Dietary Factors Affecting Asthma Outcomes among Asthmatic Children in California

Abstract

Asthma is one of the principal causes of absenteeism from school and the leading cause of emergency department visits for children in the United States. Some dietary habits are associated with asthma prevalence and play a role in the pathogenesis and control of symptoms. The objective of this study was to characterize dietary factors that may affect asthma outcomes among children with asthma in California. The California Health Interview Survey (CHIS) is the largest state health survey in the nation. This cross-sectional study included 7687 surveys, representing an estimated annual 710,534 children (ages 2–11) reported to have asthma between 2001 and 2015. Analysis was survey-weighted. We used multivariable regression, adjusting for covariates, to examine the association between dietary factors and asthma outcomes. Asthmatic children consuming two or more servings of sodas per day had more symptoms of asthma than those who did not consume soda daily (OR = 1.50, 95% CI: 1.04, 2.15). Moreover, those consuming two servings of fruits per day had lower odds of missing school due to asthma. Children with asthma may be affected by certain pro-inflammatory foods that are energy dense. This study provided an additional reason to discourage the consumption of sodas and sugary drinks due to the negative respiratory impact, in addition to their effect on childhood obesity, oral health problems, and future chronic diseases.

1. Introduction

Asthma affects 5.1 million children in the United States and is known to be the most common chronic disease among children [1]. It is a complex, inflammatory disease, affecting the respiratory tract along with many comorbidities such as obesity, diabetes, gastroesophageal reflux, hormonal disorders, anxiety, and depressive disorders [2]. In 2008, it was estimated that asthma caused approximately 10.5 million missed school days for children aged 5–17 years due to one or more asthma attacks within a 12-month period [3]. Compared to school-aged children without asthma, children with asthma had a higher rate of emergency department (ED) visits, hospitalizations, outpatient visits, and prescribed medications to control their asthma [4]. According to the Household Component of the Medical Expenditure Panel Survey (MEPS–HC), asthma is estimated to be one of the five most costly conditions for children [5].

Diet may play a role in asthma pathogenesis and control [6]. Recent studies indicate a positive association between diet and asthma prevalence among children. For example, higher consumption of fruits and vegetables has been associated with lower prevalence of asthma symptoms [7]. However, fast food consumption significantly correlated with higher prevalence of current wheeze [8], whereas high fat, sugar, and sodium intake (which are found in pastry, chocolate desserts, salty snacks, fruit juices, and soft drinks) have been linked with asthma prevalence and current severe asthma [9].

The objective of this study was to examine whether consumption of specific dietary items, such as fruits and vegetables, fruit juices, french fries, high-sugar foods, sodas, and fast food, were associated with children’s asthma outcomes, such as symptom frequency, missed days from school, and emergency department (ED) visits. Results of this study could potentially improve dietary interventions to impact asthma outcomes, including minimizing asthma symptoms.

2. Materials and Methods

2.1. Data

This was a secondary data analysis of the 2001–2015 California Health Interview Survey (CHIS) child health profiles. CHIS is the nation’s largest state survey, overseen by the University of California, Los Angeles Center for Health Policy Research (UCLA CHPR) in collaboration with the California Department of Public Health (CDPH) and the California Department of Health Care Services (CDHCS). Data on numerous underrepresented racial and ethnic populations were collected using a landline and cellphone random-digit dial method. Interviews were conducted in different languages, including English, Spanish, Chinese (Mandarin and Cantonese dialects), Vietnamese, and Korean. Surveys were weighted by a complex ranking and oversampling process that allowed for specific populations to provide statewide population estimates as calculated by UCLA CHPR. Further information about CHIS methodology is described elsewhere [10].

Analysis was conducted on a total of 7687 surveys completed between 2001 and 2015 among children who were between the ages of 2 and 11 years old and had been diagnosed with asthma. This specific age group was chosen to ensure consistency with previous literature, so the results could be compared to existing studies. The ages from 0 to 2 were excluded since they were not consistently asked about asthma status. In addition, asthma action plans were designated by age groups, with the first being 0–11 years [11].

This study was conducted in accordance with the Declaration of Helsinki, and approved by the Loma Linda University Institutional Review Board, with an IRB # 5180172—Notice of Determination, 23 May 2018. Additionally, informed consent was obtained from all subjects involved in the study by the CHIS (California Health Interview Survey) workers.

2.2. Measures

2.2.1. Diet

In the CHIS child surveys, dietary questions were reported by the parent or guardian identified as the adult who knew most about the child’s daily eating and behavioral patterns within the past week (i.e., fast food intake). Participants were allowed to self-define portions and/or serving sizes (i.e., glasses, boxes, or cans were not defined by the interviewer) which included the following food items [10]:

1. Fruit intake	“Yesterday, how many servings of fruit, such as an apple or banana, did (child) eat?”
2. Vegetable intake	“Yesterday, how many servings of vegetables like green salad, green beans, or potatoes did (he/she) have? Do not include fried potatoes.”
3. Fruit juice consumption	“Yesterday, how many glasses or cans of 100% fruit juice, such as orange or apple juice, did (child) drink?”
4. Milk consumption	“Yesterday, how many glasses or small cartons of milk did (he/she) drink?”
5. Consumption of highly sugary foods	“Yesterday, how many servings of sweets such as cookies, candy, doughnuts, pastries, cake or popsicles did (he/she) have?”
6. Soda/sweetened drink intake	“Yesterday, how many glasses or cans of soda, such as Coke or other sweetened drinks, such as fruit punch or sports drinks, did (he/she) drink? Do not count diet drinks.”
7. Consumption of fried potatoes	“Yesterday, how many servings of french fries, home fries or hash browns did (child) eat?”
8. Fast food consumption dichotomized as one, two, or more servings per week	“In the past 7 days, how many times did (he/she) eat fast food? Include fast food meals eaten at school or at home, or at fast food restaurants, carry out or drive thru.”

2.2.2. Asthma Outcomes

The CHIS child questionnaire asked the parent or guardian about asthma-related outcomes within the past 12 months. Questions related to asthma outcomes were asked in the following manner [10]:

1. “During the past 12 months, has (child) had an episode of asthma or an asthma attack?”	Yes/No
2. “During the past 12 months, how often has (child) had asthma symptoms such as coughing, wheezing, shortness of breath, chest tightness, or phlegm?”	Less than monthly/ Monthly or more
3. “During the past 12 months, has (child) had to visit a hospital emergency room because of (his/her) phlegm?”	Yes/No
4. “Is the child now taking a daily medication to control (his/her) asthma that was prescribed or given to you by a doctor?”	Yes/No
5. “How confident are you that you can control and manage (child’s) asthma?”	High self-efficacy = very confident; low self-efficacy = data combined into somewhat confident, not too confident, not at all confident [12]
6. “During the past 12 months, did (child) miss any days of school due to asthma?”	Yes/No

2.2.3. Covariates

The CHIS questionnaire allowed for the inclusion of gender (male/female), age (2–4 years, 5–6 years, 7–8 years, 9–10 years, and 11 years), race (White, Latino, African-American, Asian/Pacific Islander, other), English proficiency of parent (very good/good, not good, not at all), parent’s education (less than 12 years, high school graduate, college graduate, any graduate school), household income (0–99% FPL, 100–199% FPL, 200–299% FPL, 300% or more FPL), and geographic location of residency (urban, second city, suburban, town/rural). Body Mass Index calculations were not included in this study, as parental self-reported heights for children were not considered reliable by CHIS staff.

2.3. Statistical Analysis

Descriptive analysis were summarized in

Table 1. Sociodemographic distribution as % of children ages 2–11 with asthma (CHIS 2001–2015).

	Any Asthma
	(n = 7687)
Weighted n	710,534
Gender
Female	39.3
Male	60.7
Age group
2 to 4 years of age	21.3
5 to 6 years	21.0
7 to 8 years	21.9
9 to 10 years	22.9
11 years	12.9
Race
White	40.7
Latino	10.8
African-American	9.9
Asian/Pacific Islander	32.2
Other	6.5
Parent’s education
Less than 12 years	18.3
High School graduate	23.3
College graduate	27.8
Any graduate school	30.6
English language proficiency of parent
Very well/well	83.9
Not well	11.0
Not at all	5.1
Household income
0–99% FPL	24.5
100–199% FPL	22.7
200–299% FPL	14.1
300% + FPL	38.7
Geography
Urban	45.6
Second City	25.9
Suburban	19.4
Town and Rural	9.0
Survey Year
2001–2002	10.9
2003–2004	11.7
2005–2006	11.9
2007–2008	11.7
2009–2010	10.0
2011–2012	12.0
2013	11.6
2014	10.2
2015	10.0

Abbreviations: FPL—Federal Poverty Level.

. This study compared the diet frequency of asthmatic children (ages 2–11) with asthma outcome by using chi-square analysis (as illustrated in

Table 2. Comparison (%) of dietary practices by asthma status of children ages 2–11 ^a.

Food/ Food Item	Had Asthma Attack in 12 Months (%)			Symptom Frequency (%)			Currently Taking Prescription Meds to Control Asthma (%)
	Yes	No	p-Value	Less than Monthly	Monthly or More	p-Value	Yes	No	p-Value
Fruits, svg/d	n = 5315		0.481	n = 4946		0.778	n = 6385		0.602
0	40.9	59.1		70.5	29.5		36.1	64.0
1	43.8	56.2		68.5	31.5		35.2	64.8
2	45.9	54.1		69.5	30.5		32.5	67.5
3 or more	46.1	53.9		71.7	28.3		34.0	66.0
Vegetables, svg/d	n = 5315		0.076	n = 4946		0.614	n = 6385		0.426
0	40.6	59.4		72.0	28.0		32.2	67.9
1	44.3	55.7		69.6	30.4		35.4	64.6
2 or more	47.3	52.8		69.1	30.9		33.9	66.2
Fruit juice, svg/d	n = 5315		0.881	n = 4931		0.910
0	45.1	54.9		69.9	30.1		ND	ND	ND
1	45.3	54.7		70.6	29.4		ND	ND
2 or more	44.0	56.0		69.4	30.6		ND	ND
Milk, svg/d	n = 3449		0.177	n = 3722		0.805	n = 4554		0.030
0	47.6	52.4		72.7	27.3		39.1	60.9
1	42.2	57.8		70.4	29.6		31.7	68.3
2 or more	47.2	52.8		70.6	29.4		37.1	62.9
High sugar, svg/d	n = 4473		0.861	n = 2996		0.335	n = 4122		0.148
0	46.1	53.9		70.5	29.5		34.4	65.6
1	46.2	53.9		70.6	29.4		29.2	70.8
2 or more	44.7	55.3		65.8	34.2		31.7	68.3
Soda, svg/d	n = 5315		0.227	n = 4947		0.002	n = 6387		0.491
0	46.2	53.8		69.7	30.3		33.4	66.6
1	42.8	57.2		75.0	25.1		34.2	65.8
2 or more	41.9	58.1		61.7	38.3		37.1	62.9
French fries, svg/d	n = 5315		0.658	n = 4949		0.204	n = 6388		0.066
0	44.6	55.4		70.7	29.3		33.1	66.9
1 or more	45.9	54.1		67.0	33.0		33.8	62.2
Fast food, svg/w	n = 3035		0.605	n = 2003		0.149	n = 3035		0.448
0	42.4	57.6		66.2	33.8		31.2	68.8
1	42.3	57.7		74.3	25.7		27.3	72.7
2 or more	45.5	54.5		66.9	33.1		31.4	68.6
Fruits, svg/d	n = 6385		0.021	n = 5174		0.295	n = 2839		0.664
0	74.3	25.7		22.6	77.4		57.7	42.4
1	62.4	37.6		28.9	71.1		61.9	38.2
2	75.2	24.8		30.4	69.6		61.4	38.6
3 or more	77.6	22.4		33.2	66.8		56.1	43.9
Vegetables, svg/d	n = 2776		0.282	n = 5174		0.990	n = 2839		0.047
0	67.2	32.8		29.7	70.3		59.8	40.2
1	74.0	26.1		30.3	69.7		66.8	33.2
2 or more	74.7	25.3		30.1	69.9		53.8	46.2
Fruit juice, svg/d	n = 2776		0.551	n = 5174		0.039	n = 2839		0.401
0	74.3	25.7		25.7	74.3		62.1	37.9
1	69.0	31.0		30.1	69.9		60.8	32.0
2 or more	74.2	25.8		35.5	64.5		54.4	45.6
Milk, svg/d				n = 2398		0.033	n = 1929		0.265
0	ND	ND	ND	30.5	69.5		62.3	37.7
1	ND	ND		27.9	72.1		59.2	40.8
2 or more	ND	ND		35.7	64.3		55.5	44.5
High sugar, svg/d	n = 1024		0.335	n = 3422		0.297	n = 1929		0.586
0	66.6	33.4		32.0	68.0		59.8	40.2
1	68.2	31.8		28.2	71.8		56.3	43.7
2 or more	75.8	24.2		32.1	67.9		55.6	44.4
Soda, svg/d	n = 2776		0.465	n = 5174		0.743	n = 2839		0.069
0	73.3	26.7		30.4	69.6		62.2	37.9
1	71.6	28.4		28.3	71.8		52.6	47.4
2 or more	64.1	36.0		31.5	68.5		53.3	46.7
French fries, svg/d	n = 1963		0.143	n = 4361		0.948	n = 2026		0.914
0	74.2	25.8		31.2	68.8		60.0	40.0
1 or more	66.1	33.9		30.9	69.1		59.6	40.5
Fast food	n = 2776		0.084	n = 3945		0.185	n = 910		0.269
0	78.6	21.4		25.4	74.6		69.9	30.1
1	73.9	26.1		27.5	72.6		61.4	38.6
2 or more	68.2	31.8		32.3	67.7		56.6	43.4

^a Not every diet or outcome question was asked every survey year; svg/d = servings per day; svg/w = servings per week; ND = no data. We bolded the statistically significant results.

). Significance was set to p < 0.05. Significant dietary factors were followed up with logistic regression that adjusted for covariates such as gender, age, race, parent’s education, household income, and geographic location (as illustrated in

Table 3. Adjusted odds ratios (95% CI) for asthma outcome by dietary practices for children ages 2–11 (CHIS 2001–2015).

	Symptoms Frequency (n = 3655)		Currently Taking Prescription Meds to Control Asthma (n = 4485)		Low Self-Efficacy to Control Asthma (n = 2776)		Any ED Visits (n = 2398)		Any Missed School Due to Asthma in Past 12 Months (n = 1929)
	OR (95% CI)	p-Value	OR (95% CI)	p-Value	OR (95% CI)	p-Value	OR (95% CI)	p-Value	OR (95% CI)	p-Value
Fruits i
0	1.0 Ref		1.0 Ref		1.0 Ref		1.0 Ref		1.0 Ref
1	1.07 (0.75, 1.54)	0.685	0.88 (0.64, 1.20)	0.411	1.85 (0.85, 1.05)	0.123	1.30 (0.76, 2.23)	0.338	0.73 (0.48, 1.09)	0.121
2	0.99 (0.69, 1.43)	0.970	0.85 (0.61, 1.18)	0.325	0.99 (0.49, 2.01)	0.993	1.54 (0.89, 2.67)	0.121	0.68 (0.46, 0.99)	0.046
3+	0.80 (0.54, 1.19)	0.275	0.85 (0.60, 1.20)	0.355	0.91 (0.41, 1.99)	0.810	1.21 (0.68, 2.15)	0.511	0.89 (0.56, 1.42)	0.621
Vegetables i
0	1.0 Ref		1.0 Ref		1.0 Ref		1.0 Ref		1.0 Ref
1	0.92 (0.69, 1.24)	0.590	0.99 (0.78, 1.28)	0.989	0.73 (0.43, 1.22)	0.223	0.99 (0.63, 1.55)	0.951	1.02 (0.70, 1.49)	0.898
2	1.15 (0.85, 1.56)	0.351	0.98 (0.74, 1.30)	0.900	0.92 (0.57,1.51)	0.756	0.82 (0.53, 1.27)	0.376	0.86 (0.59, 1.26)	0.445
Fruit Juice i
0	1.0 Ref		1.0 Ref		1.0 Ref		1.0 Ref		1.0 Ref
1	0.93 (0.71, 1.21)	0.578	1.01 (0.80, 1.29)	0.912	1.24 (0.70, 2.19)	0.460	0.99 (0.63, 1.55)	0.951	0.89 (0.64, 1.25)	0.502
2	1.14 (0.87, 1.49)	0.324	1.17 (0.94, 1.47)	0.167	0.82 (0.48, 1.42)	0.487	0.82 (0.53, 1.27)	0.376	1.04 (0.74, 1.48)	0.803
Milk i
0	1.0 Ref		1.0 Ref		---	---	1.0 Ref		1.0 Ref
1	1.14 (0.87, 1.49)	0.508	0.79 (0.58, 1.08)	0.137	---	---	0.99 (0.61, 1.62)	0.972	1.21 (0.78, 1.89)	0.382
2	1.06 (0.75, 1.49)	0.748	0.99 (0.75, 1.32)	0.978	---	---	1.19 (0.75, 1.89)	0.456	1.34 (0.88, 2.04)	0.174
Soda i
0	1.0 Ref		1.0 Ref		1.0 Ref		1.0 Ref		1.0 Ref
1	0.87 (0.67, 1.15)	0.331	1.07 (0.85, 1.34)	0.564	1.10 (0.69, 1.74)	0.690	0.98 (0.70, 1.36)	0.895	1.02 (0.78, 1.33)	0.884
2+	1.50 (1.04, 2.15)	0.027	1.10 (0.79, 1.53)	0.572	1.39 (0.74, 2.62)	0.301	0.77 (0.50, 1.17)	0.220	0.93 (0.63, 1.37)	0.715
Gender
Female	1.0 Ref		1.0 Ref		1.0 Ref		1.0 Ref		1.0 Ref
Male	1.11 (0.89, 1.37)	0.351	1.07 (0.88, 1.31)	0.477	0.94 (0.62, 1.42)	0.769	1.13 (0.85, 1.49)	0.403	0.78 (0.58, 1.06)	0.109
Age group
2 to 4 years of age	1.0 Ref		1.0 Ref		1.0 Ref		1.0 Ref		1.0 Ref
5 to 6 years	0.81 (0.57, 1.16)	0.257	0.95 (0.72, 1.25)	0.720	1.28 (0.74, 2.22)	0.371	0.54 (0.37, 0.78)	0.001	1.01 (0.65,1.57)	0.961
7 to 8 years	0.96 (0.68, 1.36)	0.829	0.86 (0.64, 1.15)	0.305	0.80 (0.47, 1.35)	0.402	0.46 (0.30, 0.69)	<0.01	0.98 (0.65, 1.46)	0.913
9 to 10 years	0.67 (0.48, 0.95)	0.024	0.73 (0.55, 0.98)	0.036	0.88 (0.49, 1.56)	0.656	0.46 (0.31, 0.69)	<0.01	0.71 (0.47, 1.07)	0.104
11 years	0.82 (0.55, 1.24)	0.356	0.92 (0.65, 1.30)	0.635	0.72 (0.30, 1.73)	0.459	0.47 (0.28, 0.81)	0.006	0.77 (0.49, 1.20)	0.243
Race
White	1.0 Ref		1.0 Ref		1.0 Ref		1.0 Ref		1.0 Ref
Latino	0.83 (0.51, 1.34)	0.448	0.62 (0.43, 0.90)	0.012	0.45 (0.17, 1.18)	0.105	0.29 (0.16, 0.55)	<0.01	0.43 (0.24, 0.77)	0.004
African American	1.20 (0.79, 1.84)	0.392	1.25 (0.87, 1.81)	0.223	0.85 (0.42, 1.73)	0.663	1.33 (0.76, 2.31)	0.318	0.83 (0.48, 1.42)	0.490
Asian/Pacific Islander	0.98 (0.72, 1.33)	0.905	0.83 (0.64, 1.09)	0.188	0.70 (0.43, 1.13)	0.142	0.76 (0.52, 1.11)	0.158	0.66 (0.45, 0.97)	0.034
Other	0.79 (0.47, 1.33)	0.380	0.88 (0.58, 1.34)	0.551	0.58 (0.30, (1.11)	0.102	1.08 (0.64, 1.82)	0.769	0.86 (0.44, 1.67)	0.655
English language proficiency of parent
Very well/well	1.0 Ref		1.0 Ref		1.0 Ref		1.0 Ref		1.0 Ref
Not well	0.73 (0.45, 1.20)	0.219	0.56 (0.38, 0.83)	0.004	2.57 (1.32, 5.02)	0.006	0.69 (0.40, 1.17)	0.165	0.78 (0.45, 1.35)	0.374
Not at all	0.78 (0.35, 1.73)	0.556)	0.76 (0.40, 1.44)	0.398	2.13 (1.01, 4.50)	0.047	0.40 (0.15, 1.05)	0.063	1.78 (0.87, 3.64)	0.113
Parent’s education
Less than 12 years	1.0 Ref		1.0 Ref		1.0 Ref		1.0 Ref		1.0 Ref
High School graduate	0.91 (0.61, 1.36)	0.647	1.01 (0.72, 1.43)	0.946	1.02 (0.52, 2.01)	0.945	0.87 (0.54, 1.41)	0.576	0.97 (0.62, 1.53)	0.910
College graduate	1.04 (0.68, 1.61)	0.828	0.93 (0.64, 1.35)	0.712	0.90 (0.45, 1.83)	0.781	1.08 (0.64, 1.81)	0.769	1.07 (0.63, 1.82)	0.793
Any graduate school	0.91 (0.57, 1.45)	0.677	0.85 (0.58, 1.24)	0.402	0.90 (0.44, 1.86)	0.785	0.74 (0.42, 1.31)	0.304	0.74 (0.43, 1.28)	0.286
Household income
0–99% FPL	1.0 Ref		1.0 Ref		1.0 Ref		1.0 Ref		1.0 Ref
100–199% FPL	0.65 (0.45, 0.95)	0.026	0.91 (0.65, 1.26)	0.563	0.62 (0.34, 1.11)	0.109	0.65 (0.40, 1.06)		1.37 (0.89, 2.09)	0.147
200–299% FPL	0.73 (0.49, 1.11)	0.140	0.74 (0.52, 1.05)	0.091	1.27 (0.57, 2.87)	0.556	0.67 (0.41, 1.08)		1.11 (0.66, 1.86)	0.679
300%+ FPL	0.60 (0.40, 0.88)	0.010	0.65 (0.47, 0.92)	0.015	0.78 (0.39, 1.58)	0.495	0.65 (0.41, 1.03)		1.20 (0.78, 1.85)	0.395
Geography
Urban	1.0 Ref		1.0 Ref		1.0 Ref		1.0 Ref		1.0 Ref
Second City	1.10 (0.84, 1.44)	0.468	1.02 (0.81, 1.29)	0.835	0.66 (0.42, 1.04)	0.076	0.96 (0.69, 1.33)	0.806	1.03 (0.75, 1.42)	0.860
Suburban	0.91 (0.69, 1.20)	0.515	0.89 (0.69, 1.14)	0.364	0.49 (0.28, 0.86)	0.012	0.83 (0.57, 1.20)	0.315	1.22 (0.84, 1.79)	0.293
Town and Rural	1.07 (0.73, 1.56)	0.723	1.09 (0.81, 1.46)	0.563	0.83 (0.45, 1.54)	0.553	1.31 (0.88, 1.96)	0.186	1.08 (0.69, 1.69)	0.734

ⁱ = foods are in serving per day. Note: model adjusted for survey year. We bolded the statistically significant results.

). Odds ratios were computed with 95% confidence intervals. All analyses were conducted using STATA 14.2 (Stata Corp., College Station, TX, USA), and were survey-weighted using CHIS raking variables to adjust for survey sampling, non-response, and to reflect California Department of Finance Population demographic estimates for each survey period.

3. Results

There was an annual estimated sample of 710,534 children with asthma represented between 2001 and 2015 (Table 1). Most asthmatics were boys (60.7%). Most asthmatic children were from families who were either White (40%) or Asian (32.2%). Most of the reporting parents had at least a college degree and were fluent in the English language (83.9%).

Table 2 shows significant findings among the following variables (p < 0.05): (1) higher fruit consumption (greater than 3 or more servings) was associated with higher self-efficacy to control asthma; (2) increased fruit juice consumption was associated with a higher frequency of ED visits within the prior 12 months; (3) consumption of two or more daily servings of milk was associated with increased visits to the ED within the prior 12 months; (4) two or more daily servings of vegetables were associated with a reduced percentage of individuals reporting missed school days due to asthma within the past 12 months; (5) one daily serving of soda was associated with higher frequency of asthma symptoms; and (6) one daily serving of milk was associated with a decrease in individuals currently taking prescription medications to control asthma. All findings were in comparison to zero reported intake of each food or beverage item.

All combinations from Table 2 with p < 0.05 were further analyzed with logistic regression while adjusting for covariates (see Table 3). There were two significant food-related findings (p < 0.05): (1) asthmatic children who consumed two or more servings of soda per day had a higher frequency of asthma symptoms and (2) asthmatic children eating at least two fruit servings per day had lower odds of missing days at school due to asthma.

In addition to significant associations between food and asthma outcomes, there were some significant associations with non-food measures. For example, children ages 5–11 were less likely to visit the ED compared to those 2–4 years of age. Latinos were less likely to visit the ED, take asthma medications, or have missed days at school. Furthermore, parents who were not proficient in speaking English or who spoke no English at all had low self-efficacy to control their child’s asthma.

4. Discussion

The main regression finding from this study was that soda consumption (two or more servings per day) was positively associated with higher frequency of asthma symptoms (i.e., coughing, wheezing, and shortness of breath). Multiple logistic regression findings included soda consumption (two or more servings per day) being associated with higher frequency of asthma symptoms. On the other hand, consumption of at least two fruit servings per day showed a negative association with missing days at school due to asthma.

Other research supports the findings of the present study. For example, an analysis of over 2000 children in the Dutch Prevention and Incidence of Asthma and Mite Allergy (PIAMA) birth cohort found that asthma risk was increased among 11-year-old children who consumed high quantities (≥10 glasses per week) of drinks with added sugar [13]. A similar trend was found among U.S. high school students, in which the odds of having current asthma were higher among those who drank regular soda at least 2 or more times per day compared to students who did not drink regular soda [14]. Increased consumption of sugar-sweetened beverages may indeed play a role in increasing asthma symptoms by promoting an inflammatory response of the airways, which can lead to wheezing and other respiratory problems [15].

Previous studies have demonstrated a positive relationship between consuming fast food and asthma risk. For instance, research by the International Study of Asthma and Allergies in Childhood (ISAAC) found that there was an increased risk of asthma and wheezing among a large sample of children and adolescents when consuming fast food at least 3 times per week [16]. Similarly, a systematic review of 16 studies (including 13 cross-sectional and 3 case control studies) found that consumption of fast food was significantly related to current and severe asthma risk [17]. Therefore, poor quality of diet associated with fast food consumption may contribute to increased asthma risk among children related to an exacerbation of the airways, which can reduce a parent’s belief in their ability to manage their child’s condition effectively.

The present study did not find significant associations between asthma outcomes and dietary measures such as fast food, french fries, and highly sugary foods. Others have shown that Taiwanese children with energy-dense/nutrient-poor foods and dietary practices were more likely to have current asthma (OR = 2.42, 95% CI: 1.19–4.93) and current severe asthma (OR = 3.21, 95% CI: 1.11–9.25) [18]. High-fat and -sugar snacks were included in their analysis; however, soda was excluded due to it not being a part of their typical diet.

Fruit was observed through this study to have protective effects on asthma outcomes. A recent randomized control trial on the DASH (Dietary Approach to Stop Hypertension) diet showed efficacy to improve asthma control in adults. After 6 months on the DASH diet, there were clinically significant improvements in the participants’ overall well-being reflected by their scores on Asthma Control Questionnaire (ACQ) and Mini Asthma-specific Quality of Life Questionnaire (MiniAQLQ) [19]. In addition, there have been multiple studies on the Mediterranean diet and its association with decreased symptoms related to wheeze and cough [20].

It is understood that establishing healthy lifestyle practices and dietary habits prevent long-term health consequences. Previous research found that increased fast-food accessibility in a child’s neighborhood was significantly associated with decreased fruit and vegetable consumption. In addition, younger children were found to drink more juice than older children and the mother’s education level played a significant role on the children’s consumption of sweets, juice, and milk [21].

Older children, especially those between 9 and 10 years old, were less likely to have asthma symptoms, visit the ED, take asthma medications, and miss days at school due to asthma. These results corroborate with the literature findings, which approximate that 80% of asthma cases are diagnosed within the first six years of life [22].

Our results also showed that Latinos were less likely to visit the ED, take asthma medications, or have missing days at school. This also agrees with the literature findings where Mexican-Americans present lower asthma prevalence than other groups, even though they have lower incomes and insurance coverages than non-Hispanic whites [23].

Limitations to this study include being unable to draw any causal inferences from the results due to its observational, cross-sectional nature. Furthermore, as survey questions changed over time, we were not able to include factors such as physical activity. Since food questions also changed over time, we were not able to use the same subjects for all analyses. Due to CHIS concerns regarding accuracy of anthropometric data, we did not adjust for the children’s body mass index or weight, which is an unfortunate limitation since obesity has been associated with increased incidence of asthma and increased risk for severe asthma [24]. This public dataset also presented limited questions regarding parents’ habits and behaviors, such as smoking. The presence of self-reporting bias may have led to discrepancies in how the participants classified foods and approximated serving sizes. In addition, the quality of food items was also not distinguishable. For example, fresh/organic fruits and vegetables were counted the same as canned fruits or vegetables. However, despite the methodological limitations, statistical significance was found among key food items and asthma outcome variables.

5. Conclusions

In conclusion, considerations should be made when educating families that have children with asthma on appropriate dietary choices that may affect the asthma outcomes. Although avoiding fast food and increasing fruits and vegetables are measures that can generally improve overall health, these may also be recommended for the prevention and management of asthma in children.

Overall, consumption of energy-dense foods should be reduced while families should be encouraged to eat more whole fruits, vegetables, and legumes to avoid childhood diseases such as asthma, as our study proposes, and future chronic diseases such as type 2 diabetes, obesity, and heart disease.

Asthma may be considered a lifestyle-related disease which can be prevented by behavior modification and supported by environmental changes to improve outcomes. Therefore, more policies and school-based activities should be in place to promote healthy lifestyles for school-aged children, such as healthy cafeteria food choices, healthy culinary activities, on-site gardening, and education on how to purchase healthy food items.

In general, policies should be made to improve the affordability of food for food deserts, providing access to more fresh fruits and vegetables. This is a pertinent public health issue that would benefit populations at large, but especially children who are vulnerable to diseases related to access to healthy dietary choices.