Analysis of Effects of Mental Health on Physical Health
Authors: Alvin Huang (Project Lead), Rachel Szeto, Jasmine Yung, Jordan Nguyen
Mental health awareness is becoming increasingly important and can even be considered just as important, if not more, than physical health. There may even be connections between physical and mental health. This project examines the relationship between the two through several aspects of life, including jobs, life expectancy, overall mood, and mental illness.
It is imperative to understand whether mental health might affect physical health, and vice versa, to dictate technological and medical advancements in improving overall health. For instance, can being depressed increase the chances of the development of a long-term illness such as diabetes or heart disease? On the other hand, can the presence of physical illness increase the chances of feeling mentally unwell?
All mental health problems should be taken seriously, as many people struggle with them daily. As people tend to struggle with one mental health problem, does this also lead to them working with others with mental health problems as well? The heat correlation map shows if there are any correlations between Schizophrenia, Depression, Anxiety, Bipolar, and Eating disorders.
Something surprising found within the data is depression and schizophrenia have a negative correlation. A reason behind this negative correlation is schizophrenia is characterized by symptoms such as delusions, hallucinations, and disorganized thinking. At the same time, depressive disorders are primarily involved with feelings of sadness, hopelessness, and a lack of interest or pleasure in activities. The mental health problems with the highest correlation are eating and bipolar disorder, as both are linked to difficulties managing emotions. An example can be seen as the mood swings in bipolar disorder can affect eating behaviors, leading to episodes of either overeating or restricted eating depending on the mood phase.
In the quest to understand the complex interplay between different mental health conditions, our visualization provides a detailed examination of the relationships between anxiety, bipolar, depressive, and eating behaviors.
The scatter plot of “Anxiety vs. Bipolar” highlights a positive correlation between anxiety and bipolar disorder metrics, suggesting as stress increases, so do the manifestations or severity of bipolar disorder. This relationship is underscored by a trend line that climbs steadily across the graph, emphasizing a significant overlap in how these conditions might influence an individual’s behavior or treatment needs. To the right of the scatter plot, a violin plot illustrates the distribution of eating rates, categorized by color in the scatter plot itself. This plot is not only visually striking but provides a deep dive into how eating behaviors may correlate with anxiety and bipolar disorder.
For bipolar disorder and eating behaviors, the trendline in the scatter plot shows a steady increase, which indicates bipolar symptoms intensified. The relationship is further illustrated by the box and violin plots as they highlight the variability in eating disorders, which may be influenced by the different phases of bipolar disorder.
Moving on, the Depressive vs. Anxiety reveals a moderate positive correlation, with a dense cluster of data points around the trendline. The clustering can suggest individuals with higher levels of depressive symptoms also tend to experience elevated anxiety levels. The spread and density indicate a general trend, but individual experiences may vary.
Lastly, depression and eating disorders show a flat trendline, suggesting some individuals with higher depression metrics exhibit distinct eating behavior patterns.
The included graphs reveal patterns and correlations that can help clinicians and researchers develop better treatment strategies. They also emphasize the interconnected nature of mental health disorders. By understanding these relationships, healthcare providers can create more holistic and integrated treatment plans that address multiple conditions simultaneously.
Furthermore, understanding mental health trends globally is crucial for developing effective policies and interventions.
The first heatmap illustrates the proportion of respondents per country who reported their tech company offered mental health benefits, such as access to mental health professionals. The heatmap shows a stark contrast, where most companies in a country provide benefits, and only a few do. Countries like the United States, the United Kingdom, and Canada show more companies offering mental health benefits. However, it is worth noting that most respondents are from these countries; a more extensive dataset could provide different insights, emphasizing the need for global data representation to understand the complete picture of mental health benefits available in tech companies.
Moving to a broader view of mental health issues, the second heatmap presents rates of schizophrenia per country, derived from a comprehensive dataset representing a wider variety of countries more evenly. In general, most countries exhibit a significant prevalence of individuals with schizophrenia, with heightened rates in the United States, Greenland, and Australia. This could be attributed to the availability of mental health resources in these countries, allowing for more accurate diagnosis and reporting. The heatmap underscores the global challenge of schizophrenia and highlights the importance of robust mental health systems to ensure early detection, treatment, and support for individuals with schizophrenia.
The comparative histogram of life expectancy for men and women illustrates an apparent gender disparity. Women generally live longer than men, with their life expectancy clustering around 80 years. The histogram shows a peak in the 75–80-year range for women, indicating a higher concentration of female life expectancy around these ages. In contrast, men are more likely to have a life expectancy of around 75 years, with a broader distribution ranging from 55 to 85 years. This wider distribution for men suggests more variability in male life expectancy, which could be influenced by higher rates of risky behavior, occupational hazards, and mental health issues. These factors contribute to the gender disparity in longevity, highlighting the need for targeted health interventions for men to address these issues.
Shifting the focus to suicide rates, the bar chart indicates Armenia, Latvia, and Albania have the highest suicide rates, approaching or exceeding 35 per 100,000 people. This data underscores the urgent need for targeted mental health interventions in these countries. High suicide rates often reflect underlying societal issues such as economic hardship, lack of mental health resources, and social stigmatization of mental illness. The high rates in these countries call for comprehensive mental health policies, increased funding for mental health services, and community-based support programs to address and mitigate the factors contributing to these alarming statistics.
When examining the relationship between economic wealth and suicide rates, the scatter plot reveals a weak negative correlation. The best-fit line, with an equation of y = -639.31x + 25685.67, suggests that as GDP per capita increases, the suicide rate tends to decrease slightly. However, the data points are widely scattered, indicating that this relationship is not strong. This implies that while economic stability can provide better access to mental health care and social services, it is not the sole determinant of mental health. Countries with high GDP still face significant mental health challenges, indicating that wealth alone cannot address the complex factors contributing to suicide. This highlights the importance of addressing other determinants of mental health, such as social support, mental health education, and reducing stigma.
Interestingly, the analysis of happiness scores versus suicide rates presents an intriguing observation. Despite the expectation higher happiness scores would correlate with lower suicide rates, the scatter plot shows no strong correlation, with an R-squared value of 0.00047. The best-fit line is almost flat, suggesting no significant relationship between these variables. This indicates happiness might not fully capture the mental health landscape as it is a subjective measure. Cultural factors, social support systems, and individual coping mechanisms also play critical roles in shaping mental health outcomes, highlighting the multifaceted nature of mental health. This suggests improving mental health requires a holistic approach beyond simply increasing economic wealth or happiness scores.
To get an initial idea of how mental health can affect the daily lives of people, it is insightful to look at the relationships between mental health and employment.
The graph provides a visualization of how many employees seek treatment based on the availability of mental health benefits, whether or not resources to learn more about mental health issues, and how to seek help are provided by employers. All data points are unique, so each falls in only one category.
From the graph, the results seem pretty intuitive for the top row, “Availability of mental health benefits: Yes.” We would expect individuals to feel more comfortable seeking treatment if mental health benefits are available to them. However, the other two categories are different from expected. The second row, “Availability of mental health benefits: No,” shows when mental health benefits are not available to individuals, a vast majority of employees seek treatment when resources are not provided by employers. Unexpectedly, many people would seek treatment when no benefits or resources are available from their employers. Lastly, the third row, “Availability of mental health benefits: Don’t know,” is also different than expected. Again, the largest count of people seeking treatment are those who are unsure about the availability of resources and mental health benefits.
A possible reason for the second and third-row results may be those who don’t know of or aren’t provided mental health benefits and resources seek treatment outside of their workplace. Furthermore, those who seek treatment may have already found resources before their job.
Conclusion
Mental health is a complex issue influenced by a myriad of factors, including gender, economic status, and societal norms. The graphs analyzed in this article highlight the diverse challenges and underscore the importance of comprehensive, culturally sensitive approaches to improve mental health outcomes globally. By integrating economic, social, and psychological strategies, we can better address the root causes of mental health issues and foster healthier, more resilient communities.
