🌍 Global Health Insights Analysis: Causes of Death

Introduction

This project provides a comprehensive analysis of global health data, focusing on the leading causes of death worldwide. By leveraging Power BI, we explored trends, correlations, and insights that could inform public health initiatives. The creation of an interactive dashboard allowed for dynamic data exploration and real-time filtering.

Data Overview

The dataset titled cause_of_deaths_FINAL (Recovered).xlsm includes key information regarding global mortality rates:

• Year: The year the data point represents.
• Country/Territory: The geographical area the data pertains to.
• Causes of Death: Various causes, including:
  • Cardiovascular Diseases
  • HIV/AIDS
  • Diabetes Mellitus
  • Respiratory Diseases
  • Neoplasms
  • Other communicable and non-communicable diseases

Objectives

The main objectives of this analysis are:

• Identify the top causes of death globally and regionally.
• Perform data cleaning and preparation using Power BI's built-in tools.
• Analyze key aspects of the dataset with interactive visualizations (e.g., pie charts, bar charts, maps).
• Create a responsive and interactive dashboard that provides a comprehensive view of the data.

Dataset Description

The dataset provides an in-depth view of mortality causes, enabling rich exploration across multiple countries and years.

Key Features:

• Geographic Scope: Data from countries across various continents.
• Temporal Scope: Data spans multiple years to observe trends over time.
• Categories of Death: Covers a broad range of death causes for comparative analysis.

Process Details

1. Data Loading and Cleaning: The dataset was imported into Power BI and cleaned using Power Query, removing inconsistencies and handling missing data.
2. Exploratory Data Analysis (EDA):
   • Power BI’s built-in visualizations were used to summarize the data effectively, including bar charts, line charts, and pie charts.
3. Data Transformation:
   • Grouped and transformed the data using DAX functions for deeper insights, such as calculating year-over-year changes.
4. Visualization Creation:
   • Visualizations were created using Power BI’s interactive charts, such as:
     • Pie Charts for the proportion of causes of death.
     • Bar and Line Charts for trend analysis over time.
     • Map Visualizations to track global disease impacts.
5. Interactive Dashboard

• Compare 5 Countries' Total Deaths Over Years:

  • An interactive bar chart was created to compare the total deaths across five selected countries over a range of years. This allows for visualizing which countries experienced the highest mortality rates, with the ability to filter by year or cause of death.

• Neoplasms Disease in Map Visualization:

  • A geographic map visualization highlights the global distribution of deaths due to Neoplasms. This map showcases how the disease impacts various regions, providing a clear understanding of high-impact areas.

• Compare Between Two Selected Causes of Death:

  • A dynamic comparison between two user-selected causes of death was implemented, enabling users to analyze trends for both causes across multiple countries or globally. This comparison helps in identifying correlations or disparities in disease mortality rates.

• Trend of Cardiovascular Disease Over Time in Line Chart:

  • A line chart visualizes the trend of cardiovascular disease deaths over time, displaying year-by-year fluctuations and long-term trends. This visualization helps highlight increases or decreases in mortality due to cardiovascular diseases.

• Sum of Total Diseases/Causes by Code in Stacked Area Chart:

  • A stacked area chart was created to represent the sum of total deaths for all diseases or causes, categorized by their respective codes. This visualization allows users to observe the relative contribution of different causes to overall mortality rates over time.

• Top 3 Causes of Death in Pie Chart:

  • A pie chart presents the top 3 causes of death globally or for selected countries. The chart focuses on:
    • Total Deaths from Self-harm
    • Total Deaths from Diabetes Mellitus
    • Total Deaths from another selected cause
  • This breakdown provides a clear view of how these causes contribute to overall mortality.

• Total Death of Self-harm, Diabetes Mellitus, and One Other Cause in Gauge:

  • Three gauge charts were set up to compare the total deaths for Self-harm, Diabetes Mellitus, and a user-selected third cause. The gauges provide an intuitive view of how these causes compare in terms of total deaths.

• Compare Between Two Selected Causes of Death in Line Chart:

  • Another line chart was created for a more detailed comparison between two user-selected causes of death, allowing users to see trends and variations over time. This interactive feature helps in understanding the progression of these causes of death side by side.

Analysis

• Top Causes of Death: Cardiovascular Diseases consistently ranked as the leading cause, with increasing trends over time.
• Trend Analysis: Various causes of death displayed fluctuating trends across regions and years.
• Comparison: Power BI allowed for comparing two causes of death in countries like Afghanistan and Zimbabwe, revealing variations in mortality.

Key Insights 🌟

• Cardiovascular Diseases are the primary cause of death, with rising global rates.
• Power BI's ability to integrate geographic visualizations enabled insights into regions with the highest disease impact.
• Regional mortality differences highlight the need for localized health strategies.

Tools & Techniques

• Software: Power BI
• Features:
  • Data Cleaning and Transformation: Using Power Query and DAX.
  • Visualization:
    • Pie, Bar, Line, and Map Charts for effective data storytelling.
  • Dynamic Filters: Power BI slicers were used to filter data by year, country, and cause of death.
  • Geospatial Analysis: Power BI’s map features to track diseases like Neoplasms globally.
  • Interactive Dashboard: Combined all visual elements for seamless data exploration.

Results

Descriptive Statistics

• Summary statistics were generated to reveal mortality distributions across causes of death.

Trends Over Time

• A steady upward trend in cardiovascular disease deaths was observed.
• Diseases like HIV/AIDS showed region-specific variations.

Visualizations

• Interactive visualizations, such as comparative death rates and time trends, were created for better understanding.

Reports:

Reports were created to effectively communicate the insights and finding from the data analysis.

Conclusion

This project provided crucial insights into global health trends, particularly the rise of cardiovascular diseases. Power BI proved to be a powerful tool for data manipulation, visualization, and dynamic analysis, offering a user-friendly interface to explore data interactively.

Future Directions

• Explore additional datasets to include other health metrics.
• Implement machine learning models for predictive analysis on mortality trends.
• Use Power BI’s AI Insights to extract more predictive insights.

Contact Our Team

For collaboration or inquiries, feel free to reach out.

• Name: Sara ELdamarany

• Email: [email protected]

• LinkedIn: Sara Eldamarany

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• Name: Manar Alaa

• Email: [email protected]

• LinkedIn: Manar Alaa

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• Name: Ahmed Khater

• Email: [[email protected]]

• LinkedIn: Ahmed Khater