J Autism Dev Disord (2014) 44:3083–3088 DOI 10.1007/s10803-014-2174-y
ORIGINAL PAPER
Co-occurrence of Autism and Asthma in a NationallyRepresentative Sample of Children in the United States Stanley Kotey • Karen Ertel • Brian Whitcomb
Published online: 6 July 2014 Ó Springer Science+Business Media New York 2014
Abstract Few large epidemiological studies have examined the co-occurrence of autism and asthma. We performed a cross-sectional study to examine this association using the 2007 National Survey of Children’s Health dataset (n = 77,951). We controlled for confounders and tested for autism-secondhand smoke interaction. Prevalence of asthma and autism were 14.5 % (n = 11,335) and 1.81 % (n = 1,412) respectively. Unadjusted odds ratio (OR) for asthma among autistic children was 1.35 (95 % CI 1.18–1.55). Adjusting for covariates (age, gender, body mass index, race, brain injury, secondhand smoke and socio-economic status) attenuated the OR to 1.19 (95 % CI 1.03–1.36). Autism-secondhand smoke interaction was insignificant (p = 0.38). Asthma is approximately 35 % more common in autistic children; screening may be an efficient approach to reduce risk of morbidity due to asthma. Keywords Allergy
Autism Asthma Autoimmune Screening
Introduction Asthma is the second leading cause of hospital admissions of children in the US and places a large burden on affected children and their families (‘‘Hospital Stays for Children 2009’’). According to the National Health Interview Survey (NHIS), 9.5 %, or 1 in 10 children have asthma in the US (‘‘Trends in Asthma Prevalence, Health Care Use, and Mortality in the
S. Kotey (&) K. Ertel B. Whitcomb School of Public Health and Health Sciences, University of Massachusetts Amherst, 715 N. Pleasant Street, Amherst, MA 01003, USA e-mail: [email protected]
United States 2001’’). Potential complications are significant, and include recurrent pneumonia and occasionally fatal attacks. Between 1980 and 1998, the average mortality rate due to asthma was 3.4 %; asthma was found to be the second most common reason for hospital admissions among children in the Healthcare Cost and Utilization Project, with a hospital discharge rate of 18.4 per 10,000 recorded in 2009 (Akinbami and Schoendorf 2002; ‘‘Trends in Asthma Prevalence, Health Care Use, and Mortality in the United States 2001’’; Yu et al. 2006). Hospitalization and/or fatalities related to asthma result primarily when there is ineffective ambulatory care or a lack of asthma-management education on the part of the patient and/or family (Akinbami and Schoendorf 2002). Children with ASD are more likely to experience unmet medical needs compared to typically developing children (Chiri and Warfield 2012). Also, it has been recognized that caretakers of autistic children with asthma are under significant strain and are more likely to experience depression, poorer parenting and competing demands (Koehler et al. 2014). Timely diagnosis and educating caregivers to promote timely recognition of signs and symptoms of asthma are important to reduce adverse outcomes in children who have asthma. This will also enable health care providers to identify at risk caretakers which would have implications for the child’s health management. Finally, if autistic children are at elevated risk of asthma, this association would promote more active strategies to diagnose and manage asthma in autistic children. Asthma is a chronic inflammatory disorder of the airways that causes recurrent episodes of wheezing, breathlessness, chest tightness and cough particularly at night and/or early in the morning (Abbas and Aster 2012). Risk factors for asthma include genetic predisposition, exposure to secondhand smoke and past smoking history (Ghosh et al. 2009). In a study to examine the trend of admissions of asthma in California, cases of pediatric asthma admissions at emergency
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departments had disproportionately higher representation of individuals of low socioeconomic status (SES). The significance of this public health issue suggests the importance of continuing research that may help to identify individuals who may benefit from prevention strategies such as targeted education (Largent et al. 2012). Though its etiology is not fully understood, autism is characterized by imbalances in immune and inflammatory processes. These similarities in conjunction with tandem increases in prevalence of both asthma and autism suggest common etiologies (Becker and Schultz 2010; Becker 2007). Altered levels of immunoglobulin, cytokines and evidence of inflammatory markers present in serum, cerebral spinal fluid and autopsy brain tissues have been seen in autistic children (Becker and Schultz 2010). Also, abnormalities in macrophages and mast cells have been noted. These inflammatory mediators are also important in the pathogenesis of asthma (Kazani and Israel 2012). Instructively, genome wide scans have identified several genetic variants common to both autism and asthma (Becker 2007), which may explain why autistic patients are at elevated risk of developing autoimmune diseases such as asthma (Becker 2007). Accordingly, examining the risk of asthma in autism patients from an epidemiologic perspective may promote efforts to implement preventive public health strategies in this subpopulation. A role of secondhand smoke in asthma pathogenesis has been attributed to the tiny particulate matter which initiates an inflammatory cascade. This mechanism has been noted in the exacerbation of asthmatic attacks (Ghio 2008). It is important to consider the role of secondhand smoke exposure in the relationship between autism and asthma to assess potential synergy in their inflammatory mechanisms. Only five epidemiological studies have examined the association between autism and asthma. These studies have produced conflicting results; two reporting no association, two reporting a positive association and one reporting a negative association. These studies have faced numerous limitations (Bakkaloglu et al. 2008; Becker and Schultz 2010; Croen and Grether 2005; Jyonouchi et al. 2008; Jyonouchi 2009, 2010), including small sample sizes, insufficient power to detect a relationship, and limited control for potential confounding covariates. To address these limitations, we examined the association between autism and asthma using the National Survey of Children’s Health 2007, a large nationally representative sample of children aged 0–18 years.
Methods Study Population and Design The National Survey of Children’s Health (NSCH) study is a nationwide survey conducted with funding from the
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Health Resources and Services Administration’s Maternal and Child Health Bureau, the Centers for Disease Control and Prevention’s National Center for Health Statistics. The 2007 survey was conducted nationwide as a random-digitdialing telephone survey that collected information on the health and well-being of children \18 years, based on interviews with their parents or guardians. Interviews were conducted in English, Spanish and four Asian languages. In total, interviews of over 90,000 parents or guardians were completed between April 2007 and July 2008. The design and inclusion criteria of the NSCH study has been described elsewhere (Kogan et al. 2009). A total of 78,042 subjects meeting our age cut-off criteria of C3 years (age by which ASD is typically diagnosed) were included from the NSCH 2007 study dataset (n = 9,1642) which had an overall weighted response rate of 51.2 % (Kogan et al. 2009). The sample size used in the analysis was 77,951 after exclusion of subjects with missing responses to asthma or autism diagnosis or both (n = 2,147). In our analysis, 14.52 % (n = 11,311) of the children had asthma and 1.81 % (n = 1,412) had autism. Measures Interviewers in the 2007 NSCH survey asked parents whether their children had ever been diagnosed with autism spectrum disorder (ASD), whether they currently had the condition, and the severity of the condition. For the purposes of our analysis, we classified subjects as having autism if the parent said a health worker had ever informed him/her that the child had autism. Autism status was coded as a dichotomous variable with ‘Yes’ and ‘No’ designations. Similarly, study interviewers asked parents or guardians whether children had asthma. Using the question, ‘‘Does your child currently have asthma?’’ we coded parental responses as a dichotomous variable with ‘Yes’ and ‘No’ categories. Covariates were chosen based on their associations with autism and asthma in literature reviews and availability in the NSCH dataset. The NSCH questionnaire asked parents: ‘‘Does anyone smoke inside your child’s home?’’ In our analysis we coded secondhand smoke exposure as a dichotomous variable as ‘Yes’ and ‘No’ based on responses to this question. Body mass index (BMI) been identified as an important risk factor for asthma (Porter et al. 2012). We computed and generated BMI percentiles from reported weight and height, (‘‘Growth Chart Training’’) and included it as a categorical variable using categories of \5th, 5th–85th and 95th percentiles in our analysis. In addition to brain injury which has been identified as a significant risk factor for autism, we abstracted age, gender, and SES, which were important covariates for asthma and autism (Becker 2007; Gardener et al. 2011; Largent et al. 2012;
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Porter et al. 2012). We coded gender and brain injury as dichotomous variables and age as a continuous variable. We assessed SES using average family income following the US Department of Human Services guidelines (‘‘Design and Operation of the National Survey of Children’s Health, 2007’’). We categorized the range of incomes into tertiles with levels labeled as high, middle and low. Statistical Analysis Associations of covariates with autism status and asthma status were evaluated to assess potential confounding. We used Chi square tests to evaluate categorical variables and t-tests for continuous variables and report p values accordingly. Where we encountered small cell frequencies, Fisher‘s exact test was used. We calculated the unadjusted odds ratios and the 95 % confidence intervals (CI) and reported the crude association of autism status with likelihood of asthma. We used multivariable logistic regression to examine the relationship between asthma and autism adjusting for covariates. In our analysis we included race, gender and SES as dummy variables and designated non-autistic children as the referent group and used standard techniques to model asthma and autism (Hosmer and Lemeshow 2004). Using parent-reported autism diagnosis as the principal predictor, we retained covariates in the model if the likelihood ratio test comparing models with and without the factor was significant at the p \ 0.05 level. In order to assess whether secondhand smoke modifies the association between autism and asthma, we included an interaction term in our full multivariate model corresponding to joint exposure (parent-reported autism and exposure to secondhand smoke). We also considered age as a categorical variable and examined it as an effect modifier between autism and asthma. We evaluated interaction terms by likelihood ratio tests, and included it in final models only if the interaction term was significant at p \ 0.05.
Results In the examination of the association between asthma and our covariates, we found that compared to children without asthma, children with asthma were older (11.05 vs. 10.5, p \ 0.001), had a higher BMI (24.28 vs. 24.04, p \ 0.001), were more likely to be African American (14.4 vs. 9.4 %, p \ 0.001) and less likely to be female (41.9 vs. 49.1 %, p \ 0.001). They were also more likely to have secondhand smoke exposure (66.8 vs. 6.1 %, p \ 0.001) and less likely to have high SES (46.5 vs. 50.3 %) (Table 1).
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The association between autism and covariates showed that compared to children without autism, those who had autism had similar mean BMI (24.05 vs. 24.10, p = 0.96), were less likely to be female (22 vs. 48.5 %, p \ 0.001), Hispanic (8.5 vs. 11.3 %, p = 0.004) and have high SES (44.1 vs. 49.8 %, p \ 0.001). They were more likely to have secondhand exposure (27.7 vs. 32 %, p = 0.05). However, there was no statistically significant difference in age (p = 0.36) between the two groups (Table 1). In unadjusted logistic regression models (Table 2), autistic children were more likely to have asthma compared to non-autistic children (OR = 1.35, 95 % CI: 1.18–1.55). We identified age, gender, BMI, race, history of brain injury, exposure to secondhand smoke and SES as significant predictors of asthma, and were included in our multivariable logistic regression model. In this model adjusting for the above covariates, the odds of asthma remained higher in autistic children compared to typically developing children, though the estimate was slightly attenuated (OR = 1.19, 95 % CI: 1.03–1.36) (Table 2). In evaluation of effect modification (Table 3), we observed that model fit was not improved by inclusion of interaction terms for autism and exposure to secondhand smoke (p = 0.38) or autism and age (p = 0.58), and neither interaction was included in final models.
Discussion In this large, nationally-representative cross-sectional survey of US children, we observed significantly higher odds of asthma in children diagnosed with autism compared to typically developing children. A statistically significant association persisted after adjusting for covariates. This association was not modified by secondhand smoke or age. As part of the atopy symptom complex, asthma often involves production of auto-antibodies and therefore has been observed commonly in families with autoimmune diseases (Croen and Grether 2005; Rottem and Shoenfeld 2003). Inflammatory markers such as eosinophils, mast cells, macrophages and interleukins play roles in the pathophysiology of asthma (Kazani and Israel 2012). Similarly, production of auto-antibodies and an imbalance in inflammatory mediators have been identified in autistic patients and their families (Bauman et al. 2013; Becker and Schultz 2010; Braunschweig et al. 2013; Korvatska et al. 2002). Genome wide scans (GWAS) have identified several genes with potential involvement in both autism and asthma. These genes include PTEN, MET, SERPINE1, PLAUR, ITGB3, ADRB2, and MIF. Of these, PTEN, MET, SERPINE1 have been identified to be important in the regulation of mast cells and macrophages. These mediators are important in the development of asthma and
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Table 1 Characteristics of children by autism and asthma: National Survey of Children’s Health, 2007 Autism Autism n = 1,412 (1.8 %) Age (SD)
10.47 (4.18)
Asthma No autism n = 76,545 (98.1 %) 10.58 (4.43)
Female
0.361
Asthma n = 11,335 (14.5 %) 11.05 (4.25)
No asthma n = 66,566 (88.5 %) 10.50 (4.44)
\0.001
Gender Male
p values
p values
\0.001 \0.001
1,100 (77.9 %)
39,338 (51.5 %)
6,575 (58.1 %)
33,866 (50.9 %)
312 (22.1 %)
37,105 (48.5 %)
4,744 (41.9 %)
32,664 (49.1 %)
BMI Continuous (m2/kg)
24.05 (16.27)
24.10 (20.91)
p = 0.960
24.28 (18.11)
24.04 (21.29)
\0.001
4,604 (40.62 %)
30,946 (46.45 %)
\0.001
Categorical Underweight (\5 %)
669 (47.38 %)
34,892 (45.58 %)
Normal (5–85 %)
396 (28.05 %)
27,987 (36.56 %)
4,065 (35.86 %)
24,305 (36.49 %)
Overweight (85–95 %)
163 (11.54 %)
7,301 (9.54 %)
1,263 (11.14 %)
6,208 (9.32 %)
Obese ([95 %)
184 (13.03 %)
6,365 (8.32 %)
1,403 (12.38 %)
5,157 (7.74 %)
Race
\0.001
0.004
Caucasian
990 (71.4 %)
51,703 (68.6 %)
7,008 (62.8 %)
45,677 (69.7 %)
African American Hispanic
128 (9.2 %) 118 (8.5 %)
7,601 (10.1 %) 8,541 (11.3 %)
1,607 (14.4 %) 1,212 (10.9 %)
6,130 (9.4 %) 7,439 (11.4 %)
Other
150 (10.8 %)
7,488 (9.9 %)
1,333 (11.9 %)
6,305 (9.6 %)
476 (4.2 %)
1,495 (2.2 %)
\0.001
Brain injury Yes No
96 (6.8 %) 1,310 (93.2 %)
1,874 (2.5 %) 74,639 (97.6 %)
Yes No DK/refused
\0.001 10,844 (95.8 %)
65,098 (97.8 %)
1,893 (3.2 %)
16,055 (66.8 %)
3,265 (6.1 %)
57,045 (96.7 %)
7,984 (33.2 %)
50,068 (93.8 %)
Second hand smoke
\0.001
0.05 389 (27.7 %) 1,013 (72.2 %) 1 (0.1 %)
39 (0.1 %)
8 (0 %)
32 (0.1 %)
\0.001
Socioeconomic status tertiles
\0.001
\0.001
Tertile 1, low
404 (28.6 %)
19,743 (25.8 %)
3,295 (29.1 %)
16,867 (25.3 %)
Tertile 2, middle
386 (27.3 %)
18,658 (24.4 %)
2,768 (24.4 %)
16,274 (24.4 %)
Tertile 3, high
622 (44.1 %)
38,144 (49.8 %)
5,272 (46.5 %)
33,475 (50.3 %)
Comparison of continuous variables assessed by t test; comparison of categorical variables assessed by Chi square or Fisher’s exact test BMI body mass index, DK don’t know
other autoimmune/inflammatory disorders (Becker and Schultz 2010; Becker 2007). Additionally, the ‘‘hygiene hypothesis’’ has been proposed as a common antecedent of both autism and asthma. The hygiene hypothesis proposes that asthma and other forms of atopy occur due to lack of early immune system challenge by environmental microbial, parasitic infection or gut commensals. The naivety of the immune system leads to immune hypersensitivity including inflammatory disorders such as asthma (Becker 2007). According to the theory, the drive to maintain a sterile environment for childhood development in Western countries explains, in part, the increasing prevalence of asthma in urban areas compared to rural areas Becker and Schultz 2010). The same hypothesis has been applied to explain the pathogenesis of autism considering the correlation of prevalences of the two chronic diseases across
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geographical divisions (Becker 2007). It is acknowledged that autistic patients have abnormal immune responses and experience increased incidence of autoimmune diseases such as type 1 diabetes and inflammatory bowel disease (Kohane et al. 2012). The common occurrence of food allergy in autism patients has been explained by immune dysfunction (Jyonouchi 2010). Also, inappropriate inflammatory response to otherwise benign allergens in children who have autism could explain the increased risk of asthma in this group. Our study is one of few to have considered an association between autism diagnosis and asthma. To date five epidemiological studies have examined asthma risk in autistic patients: two case control (Bakkaloglu et al. 2008; Croen and Grether 2005), two cross-sectional (Jyonouchi et al. 2008; Jyonouchi 2010) and one ecological study
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Table 2 Association between autism and asthma: National Survey of Children’s Health, 2007 Unadjusted
Adjusted
OR
95 % CI
OR
95 % CI
1.00
Referent
1.00
Referent
1.35 1.03
(1.18–1.55) (1.02–1.03)
1.19 1.02
(1.03–1.36) (1.01–1.03)
Male
1.00
Referent
1.00
Referent
Female
0.75
(0.72–0.78)
0.77
(0.74–0.80)
Autism status No autism Autism Age Gender
BMI Categorical Underweight (\5 %)
1.00
Referent
1.00
Referent
Normal (5–85 %)
1.12
(1.07–1.18)
0.98
(0.91–1.06)
Overweight (85–95 %)
1.37
(1.28–1.46)
1.13
(1.04–1.24)
Obese ([95 %)
1.83
(1.71–1.96)
1.44
(1.32–1.56)
Race Caucasian
1.00
Referent
1.00
Referent
African American
1.71
(1.61–1.81)
1.64
(1.54–1.74)
Hispanic
1.06
(0.99–1.13)
1.05
(0.99–1.13)
Other Brain injury
1.38
(1.29–1.47)
1.38
(1.30–1.48)
No
1.00
Referent
1.00
Referent
Yes
1.91
(1.72–2.12)
1.81
(1.63–2.01)
No
1.00
Referent
1.00
Referent
Yes
1.28
(1.22–1.33)
1.21
(1.16–1.27)
DK/refused
1.57
(0.72–3.40)
1.49
(0.68–3.27)
Referent
1.00
Referent
Second hand smoke
Socioeconomic status tertiles Tertile 1, low
1.00
Tertile 2, middle
0.87
(0.82–0.92)
0.91
(0.86–0.96)
Tertile 3, high
0.81
(0.77–0.85)
0.90
(0.85–0.94)
Table 3 Examination of age as an effect modifier in the association between autism and asthma: National Survey of Children’s Health, 2007 Age
Total
No autism
Autism
OR
p value
3–\8
2,789
2,721 (97.6 %)
68 (2.4 %)
1.45 (1.11–1.88)
0.518
8–\13
3,633
3,538 (97.4 %)
95 (2.6 %)
1.37 (1.09–1.72)
13– \18
4,889
4,790 (98.0 %)
99 (2.0 %)
1.29 (1.03–1.61)
(Becker and Schultz 2010). A positive association between autism and asthma was seen in two studies study (Becker and Schultz 2010; Croen and Grether 2005), a negative association another (Bakkaloglu et al. 2008), and no association observed in the other two studies (Jyonouchi
et al. 2008; Jyonouchi 2009). In addition, no study thus far has examined possible effect modification by secondhand smoking. A prior study by Bakkaloglu et al. looked at the risk of autism in children who have atopic symptoms and found no significant association (Bakkaloglu et al. 2008). This matched case control study was limited by a small sample size, with only 39 autism cases and 29 controls. In contrast, our study included a large sample size and thus had much greater statistical power to evaluate the association between autism status and asthma. Finally in a related study, maternal asthma diagnosis was found to be associated to elevated risk of having an autistic child (OR = 2.7, 95 % CI 1.3–5.8) (Croen and Grether 2005). Our results are in line with this finding though our study looked at the direct risk of asthma in autistic children. A number of potential limitations regarding this study should be noted including potential misclassification of autism and asthma status of participating children. Undiagnosed cases of autism might have occurred if parents missed early autistic behavior, lacked access to healthcare for confirmation of diagnosis (Kogan et al. 2009), or due to clinician misdiagnosis. However, previous studies using a similar methodology to that of the NSCH have found autism diagnosis prevalence assessed via parental selfreport to be comparable to diagnoses by trained professionals (Furu et al. 2011; Kogan et al. 2009). Such misclassification would be expected to be non-differential, and would tend to bias the estimate towards the null. Similarly, misclassification of asthma might have occurred for children with undiagnosed asthma or if misinterpretation of minor respiratory symptoms resulted in false positives. However, asthma related questionnaires have been shown to have high mean sensitivity and specificity to clinical diagnosis, suggesting that the NSCH questions may be adequate for asthma status measurement (Moorman et al. 2007). Residual/uncontrolled confounding cannot be ruled out. Birth order has been identified as a potential confounder to both asthma and autism (Becker 2007), however this data was not available in our data set. Also, mismeasurement of confounders might have resulted in residual confounding due to inadequate control for those confounders. However, we evaluated a large number of potential confounding factors, including those used in prior studies (Becker and Schultz 2010; Becker 2007; Croen et al. 2002). Moreover, because of the available data of the NHCS dataset, we were able to consider confounding by factors not evaluated in prior studies. In conclusion, we observed significantly elevated diagnosis of asthma in children diagnosed with autism. Although we observed minimal incremental predictive ability of autism status for identification of asthma, it is essential that parents and health workers actively look out for asthma symptoms in these patients. Atopy tends to be
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under-diagnosed in patients with autism because symptoms could be masked by associated behavioral problems (Jyonouchi 2010). If our results are replicated in future studies and due to the morbidity and potential death that comes with poorly managed asthma, it may be advisable to incorporate screening for asthma in the care of autistic children to increase early detection and improve clinical management if these results are replicated in other studies.
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ORIGINAL PAPER
Co-occurrence of Autism and Asthma in a NationallyRepresentative Sample of Children in the United States Stanley Kotey • Karen Ertel • Brian Whitcomb
Published online: 6 July 2014 Ó Springer Science+Business Media New York 2014
Abstract Few large epidemiological studies have examined the co-occurrence of autism and asthma. We performed a cross-sectional study to examine this association using the 2007 National Survey of Children’s Health dataset (n = 77,951). We controlled for confounders and tested for autism-secondhand smoke interaction. Prevalence of asthma and autism were 14.5 % (n = 11,335) and 1.81 % (n = 1,412) respectively. Unadjusted odds ratio (OR) for asthma among autistic children was 1.35 (95 % CI 1.18–1.55). Adjusting for covariates (age, gender, body mass index, race, brain injury, secondhand smoke and socio-economic status) attenuated the OR to 1.19 (95 % CI 1.03–1.36). Autism-secondhand smoke interaction was insignificant (p = 0.38). Asthma is approximately 35 % more common in autistic children; screening may be an efficient approach to reduce risk of morbidity due to asthma. Keywords Allergy
Autism Asthma Autoimmune Screening
Introduction Asthma is the second leading cause of hospital admissions of children in the US and places a large burden on affected children and their families (‘‘Hospital Stays for Children 2009’’). According to the National Health Interview Survey (NHIS), 9.5 %, or 1 in 10 children have asthma in the US (‘‘Trends in Asthma Prevalence, Health Care Use, and Mortality in the
S. Kotey (&) K. Ertel B. Whitcomb School of Public Health and Health Sciences, University of Massachusetts Amherst, 715 N. Pleasant Street, Amherst, MA 01003, USA e-mail: [email protected]
United States 2001’’). Potential complications are significant, and include recurrent pneumonia and occasionally fatal attacks. Between 1980 and 1998, the average mortality rate due to asthma was 3.4 %; asthma was found to be the second most common reason for hospital admissions among children in the Healthcare Cost and Utilization Project, with a hospital discharge rate of 18.4 per 10,000 recorded in 2009 (Akinbami and Schoendorf 2002; ‘‘Trends in Asthma Prevalence, Health Care Use, and Mortality in the United States 2001’’; Yu et al. 2006). Hospitalization and/or fatalities related to asthma result primarily when there is ineffective ambulatory care or a lack of asthma-management education on the part of the patient and/or family (Akinbami and Schoendorf 2002). Children with ASD are more likely to experience unmet medical needs compared to typically developing children (Chiri and Warfield 2012). Also, it has been recognized that caretakers of autistic children with asthma are under significant strain and are more likely to experience depression, poorer parenting and competing demands (Koehler et al. 2014). Timely diagnosis and educating caregivers to promote timely recognition of signs and symptoms of asthma are important to reduce adverse outcomes in children who have asthma. This will also enable health care providers to identify at risk caretakers which would have implications for the child’s health management. Finally, if autistic children are at elevated risk of asthma, this association would promote more active strategies to diagnose and manage asthma in autistic children. Asthma is a chronic inflammatory disorder of the airways that causes recurrent episodes of wheezing, breathlessness, chest tightness and cough particularly at night and/or early in the morning (Abbas and Aster 2012). Risk factors for asthma include genetic predisposition, exposure to secondhand smoke and past smoking history (Ghosh et al. 2009). In a study to examine the trend of admissions of asthma in California, cases of pediatric asthma admissions at emergency
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departments had disproportionately higher representation of individuals of low socioeconomic status (SES). The significance of this public health issue suggests the importance of continuing research that may help to identify individuals who may benefit from prevention strategies such as targeted education (Largent et al. 2012). Though its etiology is not fully understood, autism is characterized by imbalances in immune and inflammatory processes. These similarities in conjunction with tandem increases in prevalence of both asthma and autism suggest common etiologies (Becker and Schultz 2010; Becker 2007). Altered levels of immunoglobulin, cytokines and evidence of inflammatory markers present in serum, cerebral spinal fluid and autopsy brain tissues have been seen in autistic children (Becker and Schultz 2010). Also, abnormalities in macrophages and mast cells have been noted. These inflammatory mediators are also important in the pathogenesis of asthma (Kazani and Israel 2012). Instructively, genome wide scans have identified several genetic variants common to both autism and asthma (Becker 2007), which may explain why autistic patients are at elevated risk of developing autoimmune diseases such as asthma (Becker 2007). Accordingly, examining the risk of asthma in autism patients from an epidemiologic perspective may promote efforts to implement preventive public health strategies in this subpopulation. A role of secondhand smoke in asthma pathogenesis has been attributed to the tiny particulate matter which initiates an inflammatory cascade. This mechanism has been noted in the exacerbation of asthmatic attacks (Ghio 2008). It is important to consider the role of secondhand smoke exposure in the relationship between autism and asthma to assess potential synergy in their inflammatory mechanisms. Only five epidemiological studies have examined the association between autism and asthma. These studies have produced conflicting results; two reporting no association, two reporting a positive association and one reporting a negative association. These studies have faced numerous limitations (Bakkaloglu et al. 2008; Becker and Schultz 2010; Croen and Grether 2005; Jyonouchi et al. 2008; Jyonouchi 2009, 2010), including small sample sizes, insufficient power to detect a relationship, and limited control for potential confounding covariates. To address these limitations, we examined the association between autism and asthma using the National Survey of Children’s Health 2007, a large nationally representative sample of children aged 0–18 years.
Methods Study Population and Design The National Survey of Children’s Health (NSCH) study is a nationwide survey conducted with funding from the
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Health Resources and Services Administration’s Maternal and Child Health Bureau, the Centers for Disease Control and Prevention’s National Center for Health Statistics. The 2007 survey was conducted nationwide as a random-digitdialing telephone survey that collected information on the health and well-being of children \18 years, based on interviews with their parents or guardians. Interviews were conducted in English, Spanish and four Asian languages. In total, interviews of over 90,000 parents or guardians were completed between April 2007 and July 2008. The design and inclusion criteria of the NSCH study has been described elsewhere (Kogan et al. 2009). A total of 78,042 subjects meeting our age cut-off criteria of C3 years (age by which ASD is typically diagnosed) were included from the NSCH 2007 study dataset (n = 9,1642) which had an overall weighted response rate of 51.2 % (Kogan et al. 2009). The sample size used in the analysis was 77,951 after exclusion of subjects with missing responses to asthma or autism diagnosis or both (n = 2,147). In our analysis, 14.52 % (n = 11,311) of the children had asthma and 1.81 % (n = 1,412) had autism. Measures Interviewers in the 2007 NSCH survey asked parents whether their children had ever been diagnosed with autism spectrum disorder (ASD), whether they currently had the condition, and the severity of the condition. For the purposes of our analysis, we classified subjects as having autism if the parent said a health worker had ever informed him/her that the child had autism. Autism status was coded as a dichotomous variable with ‘Yes’ and ‘No’ designations. Similarly, study interviewers asked parents or guardians whether children had asthma. Using the question, ‘‘Does your child currently have asthma?’’ we coded parental responses as a dichotomous variable with ‘Yes’ and ‘No’ categories. Covariates were chosen based on their associations with autism and asthma in literature reviews and availability in the NSCH dataset. The NSCH questionnaire asked parents: ‘‘Does anyone smoke inside your child’s home?’’ In our analysis we coded secondhand smoke exposure as a dichotomous variable as ‘Yes’ and ‘No’ based on responses to this question. Body mass index (BMI) been identified as an important risk factor for asthma (Porter et al. 2012). We computed and generated BMI percentiles from reported weight and height, (‘‘Growth Chart Training’’) and included it as a categorical variable using categories of \5th, 5th–85th and 95th percentiles in our analysis. In addition to brain injury which has been identified as a significant risk factor for autism, we abstracted age, gender, and SES, which were important covariates for asthma and autism (Becker 2007; Gardener et al. 2011; Largent et al. 2012;
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Porter et al. 2012). We coded gender and brain injury as dichotomous variables and age as a continuous variable. We assessed SES using average family income following the US Department of Human Services guidelines (‘‘Design and Operation of the National Survey of Children’s Health, 2007’’). We categorized the range of incomes into tertiles with levels labeled as high, middle and low. Statistical Analysis Associations of covariates with autism status and asthma status were evaluated to assess potential confounding. We used Chi square tests to evaluate categorical variables and t-tests for continuous variables and report p values accordingly. Where we encountered small cell frequencies, Fisher‘s exact test was used. We calculated the unadjusted odds ratios and the 95 % confidence intervals (CI) and reported the crude association of autism status with likelihood of asthma. We used multivariable logistic regression to examine the relationship between asthma and autism adjusting for covariates. In our analysis we included race, gender and SES as dummy variables and designated non-autistic children as the referent group and used standard techniques to model asthma and autism (Hosmer and Lemeshow 2004). Using parent-reported autism diagnosis as the principal predictor, we retained covariates in the model if the likelihood ratio test comparing models with and without the factor was significant at the p \ 0.05 level. In order to assess whether secondhand smoke modifies the association between autism and asthma, we included an interaction term in our full multivariate model corresponding to joint exposure (parent-reported autism and exposure to secondhand smoke). We also considered age as a categorical variable and examined it as an effect modifier between autism and asthma. We evaluated interaction terms by likelihood ratio tests, and included it in final models only if the interaction term was significant at p \ 0.05.
Results In the examination of the association between asthma and our covariates, we found that compared to children without asthma, children with asthma were older (11.05 vs. 10.5, p \ 0.001), had a higher BMI (24.28 vs. 24.04, p \ 0.001), were more likely to be African American (14.4 vs. 9.4 %, p \ 0.001) and less likely to be female (41.9 vs. 49.1 %, p \ 0.001). They were also more likely to have secondhand smoke exposure (66.8 vs. 6.1 %, p \ 0.001) and less likely to have high SES (46.5 vs. 50.3 %) (Table 1).
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The association between autism and covariates showed that compared to children without autism, those who had autism had similar mean BMI (24.05 vs. 24.10, p = 0.96), were less likely to be female (22 vs. 48.5 %, p \ 0.001), Hispanic (8.5 vs. 11.3 %, p = 0.004) and have high SES (44.1 vs. 49.8 %, p \ 0.001). They were more likely to have secondhand exposure (27.7 vs. 32 %, p = 0.05). However, there was no statistically significant difference in age (p = 0.36) between the two groups (Table 1). In unadjusted logistic regression models (Table 2), autistic children were more likely to have asthma compared to non-autistic children (OR = 1.35, 95 % CI: 1.18–1.55). We identified age, gender, BMI, race, history of brain injury, exposure to secondhand smoke and SES as significant predictors of asthma, and were included in our multivariable logistic regression model. In this model adjusting for the above covariates, the odds of asthma remained higher in autistic children compared to typically developing children, though the estimate was slightly attenuated (OR = 1.19, 95 % CI: 1.03–1.36) (Table 2). In evaluation of effect modification (Table 3), we observed that model fit was not improved by inclusion of interaction terms for autism and exposure to secondhand smoke (p = 0.38) or autism and age (p = 0.58), and neither interaction was included in final models.
Discussion In this large, nationally-representative cross-sectional survey of US children, we observed significantly higher odds of asthma in children diagnosed with autism compared to typically developing children. A statistically significant association persisted after adjusting for covariates. This association was not modified by secondhand smoke or age. As part of the atopy symptom complex, asthma often involves production of auto-antibodies and therefore has been observed commonly in families with autoimmune diseases (Croen and Grether 2005; Rottem and Shoenfeld 2003). Inflammatory markers such as eosinophils, mast cells, macrophages and interleukins play roles in the pathophysiology of asthma (Kazani and Israel 2012). Similarly, production of auto-antibodies and an imbalance in inflammatory mediators have been identified in autistic patients and their families (Bauman et al. 2013; Becker and Schultz 2010; Braunschweig et al. 2013; Korvatska et al. 2002). Genome wide scans (GWAS) have identified several genes with potential involvement in both autism and asthma. These genes include PTEN, MET, SERPINE1, PLAUR, ITGB3, ADRB2, and MIF. Of these, PTEN, MET, SERPINE1 have been identified to be important in the regulation of mast cells and macrophages. These mediators are important in the development of asthma and
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Table 1 Characteristics of children by autism and asthma: National Survey of Children’s Health, 2007 Autism Autism n = 1,412 (1.8 %) Age (SD)
10.47 (4.18)
Asthma No autism n = 76,545 (98.1 %) 10.58 (4.43)
Female
0.361
Asthma n = 11,335 (14.5 %) 11.05 (4.25)
No asthma n = 66,566 (88.5 %) 10.50 (4.44)
\0.001
Gender Male
p values
p values
\0.001 \0.001
1,100 (77.9 %)
39,338 (51.5 %)
6,575 (58.1 %)
33,866 (50.9 %)
312 (22.1 %)
37,105 (48.5 %)
4,744 (41.9 %)
32,664 (49.1 %)
BMI Continuous (m2/kg)
24.05 (16.27)
24.10 (20.91)
p = 0.960
24.28 (18.11)
24.04 (21.29)
\0.001
4,604 (40.62 %)
30,946 (46.45 %)
\0.001
Categorical Underweight (\5 %)
669 (47.38 %)
34,892 (45.58 %)
Normal (5–85 %)
396 (28.05 %)
27,987 (36.56 %)
4,065 (35.86 %)
24,305 (36.49 %)
Overweight (85–95 %)
163 (11.54 %)
7,301 (9.54 %)
1,263 (11.14 %)
6,208 (9.32 %)
Obese ([95 %)
184 (13.03 %)
6,365 (8.32 %)
1,403 (12.38 %)
5,157 (7.74 %)
Race
\0.001
0.004
Caucasian
990 (71.4 %)
51,703 (68.6 %)
7,008 (62.8 %)
45,677 (69.7 %)
African American Hispanic
128 (9.2 %) 118 (8.5 %)
7,601 (10.1 %) 8,541 (11.3 %)
1,607 (14.4 %) 1,212 (10.9 %)
6,130 (9.4 %) 7,439 (11.4 %)
Other
150 (10.8 %)
7,488 (9.9 %)
1,333 (11.9 %)
6,305 (9.6 %)
476 (4.2 %)
1,495 (2.2 %)
\0.001
Brain injury Yes No
96 (6.8 %) 1,310 (93.2 %)
1,874 (2.5 %) 74,639 (97.6 %)
Yes No DK/refused
\0.001 10,844 (95.8 %)
65,098 (97.8 %)
1,893 (3.2 %)
16,055 (66.8 %)
3,265 (6.1 %)
57,045 (96.7 %)
7,984 (33.2 %)
50,068 (93.8 %)
Second hand smoke
\0.001
0.05 389 (27.7 %) 1,013 (72.2 %) 1 (0.1 %)
39 (0.1 %)
8 (0 %)
32 (0.1 %)
\0.001
Socioeconomic status tertiles
\0.001
\0.001
Tertile 1, low
404 (28.6 %)
19,743 (25.8 %)
3,295 (29.1 %)
16,867 (25.3 %)
Tertile 2, middle
386 (27.3 %)
18,658 (24.4 %)
2,768 (24.4 %)
16,274 (24.4 %)
Tertile 3, high
622 (44.1 %)
38,144 (49.8 %)
5,272 (46.5 %)
33,475 (50.3 %)
Comparison of continuous variables assessed by t test; comparison of categorical variables assessed by Chi square or Fisher’s exact test BMI body mass index, DK don’t know
other autoimmune/inflammatory disorders (Becker and Schultz 2010; Becker 2007). Additionally, the ‘‘hygiene hypothesis’’ has been proposed as a common antecedent of both autism and asthma. The hygiene hypothesis proposes that asthma and other forms of atopy occur due to lack of early immune system challenge by environmental microbial, parasitic infection or gut commensals. The naivety of the immune system leads to immune hypersensitivity including inflammatory disorders such as asthma (Becker 2007). According to the theory, the drive to maintain a sterile environment for childhood development in Western countries explains, in part, the increasing prevalence of asthma in urban areas compared to rural areas Becker and Schultz 2010). The same hypothesis has been applied to explain the pathogenesis of autism considering the correlation of prevalences of the two chronic diseases across
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geographical divisions (Becker 2007). It is acknowledged that autistic patients have abnormal immune responses and experience increased incidence of autoimmune diseases such as type 1 diabetes and inflammatory bowel disease (Kohane et al. 2012). The common occurrence of food allergy in autism patients has been explained by immune dysfunction (Jyonouchi 2010). Also, inappropriate inflammatory response to otherwise benign allergens in children who have autism could explain the increased risk of asthma in this group. Our study is one of few to have considered an association between autism diagnosis and asthma. To date five epidemiological studies have examined asthma risk in autistic patients: two case control (Bakkaloglu et al. 2008; Croen and Grether 2005), two cross-sectional (Jyonouchi et al. 2008; Jyonouchi 2010) and one ecological study
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Table 2 Association between autism and asthma: National Survey of Children’s Health, 2007 Unadjusted
Adjusted
OR
95 % CI
OR
95 % CI
1.00
Referent
1.00
Referent
1.35 1.03
(1.18–1.55) (1.02–1.03)
1.19 1.02
(1.03–1.36) (1.01–1.03)
Male
1.00
Referent
1.00
Referent
Female
0.75
(0.72–0.78)
0.77
(0.74–0.80)
Autism status No autism Autism Age Gender
BMI Categorical Underweight (\5 %)
1.00
Referent
1.00
Referent
Normal (5–85 %)
1.12
(1.07–1.18)
0.98
(0.91–1.06)
Overweight (85–95 %)
1.37
(1.28–1.46)
1.13
(1.04–1.24)
Obese ([95 %)
1.83
(1.71–1.96)
1.44
(1.32–1.56)
Race Caucasian
1.00
Referent
1.00
Referent
African American
1.71
(1.61–1.81)
1.64
(1.54–1.74)
Hispanic
1.06
(0.99–1.13)
1.05
(0.99–1.13)
Other Brain injury
1.38
(1.29–1.47)
1.38
(1.30–1.48)
No
1.00
Referent
1.00
Referent
Yes
1.91
(1.72–2.12)
1.81
(1.63–2.01)
No
1.00
Referent
1.00
Referent
Yes
1.28
(1.22–1.33)
1.21
(1.16–1.27)
DK/refused
1.57
(0.72–3.40)
1.49
(0.68–3.27)
Referent
1.00
Referent
Second hand smoke
Socioeconomic status tertiles Tertile 1, low
1.00
Tertile 2, middle
0.87
(0.82–0.92)
0.91
(0.86–0.96)
Tertile 3, high
0.81
(0.77–0.85)
0.90
(0.85–0.94)
Table 3 Examination of age as an effect modifier in the association between autism and asthma: National Survey of Children’s Health, 2007 Age
Total
No autism
Autism
OR
p value
3–\8
2,789
2,721 (97.6 %)
68 (2.4 %)
1.45 (1.11–1.88)
0.518
8–\13
3,633
3,538 (97.4 %)
95 (2.6 %)
1.37 (1.09–1.72)
13– \18
4,889
4,790 (98.0 %)
99 (2.0 %)
1.29 (1.03–1.61)
(Becker and Schultz 2010). A positive association between autism and asthma was seen in two studies study (Becker and Schultz 2010; Croen and Grether 2005), a negative association another (Bakkaloglu et al. 2008), and no association observed in the other two studies (Jyonouchi
et al. 2008; Jyonouchi 2009). In addition, no study thus far has examined possible effect modification by secondhand smoking. A prior study by Bakkaloglu et al. looked at the risk of autism in children who have atopic symptoms and found no significant association (Bakkaloglu et al. 2008). This matched case control study was limited by a small sample size, with only 39 autism cases and 29 controls. In contrast, our study included a large sample size and thus had much greater statistical power to evaluate the association between autism status and asthma. Finally in a related study, maternal asthma diagnosis was found to be associated to elevated risk of having an autistic child (OR = 2.7, 95 % CI 1.3–5.8) (Croen and Grether 2005). Our results are in line with this finding though our study looked at the direct risk of asthma in autistic children. A number of potential limitations regarding this study should be noted including potential misclassification of autism and asthma status of participating children. Undiagnosed cases of autism might have occurred if parents missed early autistic behavior, lacked access to healthcare for confirmation of diagnosis (Kogan et al. 2009), or due to clinician misdiagnosis. However, previous studies using a similar methodology to that of the NSCH have found autism diagnosis prevalence assessed via parental selfreport to be comparable to diagnoses by trained professionals (Furu et al. 2011; Kogan et al. 2009). Such misclassification would be expected to be non-differential, and would tend to bias the estimate towards the null. Similarly, misclassification of asthma might have occurred for children with undiagnosed asthma or if misinterpretation of minor respiratory symptoms resulted in false positives. However, asthma related questionnaires have been shown to have high mean sensitivity and specificity to clinical diagnosis, suggesting that the NSCH questions may be adequate for asthma status measurement (Moorman et al. 2007). Residual/uncontrolled confounding cannot be ruled out. Birth order has been identified as a potential confounder to both asthma and autism (Becker 2007), however this data was not available in our data set. Also, mismeasurement of confounders might have resulted in residual confounding due to inadequate control for those confounders. However, we evaluated a large number of potential confounding factors, including those used in prior studies (Becker and Schultz 2010; Becker 2007; Croen et al. 2002). Moreover, because of the available data of the NHCS dataset, we were able to consider confounding by factors not evaluated in prior studies. In conclusion, we observed significantly elevated diagnosis of asthma in children diagnosed with autism. Although we observed minimal incremental predictive ability of autism status for identification of asthma, it is essential that parents and health workers actively look out for asthma symptoms in these patients. Atopy tends to be
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under-diagnosed in patients with autism because symptoms could be masked by associated behavioral problems (Jyonouchi 2010). If our results are replicated in future studies and due to the morbidity and potential death that comes with poorly managed asthma, it may be advisable to incorporate screening for asthma in the care of autistic children to increase early detection and improve clinical management if these results are replicated in other studies.
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