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What is already known on this topic
Environmental tobacco smoke is associated with worsened asthma symptoms among children.
Assessments of children's environmental tobacco exposure frequently rely on caregiver report.
Caregiver reports may misclassify smoking exposure.
What this study adds
A biological marker of tobacco exposure, but not caregiver-reported household smoking, predicted frequency of exacerbations.
Use of biological marker of environmental tobacco smoke exposure, rather than reported household smoking alone, may lead to improved identification of asthma-related risk factors.
About 9.1% of USA children have a diagnosis of asthma.1 More severe or poorly controlled asthma leads to asthma exacerbations2 and to chronic cough or wheezing and worsened lung function.3 More than 60% of asthmatics experienced at least one asthma attack and a third had five or more asthma attacks in the previous year.1 ,4 Incident asthma, wheezing episodes and worsened lung function have been associated with environmental tobacco smoke (ETS) exposure.5–9 Treatment guidelines recommend that asthmatics avoid ETS,3 but most asthmatic children have some ETS exposure.10
The best way to identify ETS exposure is unclear.11 Classifying smoking status by self-report alone may be unreliable because children may have ETS exposure outside their own homes12 ,13 or because caregivers may under-report household smoking.
Cotinine is a nicotine biomarker measurable in the blood, urine or saliva.14–16 Salivary cotinine levels increase with greater exposure to ETS.16–19 Household smoking is associated with higher cotinine levels among asthmatic children.20–23 Many children considered non-exposed to tobacco smoke have elevated cotinine levels.24 ,25 Parental surveys about sources of ETS do not adequately predict children's cotinine levels.26
Further studies are needed to characterise caregiver-reported tobacco exposure's reliability in predicting asthma outcomes. Our objective was to assess the association of measured and reported tobacco exposure with asthma severity and exacerbations in an urban paediatric population.
Data source and study sample
We used data from the Chicago Initiative to Raise Asthma Health Equity (CHIRAH) study for which the design and procedures have been previously described.27 Between 2004 and 2005, CHIRAH enrolled a cohort of 561 asthmatic children aged 8–14. Participants and their caregivers completed an inperson interview to determine household sociodemographic and psychosocial characteristics, the home environment, and participants’ asthma symptoms. Participants underwent anthropometric and spirometry measurements and had their saliva sampled. We excluded 76 participants missing key data (valid cotinine level, detailed exacerbation data, race/ethnicity, home location and/or body mass index (BMI)), and four children with exceptionally high numbers (≥25) of caregiver-reported asthma exacerbations that were likely errors in data collection or entry. We additionally excluded participants with a salivary cotinine level of >10 ng/ml (n=15, or 2.6% of the CHIRAH cohort) as extremely high cotinine values are more likely to represent active smoking.18 ,28 ,29 Our final sample included 466 children.
This study was approved by the institutional review boards of the Ann and Robert H. Lurie Children's Hospital and Northwestern University.
We used two outcome measures, asthma exacerbation frequency and asthma severity. Asthma exacerbation frequency was assessed by caregiver report to the prompt, ‘Total number of asthma exacerbations requiring hospitalisation, ER [emergency room], or same day medical care in the past 12 months.’ Asthma severity was assessed by the participant's classification based on the National Heart, Lung, and Blood Institute/National Asthma Education and Prevention Program guidelines which divided asthma severity into intermittent or mild-, moderate-, or severe-persistent categories on the basis of symptoms, short-acting β2-agonist use, interference with normal activity, exacerbations requiring systemic corticosteroids in the prior year and, if available, spirometry measurements, available for 71% of the CHIRAH cohort.30
We measured exposure to ETS by caregiver report of smoking in the household and by measured salivary cotinine. Household smoking was assessed by caregiver report to the question, ‘Does anyone in your house smoke?’ Saliva was collected with a Quanitsal collection pad (Immunalysis, Pomona, California, USA). Cotinine level was determined using a high-sensitivity immunoassay (Salimetrics, Inc., State College, Pennsylvania, USA). For comparisons of tobacco exposure by report versus by biomarker, we considered participants with salivary cotinine levels greater than or equal to 1 to be exposed to ETS, consistent with prior studies.21
Potential confounders were identified from caregiver reports and included child age, sex, race/ethnicity, household income (greater than or less than or equal to $50 000 per year) and access to any asthma controller medication. Because salivary cotinine is negatively associated with BMI,31 we included participant BMIs, categorised into normal or underweight, overweight, and obese (BMI <85th, 85–94th, and ≥95th percentiles, respectively).
Our outcomes were: (1) total number of caregiver-reported asthma exacerbations in the previous year and (2) asthma severity in four ordered categories (intermittent, mild persistent, moderate persistent, severe persistent). Our primary predictors were (1) caregiver-reported household smoking as a dichotomous variable and (2) salivary cotinine as a continuous variable. Because cotinine levels were not normally distributed they were log-transformed.
For each outcome, we estimated two multivariable models, one with caregiver-reported smoking as the primary predictor and one with log-transformed cotinine level as the primary predictor, with age, sex, race/ethnicity, household income, controller medicine access and BMI category as covariates. Exacerbation frequency was modelled using negative binomial regressions because exacerbation count data were overdispersed. Asthma severity was modelled using ordered logit models after we verified that the model met the proportional odds assumption.32
We used a two-sided Type I error <5% level to determine statistical significance. STATA SE V.12 (StataCorp LP, College Station, Texas, USA) was used for all data analyses.
Participant characteristics and asthma symptoms
Table 1 displays participants’ demographic, socioeconomic and asthma control characteristics. Participants reported an average of 2.3 exacerbations in the previous year. Most participants had mild or moderate persistent asthma and 63.3% had access to at least one asthma controller medication (table 1).
Participants excluded due to missing key data (n=76) or extremely large number of reported exacerbations (n=4) were not significantly different from the study sample in race/ethnicity, household income, reported household smoking, BMI category or asthma severity. Excluded participants were significantly more likely to be in the youngest (8–9 years) and oldest (14–15 years) age categories (χ2=7.94, df=3, p=0.047).
About half (50.4%) of caregivers reported that at least one household member smoked. Participants had a mean cotinine level of 1.44 ng/ml; 69.3% had a cotinine level ≥1, consistent with ETS exposure. Participants with caregiver-reported household smoking had significantly higher cotinine levels than those without caregiver-reported household smoking (geometric mean cotinine level 2.00 vs 1.07, p<0.001). Characteristics of participants with ETS exposure by report or by cotinine level are shown in table 2.
Asthma exacerbations and tobacco exposure
In multivariable analysis, cotinine was significantly associated with increased exacerbations (incidence rate ratio (IRR)=1.39, 95% CI 1.08 to 1.78) (table 3), independent of potential confounders. In an identically specified model that included reported smoking in place of cotinine, reported smoking was not significantly associated with exacerbation frequency (IRR=1.04, 95% CI 0.83 to 1.31) (table 3).
Asthma severity and tobacco exposure
In a multivariable ordered logit regression to predict asthma severity levels, there was no significant association between salivary cotinine and asthma severity, although the CI is in the direction of a positive association (OR 1.47; 95% CI 0.91 to 2.38) (table 3). When reported household smoking was used to predict asthma severity, we found no significant association between reported smoking and asthma severity (OR 0.90; 95% CI 0.63 to 1.3) (table 3).
Although objectively measured ETS exposure was significantly associated with exacerbation frequency among asthmatic children, caregiver-reported ETS exposure was not. Salivary cotinine may more accurately characterise children's asthma risks from ETS exposure than caregiver-reports of smoking behaviour. The limited number of studies on asthma-related outcomes that have directly compared the effects of biomarkers of tobacco to those of reported smoking have had mixed findings. Two studies demonstrated that both maternal report of smoking and cotinine levels are associated with asthma exacerbations, lung function, and incidence of asthma or asthma-like symptoms.33 ,34 However, other researchers found that maternal serum cotinine was a better predictor of early life wheezing than parent-reported exposure35 and salivary cotinine was a better predictor of lung function than passive ETS exposure determined by questionnaire.36
Discrepancies between reported smoking and biological measures of tobacco exposure may be due to under-reporting of reported household smoking in the research setting due to social desirability bias or to recall bias. Alternatively, children may have tobacco exposures outside of the home that are unrecognised by caregivers, such as through incursions of smoke from other units in multiunit housing13 or exposures in non-household social settings.25 Our findings add to prior research that suggests that a biological measure of smoking exposure may be an important addition to reports of household smoking in order to accurately classify risk factors for more severe asthma outcomes.
The strengths of our study include a relatively large sample size and a predominantly minority inner-city population who are at a high risk of asthma complications. Our study has several limitations. Cotinine levels may vary due to factors other than tobacco exposure because of variation in nicotine metabolism, although we were able to account for some of these factors, including BMI and race/ethnicity, in our multivariable models.21 We have limited information health services-related variables, such as measures of participants’ access to a usual source of care. A total of 95 participants (17%) were excluded from our analysis which could bias our results, although our comparison of the excluded and included subjects suggests that the two groups do not significantly differ in terms of factors that are likely to bias the results.
We would like to thank the CHIRAH team, the Chicago Public Schools, and the schools of the Archdiocese of Chicago.
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