Indoor Allergens And Asthma

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Our research program focuses on the role of the environment in the development and exacerbation of asthma and allergic diseases. In collaboration with investigators at the CDC/NCHS, we developed and implemented an allergy/asthma focused component for the National Health and Nutrition Examination Survey (NHANES). This component, included in NHANES 2005-2006, queried on allergy and asthma prevalence and morbidity, measured levels of common indoor allergens and endotoxin in bedroom dust, and quantified total and allergen-specific IgE levels in serum of more than 9000 participants. Analysis of this large data set has allowed us to 1) estimate nationwide prevalence of indoor allergen and endotoxin exposures, 2) estimate nationwide prevalence of allergic sensitization to indoor, outdoor and food allergens, 3) estimate nationwide prevalence of allergic diseases, including asthma, and 4) investigate the complex relationships between allergen and endotoxin exposures, allergic sensitization and allergic diseases. This component not only tested a greater number of allergens across a wider age range than prior studies, but also provided quantitative information on the extent of allergic sensitization and exposures to indoor allergens and endotoxin. It established a second point-in-time estimate for evaluating allergen and endotoxin exposure trends in U.S. homes, the first being established in the National Survey of Lead and Allergens in Housing, which we completed in collaboration with the Department of Housing and Urban Development. The data have enabled more robust and generalizable investigations of the role of allergen/endotoxin exposures and IgE-mediated sensitization in allergic diseases than previously possible. We have made significant advances in our understanding of the prevalence and determinants of indoor allergen/endotoxin exposures, and their relationships with allergic disease. Our research has demonstrated that exposure to indoor allergens and endotoxin is common but highly variable in U.S. homes. Our findings highlight the impacts and importance of environmental factors in human health and disease, including asthma. Although our focus continues to be on asthma/allergy-related outcomes, we have extended interest into other health outcomes as well as areas of methodological research in epidemiology. Despite the growing popularity of machine learning as a research tool, few studies have investigated implications of implementing machine learning approaches with complex survey data. We used data from NHANES and simulations of different design scenarios to assess the impact of accounting for sampling weights in gradient boosting, a powerful ensemble classification algorithm used on its own or as a component of the popular SuperLearner in R software. To evaluate the role of hyper-parameter tuning, we performed all analyses using the default hyper-parameters as well as hyper-parameters selected for each simulation from a randomized search of the hyper-parameter space in a massively parallelized computing environment (NIH Biowulf). This high-performance cluster, which ranks within the top 500 most powerful computers in the world, enabled us to use 10,000 simultaneous CPUs to fit approximately 2.2 billion tree models and obtain a high level of confidence in model cross-validation and bootstrapped confidence intervals. We demonstrated that models configured using complex survey data without sampling weights may not accurately reflect prediction in target populations, dependent on sample size and other analytic properties. We also demonstrated that, in the absence of software for configuring weighted algorithms, a post-hoc re-calculation of model performance with weighted observed outcomes might more validly represent prediction in target populations than ignoring weights entirely. Our findings underscore the need for further research; as the popularity of machine learning keeps increasing, failure to give more attention to appropriately analyzing complex survey data may represent a missed opportunity to leverage these novel and powerful approaches. In another collaborative project, we investigated prevalence of antinuclear antibodies, the most common biomarker of autoimmunity, in the general U.S. population. The findings from NHANES demonstrated an increasing trend in the antibody prevalence over a 25year time span, with a greater increase in recent years. Although the rate and timing of this increase varied by subgroups, both age and sex were consistently associated with prevalence of antinuclear antibodies. Increases over time were most marked in adolescents, men, and non-Hispanic White participants. The results provide valuable epidemiolocal insights on autoimmune disorders and will help design further studies to better understand factors underlying the increased prevalence and causes of autoimmunity. We continue to study the complex relationships between allergen exposures, allergic sensitization, and disease in more detail, as the NHANES data allow for the investigation of many interesting relationships. Our research will lead to a better understanding of the characteristics of environmental exposures, such as indoor allergen and endotoxin exposures, and their role in allergic disorders, which in turn provides insights into development of effective environmental intervention approaches for the management of allergic diseases such as asthma.