University of Southern California Dissertations and Theses

Association of traffic-related air pollution and age-related macular degeneration in the Los Angeles Latino Eye Study

(USC Thesis Other)

Association of traffic-related air pollution and age-related macular degeneration in the Los Angeles Latino Eye Study

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content

ASSOCIATION OF TRAFFIC-RELATED AIR POLLUTION AND AGE-RELATED
MACULAR DEGENERATION IN THE LOS ANGELES LATINO EYE STUDY

by

Lulu Song

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)

December 2020

Copyright 2020 Lulu Song

ii
Acknowledgements
First of all, I would like to express my sincere gratitude to the chair of my committee,
Dr. Farzana Choudhury, for her instructive advice and useful suggestions on my thesis.
Second, I would like to express my heartfelt thanks to Dr. Towhid Salam, who gave me
valuable advice on data analysis and manuscript preparation. I am also grateful to Dr.
Meredith Franklin, for her constant encouragement and guidance. Special thanks to my
friends living in 1210 who have spent a considerable amount of time and energy on the draft.
Finally, I thank my family for their continued support and encouragement.

iii
Table of Contents
Acknowledgements ............................................................................................................................................. ii
Table of Contents ............................................................................................................................................... iii
List of Figure and Tables ................................................................................................................................... iv
Abstract ............................................................................................................................................................... v
Introduction ......................................................................................................................................................... 1
Methods ............................................................................................................................................................... 4
Study Design .................................................................................................................................................. 4
Study Variable ..................................................................................................................................................... 5
Traffic Exposures ........................................................................................................................................... 5
Other Risk Factors .......................................................................................................................................... 5
Outcome ......................................................................................................................................................... 6
Statistical Analysis ......................................................................................................................................... 7
Results ................................................................................................................................................................. 9
Study Cohort ................................................................................................................................................... 9
Comparison of Participants’ Baseline Characteristics by AMD Status: ...................................................... 10
Traffic-related air pollution indicators and AMD ........................................................................................ 12
Multivariate Associations of Traffic-Related Air Pollution and any AMD ................................................. 14
Multivariate Associations of Traffic-Related Air Pollution and early AMD ............................................... 15
Discussion ......................................................................................................................................................... 16
References ......................................................................................................................................................... 19

iv
List of Figure and Tables
Figure 1-Flow-chat of analysis cohort ............................................................................................................... 9
Table 1-Distribution of Sociodemographic, Behavior and Clinical Risk Indicators Stratified by Category of
AMD. ............................................................................................................................................................... 11
Table 2-Distribution Summaries for Traffic Related Exposures Metrics ........................................................ 12
Table 3-Correlation Between Each Traffic Variable ....................................................................................... 13
Table 4-Univariate and Multivariate Associations of Traffic-Related Air Pollution and any AMD ............... 14
Table 5-Multivariate Associations of Traffic-Related Air Pollution and early AMD ..................................... 15

v
Abstract
Exposure to tobacco smoke has been associated with age-related macular
degeneration (AMD). Epidemiologic studies have implicated oxidative stress for these
associations. Traffic-related air pollution (TRAP) results in similar oxidative damage. We
investigated the relationship between TRAP and AMD using data from the Los Angeles
Latino Eye Study (LALES), a population-based cross-sectional study. Participants aged 40
or older underwent standardized comprehensive ocular examinations at baseline with 4 years
of follow-up. AMD was graded using a modification of the Wisconsin Age-Related
Maculopathy Grading System. TRAP was estimated using a Geographical Information
System (GIS) with two surrogate measures of exposures: distance from highway/freeway to
each participant’s home and total road lengths within circular areas with radiuses of 50m,
100m, 200m and 300m centered at the participant’s home. Multiple logistic regressions were
used to assess the association between TRAP metrics and the prevalence of AMD (including
any AMD and early AMD) among 5,506 participants. We adjusted for age, gender,
employment status, education, annual household income, medical insurance, smoking status,
history of hypertension, cataract surgery and lens opacities in the final models. TRAP
metrics were not found to be statistically significantly associated with AMD after adjusting
for these variables. The distance from highway/freeway was not statistically significantly
associated with the prevalence of any AMD [odds ratio (OR) = 1.05; 95% Confidence
interval (95% CI): 0.96 - 1.15] and early AMD [OR = 1.05; 95% CI: 0.95 - 1.15]. None of
the measures for road length were associated with AMD. Thus, the results indicate that these
TRAP metrics were not statistically significantly associated with the prevalence of AMD in
this study.

1
Introduction
Visual impairment has significant adverse effects on physical and psychosocial health (Li et
al., 2011). In particular, individuals who are visually impaired or blind have a higher risk of chronic
health conditions (Crews & Campbell, 2004), unintentional injuries (Ivers et al., 2000), social
withdrawal (Jones et al., 2009), depression (Jones et al., 2009; Zhang et al., 2013), and mortality
(Lee et al., 2002). In 2015, approximately 3.22 million people in the United States had visual
impairment (Varma et al., 2016). By 2050, this number is projected to reach 6.95 million (Varma
et al., 2016). One of the primary causes of visual impairment is age-related macular degeneration
(AMD), which leads to irreversible visual impairment among people aged 65 or more in many
regions or countries, such as Europe, the United States, and India (Chopdar et al., 2003). By 2020,
the number of people with AMD globally is expected to be around 200 million, and this number
will increase to nearly 300 million by 2040 (Wong et al., 2013). Additionally, AMD accounts for
more than 50% of all blindness in the United States (Congdon et al., 2004)

and leads to a significant
public health problem (Wong et al., 2014). The number of persons with advanced AMD is expected
to double over the next 20 years, resulting in an increased socioeconomic burden (Friedman et al.,
2004). Therefore, there is significant interest in identifying the risk factors that are associated with
AMD.
Age-related macular degeneration (AMD), a disease characterized by the formation of
lipid-rich extracellular deposits, localized inflammation, and ultimately neurodegeneration in the
central part of the retina (termed the macula) (Chang et al., 2019), can be divided into early-stage
AMD and late-stage AMD (Gehrs et al., 2006). In brief, early-stage AMD includes clinical signs
such as drusen and abnormalities of the retinal pigment epithelium, the pigmented cell layer
outside the neurosensory retina. Late-stage AMD can be neovascular (also known as wet or

2
exudative) or non-neovascular (known as atrophic, dry, or non-exudative), which is more severe
than early-stage AMD. Late AMD results in diminished central visual acuity, leading to severe
and permanent visual impairment and legal blindness.
Among the known risk factors of AMD, age is most strongly associated with AMD
(Fritsche et al., 2016). Certain genetic variants are also associated with AMD (Group, 2000). By
2017, 52 common and rare variants at 34 genetic loci had been tested to be independently
associated with late AMD (Group, 2000). Previous studies evaluated a number of other risk factors
related to AMD, including family history, sex, and cigarette smoking (Fritsche et al., 2016). Diet
is also identified to be a risk factor of AMD (Lambert et al., 2016).. Sunlight exposure, iris colour
(Tomany et al., 2004), and alcohol consumption are also associated with AMD (Adams et al.,
2012).
Traffic sources contribute to air pollution in urban areas, yet very little is currently known
about the effects they may have on the eyes. There are three reasons why traffic-related air
pollution (TRAP) might be associated with AMD. At first, exposure to TRAP during infancy can
affect lung function in children up to 8 years of age (Schultz et al., 2012), which indicates that air
pollution can cause oxidative damage and impact lung function. Therefore, air pollution can
potentially impact eyes, since air pollution could contribute to similar oxidative damage to eyes.
Understanding the relationship between TRAP and oxidative-caused eye disease, such as AMD,
could aid in preventing the disease. Secondly, tobacco smoke also influences oxidative damage,
and exposure to tobacco smoke has been associated with AMD in epidemiologic studies (Fritsche
et al., 2016). It is plausible that exposure to air pollution could be related to AMD. Furthermore,
the other study has been demonstrated that traffic-related air pollutants increase the risk of age-

3
related macular degeneration in Taiwan (Chang et al., 2019). However, the association between
TRAP and Latinos in the United States has never been explored. Better understanding the
relationship between TRAP and oxidative-caused eye disease, such as AMD, could aid in
preventing the disease. Given the evidences above, we hypothesized that there is an association
between TRAP and AMD. We tested this hypothesis using data from The Los Angeles Latino Eye
Study (LALES).
As of 2018, the Census Bureau estimated that there were almost 60 million Hispanics living
in the United States (about 18% of the overall population), which was the largest and fastest-
growing racial/ethnic group (Mark, 2019). By 2050, the highest prevalence of visual impairment
among minorities is expected to shift from African American individuals (15.2% in 2015 to 16.3%
in 2050) to Hispanic individuals (9.9% in 2015 to 20.3% in 2050) (Varma et al., 2016). However,
the epidemiology studies related to Latinos are relatively scant and needed to be explored. LALES
was a population-based study (from February 2000 to May 2003) designed to determine both
modifiable and nonmodifiable risk indicators that may be associated with ocular diseases among
Latinos of predominantly Mexican ancestry (Fraser-Bell et al., 2006). The LALES is the largest
study of eye disease among a predominantly Mexican-American population in the United States
(Fraser-Bell et al., 2006) and funded by the National Eye Institute and the National Center on
Minority Health and Health Disparities of the National Institutes of Health (Varma et al., 2004).
Institutional review board/ethics committee approval was obtained from the Los Angeles
County/University of Southern California Medical Center Institutional Review Board (Fraser-Bell
et al., 2006).

4
Methods
This study used data from the Los Angeles Latino Eye Study (LALES). The data from Los
Angeles Latino Eye Study (LALES) was collected by interviews and comprehensive ocular
examinations (Varma et al., 2004). Given these data, we have utilized a cross-sectional study to
investigate the association between TRAP and the prevalence of AMD in Latinos aged more than
40 years in Los Angeles County.

Study Design
Latinos are Americans who are descended from a Spanish-speaking community. The Los
Angeles Latino Eye Study (LALES) is a population-based study of vision and common eye
diseases among Latinos living in 6 census tracts in the city of La Puente, Los Angeles County.
Details of the survey design have been reported elsewhere (Varma et al., 2004). In brief, eligible
participants had to be aged more than 40 years old and self-reported as Latinos. They were
informed of the study and invited to participate in a standardized questionnaire and clinical
examination. Sociodemographic indicators and behavior indicators were collected by
questionnaire. Clinical indicators were collected by clinical examination. The procedures for
clinical data collection were standardized and included a series of measurements taken by
ophthalmic technicians, interviewers, and an ophthalmologist. Similar questionnaires and clinical
examinations were performed in both baseline and follow-up studies. The ophthalmic examination
included 30-degree stereoscopic color fundus photographs of diabetic retinopathy study fields 1
and 2 and a modified field 3 for all participants.

5
Study Variable
Traffic Exposures
There were two metrics used to measure traffic air pollution exposures: the distance from
highway/freeway to each participant’s home and the total road length within circular areas with
radiuses of 50m, 100m, 200m and 300m centered at each participant’s home. The home address
of each participant was geocoded and these two traffic exposures metrics were calculated by
ArcGIS version 10.1.

Other Risk Factors
Potential risk factors included a number of sociodemographic, behavioral, and clinical
characteristics. Potential sociodemographic characteristics include age, gender, working status,
income, education, vision insurance plan, and medical insurance. Variables of potential
sociodemographic characteristics and behavior characteristics were self-reported. Clinical
characteristics, including diabetes, hypertension, were clinically assessed. Ocular factors such as
cataracts, lens opacities, and heart attacks were assessed by comprehensive ophthalmologic
examination. Age was defined as the participant’s age at the time of the baseline examination.
Smoking status was categorized into three groups, non-smoker, ex-smoker, and current smoker.
Non-smokers were participants who had never smoked before the baseline examination. Ex-
smokers were participants who had smoked but stopped smoking before the baseline examination.
Current smokers were participants who smoked during the time of the baseline examination. In
LALES data, 45% of participants had less than six years of education, and 52% of participants had
annual household income less than $20,000 (Varma et al., 2004). Thus, education was

6
dichotomized into two groups: those with education for less than six years and those with education
equal to or more than six years. Annual household income was dichotomized into two groups:
those with annual income less than $20,000 and those with annual income equal to or more than
$20,000.

Outcome
Grading of fundus photographs for AMD
A modification of the Wisconsin age-related maculopathy grading system was used to
grade individual AMD lesions (Klein et al., 1991). A more detailed description of all grading
procedures and definitions has been presented elsewhere (Klein et al., 1991). In short, lesion-by-
lesion evaluation was done and two gradings were performed for each eye: a preliminary masked
grading was done by one of two graders and detailed gradings were performed by one of three
other experienced graders. Clinical differences between the two graders were edited and reviewed
using standardized edit rules. These edits were masked as to whether the photographs were taken
at baseline or follow-up.

Definitions of AMD
Definitions of AMD component lesions and prevalence of AMD have been described in
the previous study (Varma et al., 2004).

In short, early AMD was defined as the absence of signs
of advanced AMD and the presence of soft indistinct or reticular drusen or hard distinct or soft
distinct drusen with pigmentary abnormalities (retinal pigment epithelial depigmentation or

7
increased retinal pigment). Advanced AMD was defined as the presence of either geographic
atrophy or exudative AMD.

Statistical Analysis
The sociodemographic indicators, behavior indicators, and clinical indicators between
participants with AMD and without AMD were examined using t-tests for comparison of means
for continuous variables and Pearson chi-square tests for comparison of proportions for categorical
variables. The distributions for traffic-related exposures metrics were investigated. The means and
standard deviations of TRAP were also calculated. Correlations between each traffic variable were
examined using Spearman rank correlations. Logistic regressions were used to examine the
association between TRAP and the prevalence of AMD, including any AMD and early AMD. We
scaled each traffic metric by the standard deviation (SD) to obtain odds ratios (ORs) and 95%
confidence intervals 95% CIs) reflecting risk estimates per SD of TRAP exposure. For multivariate
associations, we initially adjusted for different kinds of potential risk factors independently, then
added all of them in the final model. We ran logistic regression for the basic model (unadjusted).
We then adjusted for sociodemographic indicators (age, gender, income, education, unemployed,
and vision plan) to model 2. In model 3, we added behavior indicators (smoking status) to the base
model. In model 4, we just added clinical indicators (hypertension, Cataract surgery, and Lens
Opacities) that were independently significantly associated with any AMD and early AMD to the
base model. In the final model, all the indicators in model 2, model 3, and model 4 were added to
the base model. Missing indicators were included as covariates in the model to handle missing

8
data. All statistical tests were two-sided at a 5% significance level. All statistical analyses were
performed using SAS software v. 9.4 (SAS Institute, Inc., Cary, NC, USA).

9
Results
Study Cohort
Of the 6,357 participants examined at baseline, 5,506 (87%) participated with AMD data,
environmental and ocular data. 4,968 of them had no AMD, 538 participants had any AMD. For
certain types of AMD, 515 of participants had early AMD, while only 23 participants carried late
AMD. Figure 1 is a flowchart of the study cohort in this analysis.

Figure 1-Flow-chat of analysis cohort

10
Comparison of Participants’ Baseline Characteristics by AMD Status:
Sociodemographic indicators and AMD
The distributions of sociodemographic, behavior and clinical risk factors by different
statuses of AMD are presented in Table 1. Participants with early AMD and AMD were about 5-
6 years older than participants without AMD (all p values < 0.01). The proportion of male were
significantly higher than proportion of female for participants with early AMD and AMD
compared with those without AMD (All p values < 0.01). For employment status, proportion of
unemployed or retired participants were significantly higher for participants with early AMD and
AMD compared with those without AMD (all p < 0.01). Participants with early AMD and AMD
were more likely to have lower income compared with those without AMD (all p < 0.01).
Participants with early AMD and AMD are more likely to have less education compared with those
without AMD (all p values < 0.01). Participants with early AMD and AMD were more likely to
have vision plan compared with those without AMD (all p values < 0.05).

Behavior indicators, clinical indicators and AMD
The proportion of current smokers were significantly more among participants with early
AMD compared with participants without AMD (p value < 0.05). Participants with early AMD
and AMD were more likely to have hypertension and cataract surgery compared with those without
AMD (all p values < 0.05). Participants with early AMD and AMD were more likely to have the
history of lens opacities (all p values < 0.01).

11
Table 1-Distribution of Sociodemographic, Behavior and Clinical Risk Indicators Stratified by Category of AMD.
Risk Indicators No AMD
(N=4968)
Early
AMD(N=515)
Any AMD
(N=538)
Sociodemographic indicators
Age[mean(SD)] 53.49 (9.96) 58.72 (11.92)** 59.39 (12.37)**
Gender
Male 1996 (40.18%) 281 (54.56%)** 293 (54.56%)**
Female 2972 (59.82%) 234 (45.44%) 245 (45.44%)
Unemployed/retired 2374 (47.98%) 297 (57.89%)** 316 (58.96%)**
Annual household
Income (US $)

< 20,000 2148 (49.55%) 251 (56.53%)** 264 (56.65%)**
≥ 20,000 2187 (50.45%) 193 (43.47%) 202 (43.45%)
Education
< 6 years 2801 (56.47%) 258 (50.19%)** 271 (50.47%)**
≥ 6 years 2159 (43.53%) 256 (49.81%) 266 (49.53%)
Vision Plan 2446 (49.33%) 280 (54.37%)* 296 (55.02%)*
Medical insurance 3171 (63.96%) 347 (67.38%) 367 (68.22%)
Behavior indicators
Smoking status
Non-smoker 3077 (62.25%) 293 (57.23%) 303 (56.64%)*
Ex-smoker 1175 (23.77%) 143 (27.93%) 152 (28.41%)
Current-smoker 691 (13.98%) 76 (14.84%) 80 (14.95%)
Clinical indicators
History of diabetes 759 (15.31%) 81 (15.73%) 86 (15.99%)
hypertension 1399 (28.29%) 171 (33.20%)* 183 (34.01%)**
Cataract surgery 260 (69.71%) 36 (52.17%)** 40 (50.63%)**
history of Lens
Opacities
373 (7.55%)

69 (13.5%)**

79 (14.79%)**
History of heart
attack
145 (2.93%) 18 (3.50%) 21 (3.91%)
Take vitamins 1279 (44.83%) 142 (45.22%) 147 (44.55%)
1)SD = standard deviation; * stands for p-values < 0.05, ** stands for p-value < 0.01.
2)Difference of risk indicators in each category of AMD compared to subjects without AMD were analyzed
by independent t-tests for continuous variables and Pearson chi-square test for categorical variables. P-
values < 0.05 were considered statistically significant.

12
Traffic-related air pollution indicators and AMD
The distribution summaries for traffic-related exposures metrics are in Table 2. The
average distance to the nearest highway or freeway was around 2.1km. With increase in the radius
of the buffer, the length of roads within the buffer increased such that there was about 128m of
road length within the 50m buffer and about 2,880m road lengths within the 300m buffer. The
correlations among these traffic measures are summarized in Table 3. Correlations between the
distance to the nearest highway/freeway and the total road length in different buffers were weak
(Spearman correlation coefficients ranged from 0.11 to 0.21). The correlations between total road
length within different buffers ranged between very weak to strong (Spearman correlation
coefficients ranged 0.13 to 0.62).
Table 2-Distribution Summaries for Traffic Related Exposures Metrics
Variable Mean
(SD)
Percentile
25th 50th 75th
Distance to nearest
highway/freeway
(meter)
2100.58
(874.79)
1460.57 2347.45 2779.07
Total road length in 50m
buffer around home
(meter)
128.21
(48.48)
97.86 114.42 155.73
Total road length in 100m
buffer around home
(meter)
485.02
(135.72)
402.25 483.00 573.84
Total road length in 200m
buffer around home
(meter)
1869.13
(374.15)
1613.60 1887.60 2141.97
Total road length in 300m
buffer around home
(meter)
2879.59
(1457.11)
1452.81 3099.88 4095.39
1) SD = standard deviation
2) N=5,506, which is the number of participants with AMD data

13
Table 3-Correlation Between Each Traffic Variable
Variables #1 #2 #3 #4
1). Distance to nearest
highway/freeway
(meter)
1
2). Total road length in 50m
buffer around home
(meter)
0.11** 1
3). Total road length in 100m
buffer around home
(meter)
0.10** 0.42** 1
4). Total road length in 200m
buffer around home
(meter)
0.11** 0.13** 0.62** 1
5). Total road length in 300m
buffer around home
(meter)
0.21** 0.19** 0.26** 0.32**
**P <0.0001

14
Univariate and Multivariate Associations of Traffic-Related Air Pollution and any AMD
The univariate and multivariate associations between traffic-related air pollution and any
AMD are shown in Table 4. For univariate associations, we did not find any statistically significant
associations between TRAPs (distance from highway/freeway to each participant’s home and total
road length in different buffers around home) and any AMD. For multivariate associations, we did
not find any statistically significant associations between TRAPs and any AMD after adjusting for
sociodemographic, behavior and clinical indicators.
Table 4-Univariate and Multivariate Associations of Traffic-Related Air Pollution and any AMD
Traffic related
exposures
Model 1 Model 2 Model 3 Model 4 Model 5
OR
(95% CI)
OR
(95% CI)
OR
(95% CI)
OR
(95% CI)
OR
(95% CI)
Distance to nearest
highway/freeway
1.054
(0.964 - 1.154)
1.049
(0.956 - 1.151)
1.046
(0.956 - 1.145)
1.056
(0.964 - 1.156)
1.050
(0.956 - 1.153)
Total road length in
50m
buffer around home
1.041
(0.953 - 1.136)
1.038
(0.948 - 1.136)

1.038
(0.951 - 1.134)
1.037
(0.949 - 1.133)
1.035
(0.946 – 1.133)
Total road length in
100m
buffer around home
1.009
(0.923 - 1.103)
0.983
(0.896 - 1.077)
1.007
(0.921 - 1.101)
1.006
(0.920 - 1.101)

0.983
(0.897 - 1.078)
Total road length in
200m
buffer around home
1.019
(0.932 - 1.114)
0.972
(0.887 - 1.065)
1.018
(0.931 - 1.113)
1.012
(0.925 - 1.107)
0.973
(0.887 - 1.066)
Total road length in
300m
buffer around home
1.052
(0.962 - 1.150)
1.044
(0.953 - 1.144)
1.050
(0.960 - 1.149)
1.052
(0.962 - 1.151)
1.047
(0.956, 1.148)
1) Model 1: Base model (unadjusted)
Model 2: Base model + sociodemographic indicators (age, gender, income, education, unemployed and vision plan)
Model 3: Base model + behavior indicators (smoking status)
Model 4: Base model + clinical indicators (hypertension, Cataract surgery and Lens Opacities)
Model 5: Base model + sociodemographic indicators (age, gender, income, education, unemployed and vision plan) + behavior
indicators (smoking status) + clinical indicators (hypertension, Cataract surgery and Lens Opacities)
2) OR = Odds Ratio, CI = Confidence Interval. Odds ratios reflects associations per 1 standard
deviation (presented in table 2) change in TRAP metrics.
3) Multivariate associations were analyzed by logistic regression and the statistical significance were
presented with ORs (95%CIs).

15
Univariate and Multivariate Associations of Traffic-Related Air Pollution and early AMD
The univariate and multivariate associations between traffic-related air pollution and early
AMD are shown in Table 5. For univariate associations, we did not find any statistically significant
associations between TRAPs and early AMD. For multivariate associations, we still did not find
any statistically significant associations between TRAPs and early AMD in the final model.
Table 5-Multivariate Associations of Traffic-Related Air Pollution and early AMD
Traffic related
exposures
Model 1 Model 2 Model 3 Model 4 Model 5
OR
(95% CI)
OR
(95% CI)
OR
(95% CI)
OR
(95% CI)
OR
(95% CI)
Distance to nearest
highway/freeway
1.049
(0.964 – 1.154)
1.047
(0.953 – 1.151)
1.041
(0.950 – 1.141)
1.050
(0.957 – 1.151)
1.048
(0.954 – 1.152)
Total road length in
50m
buffer around home
1.036
(0.947 – 1.133)
1.035
(0.944 – 1.135)
1.033
(0.944 – 1.131)
1.033
(0.944 – 1.131)
1.033
(0.942 – 1.133)
Total road length in
100m
buffer around home
0.992
(0.906 – 1.087)
0.968
(0.882 – 1.063)
0.990
(0.904 – 1.084)
0.989
(0.903 – 1.084)
0.968
(0.882 – 1.064)
Total road length in
200m
buffer around home
0.999
(0.913 – 1.094)
0.957
(0.872 – 1.050)
0.998
(0.912 – 1.093)
0.993
(0.906 – 1.088)
0.958
(0.972 – 1.051)
Total road length in
300m
buffer around home
1.047
(0.956 – 1.147)
1.040
(0.948 – 1.141)
1.046
(0.955 – 1.146)
1.048
(0.956 – 1.149)
1.044
(0.951 – 1.145)
1) Model 1: Base model (unadjusted)
Model 2: Base model + sociodemographic indicators (age, gender, income, education, unemployed and vision plan)
Model 3: Base model + behavior indicators (smoking status)
Model 4: Base model + clinical indicators (hypertension, Cataract surgery and Lens Opacities)
Model 5: Base model + sociodemographic indicators (age, gender, income, education, unemployed and vision plan) + behavior
indicators (smoking status) + clinical indicators (hypertension, Cataract surgery and Lens Opacities)
2) OR = Odds Ratio, CI = Confidence Interval. Odds ratios reflects associations per 1 standard
deviation (presented in table 2) change in TRAP metrics.
3) Multivariate associations were analyzed by logistic regression and the statistical significance were
presented with ORs (95%CIs).

16
Discussion
Using data from the Los Angeles Latino Eye Study (LALES), we found that traffic-related
exposures metrics were not statistically significantly associated with any AMD and early AMD
after adjusting for age, gender, employment status, education, annual household income, medical
insurance, smoking status, history of hypertension, cataract surgery and lens opacities. The
association between TRAP and AMD remain an understudied topic. Paucity in literature makes it
difficult to compare and contrast our findings.
A few studies have investigated the relationship between pollution and central nervous
system. One study has documented the association between TRAP and brain connectivity in
children (Pujol et al., 2016). Besides, long-term exposure to traffic-related particulate matter has
been demonstrated to contribute to the development of Alzheimer's disease (Ranft et al., 2009).
Another study has linked TRAP to brain pathologies associated with Parkinson’s disease (Ritz et
al., 2016). These studies have shown the relationship between TRAP and various brain diseases.
Since the retina is a part of the central nervous system developmentally, it is biologically plausible
that TRAP could be related to AMD. The relationship between TRAP and AMD needs more
investigation in future.
A recent study in 2019 conducted in Taiwan reported an increased the risk for AMD with
exposure to traffic related air pollution (Chang et al., 2019). This study was one of the very few
studies that reported an association between TRAP and AMD. In this longitudinal cohort where
study participants were followed for 11 years, highest quartile of NO2 and CO were associated
with increased risk of AMD. The residential history of the participants was also recorded in detail
in the follow-up. Unlike this study, we used data from a cross-sectional study. This approach

17
introduces a possibility of not knowing how long the participants have lived in their residency.
Thus, the TRAP exposure measured in the baseline examination may not represent the pre-disease
exposure. For instance, if someone identified with AMD moved to the urban area of the study
communities from a rural area before the time of baseline examination, his/her TRAP exposure
measured at baseline examination would be estimated to be higher than the true TRAP exposure
during at-risk period, since TRAP exposure in the urban area is higher than in rural area. This may
then lead to reduce contrast of TRAP exposures among study participants and prevent detecting
any significant association between TRAP and AMD.
It is possible that we do not have enough power to detect the association between TRAP
and AMD. Traffic-related air pollutants have been demonstrated to increase the risk for AMD in
a previous study (Chang et al., 2019). The sample size of that study was 39,819, which is much
larger than sample size of our study (N=5,506). With sample size of 5,506, the maximum statistical
power is 0.18 among the models. Thus, it is possible that the lack of power could account for not
finding any statistically significant association between TRAP and AMD.
There are three main reasons why the road traffic distance data could not properly quantify
true exposures of TRAP. Firstly, seasonal variation of exposure cannot be quantified by these
surrogate metrics, since air pollution composition can vary with meteorological influences (Ritz
et al., 2016).

Secondly, traffic emissions of gasoline vehicles and diesel trucks vary across areas.
Third, road traffic distances are surrogate metrics of TRAP, and they may not have captured the
true exposure to find an association between traffic-related air pollution and AMD. Further
research is warranted to take traffic emission of gasoline vehicles and diesel trucks, vehicle
emission rates, and meteorology into consideration to capture the true exposure of TRAP.

18
The major strength of this study is the fact that LALES is the largest population-based
ocular epidemiology study among the largest ethnic minority group of the United States. LALES
includes details of sociodemographic, behavior and clinical indicators for participants. The process
of AMD grading was standard and reliable. Moreover, all anthropometric measures were made by
direct physical examination according to standard protocols as opposed to self-reported.
24
The
relationship of TRAP on the overall and subtypes of AMD could be investigated using the data
from LALES.
Overall, in this study, we did not find statistically significant association between traffic-
related exposures metrics and prevalence of AMD after adjusting potential confounders.
Longitudinal studies, with refined estimates of time-resolved residential-level TRAP exposures,
are warranted to further explore the relationship between traffic-related air pollution and AMD.

19
References
Li, Y., Crews, J. E., Elam-Evans, L. D., Fan, A. Z., Zhang, X., Elliott, A. F., & Balluz, L. (2011).
Visual impairment and health-related quality of life among elderly adults with age-related
eye diseases. Quality of life research, 20(6), 845-852.

Crews, J. E., & Campbell, V. A. (2004). Vision impairment and hearing loss among community-
dwelling older Americans: implications for health and functioning. American journal of
public health, 94(5), 823-829.

Ivers, R. Q., Norton, R., Cumming, R. G., Butler, M., & Campbell, A. J. (2000). Visual impairment
and hip fracture. American journal of epidemiology, 152(7), 633-639.

Jones, G. C., Rovner, B. W., Crews, J. E., & Danielson, M. L. (2009). Effects of depressive
symptoms on health behavior practices among older adults with vision loss. Rehabilitation
Psychology, 54(2), 164.

Zhang, X., Bullard, K. M., Cotch, M. F., Wilson, M. R., Rovner, B. W., McGwin, G., . . . Saaddine,
J. B. (2013). Association between depression and functional vision loss in persons 20 years
of age or older in the United States, NHANES 2005-2008. JAMA ophthalmology, 131(5),
573-581.

Lee, D. J., Gómez-Marín, O., Lam, B. L., & Zheng, D. D. (2002). Visual acuity impairment and
mortality in US adults. Archives of ophthalmology, 120(11), 1544-1550.

Varma, R., Vajaranant, T. S., Burkemper, B., Wu, S., Torres, M., Hsu, C., . . . McKean-Cowdin,
R. (2016). Visual impairment and blindness in adults in the United States: demographic
and geographic variations from 2015 to 2050. JAMA ophthalmology, 134(7), 802-809.

Chopdar, A., Chakravarthy, U., & Verma, D. (2003). Age related macular degeneration. Bmj,
326(7387), 485-488.

Wong, T., Xiang, L., Su, X., Cheung, G., & Cheng, C.-Y. (2013). The Number and Distribution
of People with Age-Related Macular Degeneration Worldwide in 2020 and 2040: A
Systematic Review and Hierarchical Bayesian Meta-Analysis. Investigative
Ophthalmology & Visual Science, 54(15), 220-220.

Congdon, N., O'Colmain, B., Klaver, C., Klein, R., Muñoz, B., Friedman, D. S., . . . Mitchell, P.
(2004). Causes and prevalence of visual impairment among adults in the United States.
Archives of Ophthalmology (Chicago, Ill.: 1960), 122(4), 477-485.

Wong, W. L., Su, X., Li, X., Cheung, C. M. G., Klein, R., Cheng, C.-Y., & Wong, T. Y. (2014).
Global prevalence of age-related macular degeneration and disease burden projection for
2020 and 2040: a systematic review and meta-analysis. The Lancet Global Health, 2(2),
e106-e116.

20
Friedman, D. S., O’Colmain, B. J., Munoz, B., Tomany, S. C., McCarty, C., De Jong, P., . . .
Kempen, J. (2004). Prevalence of age-related macular degeneration in the United States.
Arch ophthalmol, 122(4), 564-572.

Chang, K.-H., Hsu, P.-Y., Lin, C.-J., Lin, C.-L., Juo, S.-H. H., & Liang, C.-L. (2019). Traffic-
related air pollutants increase the risk for age-related macular degeneration. Journal of
Investigative Medicine, 67(7), 1076-1081.

Gehrs, K. M., Anderson, D. H., Johnson, L. V., & Hageman, G. S. (2006). Age‐related macular
degeneration—emerging pathogenetic and therapeutic concepts. Annals of medicine, 38(7),
450-471.

Fritsche, L. G., Igl, W., Bailey, J. N. C., Grassmann, F., Sengupta, S., Bragg-Gresham, J. L., . . .
Gorski, M. (2016). A large genome-wide association study of age-related macular
degeneration highlights contributions of rare and common variants. Nature genetics, 48(2),
134-143.

Group, A.-R. E. D. S. R. (2000). Risk factors associated with age-related macular degeneration: a
case-control study in the age-related eye disease study: age-related eye disease study report
number 3. Ophthalmology, 107(12), 2224-2232.

Lambert, N. G., ElShelmani, H., Singh, M. K., Mansergh, F. C., Wride, M. A., Padilla, M., . . .
Ambati, B. K. (2016). Risk factors and biomarkers of age-related macular.

Tomany, S. C., Wang, J. J., van Leeuwen, R., Klein, R., Mitchell, P., Vingerling, J. R., . . . De
Jong, P. T. (2004). Risk factors for incident age-related macular degeneration: pooled
findings from 3 continents. Ophthalmology, 111(7), 1280-1287.

Adams, M. K., Chong, E. W., Williamson, E., Aung, K. Z., Makeyeva, G. A., Giles, G. G., . . .
Baird, P. N. (2012). 20/20—Alcohol and age-related macular degeneration: the Melbourne
Collaborative Cohort Study. American journal of epidemiology, 176(4), 289-298.

Schultz, E. S., Gruzieva, O., Bellander, T., Bottai, M., Hallberg, J., Kull, I., . . . Pershagen, G.
(2012). Traffic-related air pollution and lung function in children at 8 years of age: a birth
cohort study. American journal of respiratory and critical care medicine, 186(12), 1286-
1291.)

Li, Z., Bian, X., Yin, J., Zhang, X., & Mu, G. (2016). The effect of air pollution on the occurrence
of nonspecific conjunctivitis. Journal of Ophthalmology, 2016.

Mark, H. L., J. M. K. a. J. S. P. (2019). Who Is Hispanic? .

Fraser-Bell, S., Wu, J., Klein, R., Azen, S. P., Varma, R., & Group, L. A. L. E. S. (2006). Smoking,
alcohol intake, estrogen use, and age-related macular degeneration in Latinos: the Los
Angeles Latino Eye Study. American journal of ophthalmology, 141(1), 79-87.

21
Varma, R., Paz, S. H., Azen, S. P., Klein, R., Globe, D., Torres, M., . . . Group, L. A. L. E. S.
(2004). The Los Angeles Latino eye study: Design, methods, and baseline data.
Ophthalmology, 111(6), 1121-1131.

Klein, R., Davis, M. D., Magli, Y. L., Segal, P., Klein, B. E., & Hubbard, L. (1991). The
Wisconsin age-related maculopathy grading system. Ophthalmology, 98(7), 1128-1134.

Klein, R., Klein, B. E., Jensen, S. C., & Meuer, S. M. (1997). The five-year incidence and
progression of age-related maculopathy: the Beaver Dam Eye Study. Ophthalmology,
104(1), 7-21. degeneration. Progress in retinal and eye research, 54, 64-102.

Varma, R., Fraser-Bell, S., Tan, S., Klein, R., Azen, S. P., & Group, L. A. L. E. S. (2004).
Prevalence of age-related macular degeneration in Latinos: the Los Angeles Latino eye
study. Ophthalmology, 111(7), 1288-1297.

Pujol, J., Martínez-Vilavella, G., Macià, D., Fenoll, R., Alvarez-Pedrerol, M., Rivas, I., . . . Querol,
X. (2016). Traffic pollution exposure is associated with altered brain connectivity in school
children. Neuroimage, 129, 175-184.

Ranft, U., Schikowski, T., Sugiri, D., Krutmann, J., & Krämer, U. (2009). Long-term exposure to
traffic-related particulate matter impairs cognitive function in the elderly. Environmental
research, 109(8), 1004-1011.

Ritz, B., Lee, P.-C., Hansen, J., Lassen, C. F., Ketzel, M., Sørensen, M., & Raaschou-Nielsen, O.
(2016). Traffic-related air pollution and Parkinson’s disease in Denmark: a case–control
study. Environmental health perspectives, 124(3), 351-356.
Ito, K., Thurston, G. D., & Silverman, R. A. (2007). Characterization of PM 2.5, gaseous pollutants,
and meteorological interactions in the context of time-series health effects models. Journal
of exposure science & environmental epidemiology, 17(2), S45-S60.

Abstract (if available)

Abstract Exposure to tobacco smoke has been associated with age-related macular degeneration (AMD). Epidemiologic studies have implicated oxidative stress for these associations. Traffic-related air pollution (TRAP) results in similar oxidative damage. We investigated the relationship between TRAP and AMD using data from the Los Angeles Latino Eye Study (LALES), a population-based cross-sectional study. Participants aged 40 or older underwent standardized comprehensive ocular examinations at baseline with 4 years of follow-up. AMD was graded using a modification of the Wisconsin Age-Related Maculopathy Grading System. TRAP was estimated using a Geographical Information System (GIS) with two surrogate measures of exposures: distance from highway/freeway to each participant’s home and total road lengths within circular areas with radiuses of 50m, 100m, 200m and 300m centered at the participant’s home. Multiple logistic regressions were used to assess the association between TRAP metrics and the prevalence of AMD (including any AMD and early AMD) among 5,506 participants. We adjusted for age, gender, employment status, education, annual household income, medical insurance, smoking status, history of hypertension, cataract surgery and lens opacities in the final models. TRAP metrics were not found to be statistically significantly associated with AMD after adjusting for these variables. The distance from highway/freeway was not statistically significantly associated with the prevalence of any AMD [odds ratio (OR) = 1.05

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

Association of traffic-related air pollution and lens opacities in the Los Angeles Latino Eye Study

PDF

Age related macular degeneration in Latinos: risk factors and impact on quality of life

PDF

An integrative approach for determining age related macular degeneration risk facors in Latinos

PDF

Genetic variation in inducible nitric oxide synthase promoter, residential traffic related air pollution and exhaled nitric oxide in children

PDF

Ocular axial length and prevalence of myopic macular degeneration among Chinese Americans

PDF

Genetic association studies of age-related macular degeneration from candidate gene to whole genome

PDF

Effect of biomass fuel exposure on infant respiratory health outcomes in Bangladesh

PDF

Assessment of the mortality burden associated with ambient air pollution in rural and urban areas of India

PDF

Associations of ambient air pollution exposures with perceived stress in the MADRES cohort

PDF

Linking air pollution to integrative gene and metabolites networks in young adult with asthma

PDF

The association between sun exposure and multiple sclerosis

PDF

Presbyopia and quality of life among the Latino population

PDF

The impact of the environment on childhood allergic rhinitis: findings from the Children’s Health Study

PDF

Effects of near-roadway air pollution exposure on obesity, obesity-related behavior, and neurobehavioral deficits during peripuberty

PDF

Age-period-cohort analysis of hepatocellular carcinoma (HCC) incidence trends among Asians and non-Hispanic Whites in California, 1988-2017

PDF

Adipokines do not account for the association between osteocalcin and insulin sensitivity in Mexican Americans

PDF

Air pollution, mitochondrial function, and growth in children

PDF

Visual acuity outcomes after cataract extraction in Chinese Americans: the Chinese American Eye Study (CHES)

PDF

Associations between perfluoroalkyl substances exposure and metabolic pathways in youth

PDF

The role of genetic ancestry in estimation of the risk of age-related degeneration (AMD) in the Los Angeles Latino population

Asset Metadata

Creator Song, Lulu (author)

Core Title Association of traffic-related air pollution and age-related macular degeneration in the Los Angeles Latino Eye Study

School Keck School of Medicine

Degree Master of Science

Degree Program Biostatistics

Publication Date 09/20/2020

Defense Date 09/17/2020

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

Tag age related macular degeneration,OAI-PMH Harvest,traffic-related air pollution

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Choudhury, Farzana (committee chair), Franklin, Meredith (committee member), Salam, Md.Towhid (committee member)

Creator Email 18811718500@163.com,lulusong@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-375125

Unique identifier UC11666584

Identifier etd-SongLulu-8987.pdf (filename),usctheses-c89-375125 (legacy record id)

Legacy Identifier etd-SongLulu-8987.pdf

Dmrecord 375125

Document Type Thesis

Rights Song, Lulu

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA