The prevalence of allergic diseases and asthma among children is increasing worldwide. For sensitized individuals with allergic asthma and rhinitis, continued exposure to indoor allergens will produce symptoms and exacerbation. Despite numerous epidemiological studies, the exposure-response relationships between indoor allergens and asthma morbidity remain poorly understood. The goal of the proposed research is to develop new Bayesian methods and software to study exposure-response relationships between indoor allergen concentrations and asthma morbidity among inner-city children with asthma. The new methods will correct measurement errors in the indoor allergen measurements using the standards data analyzed in immunoassays and provide estimates of allergen concentrations at the extreme ends of calibration curves that would previously have been identified as below limits of detection. In addition, the proposed methods will allow for assessing nonlinear exposure-response relationships between a single allergen and a binary health outcome as well as the combined health effect of co-exposure to multiple allergens and allow for inclusion of other covariates. Using Markov chain Monte Carlo simulations, we can predict the health effects of exposures to indoor allergens and obtain imputations of the allergen concentrations for those below limits of detection. We will apply the proposed methods to the data from the New York City Neighborhood Asthma and Allergy Study (NAAS) among 7-8 years old asthmatic children living in New York City. We will provide a user-friendly R package that implements the proposed methods and a Shiny app that allows interactive data exploration. This will make our work more accessible, reproducible and directly useful. Once completed, the proposed research will move forward not only statistical tools for analyzing immunoassay data measured with errors and exposure-response analysis of such data but also research in indoor allergen exposure and asthma morbidity among asthmatic children.
The proposed research will introduce new Bayesian approaches for estimating nonlinear exposure-response relationship between a continuous environmental exposure and a binary disease outcome and for assessing the combined health effect of environmental exposure mixtures, in which the exposures are measured with errors but external calibration data are available to correct the errors. The proposed methods will be applied to the New York City Neighborhood Asthma and Allergy Study to assess the effects of indoor allergens on asthma morbidity among asthmatic children, with the indoor allergen concentrations measured using immunoassays. The findings of this study will provide important insights for intervention and prevention of asthma morbidity among inner-city children with asthma.
