Depression and Related Treatments

John Foster
Giovanna Maranghi

The following is a link to our Infographic:
https://create.piktochart.com/output/47357909-my-visual?fbclid=IwAR3kAfo8KWwJEu2NLuUSXZnCGYRZY_ciU3x0QIQrxJOBYjIrOwbnwjIaB8o

 

Depression is a condition that affects 264 million people worldwide (World Health Organization 2020). This condition can be seen in countries worldwide, across age groups and income brackets, leading to 800,000 suicides per year as a result. Suicide, however, is not the only negative outcome. According Dr Robert C. Kessler in his 2011 article, depression can lead to a generally lower standard of life. Major Depressive Disorder (MDD) was found to affect work performance, leading to lower earnings. Additionally, it was found to be associated with the onset of a variety of chronic physical disorders (1).

While there are a variety of treatment options available, from talk therapy to medication to holistic solutions, it is estimated that fewer than 50% of adults suffering from depression actually receive treatment (Koskie 2020). While there are a number of factors contributing to this (healthcare costs, access to treatments, etc), one factor is the stigmas that surround depression. In some groups, mental illness is not considered to be a serious problem, and is instead viewed as something that result from an inherent weakness. These groups also view depression something that afflicts only certain demographics, primarily young people and women.

Through our infographic, this team hopes to show the widespread nature of depression, and alleviate the stigmas that pervade world populations.

 

Objectives

The infographic that we have created is designed to help facilitate an understanding of the spread of depression across different countries and demographics. By creating graphics with this information, we hope to empower those who are suffering from depression. By sharing this information, we are aiming to alleviate stigmas about depression that may exist in the broader community; for example, by showing the breakdown of depression by sex across the globe, we will show that a significant percentage of depression sufferers are men, in contrast to certain ideas about men and emotions. In addition, by including information that details common symptoms and treatments, we will give our audience a starting place to seek help for their own personal issues. The visualizations we created static, however, we are hoping that in combination they will tell a story that will appeal to audiences and give them information that will be helpful in their real lives.

This project has a wide range of potential audiences. We envision the primary audience to be members of the public who are seeking basic information about depression. Another possible audience is that of doctors and researchers who could use the information we present as a resource for patients or a building block for further work.

Data

The dataset that we are working from was created by combining several sets sourced from Our World in Data. The first set details depression percentages by country and by sex from 1990 to 2017 (Ritche et al 2018), and the second set lists GDP (Gross Domestic Product) of countries around the world from the 1950s to 2017 (Feensta et al 2019). Using Microsoft Excel and Tableau Prep, we simplified each dataset to contain just the information that we needed, before joining the sets in Tableau Prep into one dataset that contained the information for all of the visualizations we planned to create.

The datasets for depression percentages across the globe and by sex were rounded up to one digit past the decimal in Excel. Similarly, the numbers for GDP were rounded to the nearest million/billion/trillion in order to make the numbers easier to work with.

Tools

The team used Tableau Desktop and Piktochart to create the visualizations and infographic. We knew coming into this project that our limited skills with these programs could potentially affect what we were able to produce, since both of us had only used these tools for the first time during this course. We chose to stick with these tools because they were the only ones we had competency with, and in order to use others we would have to spend time researching options, and then learn how to effectively utilize them. This was not time and effort we had to spare.

Tableau Desktop was effective for creating the visualizations. The software allowed us to create simple sheets with relative ease. That being said, we had originally hoped to create slightly more complex visualizations than the outcome shows. We were made to compromise our vision because of our skills with the software.

We chose Piktochart for our infographic as it allowed the largest amount (in our opinion) of application and visual options for free. It allowed us to import our own images and layer text, pictures and charts to create a thorough and pleasing data story. Additionally, the ability to link to articles on this platform was very useful especially in the “causes and treatment” section. One limitation that was frustrating with this platform, is that once uploaded and placed, graphics could not be altered other than to resize. This led to the data visualizations we uploaded to look slightly less elegant than desired.

Analytics

When it came to designing the visualizations and infographic, we knew from the start of the process that there was a specific story we wanted to tell: that depression affects a large number of people worldwide regardless of demographic. We initially sought out datasets that would allow us to visually communicate the general spread of depression. It was our plan to find data that viewed depression through the lens of nationality, sex, age group, and income bracket, however, it proved difficult for us to find worldwide data that showed breakdowns of the latter two lenses. Instead, we found and used data on GDPs from different countries.

When making this change, we had the idea that perhaps there would be a visible link between a country’s GDP and the percentage of the population suffering from depression. After analyzing the data and creating prototype visualizations, we came to the conclusion that there was no strong correlation between the two attributes. (That being said, it is possible to see that European countries with higher GDPs have a generally higher rate of depression). Instead, it reinforced our original idea that the percentage of the population affected by depression is consistent across countries, regardless of other factors.

Design Process

When it came to designing the visualization, we began by conceptualizing what we wanted our end result to be. From there it became a matter of finding the data that would allow us to create the sorts of visualizations that would support the end goal we were aiming to. As discussed elsewhere in this report, we were unable to find data that showed the demographic breakdowns we had planned on, so we had to pivot and create something new. Throughout the design process we aimed to make the visualizations as expressive and effective as we could. Our efforts in expressiveness are shown in our use of colours. Where possible, we aimed to stick with cool colours in order to represent the issue of mental illness.

On the map we chose a yellow-green gradient. This gradient allows the viewer to pick out countries with lower rates of depression (yellow), but as that rate rises the shade of green gets darker. The end result creates points of interest which draw the eye, mainly the darkest greens, or the more pure yellows.

Similarly, the bubble chart representing the rate of depression against national GDP required some thought about the best way to express the data. We decided that the best way to express the GDP of a nation was with the size of a bubble. This would allow the viewer several points that would draw their eye. To add the percentage of depression, we once again coded with a blue gradient. The end result is that the countries with darker blues draw the eye more immediately. This works to our end goal, because it allows the depression aspect of the chart to pop out first, and not be overshadowed by the size coding.

Story

The story that we are aiming to tell is that depression does not discriminate by nationality, sex, or level of wealth. Though there are certainly extremes in the dataset we used, for the most part the percentages of depression in the populations stay fairly constant across countries, sex, and when viewed in comparison with the nation GDPs. This story will help us to achieve our goal of alleviating stigmas in that it will show the general spread of depression across demographics. There is no one small group that is the most susceptible.

In addition to this, we are delving into the specifics of the condition with our infographic of common symptoms and treatments. By showing the most common symptoms, we will help those suffering to see that they are less alone than they may be feeling. We chose to pair the symptoms with treatments so that when viewers connect with the material presented, they also are given a starting place for helping themselves.

Pros and Cons

The visualizations and infographic that we have created are effective in achieving the goals we set out to achieve. In combination, they show the widespread existence of depression.

The map viz successfully shows the spread of depression across the globe. The colours we chose to work with successfully show areas of the world with higher rates of depression, but also show that there are no countries that have a rate of zero.

Similarly, the breakdown of depression by sex shows that while there is a lower depression rate in men across the countries observed, the existence of the condition is consistent. The primary drawback of this vis is that we made the choice to reduce the number of countries we would show a full breakdown from in order to save space. The countries we chose to exclude showed similar depression rates to those we included, however, our audience may wish to see breakdowns from their own country.

The bubble chart we created to show the intersection of depression rates with national GDP has the potential to spark analytical insights. By using size to represent GDP, and colour to repression rate of depression, the chart could be used to derive trends in the relationship between the two. For example, as mentioned above, it is possible to see higher rates of depression in European countries with higher GDPs. A proper data analyst could potentially use this chart to develop more fleshed-out insights.

In creating the infographic, one issue we came across was deciding on a criteria to limit which data points to showcase as well as how to pair different visualizations in a way that would simultaneously be informative and easy for the viewers cognition.

Conclusion

Depression is a condition that makes it difficult to live. It can lead to a lower standard of living by limiting personal functionality, resulting in unfulfilling relationships and lower incomes. It can also have physical outcomes, leading to a number of chronic pain disorders.

By presenting this infographic, this team is aiming to alleviate some of the common stigmas about depression and show that the condition can be found among members of all demographics. It can be found across the globe, regardless of a country’s wealth, in members of all sexes. We hope that this project will help educate those suffering from depression and empower them to seek the treatment they need.

 

References

Depression. (2020, January 3). World Health Organization. Retrieved May 31^st 2020. https://www.who.int/news-room/fact-sheets/detail/depression

Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer. Our World in Data. (2019). National GDP. [Dataset] https://ourworldindata.org/grapher/national-gdp

Kessler, R. (2011). The Cost of Depression. Psychiatric Clinics of North America, (35)1, 1-14. https://doi.org/10.1016/j.psc.2011.11.005

Koski, B. & Legg, T. J. (2020, June 3). Depression: Facts, Statistics, and You. Healthline. https://www.healthline.com/health/depression/facts-statistics-infographic#1

Ritchie, Hannah, and Max Roser. Our World in Data. (2018). Mental Health. [Dataset] ourworldindata.org/mental-health#depression.