Preprint / Version 1

Impact of Sociodemographic Factors on COVID-19 Vaccination Rates and Infection Rates in California

##article.authors##

  • Ayush Sridhar Mission San Jose High School
  • Adrian Albert Einstein College of Medicine

DOI:

https://doi.org/10.58445/rars.670

Keywords:

Sociodemographic, COVID-19, Vaccinations

Abstract

On January 31, the first COVID-19 case was detected in California. Since then, over 4.5 million people in California have contracted the virus. California is one of the most, if not the most, diverse state in the US. The speed of the vaccine push in California and general precautions around the pandemic make the state incredibly valuable. Studying vaccination, infection, and mortality rates within sociodemographic groups (age, race, and gender) will give crucial insights into how different groups were affected by COVID-19. Data on COVID-19 vaccines, cases, and deaths from February 2021 to May 2023 were collected for analysis. The data was separated by age, race, and gender and divided by demographic. Modifications were made to the data to create several different metrics. These metrics include, percentage of a demographic that was fully vaccinated, percentage of cases represented by a certain demographic, and many more. These metrics were graphed as a function of time and compared to find discrepancies between groups. Ages 18-49 were more likely to catch the virus while ages 65+ had a far higher mortality rate than any other group. The most glaring finding between races was the incredibly low vaccination rates for American Indians/Alaska Natives in comparison. Women were more likely to catch the virus, but men were significantly more likely to die from the virus. Ideally the insights of this study can be useful in the future if a similar situation to the pandemic arises.

Author Biography

Adrian, Albert Einstein College of Medicine

An MD-PhD student and future physician-scientist with a demonstrated passion for research, leadership, and advocacy. Research interests center around using computational approaches in biomedical imaging to better identify and treat diseases. 

Interested in healthcare disparities, biomedical imaging, neuroscience, big data, AI, government, leadership, biotechnology, and the startup sector.

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Posted

2023-11-03