Evaluating the strength of evidence between long-term NO2 exposure and pediatric asthma incidence for potential inclusion into the Global Burden of Disease Study

Abstract

Several epidemiological studies have highlighted a relationship between NO2 exposure and increased incidence and prevalence of pediatric asthma. Recently, the Global Burden of Disease (GBD) developed a quantitative evidence scoring approach with the intention to assess the effect and strength of the evidence for exposure-response relationships with consistency across all risk factors. We use this evidence scoring approach to generate a quantitative effect estimate for NO2 exposure and childhood asthma that can be applied for burden of disease assessments. For the purpose of this work, we systematically compiled existing studies on the relationship between NO2 and childhood asthma and extracted relative risks (RR). In addition, we extracted a set of study-specific covariates in order to explain between-study heterogeneity in risk estimates. We then applied the GBD’s newly developed meta-regression tool MR-BRT (MetaRegression - Bayesian, Regularized, Trimmed) to provide a quantitative estimate of effect and strength of evidence based upon unexplained between-study heterogeneity. Preliminary analyses including 28 sources from 11 countries identified within a systematic literature review conducted by Khreis et al. in 2017 were included in the metaregression. Overall, we estimated a RR of 1.10 (95% confidence interval [CI]=0.94-1.30) per 5 ppb increase in annual average NO2 (p-value=0.13) when not accounting for study-specific covariates. A model accounting for all significant study-level covariates (‘outcomes self-reported`, ‘confounding uncontrolled’, ‘selection bias’, and ‘subpopulation’) revealed an effect estimate of 1.28 (95%CI=1.00-1.63) per 5 ppb (p-value=0.024). Findings provide strong evidence for a relationship between NO2 exposure and pediatric asthma when adjusting for between-study heterogeneity. In future steps, we will include 27 additional studies that were published after the 2017 systematic review conducted by Khreis et al. (2017). Furthermore, we will apply an automated covariate selection process in which variables are sub-selected by running a set of log-linear models with a range of Lasso penalty parameters.