A new model of wheezing severity in young children using the validated ISAAC wheezing module: A latent variable approach with validation in independent cohorts

Abstract

Background: The International Study of Asthma and Allergies in Children (ISAAC) Wheezing Module is commonly used to characterize pediatric asthma in epidemiological studies, including nearly all airway cohorts participating in the Environmental Influences on Child Health Outcomes (ECHO) consortium. However, there is no consensus model for operationalizing wheezing severity with this instrument in explanatory research studies. Severity is typically measured using coarsely-defined categorical variables, reducing power and potentially underestimating etiological associations. More precise measurement approaches could improve testing of etiological theories of wheezing illness.

Methods: We evaluated a continuous latent variable model of pediatric wheezing severity based on four ISAAC Wheezing Module items. Analyses included subgroups of children from three independent cohorts whose parents reported past wheezing: infants ages 0-2 in the INSPIRE birth cohort study (Cohort 1; n = 657), 6-7-year-old North American children from Phase One of the ISAAC study (Cohort 2; n = 2,765), and 5-6-year-old children in the EHAAS birth cohort study (Cohort 3; n = 102). Models were estimated using structural equation modeling.

Results: In all cohorts, covariance patterns implied by the latent variable model were consistent with the observed data, as indicated by non-significant χ2 goodness of fit tests (no evidence of model misspecification). Cohort 1 analyses showed that the latent factor structure was stable across time points and child sexes. In both cohorts 1 and 3, the latent wheezing severity variable was prospectively associated with wheeze-related clinical outcomes, including physician asthma diagnosis, acute corticosteroid use, and wheeze-related outpatient medical visits when adjusting for confounders.

Conclusion: We developed an easily applicable continuous latent variable model of pediatric wheezing severity based on items from the well-validated ISAAC Wheezing Module. This model prospectively associates with asthma morbidity, as demonstrated in two ECHO birth cohort studies, and provides a more statistically powerful method of testing etiologic hypotheses of childhood wheezing illness and asthma.

Fig 1. Latent variable model of wheezing illness severity.

Panel a shows the latent wheezing severity model used in cohorts 1 and 3. The severity of wheezing illness is estimated as a unidimensional latent variable (η₁) with four reflective ordinal indicators: wheezing episode frequency (y₁), frequency of wheeze-related sleep disturbance (y₂), wheeze-related speech disturbance (y₃), and exercise-induced wheeze (y₄). The ordinal indicators are presumed to be coarse measurements of underlying continuous variables (y₁*-y₄*). Panel b shows the multilevel wheezing severity model used in the Cohort 2 analyses. The within-schools level of the model is identical to panel a. The between-schools level of the model accounts for non-independence due to clustering within schools and study sites. Estimated parameters are depicted in red.

Fig 2. Cohort 1: Concurrent associations between the latent wheezing severity factor and markers of wheezing illness in year-one.

This figure shows estimated probabilities of having at least one respiratory hospitalization (panel a; model 1.3) and using asthma medication (panel b; model 1.5) against levels of the latent wheezing severity variable in the first year of life. As wheezing severity increases so does the estimated probability of respiratory hospitalization and medication use. These models held all covariates constant at their median values. Dotted lines represent 95% confidence intervals for estimated probability estimates.

Fig 3. Cohort 3: Prospective associations between wheezing illness severity and wheeze-related morbidity outcomes.

In the EHAAS birth cohort study, there were strong prospective associations between the latent wheezing illness severity factor representing wheezing illness severity from 60–72 months and three asthma morbidity outcomes at the 84-month follow-up: physician diagnosis of asthma (panel a; model 3.3), any asthma medical visits (panel b; model 3.4), and urgent asthma/wheeze-related medical visits to either a doctor’s office or the emergency department (panel c; model 3.5). These models held all covariates constant at their median values. Dotted lines represent 95% confidence intervals. Pseudo-R² values represent the approximate proportion of variance in the outcome accounted for by the predictors.

Fig 4. Cohort 1: Estimated probability of year-three wheeze-related medical visits as a function of wheezing severity in year-2.

These plots show the strength of associations between year-2 wheezing severity (x-axis) and year-3 wheeze-related medical visits (y-axis), with all covariates held constant at their median values. Panels a shows estimated probabilities of corticosteroid treatment being present in the third year of life as a function of the discrete severity exposure variable; whereas panel b shows estimated probabilities vs. the latent continuous severity factor. In both models, as year-2 wheezing severity increases, so does the estimated probability of acute corticosteroid treatment, though the range of estimated probabilities is larger in the latent severity model. Values and 95% confidence intervals above the blue brackets show the increase in the estimated probabilities for a given increase in wheezing severity. Dotted lines represent 95% confidence intervals for estimated probability estimates.

Fig 5. Cohort 1: Year-3 physician asthma diagnosis as a function of wheezing severity in year-2.

These plots show the strength of associations between year-2 wheezing severity (x-axis) and year-3 physician asthma diagnosis (y-axis), with all covariates held constant at their median values. Panels a shows estimated probabilities of a physician asthma diagnosis being present in the third year of life as a function of the discrete severity exposure variable; whereas panel b shows estimated probabilities vs. the latent continuous severity factor. In both models, as year-2 wheezing severity increases, so does the estimated probability of a physician asthma diagnosis being present, though the range of estimated probabilities is larger in the latent severity model. Values and 95% confidence intervals above the blue brackets show the expected increase in the estimated probabilities for a given increase in wheezing severity. Dotted lines represent 95% confidence intervals for estimated probability estimates.

Fig 6. Cohort 1: Year-3 estimated probability of acute corticosteroid treatment as a function of wheezing severity in year-2.

These plots show the strength of associations between year-2 wheezing severity (x-axis) and year-3 acute corticosteroid treatment (y-axis), with all covariates held constant at their median values. Panels a shows estimated probabilities of corticosteroid treatment being present in the third year of life as a function of the discrete severity exposure variable; whereas panel b shows estimated probabilities vs. the latent continuous severity factor. In both models, as year-2 wheezing severity increases, so does the estimated probability of acute corticosteroid treatment, though the range of estimated probabilities is larger in the latent severity model. Values and 95% confidence intervals above the blue brackets show the increase in the estimated probabilities for a given increase in wheezing severity. Dotted lines represent 95% confidence intervals for estimated probability estimates.