Available via license: CC BY 4.0
Content may be subject to copyright.
International Journal of
Environmental Research
and Public Health
Article
Assessment of Respiratory Health Symptoms and
Asthma in Children near a Drying Saline Lake
Shohreh F. Farzan 1,*, Mitiasoa Razafy 1, Sandrah P. Eckel 1, Luis Olmedo 2, Esther Bejarano 2and
Jill E. Johnston 1
1Department of Preventive Medicine, Keck School of Medicine of University of Southern California,
2001 N. Soto Street, MC 9237, Los Angeles, CA 90089, USA; razafy@usc.edu (M.R.); eckel@usc.edu (S.P.E.);
jillj@usc.edu (J.E.J.)
2Comite Civico del Valle, Brawley, CA 92227, USA; luis@ccvhealth.org (L.O.); esther@ccvhealth.org (E.B.)
*Correspondence: sffarzan@usc.edu; Tel.: +1-323-442-5101
Received: 20 September 2019; Accepted: 9 October 2019; Published: 11 October 2019
Abstract:
Residents of the Imperial Valley, a rural, agricultural border region in California, have raised
concerns over high rates of pediatric asthma symptoms. There is an urgent need to understand the
influences and predictors of children’s respiratory health in Imperial Valley. We assessed the impacts
of sociodemographic, lifestyle, and household factors on children’s respiratory health and asthma
prevalence by administering a survey to parents of elementary school children (n=357) in northern
Imperial Valley. We observed an overall asthma prevalence of 22.4% and respiratory symptoms and
allergies were widely reported, including wheezing (35.3%), allergies (36.1%), bronchitic symptoms
(28.6%), and dry cough (33.3%). Asthmatics were significantly more likely to report respiratory
symptoms, but high rates of wheezing, allergies, and dry cough were observed among nonasthmatics,
suggesting the possibility for underdiagnosis of respiratory impairment in our school-age population.
Having an asthmatic mother and exposure to environmental tobacco smoke were also associated
with greater odds of asthma. Our findings provide evidence to support community concerns about
children’s respiratory health, while also suggesting that household and demographic characteristics
have limited explanatory power for assessing asthma in this population. This work provides critical
baseline data with which to evaluate local environmental factors and their influence on asthma and
respiratory symptoms.
Keywords:
asthma; children’s respiratory health; rural areas; Imperial County,
California; environment
1. Introduction
Over six million children in the United States (USA) are living with asthma, making it the most
commonly diagnosed chronic childhood disorder [
1
]. In 2015, the reported prevalence of asthma
among U.S. children was 8.4%, with rates ranging up to 13.4% among minority populations [
1
].
Although children of Mexican heritage have an overall lower prevalence of asthma compared to other
groups of U.S. children, recent trends further suggest widening racial, ethnic, and economic disparities
in terms of pediatric asthma prevalence, which may be in part related to environmental factors [
2
–
6
].
For example, the pediatric asthma prevalence observed among USA–Mexico border populations is
frequently much higher than the national average of 8% for Hispanic children, although the reasons
for these population differences remain largely unknown [7].
There is limited research on the prevalence of asthma and related risk factors in predominately
Mexican-American rural communities in the USA. One such community in the rural agricultural
valley situated between the USA–Mexico border and a drying saline lake in southeastern California
Int. J. Environ. Res. Public Health 2019,16, 3828; doi:10.3390/ijerph16203828 www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2019,16, 3828 2 of 15
(CA) has raised concern over elevated rates of pediatric asthma symptoms in their region. In this
primarily low-income, majority Mexican-American region known as the Imperial Valley, the rate of
asthma-related emergency room visits and hospitalizations for children ages 0–17 years is double the
CA state average [
8
]. In 2005, a CA Health Department-led study in the region observed an asthma
prevalence of 20% among middle school students and high rates of respiratory symptoms among
nonasthmatics, suggesting that undiagnosed asthma was potentially common [
8
]. To date, the factors
contributing to the high rates of adverse childhood respiratory health conditions in Imperial Valley
have not been explored.
To begin to assess the factors influencing respiratory health in this unique population, we evaluated
the current prevalence of asthma and other respiratory health conditions among elementary school
children living in the northern Imperial Valley, CA. We further sought to determine whether asthma
prevalence rates varied across sociodemographic and household characteristics. We focused on the
northern end of the valley, in response to concerns regarding changing weather patterns, droughts,
and competing water demands that are poised to cause the rapid shrinking of the Salton Sea,
a 350-square-mile land-locked saline “sea” with the potential to significantly impact the future
respiratory health and quality of life for nearby residents [
9
–
12
]. The recent enactment of a federally
mandated water transfer agreement at the end of 2018 reduced the allotment of Colorado River water
for the Imperial Valley, diverting water resources to nearby urban regions and effectively ending
the supply of water to the Salton Sea [
13
]. This reallocation of water resources has resulted in an
increasingly retreating shoreline leaving behind exposed playa, which has the potential to generate
wind-blown dust, adding to ongoing community concerns about the already poor local air quality
from contributors such as agricultural burning practices and cross-border particulate matter sources.
The drying of the Salton Sea has unknown public health implications and the existing vulnerabilities of
nearby populations are largely unmeasured. Characterizing the current respiratory health risk factors
in this population could inform public health protection in the face of changing environmental factors.
2. Methods
The Imperial Valley, located along the USA–Mexico border in Imperial County, CA, primarily
consists of agricultural fields and open, undeveloped desert land, populated by a handful of small cities
and towns [
14
,
15
]. Imperial County is considered a rural medical service study area (MSSA), defined
as an area with fewer than 250 persons per square mile and no population center exceeding 50,000,
and parts of northern Imperial County are defined as a frontier MSSA, indicating a rural MSSA with
fewer than 11 residents per square mile [
16
,
17
]. A cross-sectional school-based study was conducted
between May 2017 and May 2018 in Imperial County, CA. Four elementary schools across four towns
within approximately 20 miles of the Salton Sea were invited to participate. In three of the four towns,
a single elementary school services the entire town; in the fourth town, a representative elementary
school was selected with input from community partners. Study staffvisited classrooms to introduce
the study to the children and distribute the study packets. All surveys, materials, and consent forms
were provided in both English and Spanish, and children returned all study materials to the classroom
within one week. Parents and guardians completed a 64-item health survey, adapted from validated
questionnaires from the International Study of Asthma and Allergies in Childhood (ISAAC) [
18
],
a questionnaire developed to assess asthma prevalence that has been validated in populations around
the world, as well as questions used as part of the Southern California Children’s Health Study [19].
Demographic and lifestyle questions assessed the children’s characteristics such as age, sex,
race/ethnicity, as well as parent/caregiver information including annual household income, educational
attainment, insurance coverage, and tobacco usage. Questions on the child’s medical and health history
focused on the occurrence of severe respiratory illnesses before the age of two, asthma diagnoses,
and recent respiratory conditions and symptoms. Respiratory medication use was assessed using
photographic charts of common asthma rescue and controller medications. Questions about the home
environment included the presence of pets or farm animals, as well as indoor housing conditions
Int. J. Environ. Res. Public Health 2019,16, 3828 3 of 15
(presence of mold, carpeting, water damage, pests, home heating and cooling, humidifier use, and gas
stove use).
Health surveys were distributed to 429 children during two week-long distribution periods over
the data collection year between May 2017 and May 2018, with an overall participation rate of 83.2%
(357 of 429). For the purposes of this study, if a survey was filled out for a child during both rounds of
data collection, the first survey was used in these analyses. Written informed consent was obtained
from a parent or guardian. All protocols, consent forms, and survey materials were approved by the
University of Southern California Institutional Review Board (HS-17-00204).
Statistical Analysis
All analyses were conducted using STATA IC Version 15 (Stata, College Station, TX, USA).
Chi-square tests were used to test for associations between categorical variables. Logistic regression
was used to examine associations between potential risk factors and binary outcome variables
(physician-diagnosed asthma or the presence of respiratory symptoms). We examined the associations
in unadjusted models and stepwise adjustment for a priori selected covariates (minimally adjusted
model: age and sex, language of survey response) and fully-adjusted final models (adjusted for age,
sex, language of survey response, education level of the parent/caregiver survey respondent, school of
enrollment). Statistical significance was defined as p<0.05.
Children were classified as asthmatic if the child’s parent answered affirmatively to the question
“Has your child ever received an official doctor’s diagnosis for asthma?” A child was considered to
have bronchitic symptoms if the child’s parent reported at least one of the following: 1) daily cough for
three months in a row, 2) congestion or phlegm for at least three months in a row, or 3) bronchitis in the
past year [
20
]. Health insurance was categorized into public, private, or none. Respiratory medications
were categorized as rescue, control, or other [21].
3. Results
A total of 357 students participated in this study (Table 1). The mean age was 7.6 (SD: 0.86) years.
Our sample included slightly more female (54.9%) than male children (45.1%). Most parents completed
the questionnaires in English (64.2%), with almost all participants self-identifying as Latino/a (93.3%).
Most reported having health insurance (90.5%), with the majority enrolled in a public health insurance
plan (70.3%). Approximately one-third of participants reported a total household of less than $15,000
a year (30.0%), with a minority of participants earning above $50,000 (14.3%). Approximately 20%
of the study population did not know or chose not to report their annual total household income.
Of the parents or caregivers who responded to the questionnaire, approximately 68% were the child’s
biological mother, 19% were the child’s biological father, and the remaining 13% were other relatives
or caregivers. About half of the parent or caregiver respondents had a high school education or less
(50.4%).
Int. J. Environ. Res. Public Health 2019,16, 3828 4 of 15
Table 1. Demographic characteristics of asthmatic and nonasthmatic study participants (N=357).
Demographic Characteristics All
N=357 (%)
Asthmatics
N=80 (%)
Nonasthmatics
N=277 (%) p-Value a
Language of survey
English 229 (64.2) 59 (73.8) 170 (61.4) 0.04
Spanish 128 (35.8) 21 (26.3) 107 (38.6)
Sex
Female 196 (54.9) 41 (51.3) 155 (56.0) 0.46
Male 161 (45.1) 39 (48.8) 122 (44.0)
Race/Ethnicity
Latino 333 (93.3) 74 (92.5) 259 (93.5) 0.38
White 18 (5.0) 5 (6.3) 13 (4.7)
Black 3 (0.8) 0 (0.0) 3 (1.1)
Native American 1 (0.3) 1 (1.3) 0 (0.0)
Other 2 (0.6) 0 (0.0) 2 (0.7)
Age
5–6 years 32 (9.0) 9 (11.3) 23 (8.3) 0.69
7 years 131 (36.7) 28 (35.0) 103 (37.2)
8 years 148 (41.5) 35 (43.8) 113 (40.8)
9–10 years 46 (12.9) 8 (10.0) 38 (13.7)
Health Insurance
Public 251 (70.3) 51 (63.8) 200 (72.2) 0.01
Private 72 (20.2) 25 (31.3) 47 (17.0)
None 34 (9.5) 4 (5.0) 30 (10.8)
Total Household Income
Less than $15,000 107 (30.0) 22 (27.5) 85 (30.7) 0.01
$15,000 to $29,999 71 (19.9) 15 (18.8) 56 (20.2)
$30,000 to $49,999 54 (15.1) 8 (10.0) 46 (16.6)
More than $50,000 51 (14.3) 21 (26.3) 30 (10.8)
Don’t know 74 (20.7) 14 (17.5) 60 (21.7)
Parent/caregiver education b
Less than 12th grade 90 (25.2) 13 (16.3) 77 (27.8) 0.08
Completed 12th grade 90 (25.2) 23 (28.8) 67 (24.2)
Some college or technical school 122 (34.2) 30 (37.5) 92 (33.2)
4 years of college or more 35 (9.8) 12 (15.0) 23 (8.3)
Missing 20 (5.6) 2 (2.5) 18 (6.5)
Two-adult household
Yes 202 (56.6) 46 (57.5) 156 (56.3) 0.50
No 142 (39.8) 34 (42.5) 108 (39.0)
Missing 13 (3.6) 0 (0.0) 13 (4.7)
aAs determined by chi-squared or Fisher’s exact test. bEducation level of survey respondent.
Among the participants, the overall prevalence of doctor-diagnosed asthma was 22.4%; 48.8%
were boys and 51.3% were girls. Statistically significant differences between asthmatic and
nonasthmatic children were observed for key demographic variables, including language of the
survey, health insurance, and total household income (Table 1). We also observed significant differences
between asthmatic and nonasthmatic children for respiratory health history variables including
biological mother with asthma (25.1% versus 8.7%, p<0.001), upper respiratory tract infections
before age two (16.3% versus 2.9%, p<0.001), and lower respiratory tract infections before age two
(
32.5% versus 7.9%,
p<0.001) (Table 2). Most doctor diagnoses of asthma had occurred when the child
was between two and four years old (42.5%).
Int. J. Environ. Res. Public Health 2019,16, 3828 5 of 15
Table 2. Respiratory health history of school-age asthmatic and nonasthmatic participants.
Health Characteristics All
N=357 (%)
Asthmatics
N=80 (%)
Nonasthmatics
N=277 (%) p-Value a
Biological mother has asthma
Yes 44 (12.3) 20 (25.1) 24 (8.7) <0.001
No 304 (85.2) 57 (71.3) 247 (89.2)
Missing 9 (2.5) 3 (3.8) 6 (2.2)
Biological mother smoked while
pregnant with child
Yes 16 (4.5) 5 (6.3) 11 (4.0) 0.36
No 333 (93.3) 72 (90.0) 261 (94.2)
Missing 8 (2.2) 3 (3.8) 5 (1.8)
Lower respiratory infection
during infancy
Yes 48 (13.5) 26 (32.5) 22 (7.9) <0.001
No 309 (86.6) 54 (67.5) 255 (92.1)
Upper respiratory infection
during infancy
Yes 21 (5.9) 13 (16.3) 8 (2.9) <0.001
No 336 (94.1) 67 (83.8) 269 (97.1)
Asthma during infancy
Yes 51 (14.3) 47 (58.8) 4 (1.4) <0.001
No 306 (85.7) 33 (41.3) 273 (98.6)
Age of asthma diagnosis b
Under 2 years old – 28 (35.0) – –
2–4 years old – 34 (42.5) – –
5 years old and above – 18 (22.5) – –
aAs determined by chi-squared or Fisher’s exact test. bAmong individuals reporting doctor diagnosis of asthma only.
Respiratory symptoms and allergies were widely reported among this study population (Figure 1;
Supplemental Table S1). The most frequently reported respiratory symptoms and related conditions
in children were wheezing (35.3%), allergies (36.1%), bronchitic symptoms (28.6%), and a dry cough
(33.3%) (Supplemental Table S1). Asthmatics were significantly more likely to report any respiratory
symptoms, but among nonasthmatics we still observed high rates of wheezing (19.9%), allergies (27.4%),
and dry cough (23.1%) (Figure 1). Among those who reported ever having wheezed, 7.9% stated that
wheezing impacted their ability to speak in the past 12 months, 37.3% their ability to exercise in the
past 12 months, and 17.5% their ability to sleep in the past 12 months, as defined as having woken up
due to wheezing more than one night per week (Supplemental Table S2).
Int. J. Environ. Res. Public Health 2019,16, 3828 6 of 15
Int. J. Environ. Res. Public Health 2019, 16, x 6 of 13
Figure 1. Prevalence of reported respiratory symptoms among school-age participants. Bars represent
percentage of asthmatics (n = 80; black bars) and nonasthmatics (n = 277; gray bars) who were reported
to have any of the listed respiratory symptoms in the past 12 months, unless otherwise specified.
We observed statistically significant differences between asthmatics and nonasthmatics in the
use of healthcare resources and respiratory medications, including doctor visits due to wheezing,
emergency room visits due to wheezing, rescue medication use, control medication, and nebulizer
use (Figure 2). Among those who identified as asthmatic, 45.0% and 77.5% reported having gone to
the emergency room or to a doctor, respectively, at least once in their lives for wheezing (Figure 2).
The majority of asthmatics (82.5%) reported that they were currently taking asthma medication, with
80.0% taking rescue medication, 37.5% taking control medication, and 70.0% having used a nebulizer
at least once in the past year. Among the nonasthmatic population, 14.8% of students had been to a
doctor due to wheezing, 7.6% had to go to the emergency room for wheezing, and 9.4% are currently
taking rescue asthma medication raising concerns for potential undiagnosed cases (Figure 2).
Figure 1.
Prevalence of reported respiratory symptoms among school-age participants. Bars represent
percentage of asthmatics (n=80; black bars) and nonasthmatics (n=277; gray bars) who were reported
to have any of the listed respiratory symptoms in the past 12 months, unless otherwise specified.
We observed statistically significant differences between asthmatics and nonasthmatics in the
use of healthcare resources and respiratory medications, including doctor visits due to wheezing,
emergency room visits due to wheezing, rescue medication use, control medication, and nebulizer
use (Figure 2). Among those who identified as asthmatic, 45.0% and 77.5% reported having gone to
the emergency room or to a doctor, respectively, at least once in their lives for wheezing (Figure 2).
The majority of asthmatics (82.5%) reported that they were currently taking asthma medication,
with 80.0% taking rescue medication, 37.5% taking control medication, and 70.0% having used a
nebulizer at least once in the past year. Among the nonasthmatic population, 14.8% of students had
been to a doctor due to wheezing, 7.6% had to go to the emergency room for wheezing, and 9.4% are
currently taking rescue asthma medication raising concerns for potential undiagnosed cases (Figure 2).
Int. J. Environ. Res. Public Health 2019,16, 3828 7 of 15
Int. J. Environ. Res. Public Health 2019, 16, x 7 of 13
Figure 2. Use of healthcare resources and respiratory medications among school-age asthmatic and
nonasthmatic participants. Bars represent percentage of asthmatics (n = 80; black bars) and
nonasthmatics (n = 277; gray bars) who were reported to have utilized any of the listed healthcare
resources or medications in the past 12 months.
Using logistic regression models, either minimally adjusted (age and sex) or fully adjusted for
covariates (age and sex, plus language of survey, parental education, and school of enrollment), we
examined the association between demographic and household risk factors with physician-
diagnosed asthma (Table 3). Having a biological mother with asthma was associated with a nearly 3-
fold greater odds of asthma (OR: 2.92; 95 CI: 1.44‒5.93) in fully-adjusted models. Exposure to
household environmental tobacco smoke was also associated with 4-fold greater odds of asthma (OR:
4.00; 95% CI: 1.21‒13.23) in fully adjusted models. Asthma was positively associated with being
enrolled in private insurance (OR: 2.05; 95% CI: 1.15‒3.65) in minimally adjusted models, but this
association was attenuated in the fully adjusted models and no longer statistically significant.
We also investigated the influence of various housing characteristics on the asthma prevalence
in our population (Table 3). Although most housing characteristics did not significantly differ
between asthmatics and nonasthmatics (Supplemental Table S3), we did observe differences in
asthma prevalence across minimally and fully adjusted models associated with length of cooking gas
use in the home and the presence of pets. Asthma prevalence was marginally associated with using
a gas stove for more than 1 h per day (OR: 2.58; 95% CI: 0.97–6.82) (Table 3) in fully adjusted models.
About one-third of participants reported having a furry pet at home (29.4%) (Supplemental Table S3),
which was observed to be negatively associated with odds of asthma (OR: 0.42; 95% CI: 0.21‒0.82) in
fully adjusted models (Table 3).
Table 3. Associations between individual demographic and household risk factors and the prevalence
of asthma.
Figure 2.
Use of healthcare resources and respiratory medications among school-age asthmatic
and nonasthmatic participants. Bars represent percentage of asthmatics (n=80; black bars) and
nonasthmatics (n=277; gray bars) who were reported to have utilized any of the listed healthcare
resources or medications in the past 12 months.
Using logistic regression models, either minimally adjusted (age and sex) or fully adjusted
for covariates (age and sex, plus language of survey, parental education, and school of
enrollment), we examined the association between demographic and household risk factors with
physician-diagnosed asthma (Table 3). Having a biological mother with asthma was associated with a
nearly 3-fold greater odds of asthma (OR: 2.92; 95 CI: 1.44–5.93) in fully-adjusted models. Exposure
to household environmental tobacco smoke was also associated with 4-fold greater odds of asthma
(OR: 4.00; 95% CI: 1.21–13.23) in fully adjusted models. Asthma was positively associated with being
enrolled in private insurance (OR: 2.05; 95% CI: 1.15–3.65) in minimally adjusted models, but this
association was attenuated in the fully adjusted models and no longer statistically significant.
We also investigated the influence of various housing characteristics on the asthma prevalence in
our population (Table 3). Although most housing characteristics did not significantly differ between
asthmatics and nonasthmatics (Supplemental Table S3), we did observe differences in asthma prevalence
across minimally and fully adjusted models associated with length of cooking gas use in the home and
the presence of pets. Asthma prevalence was marginally associated with using a gas stove for more
than 1 h per day (OR: 2.58; 95% CI: 0.97–6.82) (Table 3) in fully adjusted models. About one-third of
participants reported having a furry pet at home (29.4%) (Supplemental Table S3), which was observed
to be negatively associated with odds of asthma (OR: 0.42; 95% CI: 0.21–0.82) in fully adjusted models
(Table 3).
Int. J. Environ. Res. Public Health 2019,16, 3828 8 of 15
Table 3. Associations between individual demographic and household risk factors and the prevalence of asthma.
Characteristics Minimally Adjusted a
OR (95% CI) p-Value Fully Adjusted b
OR (95% CI) p-Value
Participant demographics
Household Income c1.05 (0.89–1.24) 0.53 1.05 (0.88–1.25) 0.62
Health insurance
Public Ref. – Ref. –
Private 2.05 (1.15–3.65) 0.02 1.68 (0.89–3.15) 0.11
None 0.53 (0.18–1.59) 0.26 0.49 (0.16–1.55) 0.23
Household environmental smoke
No Ref. – Ref. –
Yes 4.24 (1.37–13.07) 0.01 4.00 (1.21–13.23) 0.02
Biological mother smoked while pregnant
No Ref. – Ref. –
Yes 1.66 (0.55–4.94) 0.37 2.23 (0.59–8.30) 0.23
Biological mother has asthma
No Ref. – Ref. –
Yes 3.79 (1.94–7.39) <0.001 2.92 (1.44–5.93) 0.003
Play sports at least twice a week
No Ref. – Ref. –
Yes 1.43 (0.85–2.42) 0.17 1.62 (0.93–2.83) 0.09
Housing characteristics
Housing type
Home Ref. – Ref. –
Apartment 0.97 (0.56–1.68) 0.97 1.00 (0.55–1.80) 0.99
Mobile home or trailer 0.86 (0.35–2.10) 0.74 1.73 (0.62–4.82) 0.29
Gas cooking stove
No Ref. – Ref. –
Yes 0.59 (0.31–1.12) 0.11 0.65 (0.32–1.34) 0.25
Length of daily gas stove use
Less than 30 min Ref. – Ref. –
Less than 1 h 1.45 (0.77–2.70) 0.25 1.42 (0.74–2.75) 0.29
More than 1 h 3.17 (1.31–7.65) 0.01 2.58 (0.97–6.82) 0.06
Int. J. Environ. Res. Public Health 2019,16, 3828 9 of 15
Table 3. Cont.
Characteristics Minimally Adjusted a
OR (95% CI) p-Value Fully Adjusted b
OR (95% CI) p-Value
Home heater
No Ref. – Ref. –
Yes 2.46 (1.06–5.68) 0.04 2.08 (0.87–4.94) 0.10
Water damage in home
No Ref. – Ref. –
Yes 2.23 (1.00–4.98) 0.05 1.73 (0.74–4.02) 0.21
Mold in home
No Ref. – Ref. –
Yes 1.37 (0.69–2.73) 0.37 1.21 (0.59–2.48) 0.60
Musty odor in home
No Ref. – Ref. –
Yes 2.56 (0.85–7.74) 0.10 2.14 (0.67–6.88) 0.20
Carpet in home
No Ref. – Ref. –
Yes 1.32 (0.78–2.25) 0.30 1.53 (0.86–2.72) 0.15
Child has lived in same house for whole life
No Ref. – Ref. –
Yes 1.32 (0.77–2.30) 0.31 1.29 (0.71–2.35) 0.41
Pet ownership
None Ref. – Ref. –
Furry pet 0.48 (0.26–0.91) 0.02 0.42 (0.21–0.82) 0.01
Other pets 1.17 (0.48–2.83) 0.74 0.87 (0.34–2.24) 0.77
Regular contact with farm animals
No Ref. – Ref. –
Yes 1.21 (0.57–2.57) 0.62 1.52 (0.67–3.45) 0.32
Problem with rodents in the home
No Ref. – Ref. –
Yes 0.90 (0.36–2.20) 0.81 0.84 (0.33–2.13) 0.72
Problem with insects in the home
No Ref. – Ref. –
Yes 1.19 (0.66–2.16) 0.57 0.97 (0.51–1.82) 0.92
a
Minimally adjusted model includes age and sex as covariates.
b
Fully adjusted model includes age, sex, language, parental education, and school as covariates.
c
Modeled as a
continuous variable.
Int. J. Environ. Res. Public Health 2019,16, 3828 10 of 15
4. Discussion
Our study sought to characterize the prevalence rates of asthma and other respiratory conditions
among rural, predominantly Mexican-American elementary school children living near the Salton
Sea in the Imperial Valley, CA prior to the implementation of the water transfer agreement poised to
dramatically accelerate the shrinking of this saline lake. Using parent-reported survey information,
we observed an overall asthma prevalence of 22.4%, which is significantly higher than the national
average for children ages 5–11 years (8.8%), the national average for Mexican-American children ages
0 to 17 (6.2%), and the California state rates for children ages 0 to 17 (14.5%) [
1
,
22
,
23
]. In addition
to asthma, we observed high rates of various respiratory symptoms, including wheezing (35%),
allergies (36%), bronchitic symptoms (28%), and persistent dry cough (33%), which were widely
reported among both asthmatic and nonasthmatic children. We also identified sociodemographic
and household factors that may be related to asthma. The findings of this study provide evidence
to support community concerns about children’s respiratory health, suggesting that household and
demographic characteristics have limited explanatory power for assessing asthma in this population.
Our study provides critical baseline data with which to evaluate the effect of local environmental
exposures on asthma and respiratory symptoms.
A small number of similar studies have investigated asthma prevalence and respiratory health
in the USA–Mexico border areas. Together, these studies collectively suggest higher rates of asthma
and respiratory illness along the U.S. side [
6
,
24
–
26
]. Our findings were similar to those of several
other studies among children living in largely urban border areas, including a study conducted
on the Arizona–Sonora border, which estimated a 25.8% rate of asthma among adolescents aged
13–14 [
6
]. A study looking at the prevalence of children’s respiratory health conditions in El Paso,
TX reported a similar asthma rate of 17% among fourth and fifth graders [
24
]. Our estimates are,
however, much higher than those observed in other studies from Texas [
25
], and among similarly
aged fifth-grade students in the Nogales, Mexico/Arizona border region [
26
], which found asthma
prevalence rates ranging from 5.8% to 9.4%. To our knowledge, the Border Asthma and Allergies
(BASTA) study is the only other study of respiratory health outcomes to have been conducted in
the Imperial Valley; it found an asthma prevalence of 20% among students aged 13–14, which is
comparable to our observed rate of 22.4%. However, compared to our study, the BASTA study found
higher reported rates of allergies among participants (57% versus 36%) [
8
]. Among children living
in the Coachella Valley, a rural community north of the Salton Sea, 17.5% of sampled children were
asthmatic or had a persistent cough [
27
]. Although similarities exist across these various studies,
caution must be exercised as other studies may have used different assessment instruments or criteria
to determine asthma prevalence.
Our results also suggest the possibility of underdiagnosis of asthma and respiratory impairment
in our school-age population. A number of studies have suggested that there may be a substantial
number of children who report asthma-like symptoms but remain undiagnosed [
28
–
32
]. Prior work
in USA–Mexico border communities found that, among those who reported wheezing, there was
a high risk of possible undiagnosed asthma [
22
,
31
]. In our sample of school children, nearly one
in five nonasthmatics reported having at least one episode of wheezing in their lifetime, nearly one
in seven reported having ever visited the doctor due to wheezing, and about one in 13 reported
ever having gone to the ER due to wheezing. Substantial proportions of nonasthmatic children
reported a persistent dry cough at night, bronchitic symptoms, or the recent use of an asthma rescue
medication. Among nonasthmatics who reported having at least one episode of wheezing in their
lifetime, half experienced wheezing in the past 12 months, and a quarter experienced wheezing after
exercise. Given the large number of children in our population who reported respiratory symptoms
without an asthma diagnosis, this suggests more widespread respiratory impairment in this population.
Prior research found that children not diagnosed with asthma, but reporting wheezing-like
symptoms, experienced missed school days, limitation of physical activities, and sleep disturbances [
29
].
Similar to our findings, 7% of children with current asthma-like symptoms but no diagnosis in a
Int. J. Environ. Res. Public Health 2019,16, 3828 11 of 15
North Carolina-based school study reported an emergency department visit due to an episode of
wheezing [
29
]. In a similar study conducted by van Gent et al., children with undiagnosed asthma had
lower quality-of-life scores than healthy control individuals, and also experienced, on average, a more
than one week longer absence from school over 12 months because of respiratory symptoms compared
to healthy individuals [
28
]. Children may have increased vulnerability to environmental insults,
particularly in this region, and future work will further investigate the impact of breathing difficulties
on quality of life across both asthmatic and nonasthmatic children in our study population [33].
Our health survey collected information from parents on a number of sociodemographic, lifestyle,
and household characteristics that may be related to children’s respiratory health. When we examined
maternal health factors, we found that having a biological mother with asthma was strongly related
to child asthma and was consistent across all models tested. This is expected as family history and
genetic predisposition are important components of asthma risk [
34
,
35
]. Additionally, as reported by
numerous studies, we observed that maternal smoking during pregnancy increased the odds of child
asthma; however, this was based on a relatively small number of mothers who reported smoking,
and this association did not reach statistical significance [36–38].
Conditions in the indoor home environment have frequently been associated with children’s asthma
risk and respiratory symptoms. In line with previous studies, we observed a strong positive association
between exposure to household environmental tobacco smoke and asthma diagnosis [
34
,
39
,
40
]. We also
observed a positive association between the use of a gas stove for more than 1 h per day and asthma
diagnosis. Previous studies have found that gas stoves release respiratory irritants and may increase
the risk of respiratory symptoms and asthma exacerbations in children [
41
]. Although other studies
have reported other significant risk factors in the home such as water damage or carpeting, we did
not observe significant associations between these variables and asthma in our analyses [19,27,34,35].
Having a furry pet at home was negatively associated with asthma in our study population. This is
contrary to some other findings, although pet ownership has been inconsistently associated with
asthma and allergic symptoms in various studies [
42
–
44
]. Despite this being a heavily agricultural
community, no significant associations were found between reporting an asthma diagnosis and having
regular contact with a farm animal. Further investigation of the role of exposure to pets and other
animals in asthma is warranted, given the conflicting findings across studies.
Our study of rural, primarily Mexican-American elementary school children provides novel
insights into the factors impacting respiratory health in the Imperial Valley. Our assessment benefited
from the collection of numerous demographic, lifestyle, and home characteristics and the use of
validated questionnaires to assess a variety of respiratory health symptoms. However, our study was
also limited by several factors. In this study, we relied on the respondents’ questionnaire answers to
assess asthma diagnosis, rather than medical records or physical examination. It is possible that defining
asthma according to self-reported doctor diagnosis may have resulted in some outcome misclassification
among our participants. Furthermore, it is possible that parents/guardians of asthmatics or those
who were concerned about their child’s respiratory health were more likely to complete the survey
than parents of nonasthmatic children, which could have led to over- or under-representation of
symptoms and exposures. Lastly, although we examined numerous potential factors, we cannot rule
out the possibility of residual confounding in our models. The limited ability of demographic, lifestyle,
and other known risk factors, such as housing characteristics, to explain the high observed rates of
asthma (22.4%) and respiratory symptoms in this young population suggests that environmental
factors are likely contributors.
Rural communities in the southwestern USA and along the USA–Mexico border face changing
weather patterns, droughts, and competing water demands that are dramatically altering the landscape
and creating conditions conducive to the production of wind-blown dust. As the Salton Sea recedes,
primarily due to local changes in water supplies and reductions in agricultural runoff, it is anticipated
that 40–80 more tons of dust per day will be released into the local environment [
45
], exposing large
swaths of playa and generating wind-blown dust [
13
]. Because the particulates from dried lakebeds
Int. J. Environ. Res. Public Health 2019,16, 3828 12 of 15
have been shown to be smaller in size, they are more easily respirable by humans [
46
], and such
substantial increases in dust have the potential to significantly impact the respiratory health and quality
of life for nearby residents [
9
–
12
]. This impact is expected to be seen first along the southern edge of the
Sea, where our findings show that, overall, children are already disproportionally affected by asthma.
Follow-up studies of these children are underway to explore the effects of a changing environment on
respiratory health.
Our findings may help provide a better understanding of the health needs of the community,
as well as strategies to inform community-specific preventive programs to address the symptoms of
asthma and respiratory impairment. The disappearance of the Salton Sea will likely have unforeseen
public health implications, while children and people with preexisting health conditions, such as asthma,
may be more vulnerable to the impacts of such environmental changes [
13
,
47
,
48
]. Future research
will longitudinally examine children’s respiratory health, incorporating both ambient exposure
measurements and physiological assessments over time. While this work began as a way to provide a
baseline assessment of children’s health in relation to the shrinking of Salton Sea, the broad implications
of the present research reveal a public health and environmental justice crisis that requires action,
attention, and further research to protect the health and wellbeing of local communities.
Supplementary Materials:
The following are available online at http://www.mdpi.com/1660-4601/16/20/3828/s1,
Table S1: Prevalence of respiratory symptoms, health care utilization and medication use among school-age
participants (n=357), Table S2: Impact of wheezing among asthmatic and non-asthmatic participants reporting
any lifetime wheeze (N =126); Table S3: Housing characteristics among study participants (N =357).
Author Contributions:
Conceptualization and Methodology, S.F.F. and J.E.J.; Formal Analysis, M.R., S.P.E.,
S.F.F., and J.E.J.; Investigation, S.F.F., J.E.J.; Resources, S.F.F., J.E.J.; Data Curation, M.R.; Writing—Original Draft
Preparation, M.R., S.F.F., and J.E.J.; Writing—Review & Editing, M.R., S.P.E., L.O., E.B., S.F.F., and J.E.J.; Supervision,
L.O., E.B., S.F.F., and J.E.J.; Project Administration, L.O., E.B., S.F.F., and J.E.J.; Funding Acquisition, L.O., S.F.F.,
and J.E.J.
Acknowledgments:
We thank the participating elementary schools, parents, and students for their collaboration
and dedication to support health in their community. In particular, we are grateful for the efforts of Comite
Civico del Valle and the Children’s AIRE Study staff. Funding for this study was provided by NIEHS R01
(1R01ES029598-01), the NIEHS Southern California Environmental Health Sciences Center (5P30ES007048-20),
and a pilot grant from the Keck School of Medicine of USC Dean’s Pilot Program.
Conflicts of Interest:
The authors declare no conflict of interest. The sponsors that supported this work had no
role in the design, execution, interpretation, or writing of the study.
References
1.
Centers of Disease Control and Prevention. National Current Asthma Prevalence. Available online:
https://www.cdc.gov/asthma/most_recent_national_asthma_data.htm (accessed on 6 October 2019).
2.
Bhan, N.; Kawachi, I.; Glymour, M.M.; Subramanian, S.V. Time Trends in Racial and Ethnic Disparities in
Asthma Prevalence in the United States From the Behavioral Risk Factor Surveillance System (BRFSS) Study
(1999–2011). Am. J. Public Health 2015,105, 1269–1275. [CrossRef] [PubMed]
3.
Akinbami, L.J.; Simon, A.E.; Rossen, L.M. Changing Trends in Asthma Prevalence Among Children. Pediatrics
2016,137, e20152354. [CrossRef] [PubMed]
4.
Leong, A.B.; Ramsey, C.D.; Celedon, J.C. The challenge of asthma in minority populations. Clin. Rev.
Allergy Immunol. 2012,43, 156–183. [CrossRef] [PubMed]
5.
Moorman, J.E.; Zahran, H.; Truman, B.I.; Molla, M.T.; Centers for Disease Control and Prevention. Current
asthma prevalence—United States, 2006–2008. MMWR Surveill. 2011,60, 84–86.
6.
Carr, T.F.; Beamer, P.I.; Rothers, J.; Stern, D.A.; Gerald, L.B.; Rosales, C.B.; Van Horne, Y.O.; Pivniouk, O.N.;
Vercelli, D.; Halonen, M.; et al. Prevalence of Asthma in School Children on the Arizona-Sonora Border.
J. Allergy Clin. Immunol. Pract. 2017,5, 114–120. [CrossRef] [PubMed]
7.
Krieger, N.; Chen, J.T.; Waterman, P.D.; Rehkopf, D.H.; Subramanian, S.V. Race/ethnicity, gender,
and monitoring socioeconomic gradients in health: A comparison of area-based socioeconomic measures–the
public health disparities geocoding project. Am. J. Public Health 2003,93, 1655–1671. [CrossRef] [PubMed]
Int. J. Environ. Res. Public Health 2019,16, 3828 13 of 15
8.
Lipsett, M.; Smorodinsky, S.; English, P.; Copan, L. BASTA Border Asthma & Allergies Study: Final Report;
Impact Assessment, Inc.: Richmond, VA, USA; San Diego, CA, USA, 2009; p. 81.
9.
Abuduwaili, J.; Liu, D.; Wu, G. Saline dust storms and their ecological impacts in arid regions. J. Arid Land
2010,2, 144–150. [CrossRef]
10.
Crighton, E.J.; Elliott, S.J.; Van der Meer, J.; Small, I.; Upshur, R. Impacts of an environmental disaster on
psychosocial health and well-being in Karakalpakstan. Soc. Sci. Med. 2003,56, 551–567. [CrossRef]
11.
Kunii, O.; Hashizume, M.; Chiba, M.; Sasaki, S.; Shimoda, T.; Caypil, W.; Dauletbaev, D. Respiratory
symptoms and pulmonary function among school-age children in the Aral Sea region. Arch. Environ. Health
2003,58, 676–682. [CrossRef]
12.
Bennion, P.; Hubbard, R.; O’Hara, S.; Wiggs, G.; Wegerdt, J.; Lewis, S.; Small, I.; van der Meer, J.; Upshur, R.
The impact of airborne dust on respiratory health in children living in the Aral Sea region. Int. J. Epidemiol.
2007,36, 1103–1110. [CrossRef]
13.
Johnston, J.E.; Razafy, M.; Lugo, H.; Olmedo, L.; Farzan, S.F. The disappearing Salton Sea: A critical reflection
on the emerging environmental threat of disappearing saline lakes and potential impacts on children’s health.
Sci. Total Environ. 2019,663, 804–817. [CrossRef] [PubMed]
14.
Seto, E.; Carvlin, G.; Austin, E.; Shirai, J.; Bejarano, E.; Lugo, H.; Olmedo, L.; Calderas, A.; Jerrett, M.; King, G.;
et al. Next-Generation Community Air Quality Sensors for Identifying Air Pollution Episodes. Int. J. Environ.
Res. Public Health 2019,16, 3268. [CrossRef] [PubMed]
15.
Wong, M.; Bejarano, E.; Carvlin, G.; Fellows, K.; King, G.; Lugo, H.; Jerrett, M.; Meltzer, D.; Northcross, A.;
Olmedo, L.; et al. Combining Community Engagement and Scientific Approaches in Next-Generation
Monitor Siting: The Case of the Imperial County Community Air Network. Int. J. Environ. Res. Public Health
2018,15, 523. [CrossRef] [PubMed]
16.
Kemper, L. On the Frontier: Medi-Cal Brings Mangaged Care to California’s Rural Communities; California Health
Care Foundation: Oakland, CA, USA, 2015.
17.
California Office of Statewide Health Planning and Development. Medical Service Study Area. Available
online: https://www.arcgis.com/home/item.html?id=5c13665b10854480aa985d76cfcb9c5d (accessed on 18
September 2019).
18.
ISAAC. International Study of Asthma and Allergies in Childhood Manual. Available online: http:
//isaac.auckland.ac.nz/(accessed on 10 October 2019).
19.
Salam, M.T.; Li, Y.-F.; Langholz, B.; Gilliland, F.D. Early-life environmental risk factors for asthma:
Findings from the Children’s Health Study. Environ. Health Perspect.
2004
,112, 760–765. [CrossRef]
[PubMed]
20.
Millstein, J.; Gilliland, F.; Berhane, K.; Gauderman, W.J.; McConnell, R.; Avol, E.; Rappaport, E.B.; Peters, J.M.
Effects of ambient air pollutants on asthma medication use and wheezing among fourth-grade school children
from 12 southern California communities enrolled in the children’s health study. Arch. Environ. Health
2004
,
59, 505–514. [CrossRef] [PubMed]
21.
Sharma, S.; Chakraborty, R.K. Asthma Medications. In Treasure Island. Available online: https://www.ncbi.
nlm.nih.gov/books/NBK531455/(accessed on 10 October 2019).
22.
California Department of Public Health. California Breathing County Asthma Data
Tool. Available online: https://www.cdph.ca.gov/Programs/CCDPHP/DEODC/EHIB/CPE/Pages/
CaliforniaBreathingCountyAsthmaProfiles.aspx (accessed on 6 October 2019).
23.
Centers of Disease Control and Prevention. Most Recent National Asthma Data. Available online:
https://www.cdc.gov/asthma/most_recent_national_asthma_data.htm (accessed on 6 October 2019).
24.
Grineski, S.E.; Collins, T.W.; Chavez-Payan, P.; Jimenez, A.M.; Clark-Reyna, S.; Gaines, M.; Kim, Y.-A.
Social disparities in children’s respiratory health in El Paso, Texas. Int. J. Environ. Res. Public Health
2014
,11,
2941–2957. [CrossRef]
25.
Ramos, I.N.; Davis, L.B.; He, Q.; May, M.; Ramos, K.S. Environmental risk factors of disease in the Cameron
Park Colonia, a Hispanic community along the Texas-Mexico border. J. Immigr. Minor. Health
2008
,10,
345–351. [CrossRef] [PubMed]
26.
Stephen, G.A.; McRill, C.; Mack, M.D.; O’Rourke, M.K.; Flood, T.J.; Lebowitz, M.D. Assessment of respiratory
symptoms and asthma prevalence in a U.S.-Mexico border region. Arch. Environ. Health
2003
,58, 156–162.
[CrossRef]
Int. J. Environ. Res. Public Health 2019,16, 3828 14 of 15
27.
Sinclair, R.; Russell, C.; Kray, G.; Vesper, S. Asthma Risk Associated with Indoor Mold Contamination in
Hispanic Communities in Eastern Coachella Valley, California. J. Environ. Public Health
2018
,2018, 9350370.
[CrossRef]
28.
van Gent, R.; van Essen, L.E.; Rovers, M.M.; Kimpen, J.L.; van der Ent, C.K.; de Meer, G. Quality of life in
children with undiagnosed and diagnosed asthma. Eur. J. Pediatr. 2007,166, 843–848. [CrossRef]
29.
Yeatts, K.; Shy, C.; Sotir, M.; Music, S.; Herget, C. Health Consequences for Children With Undiagnosed
Asthma-like SymptomsHealth Consequences for Children With Undiagnosed Asthma-like Symptoms.
JAMA Pediatr. 2003,157, 540–544.
30.
Aaron, S.D.; Boulet, L.P.; Reddel, H.K.; Gershon, A.S. Underdiagnosis and Overdiagnosis of Asthma. Am. J.
Respir. Crit. Care Med. 2018,198, 1012–1020. [CrossRef] [PubMed]
31.
Sianez, M.; Highfield, L.; Collins, T.; Grineski, S. Burden of Illness, Primary Care Use, and Medication
Utilization among US-Mexico Border Children with Wheezing. J. Racial Ethn. Health Disparities
2019
,6,
594–602. [CrossRef] [PubMed]
32.
Siersted, H.C.; Boldsen, J.; Hansen, H.S.; Mostgaard, G.; Hyldebrandt, N. Population based study of risk
factors for underdiagnosis of asthma in adolescence: Odense schoolchild study. BMJ
1998
,316, 651–657.
[CrossRef] [PubMed]
33.
Leith Sly, J.; Carpenter, D.O. Special vulnerability of children to environmental exposures.
Rev. Environ. Health
2012,27, 151–157. [PubMed]
34.
Toskala, E.; Kennedy, D.W. Asthma risk factors. Int. Forum Allergy Rhinol.
2015
,5(Suppl. 1), S11–S16.
[CrossRef]
35.
Castro-Rodriguez, J.A.; Forno, E.; Rodriguez-Martinez, C.E.; Celedon, J.C. Risk and Protective Factors for
Childhood Asthma: What Is the Evidence? J. Allergy Clin. Immunol. Pract.
2016
,4, 1111–1122. [CrossRef]
[PubMed]
36.
Wu, P. Maternal Smoking during Pregnancy and Its Effect on Childhood Asthma. Am. J. Respir. Crit. Care
Med. 2012,186, 941–942. [CrossRef]
37.
Neuman, Å.; Hohmann, C.; Orsini, N.; Pershagen, G.; Eller, E.; Kjaer, H.F.; Gehring, U.; Granell, R.;
Henderson, J.; Heinrich, J.; et al. Maternal Smoking in Pregnancy and Asthma in Preschool Children. Am. J.
Respir. Crit. Care Med. 2012,186, 1037–1043. [CrossRef]
38.
Zacharasiewicz, A. Maternal smoking in pregnancy and its influence on childhood asthma. ERJ Open Res.
2016,2, 00042–2016. [CrossRef]
39.
Neophytou, A.M.; Oh, S.S.; White, M.J.; Mak, A.C.Y.; Hu, D.; Huntsman, S.; Eng, C.; Serebrisky, D.;
Borrell, L.N.; Farber, H.J.; et al. Secondhand smoke exposure and asthma outcomes among African-American
and Latino children with asthma. Thorax 2018,73, 1041–1048. [CrossRef] [PubMed]
40.
Continente, X.; Arechavala, T.; Fernandez, E.; Perez-Rios, M.; Schiaffino, A.; Soriano, J.B.; Carreras, G.;
Lopez-Nicolas, A.; Gorini, G.; Lopez, M.J. Burden of respiratory disease attributable to secondhand smoke
exposure at home in children in Spain (2015). Prev. Med. 2019,123, 34–40. [CrossRef] [PubMed]
41.
Ostro, B.D.; Lipsett, M.J.; Mann, J.K.; Wiener, M.B.; Selner, J. Indoor air pollution and asthma. Results from a
panel study. Am. J. Respir. Crit. Care Med. 1994,149, 1400–1406. [CrossRef] [PubMed]
42.
Jhun, I.; Phipatanakul, W. Early exposure to dogs and farm animals reduces risk of childhood asthma.
Evid. Based Med. 2016,21, 80. [CrossRef] [PubMed]
43.
Lodrup Carlsen, K.C.; Roll, S.; Carlsen, K.H.; Mowinckel, P.; Wijga, A.H.; Brunekreef, B.; Torrent, M.;
Roberts, G.; Arshad, S.H.; Kull, I.; et al. Does pet ownership in infancy lead to asthma or allergy at school
age? Pooled analysis of individual participant data from 11 European birth cohorts. PLoS ONE
2012
,7,
e43214. [CrossRef] [PubMed]
44.
Collin, S.M.; Granell, R.; Westgarth, C.; Murray, J.; Paul, E.; Sterne, J.A.C.; John Henderson, A. Pet ownership
is associated with increased risk of non-atopic asthma and reduced risk of atopy in childhood: Findings
from a UK birth cohort. Clin. Exp. Allergy 2015,45, 200–210. [CrossRef] [PubMed]
45.
Cohen, M.; Hyun, K. Hazard: The Future of the Salton Sea with No Restoration Project; Pacific Institute: Oakland,
CA, USA, 2006; pp. 1–60.
46.
Reid, J.S.; Flocchini, R.G.; Cahill, T.A.; Ruth, R.S.; Salgado, D.P. Local meteorological, transport, and source
aerosol characteristics of late autumn Owens Lake (dry) dust storms. Atmos. Environ.
1994
,28, 1699–1706.
[CrossRef]
Int. J. Environ. Res. Public Health 2019,16, 3828 15 of 15
47.
Chalupa, D.C.; Morrow, P.E.; Oberdorster, G.; Utell, M.J.; Frampton, M.W. Ultrafine particle deposition in
subjects with asthma. Environ. Health Perspect 2004,112, 879–882. [CrossRef]
48.
Ostro, B.; Roth, L.; Malig, B.; Marty, M. The effects of fine particle components on respiratory hospital
admissions in children. Environ. Health Perspect. 2009,117, 475–480. [CrossRef]
©
2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
