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Associations of cumulative pollution burden and environmental health vulnerabilities with gestational weight gain in a cohort of predominantly low-income Hispanic women

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Associations of cumulative pollution burden and environmental health vulnerabilities with gestational weight gain in a cohort of predominantly low-income Hispanic women

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ASSOCIATIONS OF CUMULATIVE POLLUTION BURDEN AND ENVIRONMENTAL
HEALTH VULNERABILITIES WITH GESTATIONAL WEIGHT GAIN IN A COHORT OF
PREDOMINANTLY LOW-INCOME HISPANIC WOMEN

by

Anita Yau

A Thesis Presented to the
FACULTY OF THE USC KECK SCHOOL OF MEDICINE
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(BIOSTATISTICS)

May 2020

Copyright 2020 Anita Yau
ii
Table of Contents

List of Tables ................................................................................................................................. iii

List of Figures ................................................................................................................................. iv

Abstract ............................................................................................................................................ v

Introduction ..................................................................................................................................... 1

Methods ........................................................................................................................................... 3
Participants and Study Design ..................................................................................................... 3
Longitudinal Data Analysis ......................................................................................................... 6
CES Score Subcomponent Analysis ............................................................................................ 7

Results and Conclusions .................................................................................................................. 9
Descriptive Analysis .................................................................................................................... 9
Multinomial Logistic Regression .............................................................................................. 11
Longitudinal Data Analysis ....................................................................................................... 12
Pollution Burden ........................................................................................................................ 14
Population Characteristics ......................................................................................................... 15

Discussion ...................................................................................................................................... 18

References ..................................................................................................................................... 20

Appendix ....................................................................................................................................... 23

iii
List of Tables

Table 1. Institute of Medicine Weight Gain Recommendations for Pregnancy…………………..4

Table 2. Odds Ratios (OR) for Inadequate (or Excessive) vs. Adequate GWG associated
with a 10-unit increase in CES Score, using multinomial logistic regression…...……..11

Table 3. CES Score effects on Gestational Weight Gain………………………………………...14

Table 4. Pollution Burden score Impacts on Weight Gain……………………………………....15

Table 5. Population Characteristic score Impacts on Weight Gain………………………...……16

Table 6. Population Characteristics individual indicators’
effects on Gestational Weight Gain.................................................................................17

iv
List of Figures

Figure 1. Distribution of pre-pregnancy BMI by IOM gestational weight gain………………..…9

Figure 2. Distribution of gestational age BMI by IOM gestational weight gain………………...10

Figure 3. Observed maternal weight gain trajectories (left) versus the
Institute of Medicine’s recommended maternal weight gain in
trimesters 2 and 3, assuming the observed weight gain in trimester
1 since IOM recommendations for that period are vague……………...………………13

v
Abstract
Growing evidence suggests that environmental exposures play a biological role in weight
gain and obesity, leading to increased risks for serious diseases and health concerns, but there is
little research on how these mechanisms operate during pregnancy. Excessive gestational weight
gain leading to maternal obesity can pose severe health issues for not only the mother, but also
for the child, and so this study aims to evaluate the effects of cumulative pollution burden and
environmental health vulnerabilities, measured by a CalEnviroScreen (CES) Score on gestational
weight gain in 811 Hispanic mothers in Los Angeles. When analyzing gestational weight gain
using the Institute of Medicine categorization (excessive/adequate/inadequate) tailored to pre-
pregnancy body mass index (BMI), we estimated a non-significant reduced odds of excessive
weight gain (vs. adequate) associated with a 10-point higher CES score (OR: 0.86, 95% CI: 0.68,
1.10). When analyzing gestational weight gain using all longitudinal assessments of maternal
weight during pregnancy, there was evidence that CES score impacted weight gain in trimesters
2 and 3 (p-value=0.012). Women with a 10-point higher CES score experienced, on average, a
reduction of weekly weight gain in trimesters 2 and 3 of: 0.02 kg/week (95% CI: 0.01, 0.04).
When investigating the subcomponents of the CES score separately (pollution burden and
population characteristics), we found that the negative association of CES score with trimester 2
and 3 gestational weight gain was driven by the population characteristics component and, within
the set of population characteristics, there was a negative association with the Educational
Attainment indicator (p-value=0.07).

1
Introduction

Although the prevalence of obesity continues to rise in California, not all populations are
equally impacted.
1
The Hispanic population in California has one of the highest rates of
childhood obesity, along with disproportionately high pregnancy-related obesity rates.
2,3

Maternal obesity during pregnancy poses serious health concerns for both mothers and children.
4

Some health issues of maternal obesity include: gestational diabetes, increased risk of pregnancy
complications, impacts to the in-utero environment, increased fetal risks of stillbirth, and
increased risks of heart disease for both the mother and fetus after pregnancy.
5,6
Not only do
minority groups suffer from health differences, but in particular, populations with lower income
and education levels are largely affected by environmental pollution.
7
The low-income, Hispanic
population in California not only has one of the highest rates of obesity, but they also have a
disproportionate environmental exposure burden, carrying the greatest cumulative burden.
8,9

There continues to be accumulating evidence for the etiological role and obesogenic properties
of chemical environmental exposures in the development and progression of obesity.
10,11
We can
further study how these mechanisms operate during pregnancy to understand the impact of
maternal environmental exposures on gestational weight gain (GWG).
The California Office of Environmental Health Hazard Assessment has developed a tool
for evaluating multiple pollutants and stressors in communities, called the California
Communities Environmental Health Screening Tool (CalEnviroScreen).
12
The CalEnviroScreen
(CES) Score is used as an indicator of pollution burden and population susceptibility –
communities with high total CES scores have high cumulative pollution burdens and populations
with characteristics making the population likely more vulnerable to environmental exposures.
13

With the knowledge that the low-income, Hispanic population is highly burdened by
2
environmental exposures, we aim to use the CES scores during pregnancy to address the gaps in
understanding how these mechanisms perform during pregnancy. Overall, investigating whether
environmental exposures lead to differences in excessive maternal GWG is a crucial step in
preventing long-term obesity-related health issues in minority and low-income populations.
We analyzed data from participants who enrolled and delivered between November 2015
and January 2020 in the ongoing Maternal and Developmental Risks from Environmental and
Social Stressors (MADRES) cohort. Our aim was to investigate the impact of cumulative
pollution burden and environmental health vulnerabilities, measured by CES Score, on maternal
weight gain trajectories during pregnancy.

3
Methods

Participants and Study Design
The MADRES cohort is an ongoing prospective pregnancy cohort of predominantly
lower-income, Hispanic women in Los Angeles, California. In partnerships with four prenatal
healthcare providers in Los Angeles, enrollment of participants in the MADRES cohort
commenced prior to 30 weeks gestation. Cohort participants were followed through their
pregnancies using a series of in-person visits with interviewer-administered questionnaires,
anthropometric measurements and biospecimen collection as well as through telephone
interviews conducted with the mother.
14,15

Our primary analyses relating CES score to longitudinal assessments of maternal weight
during pregnancy included 515 mothers, after the following sequential exclusions: USC OB
GYN recruitment site (N=60) due to disproportionately higher educational levels, missing pre-
pregnancy BMI (N=288), missing CES exposure (N=17), and outliers (N=3). Initial analyses
relating CES score to total GWG additionally excluded mothers without a weight measurement
within 2 weeks prior to delivery, resulting in 420 mothers.

Variables
Maternal weight (measured in kilograms) was self-reported prior to pregnancy and
measured repeatedly at all available prenatal visits during pregnancy.
14
The Institute of Medicine
(IOM) defined GWG guidelines based on pre-pregnancy BMI ranges aimed to optimize
outcomes for the mother and the infant,
15
as shown in Table 1. We classified total GWG (weight,
in kg, within 2 weeks of delivery minus pre-pregnancy weight) into categories defined by the
4
IOM as: “adequate” (falling within the recommended range), “inadequate” (falling below the
recommended range), and “excessive” (falling above the recommended range).
Pre-Pregnancy Weight Category
Body Mass
Index
Recommended Range
of Total Weight (kg)
Underweight Less than 18.5 12.7–18.1
Normal Weight 18.5–24.9 11.3–15.9
Overweight 25–29.9 6.8–11.3
Obese (includes all classes) 30 and greater 5–9.1
Table 1. Institute of Medicine Weight Gain Recommendations for Pregnancy
16
Cumulative pollution burden and environmental health vulnerability was calculated for
each mother using the CES score based on the mother’s residential address at the MADRES
screening visit. The CES score (ranges from 1-100) was calculated as a product of overall
population characteristics subscore (ranges from 1-10) and overall pollution burden subscore
(ranges from 1-10).
9
The overall pollution burden subscore was calculated based on a group of
indicators: Exposures (Ozone Concentrations, PM2.5 Concentrations, Diesel PM Emissions,
Drinking Water Contaminants, Pesticide Use, Toxic Releases from Facilities, Traffic Density)
and Environmental Effects (Cleanup Sites, Groundwater Threats, Hazardous Waste, Impaired
Water Bodies, Solid Waste Sites and Facilities). Similarly, the overall population characteristics
subscore was calculated based on a different group of indicators: Sensitive Populations (Asthma
Emergency Department Visits, Cardiovascular Disease, Low Birth-Weight Infants) and
Socioeconomic Factors (Educational Attainment, Housing Burdened Low Income Households,
Linguistic Isolation, Poverty, and Unemployment). Percentiles were used to assign scores for
each indicator in a given geographic area. Then the respective percentile-transformed indicators
were averaged to attain the Pollution Burden subscore and Population Characteristic subscore,
respectively.
12
5
We assessed a wide range of potential covariates, as they could account for observed
associations among environmental exposures, psychological and behavioral responses, and
pregnancy-related weight outcomes. Covariates that were considered included maternal age at
consent, birth order of the child, maternal education, pre-pregnancy BMI, gestational age at birth
of child, and mother’s recruitment site. The distribution of key variables was summarized using a
frequency table and chi-square tests for homogeneity.

Multinomial Logistic Regression
Initial analyses related three-level categorized total GWG to CES score adjusting for
maternal age, birth order, maternal education, BMI, and recruitment site in women with full term
pregnancies using multinomial logistic regression models (with “adequate” as the reference level
in comparison to “inadequate” and “excessive” weight gain). Multinomial logistic regression is
used to model nominal outcome variables, in which the log odds of the outcomes are modeled as
a linear combination of the predictor variables. For example, it simultaneously estimates the
following two regression equations:
𝑙𝑛#
𝑃(𝐼𝑂𝑀_𝑇𝑜𝑡𝑎𝑙 = 𝑖𝑛𝑎𝑑𝑒𝑞𝑢𝑎𝑡𝑒)
𝑃(𝐼𝑂𝑀_𝑇𝑜𝑡𝑎𝑙 = 𝑎𝑑𝑒𝑞𝑢𝑎𝑡𝑒)
5 = β
!"
+ β
!!
𝐶𝐸𝑆_3_0_𝑆𝑐𝑜𝑟𝑒_10
𝑙𝑛#
𝑃(𝐼𝑂𝑀_𝑇𝑜𝑡𝑎𝑙 = 𝑒𝑥𝑐𝑒𝑠𝑠𝑖𝑣𝑒)
𝑃(𝐼𝑂𝑀_𝑇𝑜𝑡𝑎𝑙 = 𝑎𝑑𝑒𝑞𝑢𝑎𝑡𝑒)
5 = β
#"
+ β
#!
𝐶𝐸𝑆_3_0_𝑆𝑐𝑜𝑟𝑒_10
where βs are the regression coefficients. In our analyses, we scaled the CES score by 10, so a 1-
unit change was equivalent to a 10-unit change. We considered both unadjusted models and
models accounting for key confounding and effect modification after considering potential
interaction (interaction p-value<0.05) or confounding (>10% change in beta criterion) effects of
maternal age, birth order, maternal education, pre-pregnancy BMI, and study site. To maintain
6
consistency between models that are run in parallel, significant confounders were included in
both models. We also considered sensitivity analyses stratifying by gestational age, categorized
into “before full term” (<37 weeks) and “full term” (≥37 weeks).

Longitudinal Data Analysis
Our primary data analysis was longitudinal, taking advantage of the repeated weight
measurements for each mother to evaluate whether CES score was associated with maternal
weight gain trajectories during pregnancy. First, we used spaghetti plots to visualize trends in
gestational weight versus GA at the time of the weight assessment to determine if linearity was
reasonable. Based on these exploratory data analyses, we developed linear mixed effects models
that featured a piecewise linear spline with a single knot after the first trimester (at GA equal to
13 weeks), allowing for a change in the linear weight gain trajectory at 13 weeks. Weight gain
trajectories were more similar in the second and third trimesters, so to maintain a parsimonious
model we assumed the same linear trend for these two trimesters combined. To account for the
considerable between-participant variation in pre-pregnancy weights and weight trajectories, we
included random intercepts and slopes, resulting in an “unadjusted” model of the form:
Weight ij = (b 0 + b 1CES + U 0i) + (b 2 + b 3CES + U 1i)GA ij + (b 4 + b 5CES + U 2i)(GA ij – 13)
+
+ e ij, (1)
where the mother-specific random effects (U0i, U1i,U2i)
T
are assumed to follow a multivariate
normal distribution with mean zero and an unstructured variance/covariance matrix and (GAij –
13)
+
represents the “spline term” with ()
+
denoting a heaviside function (equal to 0 for negative
arguments and 1 for positive arguments). In this piecewise linear spline, b 2 represents the mean
weekly weight gain in trimester 1 for a mother with CES score of 0 but who is otherwise
“typical” in trimester 1 weight gain (U1i=0). The sum b2 + b4 represents the mean weekly weight
gain in trimesters 2 and 3 for a mother with CES score of 0 but who is otherwise “typical” in
7
trimester 2 and 3 weight gain (U2i=0). The key parameters of interest quantify the effect of CES
score on weight gain trajectory. The first, b3, represents the additional change in trimester 1
weekly weight gain associated with a 10-unit higher CES score. The sum b3 + b5, represents the
additional change in trimester 2 and 3 weekly weight gain associated with a 10-unit higher CES
score. Interpreted alone, b5 quantifies the difference in the effect of CES score on weight
trajectory in trimesters 2 and 3 as compared to trimester 1. We reported on b3 and b3 + b5 to study
if CES score impacted trimester 1 weekly weight gain and trimesters 2 and 3 weekly weight
gain, respectively. The longitudinal models did not adjust for any covariates; however, they were
fully stratified by pre-pregnancy BMI categories to further study the effect of CES score on
weight gain trajectory within each weight class.

CES Score Subcomponent Analysis
Further exploratory analysis was performed to determine the subcomponent(s) driving the
observed associations of CES score with weight gain trajectories. As the strongest evidence was
for the Population Characteristic subscore, we evaluated the correlations of the seven indicators
(Asthma Emergency Department Visits, Cardiovascular Disease, Low Birth-Weight Infants,
Educational Attainment, Housing Burdened Low Income Households, Linguistic Isolation,
Poverty, and Unemployment). To avoid including highly correlated variables in the same model,
only one variable from each set of high correlated variables was chosen. We fit a random
intercept and slope model, analogous to that in Equation 1 but with multiple Population
Characteristic subscore components included simultaneously, to investigate which Population
Characteristic indicator(s) were associated with differences in weekly gestational weight gain in
different trimesters. The model was as follows:
8
Weightij = (b0 + b1Asthma + b2LowBirthWeight + b3Education + b4Unemployment + U0i) +
(b5 + b6Asthma + b7 LowBirthWeight + b8Education + b9Unemployment + U1i)GAij +
(b10 + b11Asthma + b12LowBirthWeight + b13 Education + b14Unemployment +
U2i)(GAij – 13)
+
+ eij
All data was analyzed using Statistical Analysis System software version 9.4.

9
Results and Conclusions

Descriptive Analysis
As shown in Table A-1 (in the Appendix), the frequency and percentage of women
gaining inadequate, adequate, or excessive weight by pre-pregnancy BMI, maternal age, birth
order of child, maternal education, gestational age, and mother’s recruitment site were studied.

Figure 1. Distribution of pre-pregnancy BMI by IOM gestational weight gain

There were significant differences in categorized total GWG by categorized pre-
pregnancy BMI (𝜒
#
p-value < 0.0001 with three classes of obesity and p-value = 0.0066 with
obese classes collapsed into one category). As shown in Figure 1 for categorized total GWG,
underweight women had similar numbers in each category (adequate: N=4, inadequate: N=5, and
excessive: N=3) as did normal weight women (adequate: N=40, inadequate: N=39, and
excessive: N=41). Overweight women tended to have more weight gain (excessive: N=66,
10
adequate: N=44, and inadequate: N=23) as did obese class I women (excessive weight: N=46,
adequate: N=24, and inadequate: N=23). However, in obese class II and obese class III women,
the number of women who had gained inadequate weight was the highest (N=16 and N=17,
respectively). We noted that there was a possibility that higher obese class women received
advice to gain less weight or even lose weight, so it was unclear if the term “inadequate” weight
gain was appropriate for these women.

Figure 2. Distribution of gestational age BMI by IOM gestational weight gain

Gestational age at birth was associated with categorized total GWG (𝜒
#
p-value of
0.0001). As illustrated in Figure 2, in pregnancies that did not reach full term it was common to
observe inadequate weight gain, as expected (inadequate: N=25, adequate: N=8, excessive:
N=11). Hence, we decided to perform sensitivity analyses restricting to full term (≥37 weeks)
pregnancies only.
11
Multinomial Logistic Regression
Three-level categorical GWG was related to CES Score. We evaluated evidence for
interactions by fitting models that adjusted for CES Score, maternal age, birth order, maternal
education, mother’s BMI category, gestational age, and mother’s site and included interaction
terms one at a time (Table A-2). There were no significant interaction terms (all interaction p-
values > 0.05). We then evaluated confounding and observed the strongest evidence for
confounding (for maternal age, birth order, maternal education, pre-pregnancy BMI, and
gestational age) of the CES association with inadequate vs. adequate gestational weight gain
(Table A-3).
The results from the multinomial logistic model relating gestational weight gain to CES
Score are in Table 2. Although not statistically significant, we estimated protective effects of
total CES score on excessive vs. adequate weight gain with a slightly attenuated effect estimate
when restricting the analysis to only women with full-term pregnancies (adjusted OR: 0.82, 95%
CI: 0.66, 1.03, p-value=0.09) in all women and OR: 0.85 (95% CI: 0.69, 1.05, p-value=0.13) in
women with full-term pregnancies).
* Adjusted for: maternal age, birth order, maternal education, pre-pregnancy BMI, gestational age, study site
** Adjusted for: maternal age, birth order, maternal education, pre-pregnancy BMI, study site

Table 2. Odds ratios (OR) for inadequate (or excessive) vs. adequate GWG associated with a 10-
unit increase in CES Score, using multinomial logistic regression

Inadequate vs Adequate Excessive vs Adequate
Participants Model OR 95% CI p-value OR 95% CI p-value
All

Unadjusted 0.96 (0.76, 1.20) 0.70 0.82 (0.66, 1.01) 0.06
Adjusted* 0.91 (0.71, 1.17) 0.46 0.80 (0.64, 0.99) 0.04
Only full-term
pregnancies
Unadjusted 0.95 (0.74, 1.20) 0.65 0.85 (0.69, 1.05) 0.13
Adjusted** 0.92 (0.72, 1.19) 0.54 0.82 (0.66, 1.03) 0.09
12
When investigating associations of the two components of total CES score (Pollution
Burden subscore and Population Characteristics subscore), we found no evidence for
associations of the Pollution Burden score with GWG (Table A-4), but we did find evidence that
mothers with higher Population Characteristics scores had a reduced odds of excessive (versus
adequate) GWG (p-value=0.03) (Table A-5). We concluded that the previously observed
protective effect of CES score was driven by the Population Characteristics component of the
CES score.
During exploratory analyses, we also discovered that the IOM classification for GWG
may be inappropriate for the highest class of obese women. 70.8% of the 24 Obese class III
participants were classified as gaining inadequate weight, but it was likely that these women
received advice to either gain less weight or lose weight. In order to address this issue, we
proceeded with additional analyses of GWG as a continuous variable using all available
longitudinal weight assessments during pregnancy.

Longitudinal Data Analysis

Average maternal weight gain trajectories for each pre-pregnancy BMI category were
displayed next to the Institute of Medicine’s recommended rates of weight gain in the second and
third trimesters in Figure 3. Generally, weight gain in trimester 1 was more modest (or negative)
and increasing in trimesters 2 and 3. Amongst women classified as obese prior to pregnancy, the
observed weight gain trajectories for obese class III of our sample population appeared to be
quite different from that of the other classes in trimesters 2 and 3. However, the IOM’s
recommended rate of weight gain for obese women collapses the three classes and recommends
the same rate of weight gain to all obesity classes.
13

Figure 3. Observed maternal weight gain trajectories (left) versus the Institute of Medicine’s
recommended maternal weight gain in trimesters 2 and 3 (right), assuming the observed weight
gain in trimester 1 since IOM recommendations for that period are vague.
* Weight gain trajectories start at zero for each pre-pregnancy BMI class, for clarity of visual display.

Using a random slope and intercept model, the maternal weight trajectories were
evaluated, considering the effect of CES Score (Table 3). In trimester 1, the average weekly
weight gain in a typical mother with no CES exposure was 0.07 kg/week, and the difference in
weekly weight gain per 10-unit increase of CES Score was -0.02 kg/week (95% CI: -0.04, 0.01).
However, there was not statistically significant evidence that CES Score impacted trimester 1
weight gain (p-valueb3 =0.26). In trimesters 2 and 3, the average weekly weight gain in a typical
mother with no CES exposure was 0.52 kg/week, and the difference in weekly weight gain per
10-unit increase of CES Score was -0.02 kg/week. There was statistically significant evidence
that CES Score impacted trimester 2 and 3 weight gain (p-valueb3 + b5 =0.012). The estimate of
average weekly weight gain was attenuated for mothers with higher CES exposure.

14

Table 3. CES Score effects on Gestational Weight Gain
Stratified analyses were performed, and the model was repeated for each level of pre-
pregnancy BMI (Tables A-6 – A-11). Although not statistically significant, in all levels of pre-
pregnancy BMI, the same attenuated effect on the estimate of average weekly weight gain was
observed in trimesters 2 and 3. However, the lack of statistical significance could be attributed to
the lack of power from stratifying, especially in the higher obese class categories.
In order to evaluate the components of CES Score, the same model was evaluated with
effects of Pollution Burden subscore and Population Characteristics subscore in place of CES
Score. Examining the correlation between the subcomponents of CES Score, we see that the two
indicators are not correlated (Pearson’s R = 0.012).

Pollution Burden
The maternal weight trajectories were evaluated, considering the effect of Pollution
Burden subscore (Table 4). In trimester 1, the average weekly weight gain in a typical mother
with no Pollution Burden exposure was -0.01 kg/week, and the difference in weekly weight gain
per 1-unit increase of Pollution Burden subscore was -0.0004 kg/week (95% CI: -0.03, 0.03).

b Estimate 95% CI p-value
Trimester 1
Average Weekly Weight
Gain
b
2
0.07 (-0.07, 0.21) 0.35
Difference in Weekly
Weight Gain per 10 unit
increase of CES Score
b
3 -0.02 (-0.04, 0.01) 0.26
Trimester 2 + 3
Average Weekly Weight
Gain
b
2 + b
4 0.52 (0.42, 0.62) <0.0001
Difference in Weekly
Weight Gain per 10 unit
increase of CES Score
b
3 + b
5 -0.02
(-0.04, -
0.01)
0.012
15
However, there was not statistically significant evidence that Pollution Burden subscore
impacted trimester 1 weight gain (p-valueb3 =0.98). In trimesters 2 and 3, the average weekly
weight gain in a typical mother with no Pollution Burden exposure was 0.49 kg/week, and the
difference in weekly weight gain per 1-unit increase of Pollution Burden subscore was -0.01
kg/week. However, there was not statistically significant evidence that Pollution Burden
subscore impacted trimester 2 and 3 weight gain (p-valueb3 + b5 =0.21).

b Estimate 95% CI p-value
Trimester 1
Average Weekly Weight Gain b
2 -0.01 (-0.21, 0.19) 0.93
Difference in Weekly Weight
Gain per 1 unit increase of
Pollution Burden Score
b
3 -0.0004 (-0.03, 0.03) 0.98
Trimester 2 + 3
Average Weekly Weight Gain b
2 + b
4 0.49 (0.35, 0.63) <0.0001
Difference in Weekly Weight
Gain per 1 unit increase of
Pollution Burden Score
b
3 + b
5 -0.01 (-0.03, 0.01) 0.21

Table 4. Pollution Burden score Impacts on Weight Gain

Population Characteristics
The same model was repeated to consider the effect of the Population Characteristics
subscore, and the maternal weight trajectories were evaluated (Table 5). In trimester 1, the
average weekly weight gain in a typical mother with no Population Characteristics exposure was
0.14 kg/week, and the difference in weekly weight gain per 1-unit increase of Population
Characteristics subscore was -0.02 kg/week (95% CI: -0.04, 0.01). However, there was not
statistically significant evidence that Population Characteristics subscore impacted trimester 1
weight gain (p-valueb3 =0.12). In trimesters 2 and 3, the average weekly weight gain in a typical
mother with no Population Characteristics exposure was 0.49 kg/week, and the difference in
16
weekly weight gain per 1-unit increase of Population Characteristics subscore was -0.02 kg/week
(95% CI: -0.04, -0.003). There was statistically significant evidence that Population
Characteristics subscore impacted trimester 2 and 3 weight gain (p-valueb3 + b5 =0.02), yielding
similar effect estimate results from CES Score. Based on our estimates of the effect of subscores
on weekly weight gain, it was determined that the Population Characteristics subscore was
driving the effect observed by CES score. We further explored the individual indicators that
comprised the Population Characteristic subscore and their impacts on gestational weight gain.

b Estimate 95% CI p-value
Trimester 1
Average Weekly Weight Gain b
2 0.14 (-0.05, 0.33) 0.16
Difference in Weekly Weight
Gain per 1 unit increase of
Population Characteristic
Score
b
3 -0.02 (-0.04, 0.01) 0.12
Trimester 2 + 3
Average Weekly Weight Gain b
2 + b
4 0.55 (0.42, 0.69) <0.0001
Difference in Weekly Weight
Gain per 1 unit increase of
Population Characteristic
Score
b
3 + b
5 -0.02
(-0.04, -
0.003)
0.02

Table 5. Population Characteristic score Impacts on Weight Gain

Indicators from Sensitive Populations (Asthma Emergency Department Visits,
Cardiovascular Disease, Low Birth-Weight Infants) and Socioeconomic Factors (Educational
Attainment, Housing Burdened Low Income Households, Linguistic Isolation, Poverty, and
Unemployment) grouped together represent Population Characteristic. Standardizing these
indicators, correlation between indicators was studied (Table A-12). Amongst Sensitive
Population indicators, Asthma Emergency Department Visits and Cardiovascular Disease were
highly correlated (r = 0.74). Amongst Socioeconomic Factors indicators, Educational Attainment
was highly correlated with Housing Burdened Low Income Households (r = 0.55), Linguistic
17
Isolation (r = 0.66), and Poverty (r = 0.86). To avoid multicollinearity, only one variable from
each set of highly correlated variables was used for the random slope and intercept model.
Based on our results in Table 6, for every 1-unit increase in standardized Educational
Attainment score, the difference in weekly gestational weight gain was -0.019 kg/week in
trimesters 2 and 3 (95% CI: -0.04, 0.001). There was marginally statistically significant evidence
that Educational Attainment impacted weekly gestational weight gain in trimesters 2 and 3 (p-
valueb8+b13=0.07). All other indicators did not significantly impact weekly weight gain.

Table 6. Population Characteristics individual indicators’ effects on Gestational Weight Gain
b Estimate 95% CI p-value
Trimester 1
Average weekly weight gain
in typical mother
b
5 -0.012 (-0.05, 0.017) 0.42
Difference in Weekly Weight Gain per 1
unit increase in standardized Asthma
Emergency Department Visits Score
b
6 -0.003 (-0.04, 0.03) 0.84
Difference in Weekly Weight Gain per 1
unit increase in standardized Low Birth-
Weight Infants Score
b
7 -0.02 (-0.05, 0.01) 0.19
Difference in Weekly Weight Gain per 1
unit increase in standardized Educational
Attainment Score
b
8 -0.02 (-0.05, 0.006) 0.12
Difference in Weekly Weight Gain per 1
unit increase in standardized
Unemployment Score
b
9 -0.005 (-0.03, 0.03) 0.77
Trimester 2 + 3
Average weekly weight gain
in typical mother
b
5+b
10 0.40 (0.38, 0.42) <0.0001
Difference in Weekly Weight Gain per 1
unit increase in standardized Asthma
Emergency Department Visits Score
b
6+b
11 -0.013 (-0.04, 0.010) 0.27
Difference in Weekly Weight Gain per 1
unit increase in standardized Low Birth-
Weight Infants Score
b
7+b
12 -0.003 (-0.02, 0.019) 0.79
Difference in Weekly Weight Gain per 1
unit increase in standardized Educational
Attainment Score
b
8+b
13 -0.019 (-0.04, 0.001) 0.07
Difference in Weekly Weight Gain per 1
unit increase in standardized
Unemployment Score
b
9+b
14 -0.006 (-0.03, 0.015) 0.60
18
Discussion
Previous studies have established a relationship between environmental exposures and
the development of obesity.
10,11
There is also evidence that low-income, Hispanic women not
only carry the greatest cumulative environmental exposure burden, but also have one of the
highest rates of obesity.
18
As CES score is an indicator of the exposure and vulnerability to
environmental hazards, we utilize it to determine if the previously mentioned relationship holds
during pregnancy.

Our study showed there is evidence that CES Score has a significant effect on maternal
weight gain trajectories during pregnancy, attenuating pregnancy weight gain in both
multinomial logistic regression and longitudinal data analyses. Mothers with higher CES scores
had a reduced odds of gaining excessive gestational weight compared to normal gestational
weight. In longitudinal analyses, mothers with higher CES scores had a significant decrease in
average weekly gestational weight gain in trimesters 2 and 3. As women who were classified in
different categories of pre-pregnancy BMI were likely recommended to gain different amounts
of weight and at different rates, the observed attenuated effects could be classified as beneficial
or disadvantageous depending on pre-pregnancy BMI of the mother.
Upon further analyzing the two subcomponents that comprise the CES Score: pollution
burden (exposures and environmental effects) and population characteristics (sensitive
populations and socioeconomic factors), we determined that the observed protective effect was
driven by the population characteristics, but more specifically educational attainment. However,
there are limitations to the reliability of our study as it applies CES census-tract level data that
uses publicly available spatial exposure information. With census data, there is potential
19
sampling error due to self-reporting bias.
19,20
Additionally, we cannot establish causality and are
unable to extrapolate inferences to the individual level.
21

Studies might eventually utilize measurements of pollution burden and population
characteristics indicators at individual levels to increase reliability. Moreover, future research
might consider using factor analysis to group the Population Characteristics indicators by their
similar patterns of responses into factors, rather than selecting only one indicator from each
group of highly correlated variables, to further explore the relationship between socioeconomic
factors and gestational weight gain.

20
References

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Clin North Am. 2011 Dec;34(4):717-32. doi: 10.1016/j.psc.2011.08.005. PMID: 22098799;
PMCID: PMC3228640.

2 Skinner AC, Ravanbakht SN, Skelton JA, Perrin EM, Armstrong SC. Prevalence of Obesity and
Severe Obesity in US Children, 1999–2016. Pediatrics. 2018;141(3):e20173459

3 Walker LO, Hoke MM, Brown A. Risk factors for excessive or inadequate gestational weight
gain among Hispanic women in a U.S.-Mexico border state. J Obstet Gynecol Neonatal
Nurs. 2009;38(4):418–429. doi: 10.1111/j.1552-6909.2009.01036.x.

4 Leddy MA, Power ML, Schulkin J. The impact of maternal obesity on maternal and fetal
health. Rev Obstet Gynecol. 2008 Fall;1(4):170-8. PMID: 19173021; PMCID:
PMC2621047.

5 Avci ME, Sanlikan F, Celik M, Avci A, Kocaer M, Göçmen A. Effects of maternal obesity
on antenatal, perinatal and neonatal outcomes. J Matern Fetal Neonatal Med.
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6 Ng SK, Cameron CM, Hills AP, McClure RJ, Scuffham PA. Socioeconomic disparities in
prepregnancy BMI and impact on maternal and neonatal outcomes and postpartum weight
retention: the EFHL longitudinal birth cohort study. BMC Pregnancy Childbirth. 2014 Sep
8;14:314. doi: 10.1186/1471-2393-14-314. PMID: 25201481; PMCID: PMC4165994.

7 Woodruff TJ, Parker JD, Kyle AD, Schoendorf KC. Disparities in exposure to air pollution
during pregnancy. Environ Health Perspect. 2003;111(7):942–946. doi: 10.1289/ehp.5317.

8 Boyd-Barrett, Claudia. “People of Color and the Poor Disproportionately Exposed to Air
Pollution, Study Finds.” California Health Report, California Health Report, 7 Feb. 2019,
www.calhealthreport.org/2019/02/08/people-of-color-and-the-poor-disproportionately-
exposed-to-air-pollution-study-finds/.

9 Office of Environmental Health Hazard Assessment California Environmental Protection
Agency: Analysis of Race/Ethnicity, Age, and CalEnviroScreen 3.0 Scores. In.; 2018.

10 Roundtable on Environmental Health Sciences, Research, and Medicine; Board on
Population Health and Public Health Practice; Health and Medicine Division; National
Academies of Sciences, Engineering, and Medicine. The Interplay Between Environmental
Chemical Exposures and Obesity: Proceedings of a Workshop. Washington (DC): National
Academies Press (US); 2016 Jul 28. Available from:
https://www.ncbi.nlm.nih.gov/books/NBK379170/ doi: 10.17226/21880

21
11 La Merrill M, Birnbaum LS. Childhood obesity and environmental chemicals. Mt Sinai J
Med. 2011;78(1):22–48. doi: 10.1002/msj.20229.

12 Office of Environmental Health Hazard Assessment California Environmental Protection
Agency: Update to the California Communities Environmental Health Screening Tool
CalEnviroScreen 3.0; 2017.

13 Liévanos RS. Retooling CalEnviroScreen: Cumulative Pollution Burden and Race-Based
Environmental Health Vulnerabilities in California. Int J Environ Res Public Health. 2018
Apr 16;15(4):762. doi: 10.3390/ijerph15040762. PMID: 29659481; PMCID: PMC5923804.

14 Bastain TM, Chavez T, Habre R, Girguis MS, Grubbs B, Toledo-Corral C, Amadeus M,
Farzan SF, Al-Marayati L, Lerner D, Noya D, Quimby A, Twogood S, Wilson M, Chatzi L,
Cousineau M, Berhane K, Eckel SP, Lurmann F, Johnston J, Dunton GF, Gilliland F, Breton
C. Study Design, Protocol and Profile of the Maternal And Developmental Risks from
Environmental and Social Stressors (MADRES) Pregnancy Cohort: a Prospective Cohort
Study in Predominantly Low-Income Hispanic Women in Urban Los Angeles. BMC
Pregnancy Childbirth. 2019 May 30;19(1):189. doi: 10.1186/s12884-019-2330-7. PMID:
31146718; PMCID: PMC6543670.

15 O'Connor SG, Habre R, Bastain TM, Toledo-Corral CM, Gilliland FD, Eckel SP, Cabison J,
Naya CH, Farzan SF, Chu D, Chavez TA, Breton CV, Dunton GF. Within-subject effects of
environmental and social stressors on pre- and post-partum obesity-related biobehavioral
responses in low-income Hispanic women: protocol of an intensive longitudinal study. BMC
Public Health. 2019 Feb 28;19(1):253. doi: 10.1186/s12889-019-6583-x. PMID: 30819155;
PMCID: PMC6396454.

16 Institute of Medicine. Weight gain during pregnancy: reexamining the guidelines .
Washington, DC: National Academies Press; 2009.

17 The American College of Obsetricians and Gynecologists. Weight Gain During Pregnancy;
2016.

18 Chasan-Taber L, Marcus BH, Rosal MC, Tucker KL, Hartman SJ, Pekow P, Stanek E 3rd,
Braun B, Solomon CG, Manson JE, Goff SL, Markenson G. Proyecto Mamá: a lifestyle
intervention in overweight and obese Hispanic women: a randomised controlled trial--study
protocol. BMC Pregnancy Childbirth. 2015 Jul 30;15:157. doi: 10.1186/s12884-015-0575-3.
PMID: 26223246; PMCID: PMC4520196.

19 United States Census Bureau: “Understanding and Using American Community Survey
Data.”; 2018.

20 Althubaiti A. Information bias in health research: definition, pitfalls, and adjustment
methods. J Multidiscip Healthc. 2016 May 4;9:211-7. doi: 10.2147/JMDH.S104807. PMID:
27217764; PMCID: PMC4862344.

22
21 Morgenstern H. Uses of ecologic analysis in epidemiologic research. Am J Public Health.
1982 Dec;72(12):1336-44. doi: 10.2105/ajph.72.12.1336. PMID: 7137430; PMCID:
PMC1650553.

23
Appendix

IOM weight gestational weight gain
(N, row %)

Chi-
square
p-value

Inadequate Adequate Excessive OVERALL
Pre-pregnancy BMI category
Underweight, < 18.5 kg/m
2
5 (41.7%) 4 (33.3%) 3 (25.0%) 12 (2.9%)
<0.0001
(0.0066 if
obese
categories
collapsed)
Normal weight, 18.5-24.9 kg/m
2
39 (32.5%) 40 (33.3%) 41 (34.2%) 120 (28.6%)
Overweight, 25–29.9 kg/m
2
23 (17.3%) 44 (33.1%) 66 (49.6%) 133 (31.7%)
Obese, ≥30.0 kg/m
2
56 (36.1%) 36 (23.2%) 63 (40.7%) 155 (36.9%)
Obese class I, 30-34.9 kg/m
2
23 (24.7%) 24 (25.8%) 46 (49.5%) 93 (22.1%)
Obese class II, 35-39.9 kg/m
2
16 (42.1%) 9 (23.7%) 13 (34.2%) 38 (9.1%)
Obese class III, ≥ 40.0 kg/m
2
17 (70.8%) 3 (12.5%) 4 (16.7%) 24 (5.7%)
Maternal age

Youngest, Ages <22 13 (15.7%) 24 (28.9%) 46 (55.2%) 83 (19.8%)
0.0030
Middle, Ages 22-35 83 (30.3%) 82 (29.9%) 109 (30.8%) 274 (65.2%)
Eldest, Age >55 27 (42.9%) 18 (28.6%) 18 (28.6%) 63 (15.0%)
Birth order of child

Uniparous 24 (20.9%) 31 (27.0%) 60 (52.2%) 115 (27.4%)
0.0388
Multiparous 74 (31.9%) 68 (29.3%) 90 (38.8%) 232 (55.2%)
Missing 25 (34.3%) 25 (34.3%) 23 (31.5%) 60 (17.1%)
Maternal Education

<12
th
grade 37 (33.0%) 30 (26.8%) 45 (40.2%) 112 (26.7%)
0.2302
Completed high school 40 (32.0%) 37 (29.6%) 48 (38.4%) 125 (29.8%)
Some college/technical school 30 (28.3%) 28 (26.4%) 48 (45.3%) 106 (25.2%)
Completed college/grad training 19 (47.5%) 17 (42.5%) 4 (10.0%) 40 (9.5%)
Missing 12 (32.4%) 12 (32.4%) 13 (35.1%) 37 (8.8%)
Gestational Age

Before Full-Term 25 (56.8%) 8 (18.2%) 11 (25.0%) 44 (10.5%)
0.0001
Full-Term 98 (26.1%) 116 (30.9%) 162 (43.1%) 376 (89.5%)
Mother’s Recruitment Site

LAC + USC 21 (20.4%) 35 (34.0%) 47 (45.6%) 103 (24.5%)
0.1722
Eisner 99 (32.5%) 84 (27.5%) 122 (40.0%) 305 (72.6%)
South Central Clinic 3 (25.0%) 5 (41.7%) 4 (33.3%) 12 (2.9%)
OVERALL 123 (29.3%) 124 (29.5%) 173 (41.2%) 420 (100.0%)
Table A-1. Distribution of IOM gestational weight gain presented as frequency count (N) and row percent (%)

24
Covariate Interaction p-value
Maternal age 0.5657
Birth Order 0.3193
Maternal Education 0.4564
Pre-pregnancy BMI 0.6015
Gestational Age 0.2893
Mother’s Recruitment Site 0.9026
Table A-2. Testing for Interactions

Covariate
% change in β11
(inadequate vs adequate)
% change in β21
(excessive vs adequate)
Maternal age 26.9% 7.6%
Birth Order 17.6% 0.7%
Maternal Education 97.3% 2.9%
Pre-pregnancy BMI 10.3% 3.3%
Gestational Age 46.2% 0.7%
Mother’s Recruitment Site 15.8% 4.3%
Table A-3. Testing for Confounders

* Adjusted for: maternal age, birth order, maternal education, pre-pregnancy BMI, gestational age, study site
** Adjusted for: maternal age, birth order, maternal education, pre-pregnancy BMI, study site

Table A-4. Odds ratios (OR) for inadequate (or excessive) vs. adequate GWG associated with a 1
unit increase in Pollution Burden Score, using multinomial logistic regression

Inadequate vs Adequate Excessive vs Adequate
Participants Model OR 95% CI p-value OR 95% CI p-value
All

Unadjusted 0.92 (0.71, 1.18) 0.50 0.98 (0.77, 1.23) 0.84
Adjusted* 0.95 (0.72, 1.25) 0.70 1.01 (0.79, 1.29) 0.95
Only full-
term
pregnancies
Unadjusted 0.92 (0.79, 1.27) 0.56 1.00 (0.79, 1.27) 0.98
Adjusted** 0.95 (0.71, 1.26) 0.72 1.03 (0.80, 1.33) 0.81
25
* Adjusted for: maternal age, birth order, maternal education, pre-pregnancy BMI, gestational age, study site
** Adjusted for: maternal age, birth order, maternal education, pre-pregnancy BMI, study site

Table A-5. Odds ratios (OR) for inadequate (or excessive) vs. adequate GWG associated with a 1
unit increase in Population Characteristic Score, using multinomial logistic regression

b Estimate 95% CI p-value
Trimester 1
Average Weekly Weight Gain b
2 0.18 (-0.54, 0.91) 0.59
Difference in Weekly Weight
Gain per 10 unit increase of
CES Score
b
3 -0.03 (-0.16, 0.11) 0.68
Trimester 2 + 3
Average Weekly Weight Gain b
2 + b
4 0.80 (0.17, 1.43) 0.017
Difference in Weekly Weight
Gain per 10 unit increase of
CES Score
b
3 + b
5 -0.04 (-0.16, 0.07) 0.42
Table A-6. CES score Impacts on Weight Gain in Underweight Women

b Estimate 95% CI p-value
Trimester 1
Average Weekly Weight Gain b
2 0.14 (-0.03, 0.32) 0.11
Difference in Weekly Weight
Gain per 10 unit increase of
CES Score
b
3 -0.02 (-0.05, 0.01) 0.26
Trimester 2 + 3
Average Weekly Weight Gain b
2 + b
4 0.56 (0.45, 0.68) <0.0001
Difference in Weekly Weight
Gain per 10 unit increase of
CES Score
b
3 + b
5 -0.012 (-0.034, 0.01) 0.28
Table A-7. CES score Impacts on Weight Gain in Normal Weight Women

Inadequate vs Adequate Excessive vs Adequate
Participants Model OR 95% CI p-value OR 95% CI p-value
All

Unadjusted
1.02 (0.81, 1.29) 0.88 0.82 (0.67, 1.00) 0.04
Adjusted* 0.92 (0.71, 1.19) 0.52 0.76 (0.61, 0.94) 0.01
Only full-
term
pregnancies
Unadjusted
0.98 (0.78, 1.25) 0.90 0.84 (0.69, 1.03) 0.09
Adjusted**
0.93 (0.72, 1.20) 0.57 0.79 (0.63, 0.97) 0.03
26

b Estimate 95% CI p-value
Trimester 1
Average Weekly Weight Gain b
2 0.04 (-0.24, 0.32) 0.78
Difference in Weekly Weight
Gain per 10 unit increase of
CES Score
b
3 -0.006 (-0.06, 0.04) 0.81
Trimester 2 + 3
Average Weekly Weight Gain b
2 + b
4 0.45 (0.27, 0.63) <0.0001
Difference in Weekly Weight
Gain per 10 unit increase of
CES Score
b
3 + b
5 -0.009 (-0.04, 0.02) 0.61
Table A-8. CES score Impacts on Weight Gain in Overweight Women

b Estimate 95% CI p-value
Trimester 1
Average Weekly Weight Gain b
2 0.04 (-0.29, 0.37) 0.81
Difference in Weekly Weight
Gain per 10 unit increase of
CES Score
b
3 -0.02 (-0.08, 0.04) 0.58
Trimester 2 + 3
Average Weekly Weight Gain b
2 + b
4 0.52 (0.31, 0.74) <0.0001
Difference in Weekly Weight
Gain per 10 unit increase of
CES Score
b
3 + b
5 -0.04 (-0.08, 0.005) 0.08
Table A-9. CES score Impacts on Weight Gain in Obese Class I Women

b Estimate 95% CI p-value
Trimester 1
Average Weekly Weight Gain b
2 0.23 (-0.29, 0.75) 0.38
Difference in Weekly Weight
Gain per 10 unit increase of
CES Score
b
3 -0.06 (-0.15, 0.04) 0.22
Trimester 2 + 3
Average Weekly Weight Gain b
2 + b
4 0.47 (0.07, 0.88) 0.02
Difference in Weekly Weight
Gain per 10 unit increase of
CES Score
b
3 + b
5 -0.03 (-0.10, 0.05) 0.48
Table A-10. CES score Impacts on Weight Gain in Obese Class II Women
27

b Estimate 95% CI p-value
Trimester 1
Average Weekly Weight Gain b
2 -0.94 (-2.15, 0.27) 0.12
Difference in Weekly Weight
Gain per 10 unit increase of
CES Score
b
3 0.13 (-0.09, 0.35) 0.22
Trimester 2 + 3
Average Weekly Weight Gain b
2 + b
4 0.59 (0.03, 1.14) 0.04
Difference in Weekly Weight
Gain per 10 unit increase of
CES Score
b
3 + b
5 -0.08 (-0.18, 0.02) 0.11
Table A-11. CES score Impacts on Weight Gain in Obese Class III Women

Pearson Correlation Coefficients

Asthma
Emergency
Department
Visits
Cardio-
vascular
Disease
Low
Birth-
Weight
Infants
Educational
Attainment
Housing
Burdened
Low
Income
Households
Linguistic
Isolation
Poverty
Unemploy-
ment
Asthma
Emergency
Department
Visits
1.0 0.74 0.33 0.23 0.28 -0.15 0.25 0.18
Cardiovascular
Disease
0.74 1.0 0.14 0.29 0.18 -0.03 0.20 0.06
Low Birth-
Weight Infants
0.33 0.14 1.0 0.12 0.19 -0.14 0.18 0.25
Educational
Attainment
0.23 0.29 0.12 1.0 0.55 0.66 0.86 0.07
Housing
Burdened Low
Income
Households
0.28 0.18 0.19 0.55 1.0 0.40 0.60 0.13
Linguistic
Isolation
-0.15 -0.03 -0.14 0.66 0.40 1.0 0.62 -0.10
Poverty 0.25 0.20 0.18 0.86 0.60 0.62 1.0 0.12
Unemploy-
ment
0.18 0.06 0.25 0.07 0.13 -0.10 0.12 1.0
Table A-12. Correlation between Population Characteristics individual indicators

Abstract (if available)

Abstract Growing evidence suggests that environmental exposures play a biological role in weight gain and obesity, leading to increased risks for serious diseases and health concerns, but there is little research on how these mechanisms operate during pregnancy. Excessive gestational weight gain leading to maternal obesity can pose severe health issues for not only the mother, but also for the child, and so this study aims to evaluate the effects of cumulative pollution burden and environmental health vulnerabilities, measured by a CalEnviroScreen (CES) Score on gestational weight gain in 811 Hispanic mothers in Los Angeles. When analyzing gestational weight gain using the Institute of Medicine categorization (excessive/adequate/inadequate) tailored to pre-pregnancy body mass index (BMI), we estimated a non-significant reduced odds of excessive weight gain (vs. adequate) associated with a 10-point higher CES score (OR: 0.86, 95% CI: 0.68, 1.10). When analyzing gestational weight gain using all longitudinal assessments of maternal weight during pregnancy, there was evidence that CES score impacted weight gain in trimesters 2 and 3 (p-value=0.012). Women with a 10-point higher CES score experienced, on average, a reduction of weekly weight gain in trimesters 2 and 3 of: 0.02 kg/week (95% CI: 0.01, 0.04). When investigating the subcomponents of the CES score separately (pollution burden and population characteristics), we found that the negative association of CES score with trimester 2 and 3 gestational weight gain was driven by the population characteristics component and, within the set of population characteristics, there was a negative association with the Educational Attainment indicator (p-value=0.07).

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Creator Yau, Anita (author)

Core Title Associations of cumulative pollution burden and environmental health vulnerabilities with gestational weight gain in a cohort of predominantly low-income Hispanic women

School Keck School of Medicine

Degree Master of Science

Degree Program Biostatistics

Publication Date 04/20/2020

Defense Date 04/20/2020

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

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