Geographic Heterogeneity in Racial and Ethnic Disparities in COVID-19 Vaccination in the US

Overview

This repository contains the manuscript and associated materials for the study: "Geographic heterogeneity in racial and ethnic disparities in COVID-19 vaccination in the US". The work explores disparities in COVID-19 vaccination coverage across racial and ethnic groups, with a focus on urban-rural differences and the role of CDC funding. This analysis provides insights into how targeted interventions and granular data collection can improve health equity.

Key Highlights

• Focus: Analysis of racial and ethnic inequities in COVID-19 vaccination across 13 U.S. states.
• Timeframe: Summer 2021 data.
• Key Variables: Urban-rural disparities, county-scale data sufficiency, and CDC funding alignment.
• Findings:
  • Greater disparities in rural regions due to limited healthcare access.
  • Insufficient county-scale data poses challenges for disparity analysis.
  • CDC funding often misaligned with regions of greatest need.
• Implications: Importance of targeted interventions and granular data collection to address vaccination inequities.

Coding Files

The repository includes Jupyter notebooks for the analyses conducted in the study:

• HD_Funding Plots.ipynb: Analyzes the impact of CDC funding on disparities affecting Hispanic and Black populations.
• HD_Scatterplots.ipynb: Examines the relationship between Black and Hispanic disparities and the number of individuals vaccinated within these groups.
• T-Test Analysis.ipynb: Performs T-tests to determine the statistical significance of observed disparities.
• HD_maps1.ipynb: Creates maps to visually demonstrate Black and Hispanic disparities in the US.

Data Folders

• demo_vacc/: Contains all the demographic data used in Compile_and_clean_state_data.py. This includes state-level vaccination data segmented by race, ethnicity, and other demographics.
• other_data/: Includes all additional datasets required for Compile_and_clean_state_data.py, such as population data and FIPS codes.
• cleaned_data/: Includes all additional datasets produced by Compile_and_clean_state_data.py, including data for states with and without normalization.

How to Use This Repository

1. Analysis: Explore the Jupyter notebooks in the code/ folder for detailed analysis workflows.
2. Data Preparation: The demo_vacc/ and other_data/ folders provide the necessary input data for Compile_and_clean_state_data.py.
3. Data Insights: If additional data is included, it will be located in the respective folders for replication or further exploration.

Citation

If you use the data or scripts from this repository, please cite as follows:

Costello, N., Merritt, A., & Bansal, S. (2024). Geographic heterogeneity in racial and ethnic disparities in COVID-19 vaccination in the US. Data repository. [Repository Link or DOI]

Contact

For questions or collaborations, please reach out to:

Shweta Bansal, Ph.D
[Institution Name]
[Email Address]