Back

An Analysis Between Trump's Presidency and Health-Seeking Behaviors of African Americans in the United States

Animasahun, A. A.; Nunez, A. M.; Jean, R. F.

2022-02-23 epidemiology
10.1101/2022.02.17.22271104 medRxiv
Show abstract

BackgroundSlavery legally ended over 150 years ago, yet African Americans are still oppressed. The lingering effects of systemic and institutional racism are still present in all walks of life, especially in healthcare. As a result, Black patients have historically been less likely to seek preventative care and subsequently, reported lower health outcomes compared to their white counterparts. The election of Donald Trump in 2016 forced a shift in that narrative. This study aimed to investigate whether there was any correlation between changing racial preferences fueled by Trumps racist rhetoric and the health-seeking behaviors of Black patients. Through this, we explored if the number of Black patients that reported not having a primary care physician has changed, and how online search history trends researching Black physicians have also changed. MethodsThis study utilized datasets from the Behavioral Risk Factor Surveillance System (BRFSS) and Google Trends. Pearsons correlation testing was run to establish any correlation between the number of google searches and positive health-seeking behaviors. ResultsGoogleTrends data does support an increased popularity for the search term "black doctor near me" over the years of 2015-2018, supporting our hypothesis. Multiple logistic regression analysis was performed to determine if race, among other variables, was a significant predictor for our predetermined health indicators. The results showed race was significant in nine out of our eleven health indicators. DiscussionDespite decades of work to minimize healthcare disparities, this study has demonstrated how much more still needs to be done. It has shown how the intersection of seemingly unrelated issues, such as politics and health, as described in this study, can drastically impact health outcomes. This study highlights the importance of targeted and equitable programming to ensure quality care for all, that can withstand political and social pressures.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.