| Skills: Statistical Analysis | Data Visualization | Distribution Analysis | Hypothesis Testing | Global Health Analytics | Python (pandas, matplotlib) |
Mental health disorders affect millions of people worldwide, yet prevalence rates vary significantly across countries. This project analyzes international mental health data to compare disorder prevalence across countries and explore potential global trends over time.
The study investigates whether differences in mental health disorder rates may be influenced by socioeconomic conditions, healthcare access, or cultural factors affecting diagnosis and reporting.
The dataset includes global prevalence estimates for multiple mental health conditions across countries and years.
Key variables analyzed included:
The analysis focused on exploratory data analysis and statistical comparisons, including:
Mental health disorder prevalence varied considerably across countries, supporting the hypothesis that socioeconomic and healthcare factors influence disorder rates.
Global depression prevalence ranged from approximately 2% to 6%, with the majority of observations concentrated between 3% and 4%.
The cumulative distribution curves suggested a potential gradual increase in depression prevalence over time.
Lower depression prevalence was generally associated with lower substance use disorder rates, though this relationship weakened at higher depression levels.
Most disorder prevalence differences between countries were statistically significant, indicating that observed variation was unlikely due to chance.
Several factors may influence mental health disorder prevalence estimates, including:
Mental health disorder prevalence varies substantially across countries, highlighting the importance of considering social, cultural, and healthcare factors when interpreting global mental health statistics.
Python, pandas, matplotlib