weight and increased pre-pregnant adiposity appear to be

contributing factors, which may be creating an unhealthy cycle. To date, there is limited

51

research on the interrelationship of socio-demographic factors and increased maternal

pre-pregnant adiposity in non-diabetic women on the risk of high birth weight indices or

its long-term consequences.

Statistical Methods Used

Of the 24 studies, reviewed regression analysis was performed in all but three,

which used chi-square analysis (Kumari, 2001; Usha Kiran et al., 2005; Vesco et al.,

2009). Sixteen studies performed logistic regression, two studies performed linear

regression (Cedergren, 2006; Sewell et al., 2006) and three studies performed both

linear and logistic regression (Frederick et al., 2008; Gilboa et al., 2008; Rode et al.,

2007). The dichotomization of birth weight to create a high birth weight category is the

most likely reason for the extensive use of logistic regression. Only one study (Gilboa

et al., 2008) provided discussion on the selection of the statistical approach.

There has been a trend of increasing BMI in both adults and children within the

United States (Ogden et al., 2006). As the mean BMI increases, the distribution

becomes skewed, with a right shift (World Health Organization, 2000). The skewed

distribution of BMI data limits the usefulness of linear or logistic regression modeling

(Beyerlein, Fahrmeir, Mansmann, & Toschke, 2008). More recently, non-parametric

regression models such as quantile regression (Koenker & Hallock, 2001) have been

developed. Research suggests that quantile regression may be more suitable to

detecting risks when using BMI data (Beyerlein, Fahrmeir et al., 2008). Quantile

regression has been used to examine prenatal care, birth outcomes, as well as

childhood and adult obesity issues (Abrevaya, 2001; Beyerlein, Fahrmeir et al., 2008;

Beyerlein, Toschke, & von Kries, 2008; Costa-Font, Fabbri, & Gil, 2009; Terry, Wei, &

52

Esserman, 2007; Wehby, Murray, Castilla, Lopez-Camelo, & Ohsfeldt, 2009). The

advantage of the quantile regression is that it can be used to identify the contribution of

risk factors across the spectrum of the variables. Beyerlein, Toschke, and von Kries

(2008) were able to identify a significant contribution from breastfeeding in reducing the

risk of obesity at both the upper and lower quantiles, which was not evident with linear

regression.

While logistic regression enables the identification of the risk of high birth weight

at the main level, it does not explain variations within groups or show at what point the

change in risk occurs. There is the chance that marginally significant results may not be

evident when using logistic regression. Additionally, the selection of a cut-off point is

arbitrary. Linear regression uses the full range of values from the outcome variable.

However, the results from linear regression are limited to the ordinary least squares; as

such, subtle and significant variations at different points in the outcome variable may be

missed. Quantile regression can provide richer information through a more flexible and

precise approach (Koenker & Hallock, 2001). Quantile regression allows the full

examination of the effect of each predictor variable on the entire distribution of the

outcome variable: birth weight. It is potentially able to show at what point the

relationship changes. To date, quantile regression has not been used to assess the

impact of maternal pre-pregnant BMI on high birth weight.

Summary

Infant birth weight is influenced by a variety of modifiable and non-modifiable

maternal characteristics, as well as by social environmental factors. Until recently,

issues of fetal overgrowth were only considered a problem in diabetic pregnancies;

53

however, new data suggest that excessive fetal growth in utero resulting in high birth

weight may contribute to an increased risk of metabolic disorders and obesity in both

childhood and adulthood. Increased pre-pregnant adiposity is associated with

increased birth weight; with the majority (59.5%) of women entering pregnancy with

excess adiposity levels (BMI > 25 kg/m2), it is essential that the risk to the infant be

examined using appropriate birth weight measures. Socio-demographic factors are

known to influence birth weight. However, limited research exists that has examined

how these factors influence the risk of high birth weight. Birth weight indices are a more

precise assessment of fetal growth and neonatal adiposity.

54

III. METHODS

This chapter describes the data set, research design, data collection tools,

variables and methods of statistical analysis. In this study, the influence of increasing

maternal adiposity as measured by pre-pregnant BMI in non-diabetic women on the risk

for high birth weight indices was examined using existing perinatal data.

An international search was initiated to identify an existing data set that

contained current linked maternal and neonatal variables relevant to the research

purpose. The maternal and neonatal variables needed were: maternal pre-pregnant

BMI or maternal height and pre-pregnant weight measurements that could be used to

calculate this concept; infant birth weight; and gestation at delivery. Other important co-

variables required in the data set included: gestational weight gain; parity; and maternal

age, as these factors are known to influence infant birth weight. In addition, infant birth

length was desired, as this variable is important in accurately calculating infant ponderal

index. The St. Joseph Hospital data set was deemed to have the required variables as

well as sufficient data to suitably address the research questions.

Source of Data

The project was the analysis of electronic data stored in the perinatal WatchChild

database at St, Joseph Hospital Family Birthplace. The Family Birthplace is a 14-bed,

level two, single-room maternity unit. The hospital is a full-service facility located two

miles north of downtown Chicago. The Family Birthplace provides antenatal,

intrapartum, and postnatal services for privately insured, publicly funded (Medicaid),

55

private pay, and indigent pregnant women. An electronic de-identified data set was

extracted from the linked maternal and neonatal records that had been collected

continuously of all deliveries at the hospital since 1998. The maternal and infant data

within the data set included anthropometric measurements from both the mother and

newborn infant, as well as maternal health history, social and demographic information.

Ten years of complete data were used. However, the final sample only included term

(37-42 weeks' gestation) singleton pregnancies. This project was a retrospective

analysis of a secondary data set.

Sample

The sample was taken from existing perinatal data from St. Joseph Hospital.

The perinatal data contained approximately 19,000 mother-infant pairs who attended

the hospital between January 1, 2001 and December 31, 2010. The sample consisted

of the women who delivered a term infant following a singleton pregnancy. Exclusion

criteria included: women who delivered prior to 37 weeks' or after 42 weeks' gestation;

multiple pregnancies; maternal pre-existing or gestational diabetes; oligohydramnios;

polyhydramnios; fetal anomalies; infants with congenital anomalies; and maternal pre-

existing medical conditions that may influence fetal growth. Such medical conditions

included thyroid disease; cardiac anomalies and or disease; chronic hypertension; and

any other health conditions known to restrict fetal growth. Women who admitted to

cocaine or methadone use were excluded. Women under the age of 18 years were

excluded, as they are known to have smaller infants than adult women. Identification of

any of the listed items resulted in the exclusion of the mother and infant pair.

56

Research Design

The research was a non-experimental, cross-sectional design of existing

quantitative data. When analyzing existing data, it is important to examine the

conceptual and methodological components of the original data. As the data were

hospital perinatal records, there was no conceptual framework for the original data

collection. However, it was important to ensure that the selected data set contained the

variables required to answer the research questions, and that the variable definitions

were congruent with the conceptual definitions of the research project (Pollack, 1999).

The methodological considerations included: sampling, measurement, and validity

(Clarke & Cossette, 2000). Sampling includes representativeness of the sample and

sufficient variability of the key concepts. Measurement includes the reliability of the

measures, data cleaning and dealing with missing data appropriately. Validity is the

accuracy of the data.

Measures

Validity of the Data Set

The perinatal data set used in this study had an extensive number of variables.

The concepts within the supporting conceptual framework were used to select the

variables to be included in the de-identified data set. It was anticipated that content

validity would be achieved, as the data set contained the relevant variables to address

the research question. Data definitions were reviewed with the data set administrator to

ensure that they were congruent with those of this research project.

57

Variable Definitions

Exposure Variable – Infant Birth Weight Index

The exposure (criterion) variable was infant birth weight index. Three indices

(birth weight centile, birth weight z-score, and ponderal index) were generated using

birth weight, birth length, gestational age, and infant gender (Table V). These indices

were chosen because they provide a more accurate representation of fetal growth than

raw birth weight. Centile values and z-scores were generated by adjusting raw birth

weight for gestational age and infant (Olsen et al., 2010). These indices provide a more

refined representation of fetal growth as it allows the comparison of infants of different

gestational ages. Birth weight centiles can be stratified into three clinical categories that

represent achievements of fetal growth: small-for-gestational-age (SGA, inadequate

fetal growth); appropriate-for-gestational-age (AGA, fetal growth within the normal

range); and large-for-gestation-age (LGA, excessive fetal growth). Centile values are

highly used and well recognized; however, they fail to consider genetic variations in

infant length.

58

TABLE V

INFANT BIRTH WEIGHT INDEX CONSTRUCTS

Construct Concept Study Variable Operational Measure

Infant birth weight Birth weight Grams

Infant length Length Centimeters

Infant gender Gender Female, Male

Gestational age Gestational age Completed weeks by maternal due

date

Birth weight indices Birth weight centile

rank: Large-for-

gestational-age

(LGA)

Standardized score generated from

birth weight and gestational age using

population reference values (Olsen et

al., 2010). Stratified to LGA > 90th

centile or Not LGA ≤ 90th centile.

Birth weight z-scores

= (Birth weight – Mean birth weight) /

Birth weight standard deviation

All values were adjusted for

gestational age and gender

Mean birth weight and standard

deviation obtained from current US

data (Olsen et al., 2010).

Fetal Growth

Ponderal index Weight in grams * 100 / Length in

centimeters3 (Davies, 1980)

59

The ponderal index was selected as it considers variations in birth length. A

ponderal index is a non-invasive measure of infant adiposity, calculated as birth weight

(grams) x 100 divided by the birth length cubed (cm3) (Davies, 1980). Ponderal index is

considered highly accurate in the identification of high birth weight from fetal overgrowth

(Gabbe et al., 2007; Lepercq, Lahlou, Timsit, Girard, & Mouzon, 1999). Ponderal index

has strong correlation to neonatal adiposity (Wolfe, Brans, Gross, Bhatia, & Sokol,

1990). The ponderal index allows the comparison of the shorter, lighter infant to the

longer and therefore heavier infant. Ponderal index is important when comparing a

heterogeneous sample, as genetic factors such as parental height, rather than fetal

growth, influence birth length (Knight et al., 2005) and in turn birth weight.

Centile values provide a more accurate representation of fetal growth, while the

ponderal index is an indicator of infant adiposity. There are only limited U.S. reference

data for ponderal index. Research performed in the United States by Miller and

Hassanein (1971) identified the mean ponderal index for a term infant to be 2.53. Using

current birth weight (Oken, Kleinman, Rich-Edwards, & Gillman, 2003) and birth length

data (Niklasson & Albertsson-Wikland, 2008) at the 50th percentile for 40 weeks, the

ponderal index would be 2.53.

Predictor Variables

Maternal Variables

Maternal variables were divided into four sub-categories according to the study

conceptual framework: non-modifiable biological characteristics, modifiable biological

factors, behavioral factors (Table VI), and social environmental factors (Table VII).

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TABLE VI

MATERNAL CONSTRUCTS

Construct Concept Study Variable Operational Measure

Age Maternal Age Completed years (on the date of the infant's birth)

Parity Maternal parity Number of times a woman has

given birth to an infant > 20 weeks'

Non-modifiable biological characteristics

Height Maternal height Height in centimeters

Pre-pregnancy BMI

Maternal pre-pregnancy BMI

Kilograms/meters squared

BMI = weight in kilograms

(height in centimeters/100)²

Modifiable biological factors

Gestational weight gain Gestational weight gain or loss Weight gain or loss in kilograms

Calculated = current weight minus

pre-pregnant weight

Smoking No

Yes

Behavioral factors Substance use

Alcohol No

Yes

61

Non-modifiable biological characteristics. The construct of maternal non-

modifiable biological characteristics included three concepts: age, medical conditions,

and parity. Age was defined as the number of completed years of the mother at the

time of delivery. Medical conditions were limited to conditions that do not influence fetal

growth. Conditions that influence fetal growth were excluded from this study, as the

purpose was to examine the impact of increased maternal pre-pregnant BMI on fetal

growth in otherwise healthy low-risk women.

Parity is the number of times that a woman has given birth to an infant with a

gestational age greater than 20 weeks prior to the index pregnancy. A primipara has

had no previous births greater than 20 weeks. A multipara has in the past given birth to

an infant greater than 20 weeks' gestation, regardless of whether the infant was live-

born or stillborn. Height is maternal height either self reported or measured.

Modifiable biological factors. The construct of modifiable maternal biological

factors included three concepts: health status, pre-pregnant BMI, and gestational weight

gain. Maternal health status may be pre-existing or gestational medical conditions that

are potentially modifiable. These conditions were identified from narrative descriptions

as recorded by nursing staff. Health conditions were assessed to identify if they were

known to influence fetal growth. Health conditions known to influence fetal growth were

excluded. Pre-existing conditions may include autoimmune disorders and others as

identified. Gestational conditions that were excluded included: diabetes mellitus;

hypertension disorders of pregnancy including pre-eclampsia, hemolysis, elevated liver

enzyme levels, and a low platelet count (HELLP) syndrome.

62

Pre-pregnant BMI was calculated from self-reported height and weight at the

onset of pregnancy. BMI is not a precise measure of adipose mass but rather a relative

estimation. However, BMI has been shown to have a correlation with percentage of fat

mass (r = .84) in women aged 20-39 years (Flegal et al., 2009). Body mass index was

maintained in interval format. Although the NHLBI (1998) BMI categories are used in

pregnancy to guide gestational weight gain, the Institute of Medicine admits that the

stratification is arbitrary (Weiss et al., 2004).

Gestational weight gain was calculated from self-reported current weight (weight

at last prenatal visit) minus self-reported pre-pregnant weight. Variations in gestational

weight gain are known to influence fetal growth (Frederick et al., 2008); however, the

influence is altered by pre-pregnant BMI (Nohr et al., 2008). Gestational weight gain is

a known covariant in the relationship between maternal pre-pregnant BMI and fetal

growth.

Behavioral factors. The construct of maternal behavioral factors was limited to

the concept of substance use, which included two variables: alcohol use and smoking

during pregnancy. Both alcohol and smoking were operationalized as whether or not

the mother self-reported use during pregnancy. Within the original data set, no other

behavioral variables were measured. Additional concepts that could have been

included in this study would have included exercise patterns, rest, and dietary intake

patterns.

Social environment factors. The construct of social environmental influences

on fetal growth included five concepts: education attainment; family structure; support;

health care assistance; and race/ethnicity of the mother (Table VII). Data were self-

63

reported and did not consider whether the individual was American born or an

immigrant. Maternal ethnicity was to be used as a marker for socio-demographic

disparities that influence health. There were five categories in the original data: African

American, Asian, Caucasian, Hispanic and other not specified.

64

TABLE VII

SOCIAL CONSTRUCTS

Construct Concept Study Variable Operational Measure

Education Level Level of education attained

Years of education completed

Marital status Single

Married or lives with partner

Single

Cohabitation

Support partner Support partner present during labor process

Partner or father of baby

Non-partner support

Medicaid Medicaid health insurance assistance recipient

No

Yes

Social Environment

Race/Ethnicity African American

Asian

Caucasian

Hispanic

Other

Maternal race: African American

Maternal race: Asian

Maternal race: Caucasian

Maternal ethnicity: Hispanic

Other race or ethnicity

65

Education was recorded as years of education completed. The original data set

recorded education as either years completed or level attained. The level attained was

converted into years using the following criteria: graduated high school = 12 years;

some college = 14 years; college graduate = 16 years; master's or graduate degree =

18 years; postdoc = 21 years. Previous studies have varied in the classification of

education; some have used total years of education, while others have stratified by

secondary and tertiary achievement.

Marital status included domestic partner and relationship with the father of the

baby: single and divorced. Support was the relationship of the support person who was

with the pregnant women during labor. It included: husband/partner/father of the baby

(coded as one variable) and non-partner support. Non partner support included a

parent of either the pregnant woman or the father of the baby; sibling; cousin, aunt,

uncle; the pregnant woman’s previous children; as well as professional support (doula).

Health care assistance was the provision of Medicaid insurance support during

the pregnancy. It was stratified into two groups: received Medicaid assistance and did

not receive Medicaid assistance.

Instrumentation

Original Data Collection Methods

The original data were collected by labor and delivery nursing staff and medical

records staff at St. Joseph Hospital. The data were collected for medical record

purposes. The data were entered electronically in real time into the hospital perinatal

database.

66

Reliability and Validity of Data

Hospital procedures for collection of anthropometric data as well as equipment

quality control standards were reviewed to ensure that data were assessed and

recorded in a manner that would ensure reliability of the data. In this study, the

anthropometric variables were both exposure and predictor variables. The precision

and accuracy of these measurements influence the integrity of the research findings.

The purpose of the original electronic database forms was data storage, and so no

instrument testing has been performed to establish reliability (Northam & Knapp, 2006).

As a retrospective study, assessment of nursing staff data accuracy was limited.

However, the database administrator routinely performed audits to assess

completeness and accuracy of documentation, using paper records to check electronic

data, as well as review of illogical or extreme values (outliers). The database from

which the data set was extracted is used to generate birth certificate and perinatal

reporting (adverse pregnancy outcomes reporting system) to the state of Illinois.

Birth certificate data are often used as a source of perinatal statistics; however,

Dobie et al. (1998) suggested that pregnancy complications may be underestimated.

Linked hospital discharge data and birth certificates showed a higher level of accuracy

than birth certificate alone (Lydon-Rochelle et al., 2005). Currently, there is no gold

standard for the accuracy of perinatal records. Costakos, Love and Kirby (1998)

compared paper medical records to an electronic perinatal database and found good

agreement between most of the clinical variables, which included birth weight (r = .98),

and alcohol number of drinks per week (r = .92). Specificity for dichotomous variables

including gestational diabetes and hypertension were high (.99); however, sensitivity

67

was lower at .63 and .67, with positive predictive values at .67 and .83, respectively.

Beyond the limitations of the nursing documentation, Reichman and Hade (2001)

suggested that some discrepancies between perinatal records (paper and electronic)

and birth certificate data related to the accuracy of maternal reporting, particularly in

regard to tobacco usage and alcohol usage in pregnancy. However, this problem is not

unique to perinatal data. Although retrospective data may limit the reliability and

accuracy of the variables, there is no evidence to suggest that electronic perinatal

database data are invalid or are less accurate than birth certificate data.

Infant anthropometric data. A registered nurse performed infant weight and

length measurements. New staff were trained and assessed in their skills to perform

these measures. Infant weight was performed using a Medela 040.7012 digital scale.

The scales are considered accurate to one centigram and are calibrated annually. The

naked infant is placed in a supine position on the scale immediately after the scale has

been digitally set to zero. The weight is recorded in grams once the infant settles and

the output is stable.

Crown to heel length was assessed with the naked infant in a supine position on

a firm flat surface using a tape measure on the first day of life. Length was measured in

centimeters to the nearest millimeter; the tape measure was accurate to one millimeter.

Weight and length measures were entered into the electronic data file by the

nurse performing the measurements. The electronic file records weight to one

decigram and length to one centimeter. Infant anthropometric weight measures have

been found to be reliable (Burke, Roberts, & Maloney, 1988); however, length measures

68

have a lower rate of reliability, with intra- and inter-rater variability ranging from 0.5 cm

to .89 cm (Rosenberg, Verzo, Engstrom, Kavanaugh, & Meier, 1992).

Maternal anthropometric data. Maternal height, pre-pregnant weight, and

current weight were self-reported. Information was obtained during the admission

interview by the nurse and recorded in the electronic charts. Maternal height and

weight were obtained in imperial units as whole numbers. The WatchChild system

converted them into metric units. The conversion rates were one inch equals 2.5

centimeters; one pound equals 0.4536 kilograms. Both measurements were to one

decimal place, one millimeter, and dekagram, respectively.

The accuracy of self-reported anthropometric to direct measurement in pregnant

women is conflicting. Research has shown that maternal recall related to pregnancy is

highly accurate with pre-pregnant weight (r = .86) and height (r = .90) (N = 154) (Tomeo

et al., 1999). The accuracy of self-reported weight during pregnancy was supported in a

larger (N = 1029) and more recent study (Herring et al., 2008). Self-reported weight of

pregnant women was assessed to direct measurement; a very high correlation was

obtained (r = .99) (Herring et al., 2008). In the ideal weight category, 13% over-

assessed their weight, while in the overweight obese group 14% underestimated their

weight, with the remaining women in each category accurately assessing their weight

(Herring et al., 2008). However, in a smaller study (N = 100) to examine BMI

calculations in the first trimester of pregnancy, self-reported weight and height were

compared to direct measurements (Fattah et al., 2009). Body mass index classification

was underestimated 60% of the time using self-reported data. Women in the obese

BMI category (≥ 30 kg/m2) had the highest correlation between self-reported and direct

69

measurement (r = .80); overweight (25-29.9 kg/m2) had the lowest correlation (r = .44);

while women in the ideal category (18.5-24.9 kg/m2) had a moderate correlation (r =

.68). There was no significant difference between self-reported and measured height,

although only 14% accurately reported their height. Fifty-nine percent of women under-

reported their weight, while 36% over-reported; a significant difference was seen

between self-reported and measured weight and BMI (Fattah et al., 2009).

Gestational weight gain or loss. Gestational weight gain or loss was

calculated using maternal self-reported weight at the last prenatal visit minus self-

reported pre-pregnant or first trimester weight. If weight at last prenatal visit was

unknown, the nursing staff performed a weight assessment using a Detecto balance

beam scale. The scale is accurate to 10 grams. First trimester pregnancy weight is

often used as a surrogate for pre-pregnant weight as weight changes in early pregnancy

are limited (Fattah et al., 2010).

Gestational age. Gestational age at time of the delivery is calculated from

expected date of delivery (EDD) using the mothers' self-reported EDD, as calculated by

the prenatal care provider. The WatchChild database system electronically generates

gestation to completed weeks plus days, with EDD being 40 weeks. Gestation was

rounded down to completed weeks. The study only included term pregnancies, 37 to 42

weeks' gestation. Women with unknown gestation were excluded from the sample.

Expected date of delivery can be calculated using either maternal last menstrual period

(LMP) or ultrasound fetal biometry. Current obstetric management recommends a first

trimester ultrasound to confirm fetal gestational age and EDD. Gestational age/EDD

70

estimations from an ultrasound before 22 weeks' gestation are considered to be

accurate to ± 4 days (Chervenak et al., 1998).

Ethical Considerations

Human Subjects

The researcher (author) was granted Institutional Review Board (IRB) approval

from the University of Illinois at Chicago Office of Protection of Research Subjects (#

*2010913-57074-1*) and Saint Joseph Hospital (2010-35) to access the data as a de-

identified data set. The de-identified data set had the following personal information

removed: names (all); street address, city, county, precinct; all elements of dates

(except year) for dates that directly related to an individual, including birth date,

admission date, discharge date; telephone numbers; facsimile numbers; electronic

email addresses; social security numbers; medical record; numbers; health plan

beneficiary numbers; account numbers; certificate/license numbers; vehicle identifiers,

including license plates; device identifiers and serial numbers; Web universal resource

locators (URL’s); internet protocol (IP) addresses; biometric identifiers; and any other

unique identifying number, characteristic, or code. Removal of these items met the

Health Insurance Portability and Accountability Act (HIPAA) of de-identified information.

Data

Data Extraction Procedure

An electronic data set was generated in Microsoft Excel format by the database

administrator. To ensure accurate extraction of the data, a meeting was set up with the

information technology (IT) consultant from the WatchChild database support company

who created the initial data storage program, the hospital administrator of the database

71

and the researcher (author). The database contained 2,880 data fields; 145 data fields

were deemed relevant, necessary to the proposed research topic, and selected for

extraction into a de-identified data set. Many of the fields were individual items, such as

medical conditions, medication usage, and intrapartum complications, which were used

to eliminate subjects who did not meet eligibility criteria for the study. The data were

extracted in one-year batches by the hospital database administrator and stored in an

Excel format.

The 10 Excel files were then merged by the researcher. The generated data set

included deliveries from January 1, 2001 to December 31, 2010. The generated data

set contained 19,021 cases and contained 145 variables; however, only 20 variables

directly related to the research question. The remaining variables were used to identify

subjects who did or did not meet inclusion/exclusion criteria or were imperial

anthropometric values used to confirm metric anthropometric data.

Assessment of Accuracy of the De-identified Data

The data set was evaluated for accuracy and completeness. The accuracy of the

data following extraction and transfer to an Excel file was assessed by the database

administrator and the researcher. Previously generated hospital reports on annual

delivery numbers and maternal medical conditions were used to compare data in the

generated data set. Descriptive analysis was performed on the anthropometric

variables in the generated data set to determine if means and standard deviations were

plausible. Additionally, cases were randomly selected to check against the original file

to confirm that continuous variables were not corrupted during the transfer.

72

Comparisons and cross-checking demonstrated consistency and accuracy of the

extracted data.

Data Cleaning

Data cleaning commenced with the identification of subjects who did not meet

inclusion criteria of a term singleton non-diabetic pregnancy not complicated by

maternal or fetal factors known to influence fetal growth. Cases were removed if any of

the following were present:

• multiple gestation

• maternal diabetes pre-existing or gestational

• maternal hypertension pre-existing or gestational

• maternal cardiac disease

• maternal thyroid dysfunction

• maternal epilepsy

• maternal use of cocaine, methadone, heroin or admitted substance abuse

• oligohydramnios or polyhydramnios during pregnancy

• fetal intrauterine growth retardation (IUGR)

• fetal anomaly or congenital defect noted at birth

• stillborn infant, birth condition not stated or neonatal death occurred

• gestation was less than 37 weeks, greater than 42 weeks or unknown

• infant gender unknown

• no birth weight data present

From the original data set, 4,624 cases were excluded, leaving 14,397 cases.

The original proposal included the predictor variables of maternal medical and health

73

conditions not associated with alterations in fetal growth. However, the generated data

set contained a very low frequency of any maternal medical or health conditions; thus,

these predictors were not able to be included in the regression model. Relevant data in

string format were recoded to numerical format.

Anthropometric data were reviewed for extreme values (outliers). Physiologically

implausible anthropometric values were cross-checked by reviewing imperial and metric

values. If a plausible imperial value was present, then a metric value was generated

using the metric conversion rates from the original dataset. Implausible values were

deleted, but the cases were retained. Maternal and infant indices were further

examined for outliers using z-score standardization. Implausible gestational weight

changes and pre-pregnant BMI values were excluded. No infant birth weight indices

were excluded following review with z-scores, although some high values were present.

During regression analysis, extreme birth weight indices were removed and analysis

repeated; the exclusion of the (5) high birth indices did not alter the regression output.

Maternal and infant indices were generated using anthropometric, gestational

age, and gender data accordingly. Maternal pre-pregnant BMI was generated using

pre-pregnant weight in kg, and maternal height in cm was converted to meters. Pre-

pregnant BMI was generated as a continuous variable. Birth weight indices' z-scores

and centile rank were generated using current U.S. mean birth weight and standard

deviation data (Olsen et al., 2010). Centile rank was stratified to LGA (> 90th percentile)

and not LGA (≤ 90th centile). Ponderal index was generated from infant birth weight and

infant length (Davies, 1980).

74

Pre-pregnant weight and gravid weight were excluded from the regression

analysis, as they were used to create pre-pregnant BMI and gestational weight gain or

loss, respectively; as such, they were highly correlated to these factors in the initial

regression analysis. Gestational age and infant gender were included as prediction

variables for ponderal index; however, they were removed from the regression model

for birth weight z-scores and birth weight centiles, as they were used to generate these

indices.

Missing Data

The data set was assessed for the presence of missing data. Frequency reports

indicated that four variables had no missing data: maternal age, infant gender, infant

birth weight and gestational age. Overall, the level of missing data was low (< 5 %).

However, two variables had very high missing data rates: education level (82%) and

alcohol usage (98%). Alcohol was removed due to the excessive level of missing data.

Bivariate analysis indicated that there was no relationship between education and birth

weight z-scores. Performing a list-wise deletion would have considerably reduced the

sample size. Education was removed from the regression model.

Predictor variables were recoded to identify missing data, and analyses were

performed to examine the randomness of the missing data. Nonrandom missing data

are a threat to validity (Pollack, 1999). Independent sample t test (p < .05) identified

that data were not missing at random. The presence of nonrandom missing data

prohibited the imputation of missing data. The data set was left as obtained with 14,397

cases that included missing values.

75

Statistical Analysis

Data Preparation for Regression

Bivariate analyses were conducted to examine the relationship of birth weight

indices and predictor variables. Correlations using Pearson’s r were performed on

continuous variables; ANOVA for categorical variables and chi-square when both the

predictor and outcome variable were categorical. All interval variables were found to be

significant (p < .05), with at least one of the outcome variables (Table VIII). Variables

that failed to achieve a significance level of .10 or less were not included in the

regression model. Prenatal care and maternal assistance program failed to

demonstrate a significant relationship with any of the birth weight indices outcome

variables (Table IX). Height, smoking and Medicaid were not included in the Ponderal

Index model.

76

TABLE VIII

BIVARIATE ANALYSIS OF CONTINUOUS INFANT BIRTH WEIGHT INDICES TO

CONTINUOUS PREDICTOR VARIABLES

Pearson Correlation z-score

Sig (2 tailed)

Ponderal index

Sig (2 tailed)

Age .001 .001

Parity .001 .001

Height .001 .45

Pre-pregnant BMI .001 .001

Gestational weight gain .001 .001

Gestational age .002 .001

Education .34 .001

Pearson Correlation p < .05

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TABLE IX

BIVARIATE ANALYSIS OF CONTINUOUS INFANT BIRTH WEIGHT INDICES TO

CATEGORICAL PREDICTOR VARIABLES

z-score

Sig (2 tailed)

Ponderal index

Sig (2 tailed)

Infant gender .45 .01

Smoker: no / yes .01 .72

Alcohol usage .16 .19

Marital status .01 .10

Support person .01 .04

Ethnicity mother .01 .01

Prenatal care: no / yes .42 .96

Medicaid: no / yes .01 .16

Maternal assistance program .22 .47

ANOVA p < .05

78

TABLE X

BIVARIATE ANALYSIS OF LARGE-FOR-GESTATIONAL-AGE INDEX TO

CONTINUOUS PREDICTOR VARIABLES

p 2-sided F N

Age .001 41.91 14,397

Parity .001 37.07 14,328

Height .001 154.03 14,059

Pre-pregnant BMI .001 148.96 13,220

Gestational weight gain .001 175.32 13,393

Gestational age .104 2.63 14,397

Education .535 0.38 2,535

ANOVA p < .05

79

TABLE XI

CHI-SQUARE ANALYSIS OF LARGE-FOR-GESTATIONAL-AGE INDEX TO

CATEGORICAL PREDICTOR VARIABLES

p 2-sided Value N

Infant gender .69 χ2, 1 = 0.158 14,397

Smoker: no / yes .06 χ2, 1 = 4.518 14,315

Alcohol usage .64 χ2, 1 =0.218 214

Marital status .01 χ2, 1 = 10.097 14,335

Support person .08 χ2, 1 = 2.922 11,737

Medicare .03 χ2, 1 = 4.518 14,081

Prenatal care: no / yes .24 χ2, 1 = 1.547 14,396

Categorical variables were recoded to a binary format to enable their inclusion in

regression equation. Marital status was stratified to single or cohabitation. Cohabitation

included married, domestic partner or father of the baby; single included divorced.

Support person was stratified to partner and non-partner support that included a parent

of the pregnant woman or her partner, siblings, children and others. Race was dummy

coded into four groups, with Caucasian as the indicator. Gestational Age and Infant

Gender were not included in the birth weight z-score and LGA regression models, as

they were used to generate the index.

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The final data set included the following variables:

Outcome Variables

Infant birth weight centile rank stratified to > 90th percentile (LGA)

Infant birth weight z-score

Infant ponderal index

Predictor Variables

Non-modifiable biological factors

Maternal age

Parity

Height

Gestational age

Infant gender

Modifiable biological factors

Pre-pregnant body mass index

Gestational weight gain or loss

Behavioral factors

Smoking

Social factors

Marital status

Support person

Medicaid health insurance

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Initial Statistical Analysis

Three levels of regression analysis were performed: logistic; linear; and non-

parametric simultaneous-quantile regression. Logistic regression was performed when

the outcome variable was dichotomous (LGA). Linear and quantile regression were

used with the interval outcome variables birth weight z-scores and ponderal index.

Preliminary analysis using linear regression included the predictor variables: age, parity,

height, pre-pregnant BMI, gestational weight gain, smoker, marital status, support

partner Medicaid, race African American, race Asian and ethnicity Hispanic. Race

Caucasian was used as the referent group for the race/ethnicity analysis.

In each regression model of each birth weight index, the inclusion of

race/ethnicity did not improve the regression model. Race/ethnicity is used as a

surrogate for socio-demographic variables when they are not available; however, this

data set contained social variables. Race/ethnicity was excluded as a predictor as it did

not improve the regression model. As race/ethnicity is a marker of social factors and not

an independent factor, perhaps the path between race/ethnicity and birth weight indices

was too indirect to be identified by the regression model.

Research Questions

Research Question 1

What maternal biological, behavioral and social factors influence the risk for a

higher birth weight indices infant in term (≥ 37 weeks’ gestation) singleton pregnancies

of non-diabetic women?

Exposure (predictor) variables.

(i) Biological: age, parity, height, pre-pregnant BMI, gestational weight gain

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(ii) Behavioral: smoking

(iii) Social: marital status, support partner, Medicaid health insurance

Outcome (criterion) variables.

(a) Birth weight centile, dichotomized to LGA (≥ 90th percentile) or Not LGA.

(b) Birth weight z-scores as a continuous variable

(c) Ponderal index as a continuous variable

Method of statistical analysis. Logistic regression was used with LGA. Linear

and quantile regression were performed with birth weight z-scores and ponderal

index.

Research Question 2

Does increasing pre-pregnant adiposity, as measured by BMI in term singleton

pregnancies of non-diabetic women, increase the risk of delivering an infant with a

higher birth weight index?

Exposure (predictor) variable.

Pre-pregnant BMI as a continuous variable with remaining predictors as

covariants

Outcome (criterion) variables.

(a) Birth weight centile, dichotomized to LGA (≥ 90th percentile) or Not LGA.

(b) Birth weight z-scores as a continuous variable

(c) Ponderal index as a continuous variable

Method of statistical analysis. Logistic regression was used with LGA. Linear

and quantile regression were performed with birth weight z-scores and ponderal

index.

83

IV. Results

The purpose of this study was to assess the biological, behavioral, and social risk

factors for excess fetal growth resulting in high birth weight indices in non-diabetic

women who had a term low-risk singleton pregnancy. Regression models were

employed to identify risks and calculate the contribution of each factor to birth weight

indices. Linear and logistic regression was initially performed. Further analysis was

undertaken using simultaneous-quantile regression in an attempt to identify when the

risks developed. Two research questions were addressed using three birth weight

indices: LGA, z-scores and ponderal index. This chapter includes the descriptive

statistics of maternal and infant characteristics and a summary of the results from each

research question.

Characteristics of Subjects

Maternal Characteristics

The final dataset contained 14,397 cases (mother-infant pairs). The mean age of

the pregnant women was 28 years (SD = 5.62). Forty-three percent (n = 6,115) of the

women were primiparous; the remaining 57 % (n = 8,213) were multiparous. Parity

ranged from 0 to 10; however, only 3% of the women had a parity greater than three.

Seventeen percent of the women had more than one delivery during the 10-year period;

11,952 women had 14,397 singleton infants. The mean pre-pregnant weight was 65 kg

(SD = 14.5), and the mean weight at the end of the pregnancy was 79 kg (SD =14.9).

Gestational weight changes in pregnancy ranged from a 5 kg loss to 33 kg gain (Figure

2). Less than 1% of the subjects had gestational weight loss. Thirty-five percent of the

84

women gained up to 12 kg; 48% gained more than 12 kg but less than 20 kg, and 15%

gained more than 20 kg. The mean gestational weight gain was 14.26 kg (SD = 6.0).

The mean pre-pregnant BMI was 24.3 kg/m2 (SD = 4.65) (Table XII). Over the 10

years, there was no significant change or trend of increase in mean pre-pregnant BMI.

The lowest mean pre-pregnant BMI of 24.13 kg/m2 (SD 4.62) occurred in 2007, while

the highest mean pre-pregnant of 24.54 kg/m2 (SD 4.75) occurred in 2003 (Figure 3).

Pre-pregnant BMI of the women from this study showed a positive skew (Figure 4).

Sixty-four percent of the women had a pre-pregnant BMI less than 25 kg/m2. Twenty-

three percent of the women were classified as overweight (25-29.99 kg/m2) at the onset

of their pregnancy, while 12% were obese (BMI ≥ 30 kg/m2) (Table XIII).

Figure 2. Distribution of gestational weight gain

85

Figure 3. Pre-pregnant BMI by year

Year of delivery

86

Figure 4. Distribution of pre-pregnant BMI

87

TABLE XII

MATERNAL CHARACTERISTICS

Mean Standard Deviation Min Max N Missing (%)

Maternal age (years) 28.37 5.62 18 50 14,397 0 (0)

Parity 0.94 1.10 0 10 14,328 69 (0.5)

Maternal height (cm) 162.55 7.23 122 198 14,059 338 (2)

Pre-pregnant weight (kg) 65 14.5 34.50 158.00 13,620 777 (5)

Pre-pregnant BMI (kg/m2) 24.30 4.65 13.33 40.39 13,416 1177(8)

Gestational weight gain or loss (kg) 14.26 6.01 - 5.00 + 33.20 13,220 1004 (7)

Gravid weight (kg) 79 14.9 44.50 181.00 14,063 334 (2)

Education (years) 13 2.6 0 21 2,535 11,862 (82)

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TABLE XIII

MATERNAL BMI CATEGORIES

BMI Category N Percentage

<18.49 kg/m2 728 5.5

18.5 - 24.99 kg/m2 7,802 59.0

25 – 29.99 kg/m2 3,089 23.1

≥ 30 kg/m2 1,631 12.3

Total 13,220 100.0

The majority (n = 9,516; 66%) of women stated they cohabited, either married or

had a domestic partner, and 71% (10,192) had their husband, partner or father of the

baby with them for support during labor. The rate of prenatal care was very high at

99%, with only 34 women in the dataset not receiving prenatal care. The hospital has a

very high rate of prenatal care. Thirty-seven percent of the women received Medicaid

assistance during their pregnancy. Ninety-five percent of women denied smoking

(Table XIV).

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TABLE XIV

MATERNAL SOCIAL ENVIRONMENT CHARACTERISTICS

Number Valid Percent

Marital Status

Single 4670 32.6

Married/Life Partner 9516 66.4

Separated, Divorced, Widowed 149 1.0

Missing (%) 62 (0.4)

Support Person Type

Husband, Partner, Father of the Baby 10,192 86.8

Parent of Mother or Father 693 5.9

Relative 474 4.0

Other 378 3.2

Missing (%) 2660 (18.5)

Maternal Ethnicity/Race

African American 1490 11.1

Asian 1499 11.1

Caucasian 5437 40.4

Hispanic 4843 36.0

Other 193 1.4

Missing (%) 935 (6.3)

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TABLE XIV (continued)

MATERNAL SOCIAL ENVIRONMENT CHARACTERISTICS

Number Valid Percent

Prenatal Care

No 34 0.2

Yes 14,362 99.8

Missing (%) 1(0)

Medicaid Recipient

No 8791 62.4

Yes 5290 37.6

Missing (%) 316 (2.2)

Smoking

No 13646 95.3

Yes 669 4.7

Missing (%) 82 (0.6)

ETOH Use

No 113 52.8

Yes 101 47.2

Missing (%) 14183 (98.5)

The data contained a racially diverse sample of pregnant women: 38%

Caucasian, 34% Hispanic, 10% African American, 10% Asian, and 8% other or not

defined. A comparison of the racial groups using chi-square indicated a significant

91

relationship between each of the social variables: marital status, support person,

Medicaid and race (Table XV). Each of the racial groups was significantly different

(ANOVA p < .05) from each other in each of these social categories: Marital status (F

(4, 13409) = 369.83, p < .001); support (F (4, 10956) = 106.79 p < .001) and Medicaid

(F (4, 13173) = 172.94 p < .001).

TABLE XV

SOCIAL VARIABLES BY RACIAL GROUPS

Cohabitation

%

Support

%

Medicaid

%

Missing education data

%

African American

42.5 69.7 44.5 83.7

Asian 87.4 88.6 35.6 80.7

Caucasian 77.5 92.0 25.5 82.4

Hispanic 53.5 85.7 49.7 85.4

n 13,410 10,957 13,174 13,461

χ2, p < .001 χ2, 4 = 1332.78 χ2, 4 = 411.32 χ2, 4 = 657.47 χ2, 4 = 36.57

Additional analysis was performed on the limited subset that included education

levels. African American and Hispanic women were significantly (Table XVI) different

from all other groups in relation to years of education, while there was no difference in

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years of education between Asian and Caucasian women. A significant relationship

was identified between racial groups and the level of data missing from the education

variable (Table XV).

TABLE XVI

YEARS OF EDUCATION BY RACIAL GROUPS

Years of Education Standard Deviation

African American 14.01 2.3*

Asian 14.79 2.3

Caucasian 14.66 2.3

Hispanic 12.53 2.8*

ANOVA f (4, 2208) p < .001; Post Hoc Tukey HSD *p < .005

Infant Characteristics

The mean gestational age of the infant was 39.2 weeks (range 37-42 weeks).

Sixty-five percent of the infants were born during Week 39 or 40 of gestation. There

were more male infants (50.8%) born than female infants (49.2%) (Table XVII); this is

consistent with the general population. The mean birth weight was 3,404 g, standard

deviation 436 g (Figure 5). The female infants had a lower mean birth weight and mean

length that the male infants, however this data does not account for variations in

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gestation. Infant birth weight z-scores ranged from -2.8 to + 5.1 (Figure 6). There were

five plausible and known high birth weight infants, with z-scores ranging from 3.54 to

5.18. The mean length was 50.9 cm (SD = 2.1). Ponderal index ranged from 1.5 to 3.9

with the mean at 2.57 (Table XVIII). The female infants had a higher mean ponderal

index than the male infants: 2.58 (SD .27) and 2.56 (SD .27), respectively, although the

difference was not significant. Of the infants, 6.5% were classified as LGA (> 90th

percentile), 88.6% were appropriate for gestational-age (AGA), and 5% were small-for-

gestational-age (SGA) (< 10th percentile) (Table XIX).

TABLE XVII

INFANT CHARACTERISTICS

Infant Gender N Percentage

Female 7,085 49.2

Male 7,312 50.8

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Figure 5. Distribution of raw birth weight

95

Figure 6. Distribution of birth weight z-scores

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TABLE XVIII

INFANT ANTHROPOMETRIC CHARACTERISTICS

Anthropometrics Mean SD Min Max Missing (%)

Gestational age (weeks) 39.1 1.0 37 41 0 (0)

Birth weight (grams) 3,404 436 1,884 5,744 0 (0)

Infant length (cm) 50.9 2.1 40 59 156 (1)

Infant z-score - 0.003 0.8 -2.8 +5.1 0 (0)

Ponderal index 2.57 0.27 1.5 3.9 160 (1)

N = 14,397

TABLE XIX

INFANT BIRTH WEIGHT PERCENTILE CATEGORIES

Percentile Categories Frequency Valid Percent

SGA (<10th percentile) 714 5.0

AGA (10th - 90th percentile) 12,746 88.6

LGA (> 90th percentile) 937 6.4

Total 14,397

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Analyses Related To Each Research Question

Research Question 1

What maternal biological, behavioral, and social factors influence the risk for a

higher birth weight indices infant in term (≥ 37 weeks’ gestation) singleton pregnancies

of non-diabetic women? (a) Logistic regression was performed on dichotomized birth

weight centile rank: LGA. Birth weight centile rank was stratified: LGA (> 90th

percentile) or not LGA (≤ 90th percentile); (b) Linear regression was performed with birth

weight z-scores maintained as a continuous variable; (c) Linear regression was

performed with ponderal index maintained as a continuous variable.

Research Question 1a

The logistic regression model of LGA used nine predictor variables: age, parity,

height, pre-pregnant BMI, gestational weight gain, smoking, marital status, support

partner and Medicaid. Four biological variables (parity, height, pre-pregnant BMI and

gestational weight gain) achieved significance as predictors of giving birth to a LGA

infant (p < .001). Parity showed the greatest increase in risk (OR 1.168, 95% CI [1.087,

1.255]). A one-unit increase in parity would result in a 17% increase in the risk of

delivering a LGA infant. Pre-pregnant BMI showed an increase of risk, with an odds

ratio of 1.11 (95% CI [1.09, 1.13]), followed by gestational weight gain, with an odds

ratio of 1.09 (95% CI [1.07, 1.10]). This represents an 11% and 9% increase,

respectively, in risk of delivering a LGA infant. The behavioral predictor smoking and

the social predictors marital status, support partner and Medicaid all failed to achieve

significance as predictors of LGA. The biological variable of age failed to achieve

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significance as a predictor, but the 95% confidence interval ranged from 0.09 to 1.03

(Table XX).

TABLE XX

LOGISTIC REGRESSION MODEL OF LARGE-FOR-GESTATIONAL-AGE (LGA)

OR 95% Confidence Interval p value

Age 1.015 0.998, 1.031 .072

Parity 1.168 1.087, 1.255 .001

Height 1.058 1.046, 1.071 .001

Pre-pregnant BMI 1.116 1.098, 1.135 .001

Gestational Weight Gain 1.092 1.078, 1.106 .001

Smoker 0.718 0.470, 1.096 .126

Cohabitation 1.167 0.955, 1.425 .130

Support Partner 1.207 0.916, 1.590 .181

Medicaid 1.091 0.910, 1.307 .343

χ2 .001, 9 = 446.53 The logistic regression analysis failed to demonstrate an influence by behavioral

and social factors on the high birth weight group (LGA). As logistic regression is only

able to demonstrate membership to the high birth weight group (LGA), additional

analysis using a continuous birth weight index and an alternate regression equation was

planned. Birth weight z-scores were selected to further examine the impact of

99

biological, behavioral, and social variables on the risk of increased birth weight indices

using linear regression.

Research Question 1b

Birth weight z-scores were initially analyzed using linear regression, followed by

simultaneous-quantile regression. The predictor variables included in both models were

age, parity, height, pre-pregnant BMI, gestational weight gain, as well as the categorical

variables of smoking, marital status, support partner, and Medicaid. The multiple linear

regression model indicated that the nine predictor variables were significant contributors

to the explained variations in birth weight z-scores F (9, 10544) = 172.19, p < .001, R2 =

.0127, Intercept -4.98. The adjusted R2 square value of .0127 (unadjusted .0128)

indicates that 13% variability in birth weight z-scores is predicted by the nine variables

included in the model. In combination, the nine variables contributed to 4.98 in shared

variability. Seven of the nine predictors achieved significance; two predictors, marital

status and Medicaid, failed to achieve significance. Smoking had the greatest impact

on z-scores, with a negative regression coefficient of .26. The remaining significant

predictors all had a positive influence on birth weight z-score: age (b = .008), parity (b =

.06), height (b = .02), pre-pregnant BMI (b = .04), gestational weight gain (b = .03) and

support partner (b = .10) (Table XXI).

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TABLE XXI

LINEAR REGRESSION MODEL OF BIRTH WEIGHT Z-SCORE

Regression Coefficient

Beta Standard Error p value

Age .008 .053 0.002 .000

Parity .059 .077 0.008 .000

Height .020 .177 0.001 .000

Pre-pregnant BMI .036 .204 0.002 .000

Gestational weight gain .030 .220 0.001 .000

Smoker -.268 -.068 0.036 .000

Cohabitation .024 .014 0.018 .174

Support partner .101 .041 0.024 .000

Medicaid -.019 -.011 0.183 .250

F (9, 10544) = 172.19, p < .001 R2 = .1274, y intercept -.0498 Quantile regression was performed at the 10th, 20th, 30th, 40th, 50th, 60th, 70th,

80th, 90th and 95th percentile. Although high birth weight z-scores were the primary

interest, it was important to fully examine the effect of the predictors across the

percentiles. Five (parity, height, pre-pregnant BMI, gestational weight gain and

smoking) of the nine variables achieved significance at all birth weight z-score

percentiles.

Age achieved significance from the 20th to the 95th percentile. The positive

regression coefficient showed a trend of increasing impact from the 20th to the 95th

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percentile (Table XXII). Support partner demonstrated a significant impact on birth

weight z-scores at all percentiles except the 90th (b = .06). The significant regression

coefficients fluctuated across the percentile levels (range .79-.12), with the highest

levels at the 10th, 60th and 95th percentile (Table XXII). Marital status (cohabitation)

failed to achieve significance at any percentile. Medicaid achieved significance at the

20th percentile; however, it failed to achieve significance at any other level.

Parity showed a positive but reducing impact from the 10th to the 50th percentile,

followed by a trend of increasing impact up to the 95th birth weight z-score percentile

(Table XXII). Height had a small increase in regression coefficient ranging from .017 at

10th percentile to .025 at the 95th percentile. Pre-pregnant BMI demonstrated a positive

and progressive increase in the regression coefficient from .022 at the 10th percentile to

.056 at the 95th percentile (Table XXII). Gestational weight gain also had a progressive

but less dramatic increase from .025 at the 10th percentile to .042 at the 95th percentile

(Table XXII). Smoking showed a negative regression coefficient, which had a

progressive reduction in the regression coefficient from -.34 at the 10th birth weight z-

score percentile to -.21 at the 80th birth weight z-score percentile. The quantile

regression demonstrated changes in the regression coefficient of the five significant

predictors across the birth weight z-score percentiles.

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TABLE XXII

REGRESSION COEFFICIENT FOR BIRTH WEIGHT Z-SCORES USING LINEAR AND QUANTILE REGRESSION

Linear 10 20 30 40 50 60 70 80 90 95

Age .0077* .0039 .0049* .0060* .0069* .0073* .0086* .0075* .0068* .0093* .0100*

Parity .0586* .0677* .0608* .0576* .0512* .0504* .0600* .0705* .0625* .0704* .0869*

Height .0203* .0176* .0165* .0190* .0201* .0210* .0206* .0220* .0225* .0233* .0258*

ppBMI .0363* .0221* .0270* .0299* .0351* .0366* .0382* .0413* .0439* .0544* .0563*

GWG .0303* .0254* .0279* .0271* .0280* .0283* .0302* .0322* .0332* .0391* .0425*

Smoker -.2685* -.3493* -.2987* -.2807* -.2527* -.2741* -.3094* -.2698* -.1609* -.1839* -.2126*

Cohabit .0245 .0179 .0210 .0248 .0292 .0309 .0269 .0332 .0583 .0192 .0165

Support .1007* .1209* .0993* .0798* .0959* .0878* .1115* .1073* .0951* .0613 .1100*

Medicaid -.0191 -.0555 -.0350* -.0293 -.0206 -.0250 -.0075 -.0148 -.0203 .0139 .0413

Pseudo R2 .0457 .0550 .0601 .0661 .0682 .0703 .0725 .0766 .0862 .0950

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:

age, parity pre-pregnant BMI, gestational weight gain, gestational age, infant gender

(female), marital status (cohabitation) and support partner. The two infant factors of

gestational age and gender were added to the ponderal index model, as birth weight

was not adjusted for these variables when the index was generated.

The eight selected predictors in the linear regression model were significant

contributors to the explained variations in the ponderal index F (8, 10423) = 47.48, p <

.001, R2 = .034, y intercept 1.733. The adjusted R2 of .034 indicated that the total

contribution of the predictors to the explained variance was low at 3%. Parity had the

highest regression coefficient (b =.024) of all the predictor variables included in the

ponderal index model. A one-unit increase in parity would equate to a .06 increase in

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the ponderal index. Using the mean ponderal index, a one-unit increase in pre-

pregnant BMI would increase the ponderal index from 2.53 to 2.59.

Gestational age achieved the second highest regression coefficient of .017,

followed by pre-pregnant BMI (b = .006) and gestational weight gain (b = .005). Three

of the eight predictors failed to achieve significance; these included both of the social

variables marital status and support, as well as the non-modifiable biological predictor of

age (Table XXIII). The ponderal index regression model did not include the behavioral

factor smoking, as it failed to show a significant relationship in the initial bivariate

analysis. The linear regression model was only able to demonstrate that biological

variables influence ponderal index.

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TABLE XXIII

LINEAR REGRESSION MODEL OF PONDERAL INDEX

Regression Coefficient

Beta Standard Error p Value

Age - .001 -.013 .001 .222

Parity .024 .094 .002 .001

Pre-pregnant BMI .007 .112 .001 .001

Gestational Weight Gain .005 .108 .001 .001

Gestational Age .017 .066 .002 .001

Female infant .023 .042 .005 .001

Cohabitation .009 .016 .006 .128

Support Partner .012 .015 .008 .133

F (8, 10569) = 47.09 p < .001 R2 = .0337 The same percentile levels were used in ponderal index (10th, 20th, 30th, 40th,

50th, 60th, 70th, 80th, 90th, and 95th) with quantile regression analysis. Five of the eight

predictor variables (parity, pre-pregnant BMI, gestational weight gain, gestational age

and infant gender) were significant across all percentiles of the ponderal index. All the

predictor variables had a positive regression coefficient. Age failed to demonstrate

significance as a predictor at any level. Cohabitation achieved significance, but only at

40th and 50th percentiles. Support partner demonstrated a significant regression

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coefficient at the 80th percentile. The impact of parity on the ponderal index fluctuated

as the percentile increased, with low regression coefficients at the 10th, 30th, 40th and

90th percentile (Table XXIV). The regression coefficient of gestational age on ponderal

index percentiles fell until the 30th percentile, then fluctuated around the same level

across the remaining percentiles (Table XXIV).

Pre-pregnant BMI showed an initial reduction in the regression coefficient until

the 30th percentile of the ponderal index. It then showed an increasing trend as the

ponderal index increased across the remaining percentiles. The gestational weight gain

regression coefficient showed a progressive increase from the 10th to the 50th ponderal

index percentile, followed by a progressive decrease until the 90th ponderal index

percentile. The 20th and 90th regression coefficients were the same (at .0044), as were

the 50th and 95th percentile values at .0056 and .058, respectively.

Infant gender (female) achieved significance at all levels; however, the level of

contribution dropped between percentiles 30 to 50 and again at the 90th percentile.

Cohabitation demonstrated a significant regression co-efficient at the 40th and 50th

ponderal index percentile, while having a support partner only achieved significance at

the 80th percentile (Table XXIV).

The eight predictors in the linear regression model of the ponderal index were

only able to explain a small proportion of the explained variance in the index. All of the

significant predictor variables (parity, pre-pregnant BMI, gestational weight gain,

gestational age and infant gender (female) had a positive impact on the ponderal index.

Five of the six biological variables achieved a significant regression coefficient at all

percentiles. The quantile regression analysis indicated that the biological variable (age)

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and the two social variables (marital status and support partner) did not have a

significant impact on the majority of the ponderal index percentiles.

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TABLE XXIV

REGRESSION COEFFICIENT FOR PONDERAL INDEX USING LINEAR AND QUANTILE REGRESSION

Linear 10 20 30 40 50 60 70 80 90 95

Age -.0006 -.0012 -.0011 -.0011 -.0010 -.0007 -.0003 -.0001 -.0001 -.0001 -.0013

Parity .0237* .0202* .0265* .0230* .0230* .0265* .0280* .0279* .0291* .0209* .0271*

ppBMI .0061* .0063* .0054* .0052* .0063* .0071* .0067* .0067* .0077* .0083* .0084*

GWG .0049* .0043* .0044* .0043* .0048* .0056* .0052* .0052* .0047* .0044* .0058*

GestAge .0172* .0256* .0188* .0147* .0164* .0145* .0136* .0163* .0179* .0155* .0167*

Female .0226* .0282* .0228* .0127* .0144* .0173* .0203* .0313* .0248* .0230* .0367*

Cohabit .0009 .0162 .0113 .0084 .0120* .0125* .0008 .0006 -.0025 .0006 .0003

Support .0123 -.0020 -.0004 .0155 .0166 .0178 .0172 .0166 .0274* .0303 .0016

Pseudo R2 .0183 .0166 .0066 .0065 .0238 .0097 .102 .0111 .0076 .0138

Linear: F (8, 10578) = 47.09 p < .001; Quantile: (8, 10578, p < . 001) t * p < .05 ppBMI: Pre-pregnant BMI, GWG: gestational weight gain, GestAge: gestational age, Cohabit: cohabitation

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Research Question 2

Does increasing pre-pregnant adiposity, as measured by BMI, in term singleton

pregnancies of non-diabetic women increase the risk of delivering an infant with a

higher birth weight index? The impact of pre-pregnant BMI was examined in each of

three birth weight indices: LGA, birth weight z-scores, and ponderal index.

In the logistic regression model of LGA, pre-pregnant BMI demonstrated a

significant increased risk for a LGA infant (OR 1.116, 95% CI [1.09, 1.13]). This risk

suggests that a one-unit increase in pre-pregnant BMI would increase the odds of

having a LGA infant by 11%. The model suggested that the risk for a LGA infant from

increased pre-pregnant BMI was greater than the risk from increases in gestational

weight gain (9%).

The birth weight z-score linear regression model identified a significant

regression coefficient for pre-pregnant BMI (b = .036). Using national population

standard deviation (446 g), a one-unit increase in pre-pregnant BMI would result in a

16-gram increase in birth weight. The quantile regression model showed a progressive

increase in the impact of pre-pregnant BMI on birth weight z-scores as the birth weight

a-score percentile increased. At the 10th percentile, the regression coefficient was .022,

which equates to a 10-gram increase in birth weight, while at the 95th percentile the

regression coefficient increased to .056 (Figure 7), which represents a 25-gram

increase in birth weight. There was a greater than twofold increase in the impact of pre-

pregnant BMI on birth weight z-scores percentile from the 10th to the 95th. Additionally,

there was a marked increase in the pre-pregnant BMI regression coefficient between

the 80th and 90th percentile, with a 105-unit increase, which was double any of the other

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increases between percentile levels (Figure 7). The regression coefficient of gestational

weight gain was greater than pre-pregnant BMI at the 10th and 20th percentile; however,

the increasing impact of gestational weight was less dramatic as birth weight z-scores

increased (Figure 7), suggesting that pre-pregnant BMI has a greater impact on the

higher birth weight z-scores.

Figure 7. Linear and quantile regression coefficient of pre-pregnant BMI and

gestational weight gain on birth weight z-scores

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The linear regression model on ponderal index identified pre-pregnant BMI as a

significant predictor; however, the regression coefficient was low at .006. A one-unit

increase in pre-pregnant BMI would result in a .001 increase in ponderal index. Using

the linear equation prediction, pre-pregnant BMI would need to increase by 8 units to

see a .01 increase in the ponderal index. The quantile regression model of ponderal

index indicated that pre-pregnant BMI had a reducing impact on lower ponderal index

percentiles (10th to 30th) but an increasing impact from the 30th to 50th ponderal index

percentile, with the greatest increase of impact from the 70th to 95th percentile. The

regression coefficient increased from .006 at the 10th percentile to .008 at the 95th

percentile (Figure 8). The ponderal index model also demonstrated that pre-pregnant

BMI had a higher regression coefficient compared to gestational weight gain. The

quantile regression analysis indicates that pre-pregnant BMI has an increasing impact

on the high ponderal index percentiles, suggesting the mechanism between pre-

pregnant BMI and ponderal index changes as the ponderal index increases.

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Figure 8. Linear and quantile regression coefficient of pre-pregnant BMI and

gestational weight gain on ponderal index

Summary

Three types of regression analyses (logistic, linear, and quantile) were performed

using an existing dataset of 14,397 low-risk women who delivered a healthy singleton

infant at term to examine the effect of risk factors for high birth weight indices. Three

birth weight indices were used as the dependent variable: LGA, z-scores, and ponderal

index. The predictors included in the analysis were maternal age, parity, maternal

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height, pre-pregnant BMI, gestational weight gain, gestational age, infant gender,

smoking, marital status, support partner, and Medicaid. Each of the three regression

equations achieved significance, suggesting that the variables included in the model

were predictors of each of the birth weight indices. The logistic regression analysis

identified a significant contribution from maternal biological variables to risk of LGA.

However, the behavioral and social variables failed to show a significant contribution to

the risk of LGA.

The birth weight z-score and ponderal index linear models both achieved

significance. However, a higher level of explained variance from the predictors was

found in the birth weight z-score model than the ponderal index model. In the birth

weight z-scores linear analysis, biological, behavioral and social variables were

identified as predictors of change in the index. Seven of the nine predictors

demonstrated a significant impact on birth weight z-scores. Two social predictors failed

to demonstrate significance: marital status and Medicaid. In the ponderal index model,

five of the six biological predictors (which included two infant factors) demonstrated

significance as predictors of the explained variance in the ponderal index. Three

predictors (age, marital status, and support partner) failed to demonstrate significance.

The birth weight z-score quantile regression showed seven significant predictors:

age, parity, height, pre-pregnant BMI, gestational weight gain, smoking and support. As

expected, the significant predictors were consistent with the linear regression. With the

exception of smoking, all had a positive regression coefficient. Parity had the highest

positive regression coefficient; however, the regression coefficient fluctuated across the

birth weight z-score percentiles, with the highest values at the 10th and 95th percentiles.

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The remaining biological predictors (age, height, pre-pregnant BMI and gestational

weight gain) demonstrated a trend of an increasing regression coefficient as the birth

weight z-score percentile increased. The sole significant social variable (support

partner) did not demonstrate a trend in the change of the regression coefficient as the

birth weight z-score percentile increased. The behavioral variable of smoking showed a

significant negative impact on birth weight z-scores, with the highest regression

coefficient at the lower birth weight z-score percentile. Marital status and Medicaid did

not achieve significance in either the linear or quantile regression.

Parity, pre-pregnant BMI, gestational weight gain, gestational age, and gender

were significant predictors in the ponderal index linear and quantile regression analysis.

Parity showed a slight trend of increasing impact between the 40th and 80th ponderal

index percentile; however, there was a marked reduction in the regression coefficient

after the 80th percentile. The 90th and 95th percentile coefficients were virtually the

same as the 10th and 20th percentile, respectively. Pre-pregnant BMI showed a trend of

increasing impact as the ponderal index increased. Gestational weight gain showed an

increasing regression coefficient up to the 50th ponderal index percentile, followed by a

progressive decrease in impact from the 50th to the 90th percentile. The impact of

gestational age on ponderal index fell from the 10th to 30th percentile, then fluctuated at

the same level as the 30th percentile across the remaining percentiles. Female gender

also showed a fluctuating impact as the ponderal index increased. The quantile

regression analysis showed only very small changes in the contribution of the predictors

as the ponderal index increased.

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Pre-pregnant BMI demonstrated a significant impact on each of the birth weight

indices that were examined. The logistic regression model of LGA showed that a one-

unit increase in pre-pregnant BMI increases the odds of having a LGA infant by 11%. In

the birth weight z-score quantile regression analysis, the pre-pregnant BMI regression

coefficient progressively increased as the birth weight z-score percentile increased.

There was a 2.5-fold increase in the regression coefficient from the 10th to the 95th

percentile. This increase was greater than the increase of the gestational weight gain

regression coefficient. The quantile regression analysis demonstrated a change in the

relationship of the predictor variable pre-pregnant BMI as the birth weight z-score

increased.

The pre-pregnant BMI regression coefficient in the ponderal index model

increased between the 10th and the 95th percentile; however, it was not a consistent

upward trend. There was a decreasing impact at the lower and mid percentiles,

followed in each case by an increasing impact. The quantile regression model suggests

that the linear regression model may underestimate the association of pre-pregnant BMI

on ponderal index.

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V. Discussion

The purpose of this study was to assess the biological, behavioral, and social risk

factors for excess fetal growth resulting in high birth weight indices in non-diabetic

women who had a term low-risk singleton pregnancy. The women in this study had a

slightly lower mean pre-pregnant BMI (24.30 kg/m2) compared to the national average

(25.3 kg/m2) (Flegal & Troiano, 2000). This study had a higher rate of underweight

(5.5%) and ideal weight (59%) women compared to the national data: 3.5 % and 41%,

respectively. The rate of overweight women (23.1%) was similar to the national data

(25.3%); however, the rate of obese women was much lower (12.3%) in this study

compared to 30% in the national data (Rasmussen & Yaktine, 2009). The lower rate of

obese women in this study is consistent with other U.S. studies (Dietz et al., 2009;

Getahun et al., 2007; Gilboa et al., 2008) that have examined pre-pregnant BMI.

The distribution of the pre-pregnant BMI of the women from this study showed a

positive skewness, consistent with national data (Flegal & Troiano, 2000). There was

no significant change in mean pre-pregnant BMI across the 10 years, although national

data suggest that the rate of obesity in women aged 20 -39 years has increased over

the last 10 years (Flegal et al., 2010). The failure to identify a trend of increasing pre-

pregnant BMI may have been due to the exclusion of women who had gestational

complications such as diabetes and hypertension as their conditions are associated with

increased pre-pregnant BMI.

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The mean infant birth weight in this study was 3,404 g (SD = 436); this value was

similar to the national mean birth weight 3,389 g (SD = 446) (Donahue et al., 2010). A

comparison of mean birth weight stratified by gender and gestational age to new data

by Olsen (2010) indicated that four of five female mean birth weights (38–41 weeks'

gestation) were within 7 g of the mean female birth weight identified by Olsen (2010),

while three of the five male mean birth weights (38–40 weeks) were within 17 grams.

The rate of LGA (6.4%) was slightly lower in this study than the national rate of 8.9%

(Donahue et al., 2010); however, the national data include infants from women with

diabetes, which is associated with increased fetal growth.

The predictors included in the final model were age, parity, height, pre-pregnant

BMI, gestational weight gain, smoking, marital status, support partner, and Medicaid.

The significant predictors for the birth weight z-score regression model were age, parity,

height, pre-pregnant BMI, gestational weight gain, smoking, and support partner. The

predictors in the linear model significantly contributed 13% (R2 = .127) of the explained

variability in birth weight z-scores. Lunde et al. (2007) identified that 50%-79% of birth

weight variance is due to fetal and maternal genetic factors and suggested that

environmental factors contribute 9%-15% of birth weight variability. Although Lunde et

al. (2007) did not study maternal environmental factors, they suggested that such

variables include lifestyle behavior, diet, and socioeconomic status. In a prospective

study, Fredrick et al. (2008) were able to demonstrate a 27% (R2 = .273) contribution to

birth weight variance using the predictor variables pre-pregnant BMI; maternal age;

race; high school education; preterm birth; parity; infant gender; marital status; smoking;

gestational diabetes; and pre-eclampsia. The inclusion of the diabetes and pre-

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eclampsia by Fredrick et al. (2008) likely may have contributed to the higher level of

explained variance, as both of these gestational conditions significantly alter fetal

growth and in turn birth weight.

Modifiable Biological Predictors

Pre-Pregnant BMI

Pre-pregnant BMI was the primary predictor variable in this study; however,

unlike in many other studies, it was not categorized using NHLBI (1998) categories.

Body mass index was a continuous variable because: (1) There is no evidence-based

research to support that the NHLBI BMI categories are relevant to pregnancy and birth

weight outcomes, although they are consistently used in the management of pregnancy

and to assess birth weight outcomes. (2) It was hoped that maintaining the pre-

pregnant BMI in an interval format would reveal more information on how the predictor

impacts birth weight indices.

This study showed that an increase in pre-pregnant BMI significantly increased

the risk of LGA (OR 1.116, 95% CI [1.098, 1.135]) as well as birth weight z-scores (b =

.036, p < .001), and ponderal index (b = .006, p < .001). A one-unit increase in pre-

pregnant BMI would create an 11% increase in risk of delivering a LGA infant. Very few

studies have maintained and reported pre-pregnant BMI as a continuous variable; thus,

direct comparisons were limited.

Seven studies examined the risk of LGA (> 90th percentile) using the pre-

pregnant BMI category overweight. Five studies (Getahun et al., 2007; Jolly et al.,

2003; Nohr et al., 2008; Sebire et al., 2001; Surkan et al., 2004) found a significant

increase in the risk of LGA, while two (Gilboa et al., 2008; Magriples et al., 2009) did

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not. Gilboa et al. (2008) further examined pre-pregnant BMI in two-unit intervals

ranging 15–47 kg/m2, with a referent of 21 kg/m2. There was an increased risk for LGA

at BMI 23 kg/m2 (OR, 1.24, 95% CI [1.11, 1.38]) and 25 kg/m2 (OR 1.30 [1.07, 1.59]) but

then BMI failed to achieve significance until BMI reached 33 kg/m2.

Only one study (Magriples et al., 2009) of the eight studies (Getahun et al., 2007;

Gilboa et al., 2008; Jolly et al., 2003; Lu et al., 2001; Nohr et al., 2008; Sebire et al.,

2001; Surkan et al., 2004) that examined pre-pregnant obesity failed to find a significant

increase in the risk for LGA from pre-pregnant obesity. Whether pre-pregnant BMI was

maintained as continuous data or stratified, the research supports that increases in pre-

pregnant BMI increase the risk for LGA. However, the stratification of pre-pregnant BMI

may inadvertently mask the risk of delivering a LGA in the lower BMI categories. NHLBI

BMI categories were implemented in prenatal care to prevent SGA infants; the use of

BMI categories may be distorting or underestimating the risk for LGA.

In the birth weight z-score regression analysis, pre-pregnant BMI achieved a

regression coefficient of .036. Birth weight z-score was selected as the outcome

variable over birth weight in grams, as it allows for the accurate comparison of infants of

different gestation and gender. Only one study (Callaway et al., 2006) examined birth

weight z-scores; however, only ANOVA was performed to identify whether there were

differences by pre-pregnant BMI categories. The study showed a significant 0.1

increase in birth weight z-scores with each increase in pre-pregnant BMI category from

ideal to obese. The findings of this study are consistent with the findings by Callaway et

al. (2006); increases in pre-pregnant BMI contributed to increases in birth weight z-

scores.

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The regression coefficient of .036 obtained in this study equates to a 16-gram

increase in birth weight at term from a one-unit increase of pre-pregnant BMI. Abrams

and Laros (1986) found that a one-unit increase in pre-pregnant BMI would result in a

15.9-gram increase in birth weight (p < .001). This research continues to be cited today,

and (to some extent) it initiated the inclusion of pre-pregnant BMI in prenatal care.

Fredrick et al. (2008) was the only other study to examine pre-pregnant BMI as a

continuous variable. Their research identified a regression coefficient of 44.67 (p <

.001) for birth weight (grams). Fredrick et al. (2008) performed extensive modeling to

identify the best fit for the data.

The regression model used by Fredrick et al. (2008) identified an R2 = .273 using

the predictors of maternal age, race, education, marital status, parity, infant gender,

smoking, preterm birth gestational diabetes, and pre-eclampsia. Abrams and Laros

(1986) obtained a slightly lower explained variance to birth weight (R2 = .24) using

maternal age, race, parity, socioeconomic status, smoking, gestational age, pre-

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

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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.

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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.

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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

Midwife and Nurse Practitioner

1998 -1999 Southwest Obstetrics & Gynecology Associates, Houston, Texas Nurse Practitioner

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