The effects social structures have on COVID-19 outcomes among three states in the United States
This study aimed at examining the effects social structures have on COVID-19 outcomes. Since the emergence of the COVID-19 virus in late 2019, there has been an ongoing increase of confirmed cases and deaths worldwide. Past literature on structural factors and health have concluded that select communities target health disparities. However, because this virus is so recent, and continues to unravel itself. This dissertation empirically analyzes the effects social structures have on COVID-19 outcomes by examining counties within three states in the U.S. to understand better health disparities related to COVID-19, particularly from a macro-level perspective. The three states in this discussion are California, Texas, and New York. Specifically, this dissertation presents three hypotheses. Generalized Least Square Regression techniques were utilized to assess the hypotheses. Results indicate that overall, county population size, county racial composition, percentage of married households, occupation, total percentage of citizenship, and disadvantage are all significant predictors of rates of COVID-19 confirmed cases. The overall unemployment rate, county population size, racial composition, occupation, and the total percentage of citizenship were significant predictors of COVID-19 death rates. When examining each state individually, I found unique results on whether and how social structural factors affect each outcome. Findings from this study can contribute to the awareness and literature on health disparities and ultimately lead to policy implications that could alleviate these disparities.