Health Disparity Analysis

Quantitative analysis of health disparities across demographic and geographic dimensions in the United States.

Project Overview

This project analyzes health inequities across three key dimensions:

• Geographic disparities (metro vs. nonmetro)
• Racial and ethnic disparities
• Income-based disparities

The analysis uses data from America's Health Rankings 2025 Annual Report to calculate disparity metrics and create a composite index ranking states by overall health inequity.

Features

• Composite disparity index combining geographic, racial, and income dimensions
• Interactive visualizations of state-level disparities
• State-specific deep-dive analysis
• Identification of health measures with largest gaps across demographic groups

Data Source

America's Health Rankings 2025 Annual Report

• 82,054 records
• 1,578 unique health measures
• 51 states plus DC
• Demographic breakdowns across race, income, geography, age, education, and other factors

Installation

1. Clone this repository
2. Install dependencies:

pip install -r requirements.txt

Usage

Run Analysis

Generate disparity metrics:

python disparity_analysis.py

This creates four CSV files:

• metro_nonmetro_gaps.csv: Geographic disparities
• racial_disparities.csv: Racial health gaps
• income_disparities.csv: Income-based disparities
• composite_disparity_index.csv: Combined disparity rankings

Launch Dashboard

Run the interactive Streamlit application:

streamlit run streamlit_app.py

Methodology

Disparity Calculation

For each dimension (geographic, racial, income), the analysis:

1. Identifies relevant demographic splits in the data
2. Calculates gaps between best and worst outcomes within each state
3. Aggregates gaps across multiple health measures
4. Standardizes metrics for comparison

Composite Index

The composite disparity index:

1. Standardizes each disparity dimension (mean=0, sd=1)
2. Averages standardized scores across dimensions
3. Ranks states from highest to lowest disparity

Key Findings

Top states by composite disparity:

1. South Dakota
2. Tennessee
3. Kentucky
4. Alabama
5. Arkansas

Technical Stack

• Python 3.12
• pandas: Data manipulation
• numpy: Numerical operations
• scikit-learn: Standardization and statistical methods
• Streamlit: Interactive dashboard
• Plotly: Visualizations

Project Structure

health_disparity_analysis/
├── disparity_analysis.py      # Core analysis functions
├── streamlit_app.py            # Interactive dashboard
├── requirements.txt            # Python dependencies
├── README.md                   # Project documentation
└── [generated CSV files]       # Analysis outputs

Future Enhancements

Potential extensions:

• Time-series analysis of disparity trends
• Machine learning models to predict disparity patterns
• Additional demographic dimensions (disability, education, age)
• County-level analysis
• Policy intervention impact assessment

Author

Jeffrey Olney
GitHub | LinkedIn

Related Projects
Health Disparity Analysis (Streamlit App)

License

This project is available for educational and research purposes.