Key Indicators of Life Expectancy in Developing Countries

Author: Adrian Chavez-Loya
Completion Date: June 2024

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Overview

This project explores key factors affecting life expectancy in developing countries, based on data from the World Health Organization (WHO) and United Nations. Using regression modeling techniques, the analysis identifies significant health and economic indicators that contribute to life expectancy variations across 193 countries between 2000 and 2015.

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Objective

The primary goal of this project is to identify actionable insights for improving life expectancy in developing countries. This is achieved through a series of statistical models that assess the relationship between life expectancy and various predictors, including healthcare expenditure, vaccination rates, economic indicators, and more.

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Dataset

• Name: Life Expectancy Data
• Source: Global Health Observatory (WHO), UN economic data
• Period: 2000–2015
• Scope: Developing countries
• Variables: Includes 22 features such as GDP, infant mortality, vaccination rates, and education levels.

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Analysis Highlights

Models

1. Model 1: Relationship between GDP and Life Expectancy

   • Explores a linear and quadratic relationship.
   • Shows GDP's diminishing returns on life expectancy.

2. Model 2: Comprehensive Analysis of All Predictors

   • Identifies significant predictors like Adult Mortality, Schooling, and HIV/AIDS prevalence.
   • High explanatory power (R² = 82.5%) but suffers from multicollinearity.

3. Model 3: Optimized Model Excluding GDP and Percentage Expenditure

   • Retains robust explanatory power (R² = 81.9%).
   • Highlights the most impactful predictors while resolving multicollinearity.

Key Predictors

• Positive Influences: Schooling, BMI, Income Composition of Resources, Vaccination Coverage (Diphtheria).
• Negative Influences: Adult Mortality, Infant Mortality, HIV/AIDS prevalence.
• Moderate Influences: Alcohol consumption, Polio and Hepatitis B vaccination rates.

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Key Findings

• Education and equitable resource distribution are pivotal for increasing life expectancy.
• Preventive healthcare, especially vaccination coverage, plays a vital role.
• Addressing diseases like HIV/AIDS and reducing child mortality are critical priorities.

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Technologies Used

• Programming Languages: Python
• Libraries: pandas, numpy, statsmodels, matplotlib, seaborn
• Statistical Techniques: Linear Regression, Quadratic Modeling, Multicollinearity Analysis (VIF)