Increased Asthma Risk and Asthma-Related Health Care Complications Associated With Childhood Obesity

Abstract

Asthma is the most common chronic condition of childhood, yet the relationship between obesity and asthma risk and the impact of obesity on clinical asthma outcomes are not well understood. For this population-based, longitudinal study, demographic and clinical data were extracted from administrative and electronic health records of 623,358 patients aged 6–19 years who were enrolled in the Kaiser Permanente Southern California health plan in 2007–2011. Crude asthma incidence ranged from 16.9 per 1,000 person-years among normal-weight youth to 22.3 per 1,000 person-years among extremely obese youth. The adjusted risks of asthma for overweight, moderately obese, and extremely obese youth relative to those of normal weight youth were 1.16 (95% confidence interval: 1.13, 1.20), 1.23 (95% confidence interval: 1.19, 1.28), and 1.37 (95% confidence interval: 1.32, 1.42), respectively (P_trend < 0.0001). The relationship between obesity and asthma risk was strongest in Asian/Pacific Islanders and in the youngest girls (aged 6–10 years), compared with other groups. Among youth who developed asthma, those who were moderately or extremely obese had more frequent asthma exacerbations requiring emergency department services and/or treatment with oral corticosteroids. In conclusion, obese youth are not only more likely to develop asthma, but they may be more likely to have severe asthma, resulting in a greater need for health care utilization and aggressive asthma treatment.

Asthma is currently the most common chronic childhood disease in the United States and has steadily increased in prevalence over the past 3 decades, rising from 3.6% in 1980 (1) to nearly 10% in 2009 (2, 3). The nationwide prevalence of childhood obesity has also increased during this period from 5.5% (4, 5) to approximately 17% (6). Few US-based cohort studies have prospectively examined the relationship between higher body weight and the risk of asthma in children (7–10). Most have investigated the combined effects of overweight and obesity (9, 10); only 1 has assessed the contribution of overweight to asthma development (7); and none has specifically examined the magnitude of asthma risk in extremely obese youth. Complex interactions between obesity and age (11–14), sex (15), and/or race/ethnicity (16–18) have been observed in some cross-sectional studies but not in others (19, 20) and have not been thoroughly investigated or adequately powered in longitudinal study designs.

Obesity may also be associated with asthma severity and/or poor asthma control in children and adults. Studies have shown that obese patients with asthma are often prescribed a greater number of β-agonists and oral corticosteroids and have more frequent emergency department visits and hospitalizations for asthma exacerbations than their normal-weight counterparts (18, 21–25). The direct and indirect health care costs of asthma are substantial and are attributed to increased utilization of medical services, as well as missed school and work days (26). However, the impact of obesity on asthma-related health care has not been fully examined in the pediatric population, and findings from the few studies that have addressed this topic have been inconsistent (23–25, 27, 28).

In the present study, we examined the relationship between body mass index (BMI) (weight (kg)/height (m)²) and asthma incidence in a large, multiethnic, population-based cohort of more than 600,000 children and adolescents in Southern California. We also investigated the degree to which obesity is associated with prescription medication use and health care utilization among youth with asthma.

MATERIALS AND METHODS

Population and data sources

The Kaiser Permanente Southern California (KPSC) Medical Care Program is a prepaid, group-practice, integrated health care organization with 3.6 million members in 2011. Members receive their health care in KPSC-owned facilities throughout a 7-county region. Our study population consisted of youth, aged 6–19 years, who received care in medical offices and hospitals owned by KPSC in 2007–2011. Youth were included if they had at least 1 height and weight measurement in their electronic health records between January 1, 2007, and December 31, 2010, and were at least 6 years of age at the time of the first measurement (baseline). Youth were required to have at least 6 months of continuous KPSC health care plan enrollment prior to baseline to allow for adequate identification and exclusion of prevalent asthma cases (n = 771,673). We excluded patients who were pregnant during the accrual period (n = 11,330), as well as those with a history of asthma recorded in their electronic health records (n = 136,985), leaving 623,358 asthma-free patients with baseline height and weight measurements for inclusion in the present study. Patients were followed from baseline until December 31, 2011, onset of asthma, or disenrollment from the health plan, whichever occurred first. The study protocol was reviewed and approved by the KPSC institutional review board.

Anthropometrics

Height and weight measurements were extracted from electronic health records and used to calculate BMI. Based on a validation study that included 15,000 patients with 45,980 medical encounters, the estimated error rate in weight and height data was less than 0.4% (29). Age- and sex-specific BMI z scores, which were derived from the Centers for Disease Control and Prevention's national standards (30, 31) in conjunction with the World Health Organization's definitions for overweight and obesity in adults (32), were used to define weight status as follows: “underweight” (<5th percentile), “normal weight” (≥5th to <85th percentiles), “overweight” (≥85th to <95th percentiles or BMI ≥25), “moderately obese” (>95th percentile or BMI ≥30), and “extremely obese” (≥1.2 × 95th percentile or BMI ≥35).

Asthma status

Incident asthma cases were identified by any of the following 3 conditions: 1) the presence of a physician diagnosis of asthma identified by the International Classification of Diseases, Ninth Revision (ICD-9), code 493 associated with any medical encounter and at least 1 pharmacy dispensing of an asthma-specific medication in the same year (n = 30,052; 94.6%), 2) the presence of 1 or more asthma-related emergency department visits or hospitalizations in the absence of asthma medication (n = 926; 2.9%), or 3) 3 or more asthma-related ambulatory visits in the absence of asthma medication (n = 799; 2.5%). “Asthma index” was defined as the first date for which a patient met 1 of the above criteria. All other patients, including those who had 2 or fewer ambulatory visits accompanied by an asthma diagnosis but no asthma medications, as well as those who had asthma medications but no asthma diagnosis in the electronic health record, were classified as not having asthma. A case validation study revealed that 88% of youth with pharmacy dispensings of asthma-related medication but no asthma diagnosis in the electronic health record had 1 or more of the following diagnoses to accompany the medication: acute nasopharyngitis, sinusitis, pharyngitis, tonsillitis, laryngitis, tracheitis, upper respiratory infection, or bronchitis (ICD-9 codes 460–466); chronic pharyngitis, nasopharyngitis, sinusitis, disease of the tonsils and adenoids, laryngitis, or laryngotracheitis (ICD-9 codes 472–476); allergic rhinitis (ICD-9 codes 477); other upper respiratory infection (ICD-9 code 478); pneumonia (ICD-9 codes 480–486); influenza (ICD-9 codes 487–488); bronchitis (ICD-9 codes 490–491); bronchiectasis (ICD-9 code 494); or allergic alveolitis (ICD-9 code 495).

Asthma medication

Pharmacy dispensings of the following medications were used for identification of asthma cases: rescue medications consisting of short-acting β-agonists (SABAs) and anticholinergics (albuterol, ipratropium, ipratropium-albuterol, levalbuterol, metaproterenol, pirbuterol, or terbutaline); inhaled corticosteroids (beclomethasone, budesonide, budesonide-formoterol, fluticasone, fluticasone-salmeterol, flunisolide, mometasone, or triamcinolone); oral steroids (dexamethasone, fludrocortisone, hydrocortisone, methylprednisolone, prednisolone, or prednisone); leukotriene modifiers (montelukast, zafirlukast, or zileuton); other inhaled medications consisting of long-acting β-agonists and mast-cell stabilizers (formoterol, salmeterol, cromolyn, or nedocromil); and other oral medications (aminophylline or theophylline). Characterization of asthma severity and control was based on the use of SABAs, which are prescribed for quick relief of asthma symptoms; controller medications, which consist primarily of inhaled corticosteroids, cromolyn, nedocromil, and leukotriene modifiers and are prescribed for the prevention of asthma symptoms; and oral steroids, which are prescribed for acute exacerbations.

For youth with asthma and at least 1 year of follow-up, the number and type of medications dispensed during the 1-year period following asthma index were examined for characterization of asthma severity and control. The number of SABA canister and controller medications was calculated according to a weighted algorithm for electronic pharmacy records (33). One SABA canister yields ∼200 puffs, and the need for more than 2 puffs per day is an indicator of poor asthma control (34). Therefore, the number of SABA canisters dispensed in the first year of follow-up was categorized as “high” (≥4 canisters) or “low” (<4 canisters) to distinguish poor versus adequate control. Medication ratio was defined as the number of controller medications divided by the sum of the number of controller medications and SABA canisters dispensed (35). As such, the ratio ranged from 0 (no controllers) to 1.0 (no SABAs) and was categorized as “high” (≥0.5) or “low” (<0.5) (35). Despite the fact that patients who are adherent to controller regimens (and who thus have higher medication ratios) are expected to have lower SABA intake requirements (36), we observed substantial variation in SABA intakes among youth with high medication ratios. Therefore, we used a combination of SABA canister and medication ratio criteria to categorize asthma in the first year postindex as “severe” (≥4 SABA canisters and medication ratio ≥0.5), “under-treated” (≥4 SABA canisters and medication ratio <0.5), “controlled” (<4 SABA canisters and medication ratio ≥0.5) or “mild” (<4 SABA canisters and medication ratio <0.5).

Demographic and clinical characteristics

Information on age, sex, and race/ethnicity was obtained from health plan administrative records and birth certificates. Youth with Hispanic ethnicity were categorized as Hispanic; all other youth (e.g., white, black, Asian/Pacific Islander) were presumed to be non-Hispanic. For those who were missing information on race/ethnicity (15.7% of the cohort), we used an imputation algorithm based on a combination of surname lists and address information derived from the US Census Bureau (37–39) to impute race/ethnicity information for 6.2% of the cohort. The resulting specificity and positive predictive values were greater than 98% for all major races/ethnicities (40). Neighborhood-level information on adult education and household incomes was derived from linkage of health plan member addresses with US census block data via geocoding (41). Participation in the California Medical Assistance Program (Medi-Cal) or another subsidized program for low-income families and elderly, blind, or disabled individuals was assessed from administrative records. The payer for health insurance was categorized as “Medi-Cal,” which includes state- and/or institution-sponsored assistance, or “private/other.”

For youth with asthma and at least 1 year of follow-up (n = 28,645; 90.1% of incident asthma cases), the number of asthma-specific health care encounters, classified as ambulatory or emergency department visits for which an asthma diagnosis code was entered in the electronic health record at the time of the visit, was identified for the 1-year period following asthma index. Asthma exacerbations were defined as having 1 or more asthma-related emergency visits and/or 1 or more oral steroid dispensings within 7 days of an asthma-related ambulatory visit during this year.

Statistical analysis

Demographic and clinical characteristics were summarized as proportions and reported by incident asthma status. χ² tests were used to assess differences in proportions of demographic and clinical characteristics. Crude incidence rates for asthma were calculated as the number of new cases divided by person-years at risk over follow-up. The relationship between weight status and asthma risk was evaluated with hazard ratios estimated from Cox proportional hazard models adjusted for age, sex, race/ethnicity, and payer for insurance. To test for effect modification by age, sex, and/or race/ethnicity, we included pairwise interaction terms between each of these variables and weight status in the multivariable Cox model. Given the differential timing of puberty in boys and girls and the potential effect this may have on asthma incidence, we also examined 3-way interaction between age, sex, and weight status. Statistical significance for interactions was determined by likelihood ratio tests (P < 0.05). Among youth with incident asthma and at least 1 year of follow-up, relationships between weight status and asthma-specific outcomes were assessed. The prevalence rates of high SABA use, high medication ratios, and asthma exacerbations were greater than 10%; therefore, their associations with weight status are reported as prevalence ratios estimated from robust Poisson regression models adjusted for age, sex, and payer for insurance. Odds ratios for the association between weight status and asthma severity categories were estimated with a multinomial regression model adjusted for age, sex, and payer for insurance. Analyses were conducted by using SAS/STAT, version 9.2, software (SAS Institute, Inc., Cary, North Carolina).

RESULTS

Among the 623,358 youth comprising 1,755,414 person-years of follow-up, we identified 31,777 new asthma cases. The median length of follow-up was 3.0 (interquartile range, 1.7–4.0) years. The overall crude incidence rate was 18.1 per 1,000 person-years at risk. Youth who developed asthma were more likely to be younger and/or of white or black race/ethnicity than were youth without asthma (Table 1). Although statistically significant, differences in sex, neighborhood-level education, and payer for insurance among youth with and without asthma were minimal; youth with incident asthma included a slightly higher proportion of girls and/or patients with a Medi-Cal type of payer for insurance. Youth who developed asthma were more likely to be overweight, moderately obese, or extremely obese than were youth without asthma (Table 1).

Table 1.

Characteristics of Kaiser Permanente Southern California Patients by Incident Asthma Status, 2007–2011

Characteristic	No Asthma, % (n = 591,581)	Asthma, % (n = 31,777)	P Valuea
Age at asthma index, years
6–10	32.16	47.16	<0.0001
11–14	28.71	30.83
15–19	35.13	22.01
Sex			0.005
Male	49.04	48.24
Female	50.96	51.76
Race/ethnicity			<0.0001
White	21.81	25.31
Black	7.68	10.35
Hispanic	51.77	48.56
Asian/Pacific Islander	6.20	6.32
American Indian/Alaskan Native	0.14	0.15
Multiple	0.42	0.60
Other	2.26	2.48
Unknown	9.73	6.23
Neighborhood-level educationb			<0.0001
Less than high school	28.71	27.53
High school graduate	21.48	21.67
Some college or associate's degree	30.13	30.90
Bachelor's degree or higher	19.67	19.90
Neighborhood-level incomec			0.90
<$15,000	10.28	10.24
$15,000–34,999	18.9	18.85
$35,000–49,999	14.2	14.19
$50,000–74,999	19.50	19.61
$75,000–99,999	13.86	13.94
$100,000–149,999	14.57	14.69
≥$150,000	8.69	8.48
Health insurance			<0.0001
Medi-Cal	19.10	20.22
Private/other	80.90	79.78
Weight statusd			<0.0001
Underweight	3.22	2.55
Normal weight	58.67	54.71
Overweight	18.17	19.05
Moderately obese	12.44	14.35
Extremely obese	7.50	9.34

Abbreviation: Medi-Cal,California Medical Assistance Program.

^a P value determined by χ² test.

^b Average neighborhood-level education among adults assessed by geocoding.

^c Average neighborhood-level annual household income assessed by geocoding.

^d Weight status was defined as follows: “underweight” (<5th percentile), “normal weight” (>5th and <85th percentiles), “overweight” (>85th and <95th percentiles, or body mass index (weight (kg)/height (m²)) >25), “moderately obese” (>95th percentile or body mass index >30), and “extremely obese” (≥1.2 × 95th percentile or body mass index ≥35).

Crude asthma incidence was generally higher for youth aged 6–10 years (24.2/1,000 person-years) compared with those aged 11–14 years (18.5/1,000 person-years) or 15–19 years (11.5/1,000 person-years) and slightly higher for girls (18.4/1,000 person-years) than for boys (17.8/1,000 person-years). Incidence was also higher for black youth (22.1/1,000 person-years) and white youth (19.5/1,000 person-years) compared with Hispanic youth (17.2/1,000 person years) and Asian/Pacific Islander youth (17.5/1,000 person-years). Crude asthma incidence ranged from 16.9 per 1,000 person-years among normal-weight youth to 22.3 per 1,000 person-years among extremely obese youth (Table 2). After adjustment for potential confounders, overweight, moderately obese, and extremely obese youth had 1.16 (95% confidence interval (CI): 1.13, 1.20), 1.23 (95% CI: 1.19, 1.28), and 1.37 (95% CI: 1.32%, 1.42%) times higher risks of asthma, respectively, than did normal-weight youth (P_trend < 0.0001).

Table 2.

Hazard Ratios Overall and by Race/Ethnicity for Risk of Asthma Among Kaiser Permanente Southern California Patients, 2007–2011

Weight Statusa	Total No.	No. of Cases	Crude Incidence per 1,000 Person-Years	Unadjusted Overall		Adjusted
						Overallb		Whitec (n = 8,042)		Blackc (n = 3,289)		Asian/Pacific Islanderc (n = 2,007)		Hispanicc (n = 15,430)
				HR	95% CI	HR	95% CI	HR	95% CI	HR	95% CI	HR	95% CI	HR	95% CI
Underweight	19,850	810	15.8	0.92	0.86, 0.99	0.89	0.83, 0.96	0.89	0.78, 1.01	1.02	0.81, 1.28	0.87	0.70, 1.09	0.88	0.78, 0.99
Normal weight	364,481	17,385	16.9	1.00	Referent	1.00	Referent	1.00	Referent	1.00	Referent	1.00	Referent	1.00	Referent
Overweight	113,555	6,053	19.0	1.13	1.09, 1.16	1.16	1.13, 1.20	1.15	1.08, 1.21	1.22	1.14, 1.33	1.07	0.94, 1.21	1.17	1.12, 1.22
Moderately obese	78,159	4,561	20.7	1.23	1.19, 1.27	1.23	1.19, 1.28	1.29	1.20, 1.39	1.19	1.07, 1.32	1.41	1.23, 1.52	1.19	1.14, 1.25
Extremely obese	47,313	2,968	22.3	1.33	1.28, 1.38	1.37	1.32, 1.43	1.37	1.24, 1.51	1.25	1.11, 1.40	1.67	1.39, 2.00	1.34	1.28, 1.41
Ptrend				<0.0001		<0.0001		<0.0001		<0.0001		<0.0001		<0.0001

Abbreviations: CI, confidence interval; HR, hazard ratio.

^a Weight status was defined as follows: “underweight” (<5th percentile), “normal weight” (>5th and <85th percentiles), “overweight” (>85th and <95th percentiles, or body mass index (weight (kg)/height (m)²) >25), “moderately obese” (>95th percentile or body mass index >30), and “extremely obese” (≥1.2 × 95th percentile or body mass index ≥35).

^b Adjusted for age, sex, race/ethnicity, and payer for insurance.

^c Adjusted for age, sex, and payer for insurance.

The association between higher weight status and asthma risk varied significantly by race/ethnicity, age, and sex (for all pairwise interaction, P < 0.05). Although black youth had the highest crude asthma incidence rate compared with other racial/ethnic groups, the association between higher weight status and asthma risk was attenuated in these youth (Table 2). The relationship between obesity and the risk of asthma was most pronounced for Asian/Pacific Islanders, among whom moderate and extreme obesity were associated with 1.41 (95% CI: 1.23, 1.52) and 1.67 (95% CI: 1.39, 2.00) times higher risks of asthma, respectively, compared with their normal-weight counterparts (Table 2). Despite the fact that girls had a slightly higher incidence of asthma than did boys, moderately and extremely obese girls were 1.21 (95% CI: 1.15, 1.27) and 1.32 (95% CI: 1.24, 1.40) times as likely, respectively, to develop asthma as were their normal-weight counterparts. Moderately and extremely obese boys were 1.26 (95% CI: 1.20, 1.32) and 1.40 (95% CI: 1.33, 1.48) times as likely, respectively, to develop asthma as their normal-weight counterparts. The crude asthma incidence rate trended linearly with decreasing age, such that the youngest age group had the highest risk of asthma. Moreover, the relationship between higher BMI and asthma risk was strongest in the youngest age group (Table 3). The age-specific relationship between weight status and asthma risk also varied by sex, such that the greatest magnitude of association was observed among the youngest girls (for 3-way interaction, P = 0.023) (Table 3).

Table 3.

Adjusted Hazard Ratios for Risk of Asthma by Age and Sex Among Kaiser Permanente Southern California Patients, 2007–2011

Weight Statusa	Patients Aged 6–10 Years						Patients Aged 11–14 Years						Patients Aged 15–19 Years
	Overallb (n = 14,987)		Femalec (n = 6,835)		Malec (n = 8,152)		Overallb (n = 9,797)		Femalec (n = 5,341)		Malec (n = 4,456)		Overallb (n = 6,993)		Femalec (n = 4,273)		Malec (n = 2,720)
	HR	95% CI	HR	95% CI	HR	95% CI	HR	95% CI	HR	95% CI	HR	95% CI	HR	95% CI	HR	95% CI	HR	95% CI
Underweight	0.95	0.86, 1.04	0.87	0.75, 1.00	1.02	0.90, 1.15	0.91	0.80, 1.05	0.81	0.66, 1.00	1.02	0.85, 1.23	0.80	0.68, 0.95	0.74	0.59, 0.93	0.88	0.70, 1.12
Normal weight	1.00	Referent	1.00	Referent	1.00	Referent	1.00	Referent	1.00	Referent	1.00	Referent	1.00	Referent	1.00	Referent	1.00	Referent
Overweight	1.24	1.19, 1.30	1.24	1.16, 1.32	1.24	1.17, 1.32	1.09	1.04, 1.15	1.09	1.01, 1.16	1.10	1.02, 1.19	1.10	1.04, 1.17	1.14	1.05, 1.23	1.04	0.94, 1.15
Moderately obese	1.32	1.26, 1.39	1.36	1.27, 1.46	1.28	1.21, 1.36	1.15	1.09, 1.22	1.08	0.99, 1.18	1.23	1.13, 1.33	1.17	1.09, 1.26	1.12	1.01, 1.24	1.24	1.10, 1.38
Extremely obese	1.49	1.41, 1.5	1.56	1.43, 1.71	1.43	1.33, 1.5	1.24	1.16, 1.34	1.12	1.00, 1.24	1.36	1.24, 1.50	1.29	1.19, 1.41	1.22	1.09, 1.38	1.38	1.21, 1.56
Ptrend			<0.0001		<0.0001				<0.0001		<0.0001				<0.0001		<0.0001

Abbreviations: CI, confidence interval; HR, hazard ratio.

^a Weight status was defined as follows: “underweight” (<5th percentile), “normal weight” (>5th and <85th percentiles), “overweight” (>85th and <95th percentiles, or body mass index (weight (kg)/height (m)²) >25), “moderately obese” (>95th percentile or body mass index >30), and “extremely obese” (≥1.2 × 95th percentile or body mass index ≥35).

^b Adjusted for sex, race/ethnicity, and payer for insurance.

^c Adjusted for race/ethnicity and payer for insurance.

In addition to a greater risk of developing asthma, higher weight status was associated with a greater frequency of asthma-related health care utilization and asthma exacerbations. Among youth with asthma, those who were extremely obese had a significantly higher rate of asthma-related emergency department visits than did those of normal weight during the first year of follow-up after adjustment for age, sex, and payer for insurance (106 vs. 87 visits/1,000 youth; P = 0.002). The adjusted rate of emergency visits was marginally higher for moderately obese youth compared with their normal-weight counterparts (97 vs. 87 visits/1,000 youth; P = 0.05). Moderately and extremely obese youth also had significantly higher adjusted rates of asthma-specific ambulatory visits compared with youth of normal weight (1,776 and 1,844 vs. 1,708 visits/1,000 youth, respectively; both P < 0.0001) and were more likely to have had oral steroids dispensed within 7 days of an ambulatory visit. Thus, we observed a linearly increasing trend in the adjusted rate of asthma exacerbations with increasing weight status in the first year of follow-up (Table 4).

Table 4.

Adjusted^a Prevalence Ratios for Asthma Outcomes by Weight Status Assessed in the First Year of Follow-up Among Kaiser Permanente Southern California Patients, 2007–2011

Weight Statusb	No.	High SABA Use (n = 3,995)		High Medication Ratioc (n = 8,375)		Exacerbation (n = 7,319)
		PR	95% CI	PR	95% CI	PR	95% CI
Underweight	727	1.08	0.81, 1.33	1.04	0.93, 1.16	1.10	0.98, 1.24
Normal weight	15,735	1.00	Referent	1.00	Referent	1.00	Referent
Overweight	5,439	1.06	0.97, 1.16	1.00	0.95, 1.05	1.08	1.03, 1.14
Moderately obese	4,082	1.12	1.01, 1.23	1.07	1.01, 1.13	1.16	1.10, 1.23
Extremely obese	2,662	1.16	1.03, 1.30	1.10	1.04, 1.17	1.15	1.07, 1.23
Ptrend		0.004		0.001		<0.0001

Abbreviations: CI, confidence interval; PR, prevalence ratio; SABA, short-acting β-agonist.

^a Adjusted for age, sex, and payer for insurance.

^b Weight status was defined as follows: “underweight” (<5th percentile), “normal weight” (>5th and <85th percentiles), “overweight” (>85th and <95th percentiles, or body mass index (weight (kg)/height (m)²) >25), “moderately obese” (>95th percentile or body mass index >30), and “extremely obese” (≥1.2 × 95th percentile or body mass index ≥35).

^c Medication ratio was defined as the number of controller medications divided by the sum of the number of controller medications and SABA canisters dispensed.

Analysis of asthma severity and control during the year after asthma index was consistent with these findings. Among youth with asthma, obesity was associated with high SABA use and high medication ratios after adjustment for covariates (Table 4). Obese youth were also more likely to have severe asthma than were normal-weight youth (Table 5). After accounting for potential confounders, we found that moderately and extremely obese youth had 1.23 (95% CI: 1.06, 1.43) and 1.44 (95% CI: 1.21, 1.70) increased odds of having severe asthma, respectively, compared with normal-weight youth.

Table 5.

Adjusted^a Odds Ratios for Asthma Severity Category by Weight Status Assessed in the First Year of Follow-up Among Kaiser Permanente Southern California Patients, 2007–2011

Weight Statusb	Controlledc (n = 6,845)		Under-Treatedc (n = 2,465)		Severec (n=1,530)
	OR	95% CI	OR	95% CI	OR	95% CI
Underweight	1.04	0.87, 1.24	1.04	0.79, 1.35	1.17	0.85, 1.62
Normal weight	1.00	Referent	1.00	Referent	1.00	Referent
Overweight	1.01	0.94, 1.09	1.10	0.98, 1.22	1.01	0.88, 1.17
Moderately obese	1.08	1.00, 1.18	1.08	0.96, 1.23	1.23	1.06, 1.43
Extremely obese	1.10	0.99, 1.21	1.04	0.90, 1.21	1.44	1.21, 1.70

Abbreviations: CI, confidence interval; OR, odds ratio.

^a Adjusted for age, sex, and payer for insurance.

^b Weight status was defined as follows: “underweight” (<5th percentile), “normal weight” (>5th and <85th percentiles), “overweight” (>85th and <95th percentiles, or body mass index (weight (kg)/height (m)²) >25), “moderately obese” (>95th percentile or body mass index >30), and “extremely obese” (≥1.2 × 95th percentile or body mass index ≥35).

^c Compared with “mild” category (n = 17,805).

DISCUSSION

Results from this large, population-based longitudinal study suggest that not only does higher BMI contribute to asthma development, buy it may also predispose youth to a more severe asthma phenotype, which can complicate asthma-related health care. We found that overweight and obese youth had significantly increased risks of asthma compared with those of normal-weight youth, and that this relationship was strongest among Asian/Pacific Islanders and the youngest girls. Among youth who developed asthma, obesity was also associated with more frequent asthma-related ambulatory and emergency department utilization, as well as greater odds of having at least 1 asthma exacerbation in the first year after asthma index. These findings are particularly important, because understanding which children are most susceptible to obesity-mediated asthma development and asthma-related health care complications is essential for improving prevention and treatment methods.

Overall, the crude asthma incidence rate in our study was 18.1 per 1,000 person-years. Other US-based studies have reported incidence rates in youth under 18 years of age ranging from 8.1 to 24.6 per 1,000 person-years (7–10). Differences between the rates reported in these studies and our study may be caused by differences in the demographics of the underlying populations; our sample was based on a Southern California population with high prevalence (∼52%) of Hispanic (primarily Mexican American) youth, for whom asthma incidence may be lower than for other racial/ethnic groups (42).

Our observation that higher BMI was associated with increased asthma risk is generally consistent with those of studies that have examined this effect. Gilliland et al. (9) found that overweight and obese youth, defined as having a BMI-for-age higher than the 85th percentile, were 1.52 (95% CI: 1.14, 2.03) times as likely to develop asthma as their normal-weight counterparts after adjustment for demographic covariates. By using this same definition of overweight/obesity, Mannino et al. (10) reported an adjusted relative risk of 1.2 (95% CI: 0.9, 1.7) for overweight and obese youth combined, relative to those of normal weight. We examined a wide range of BMI values and observed a linearly increasing trend in the adjusted relative risk of asthma with increasing weight status, ranging from 1.16 (95% CI: 1.13, 1.20) for overweight youth to 1.37 (95% CI: 1.32, 1.42) for extremely obese youth. Thus, our findings suggest that even youth with only modestly elevated BMI values are more likely to develop asthma, and that asthma risk increases with higher BMI values.

We also found that these risks vary by race/ethnicity, age, and sex. Most asthma incidence studies with anthropometric data have been conducted in populations with relatively low prevalence of ethnic minorities and were likely underpowered for estimation of race/ethnicity-specific associations of overweight and obesity with asthma risk. In general, compared with white youth, Asian/Pacific Islander and Hispanic youth had lower asthma risk and black youth had higher asthma risk. However, when we examined the association between obesity and asthma risk within each of these groups, we found the relationship to be strongest in Asian/Pacific Islander youth and attenuated in black youth; the association of higher BMI with asthma development was similar in Hispanic and white youth. In our previous cross-sectional analysis, we observed similar race/ethnicity-specific relationships between obesity and asthma prevalence (18). Brenner et al. (16) found no association between prevalent asthma and obesity in a primarily African American case-control sample of 774 adolescents. It is possible that genetic and/or other environmental factors play a larger role than that of obesity on asthma development in black youth. Consistent with our findings, Gilliland et al. (9) also observed a stronger effect of overweight and obesity on asthma risk among boys, despite the fact that girls had a slightly higher overall crude incidence rate. This finding was echoed by Mannino et al. (10), although Gold et al. (8) observed a greater effect of BMI, defined by continuous z score or quintiles, among girls. Participants in the Gold et al. study were aged 6–14 years, and approximately 80% were aged 6–9 years. These findings support our observation that the relationship between higher weight status and asthma risk varies by both sex and age, such that the association of obesity with asthma risk may be strongest in young girls.

Importantly, we also found that higher BMI was associated with more frequent health care utilization and greater odds of having at least 1 asthma exacerbation in the first year after asthma index. These observations are consistent with those of our previous cross-sectional investigation (18), as well as others (23–25). Furthermore, we found that moderately and extremely obese youth with asthma were more likely to require 4 or more SABA canisters, or an average of more than 2 puffs per day, in the first year of follow-up. These youth were also more likely to have a medication ratio of 0.5 or greater in this year, indicating more dispensings of inhaled corticosteroids and leukotriene modifiers concomitant with higher SABA use. Recently, Quinto et al. (24) reported an association between higher BMI and increased SABA consumption, although they did not examine the relationship between BMI and controller medications or medication ratio per se. Taken together, these results suggest that moderately and extremely obese youth may be more likely than their normal-weight counterparts to develop severe asthma in the first year after diagnosis. Additional prospective studies with detailed assessments of severity and control are necessary to better understand the mechanisms that underlie the relationship between obesity and asthma exacerbations.

We acknowledge that a limitation of our study is the lack of direct measurements of lung function, which could have allowed for confirmation of asthma diagnoses and/or more accurate assessment of asthma severity. Electronic health records did not include details on ethnic heritage, which precluded us from further stratifying our analyses among specific Hispanic subgroups that are known to have differential risks of asthma and that may have different obesity-specific asthma risks as well. Also, we were unable to control for certain environmental exposures or behaviors known to exacerbate asthma symptoms, such as smoking in the household, pet dander, air pollution, or physical activity levels.

Despite these limitations, our study has several strengths. Our longitudinal design, based on a large, multiethnic pediatric population, allowed us to conduct well-powered tests on a wide range of BMI values within various subgroups. The high prevalence of extreme obesity, especially among racial/ethnic minorities, allowed for stable risk estimates in most minority groups. Additional strengths of the study include the availability of asthma diagnoses made by health care providers in the electronic health records rather than reliance on self-reports and of asthma-specific prescription and health care utilization information to identify asthma cases and assess asthma severity and control.

In conclusion, the findings of our study suggest that the relationship between higher body mass and childhood asthma risk varies by race/ethnicity, sex, and age. Obesity, especially extreme obesity, may have a greater impact on asthma risk in young girls and in Asian/Pacific Islander youth than in other groups. Moderate and extreme obesity may further complicate asthma-related health care utilization and treatment, potentially because obese youth are more likely to have severe asthma. Effective interventions are needed to target high-risk groups to better prevent and treat asthma in the pediatric population.