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. 2020 Feb;57(2):217-227.
doi: 10.1080/02770903.2018.1556686. Epub 2018 Dec 28.

Comparisons between 2015 US asthma prevalence and two measures of asthma burden by racial/ethnic group

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Comparisons between 2015 US asthma prevalence and two measures of asthma burden by racial/ethnic group

Phoebe Tran et al. J Asthma. 2020 Feb.

Abstract

Objective: Asthma affects approximately 20,383,000 Americans and costs the US $56 billion dollars annually. Asthma burden is not uniform across different racial/ethnic groups. Although annual asthma prevalences by racial/ethnic group are provided by the CDC, these numbers do not account for the influence of other sociodemographic and clinical factors. In this study, we evaluated several measures of asthma burden that controlled for sociodemographic and clinical factors and compared them to 2015 CDC asthma prevalences by racial/ethnic group. Methods: We obtained unadjusted asthma predictions directly from the 2015 Behavioral Risk Factor Surveillance System (BRFSS) survey. To estimate average adjusted predictions (AAP) and adjusted predictions at the means (APM) for asthma, we controlled for sociodemographic and clinical factors through logistic models and subsequently calculated marginal probabilities for these results. Results: We found substantial differences at the national and state level between unadjusted prevalences and AAPs and APMs for asthma by race/ethnicity group. In addition, states that had the highest and lowest unadjusted prevalences differed from states that had the highest and lowest AAPs and APMs. For unadjusted prevalences, AAPs, and APMs, we found that states that had the highest prevalences or probabilities of asthma for Whites tended to have the widest disparities between the estimates for Whites and those of other races. Conclusions: The study's results highlight the need to account for sociodemographic and clinical factors when addressing asthma burden by racial/ethnic group across the US.

Keywords: Asthma burden; BRFSS; average adjusted predictions; average marginal effects; logistic regression; marginal probabilities; racial disparities.

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