Global Trend in Mental Health Disorders

Abstract Mental health disorders affect millions of people worldwide, making them a significant public health concern. Understanding the prevalence of different mental health disorders is essential for effective policymaking and intervention plans. This article explores the global trends in mental health disorders using a dataset sourced from Kaggle, titled “Global Trends in Mental Health Disorder.” The dataset provides insightful information about the prevalence of various mental health conditions, including depression, anxiety disorders, bipolar disorders, schizophrenia, eating disorders, drug use disorders, and alcohol use disorders. By analyzing this data and visualizing it on a world map, it is possible to gain a better understanding of how these disorders are distributed over the different countries.

Introduction Mental health disorders are a major challenge for healthcare systems worldwide. To address this issue effectively, policymakers and researchers need access to comprehensive and up-to-date data on the prevalence of mental health disorders across different populations and geographical regions. The dataset “Global Trends in Mental Health Disorder” from Kaggle provides a valuable resource for understanding the distribution of mental health disorders globally. This dataset contains information on the prevalence of various mental health conditions, enabling us to explore patterns and trends that may inform better decision-making and intervention strategies.

Method The dataset is sourced from Kaggle and comprises data from countries across the globe. It includes prevalence rates for mental health disorders such as schizophrenia, bipolar disorder, eating disorders, anxiety disorders, drug use disorders, depression, and alcohol use disorders. To analyze the data, I utilized the R programming language and several R packages, including ‘tidyverse,’ ‘ggplot2’, ‘sf,’ ‘rnaturalearth,’ ‘countrycode,’ and ‘ggrepel.’

First, I read the CSV file into a data frame, which I named “df.” I then processed the data by joining it with world map data, filtering out irrelevant information, and performing data cleaning tasks. Subsequently, I transformed the dataset into a format suitable for visualization.

Plot depression global prevalence

Plot schizophrenia global prevalence

Plot bipolar disorder global prevalence

Plot anxiety global prevalence

Plot drug abuse global prevalence

Plot alcohol abuse global prevalence

Plot eating disorder global prevalence

Prevalence comparison depression and axniety with facet_wrap

Prevalence comparison bipolar and schizophrenia with facet_wrap

Results Through data visualization, I explored the global prevalence of different mental health disorders. I created world maps that illustrate the prevalence of disorders such as depression, schizophrenia, bipolar disorder, anxiety, drug abuse, alcohol abuse, and eating disorders across various countries. By visualizing the prevalence rates on a continuous viridis ‘plasma’ scale of the ggplot2 package, I was able to observe patterns and differences in the distribution of these disorders.

These findings indicate significant variations in the prevalence of mental health disorders among different countries and regions. This information is important for understanding how these disorders are distributed over societies and populations and for identifying areas that require focused intervention and support.

Conclusion The dataset “Global Trends in Mental Health Disorder” provides valuable insights into the prevalence of mental health disorders worldwide. By leveraging the power of data visualization and analysis in R, I explored the global distribution of various mental health conditions. This analysis highlights the distributions of the regional and demographic variations in mental health disorders, which can aid policymakers in making informed decisions and directing resources where they are needed the most. This dataset serves as a valuable resource for researchers, policymakers, and healthcare professionals seeking to address the challenges posed by mental health disorders on a global scale. By acknowledging the prevalence and distribution of these conditions, we can work towards creating a more supportive and empathetic world for those affected by mental health disorders.