NEIGHBORHOOD AND ENVIRONMENTAL PREDICTORS OF ASTHMA IN THE U.S.A.

Over the next several decades the United States will be severely affected by climate change. Consequently, the public health of the country and its human inhabitants and communities will be compromised. Select populations such as children, the elderly, and those living in squalid conditions or disadvantaged communities, will be disproportionately vulnerable. Asthma is a disease with a complex etiology, air pollution exacerbates symptoms of asthma and can cause the onset of asthma in adults. Asthma, like climate change, disproportionately affects certain groups of people. We created a dataset at the census tract level, by collecting data from multitudes of sources on environmental quality, behavioral factors, socioeconomic status, educational attainment, neighborhood characteristics, modifiable risk factors, and comorbidities of asthma. We created a correlation matrix to analyze which variables are positively and negatively correlated with asthma prevalence. Furthermore, we created multivariate linear regression models with our variables to determine the associations between asthma prevalence and neighborhood and environmental predictors. Neighborhoods with a higher percentage of Black people are positively correlated with higher asthma prevalence with a value of 0.50. Moreover, the highest positive correlation existed between es†nytimated percent of adults ever diagnosed with heart disease and estimated percent of adults ever diagnosed with COPD, emphysema, and chronic bronchitis in 2018, with a value of 0.90. In our regression models we found that for a one unit increase of percent Black people in a neighborhood, there is an increase of 1.98 units of asthma prevalence. We also found in our models that for a one unit increase in estimated percent of adults reporting 14 or more days of poor physical health in the past 30 days in 2018, there is an increase of 0.210 units of asthma prevalence. By cultivating a greater understanding of how these factors interact simultaneously the strategic optimization of neighborhood level interventions can be implemented for asthma prevention