Date of Conferral

2021

Degree

Ph.D.

School

Public Health

Advisor

Peter B. Anderson

Abstract

Research on populations emphasizes the influence of allostatic overload, a marker of biophysical and biochemical stress, in driving adverse health outcomes. Unknown is the impact of allostatic load and its cardiovascular, metabolic, and inflammatory components as they pertain to asthma prevalence in U.S. populations. Specifically, there is a need to understand the nature of the relationship between allostatic load and asthma in light of confounders such as socioeconomic status, race, education, and gender. Using Bronfenbrenner’s bioecological model as a theoretical underpinning and binary logistic regression as a statistical modality, the predictive relationship between allostatic load and asthma was examined in a representative U.S. population. Using binary logistic regression, contemporary data from the National Health and Nutrition Examination Study covering the most recent available data (2017-2018) were analyzed for predictive association with results reported for effect size, significance, and confidence intervals. Study results highlighted the statistically significant predictive nature of allostatic load in asthma and the outsized role of inflammation in driving the relationship. Having high allostatic load predicted 28% higher odds for reporting asthma (p = .021) whereas inflammatory allostatic load predicted asthma with 36% higher odds, (p < .01). Insights from the current research study contribute a scientific basis to support the development of a validated allostatic load index for asthma where none currently exists. Additionally, clinical translation of results can inform the development of future prognostic tools for asthma that account for allostatic load and possibly reduce the associated health disparity and inequity leading to improved lives for individuals, families, and communities.

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