Representing Patterns | Mental Health Stigma
Designers are frequently tasked with analyzing and questioning dense information and identifying similarities and anomalies among the content (inward communication). They are also often tasked with representing their discoveries as a clear, concise, and engaging story that reveals patterns and invites participation (outward communication). Therefore, in this project, we will once again immerse ourselves in the learning and application of communication strategies that help us make sense of dense information and aid the accurate communication of our discoveries to others.
This article documents the process of the third project in the Communication Design Studio class at CMU’s MDes/MPS program.
Nov 3 | Investigating Data
The data I’m investigating examines mental health treatment and stigma among U.S. adults, focusing specifically on young adults (ages 18–34). The key datasets cover:
1. The percentage of U.S. adults who have received mental health treatment or counseling within the past year.
2. The percentage of respondents across age groups who perceive stigma as a significant barrier to seeking mental health or substance use treatment.
3. Reasons cited by U.S. adults for not receiving mental health services, despite having unmet needs.
My audience includes mental health advocates, policymakers, healthcare providers, and educators who are committed to reducing barriers to mental health care access. The primary points I want them to understand are:
• High levels of stigma still impact young adults despite increased mental health awareness. This age group is at a pivotal stage of life, balancing transitions in career, relationships, and self-identity, yet they often face judgment when seeking support.
• The reasons for avoiding mental health services are complex and include practical factors (such as affordability and access) in addition to stigma. Recognizing these multifaceted barriers can guide policies and programs tailored to the needs of young adults.
Understanding these points can help stakeholders develop targeted interventions to reduce stigma and improve access to mental health support among young adults.
A few important relationships among the data are key for understanding the persistence of stigma and the complexity of barriers to mental health care:
• Age vs. Stigma Perception: The data shows that younger adults (18–34) report higher levels of stigma-related barriers to seeking treatment than older groups. This indicates a disconnect where, despite increasing societal awareness, younger adults still experience significant cultural or personal stigma.
• Stigma as a Barrier vs. Treatment Rates: Another relationship is between stigma as a barrier and the rate of young adults actually receiving treatment. High perceived stigma correlates with lower treatment rates, suggesting that while young adults may acknowledge mental health challenges, they often avoid seeking help due to fear of judgment or societal expectations.
• Reasons for Avoiding Treatment Beyond Stigma: Stigma isn’t the only barrier; practical concerns like cost, time constraints, and doubts about treatment effectiveness also play a role. This combination highlights the importance of addressing both social perceptions and systemic barriers to provide comprehensive support.
In shaping the data story, I’m planning to start by establishing the high prevalence of stigma among young adults to capture the audience’s attention. From there, I’ll delve into how stigma discourages young adults from seeking treatment, then expand on other significant barriers. This sequential approach — moving from the emotional (stigma) to the practical (cost, accessibility) — aims to create a holistic understanding and motivate stakeholders to address both cultural perceptions and structural issues in mental health care access.
Nov 11 | Organizing Data
To present a coherent story with the datasets, I’m organizing the data chronologically and by geography. The prevalence data from 1990 to 2019 will serve as a foundational timeline, illustrating the progression and growth of mental health challenges globally. This will anchor the narrative, showing how mental health has become an increasingly urgent issue worldwide.
From there, I will anchor data by country — using examples from the U.S., Australia, and Argentina — to compare current mental health treatment rates, perceived barriers, and cultural attitudes toward mental health services. This geographical organization helps viewers easily grasp differences and commonalities across regions.
Pros and Cons of Coordinate Systems:
• Cartesian: Great for time trends but lacks regional context.
• Geographical: Effective for cultural comparisons but unsuitable for timelines.
• Polar: Engaging for categorical data but limited in detail and sequencing.
My best approach would be combining Cartesian for time-based trends and Geographical for country comparisons, supplemented by Polar charts for categorical breakdowns, provides a balanced narrative.
The data I’m including are:
• Prevalence of depressive and anxiety disorders from 1990 to 2019 by continent or country.
• Methods people use to manage anxiety or depression
• Rates of mental health treatment access in the U.S. from 2002 to 2023.
• Reasons people don’t seek mental health treatment in the U.S.
To keep the story focused, I’m excluding specific countries and years with limited data points and minor details within each barrier category.
The groups of my datasets are defined by location (continent/country), condition (depressive or anxiety disorders), and treatment barriers.
For the range and scales, I’m using a 0–10% range for percentages, 1990 to 2023 for tracking both prevalence and treatment trends, and a categorical scale for treatment barriers and coping methods to compare proportions among different reasons or approaches.
Nov 18 | Visualizing Data
Goodness of Fit: How Data Shapes Representation
The data naturally segments into three key areas: prevalence, treatment rates, and barriers, each requiring distinct representation.
• Red (Prevalence): Represents the widespread and pressing nature of mental illness. Higher prevalence signifies greater need, making red an intuitive choice.
• Green (Treatment): Denotes progress and positivity. It reflects treatment rates, emphasizing where care is being accessed.
• Blue (Barriers): Suggests calmness but also gaps, helping visually contrast reasons for unmet needs such as affordability or stigma.
These color-coded categories ensure cognitive resonance and make data relationships more intuitive.
Layering Information to Reveal Relationships
Key insights emerge when certain datasets are layered:
• Prevalence and Treatment Rates: Layering these datasets shows how high prevalence contrasts with lower treatment rates, highlighting unmet needs.
• Barriers by Demographics: Overlaying reasons for unmet needs (blue) with demographic data (e.g., red prevalence bars) reveals disparities, such as young adults facing stigma or males citing affordability.
Unfolding the Narrative Over Time
The narrative unfolds using McCloud’s transition types:
• Moment-to-Moment: Gradual exploration of individual datasets, such as prevalence by age and gender.
• Action-to-Action: Shifts between treatment rates and barriers, illustrating how systemic issues affect access to care.
• Scene-to-Scene: Transitions between categories (e.g., prevalence → barriers → solutions) to show how different elements connect.
Nov 25 | Pivoting Data and Design Concept Speed Dating
After a deeper analysis of the datasets, I discovered a potential mismatch between my initial hypothesis and the data. I had assumed that the percentage of mental illness prevalence would be higher than the percentage of people who received treatment. However, the data showed the opposite. This discrepancy likely exists because mental health treatment is not limited to those with diagnosed mental illnesses; people dealing with stress, trauma, or other challenges may also seek treatment.
As a result, I decided to pivot my focus. I now examine datasets that explore the prevalence of both any mental illness (AMI) and serious mental illness (SMI) across different demographics in the U.S. in 2022. Additionally, I analyze the percentage of people within these groups who receive treatment and investigate the barriers preventing others from accessing care.
Coordinate Systems, Scales, and Ranges
For this revised approach, I use a Cartesian coordinate system to effectively display the relationships between different variables. The scales I apply are primarily categorical (demographic groups such as overall, male, and female) and percentage-based (e.g., treatment rates, prevalence rates, and barriers). The ranges are defined by demographic categories and specific reasons for unmet mental health needs. This structure helps make the data more organized and allows clear comparisons between groups.
Path and Visual Approach
The narrative follows a mixed path, combining a linear progression with indexical exploration. The story flows linearly by first presenting the prevalence of mental illness, then moving to treatment rates, and finally addressing the barriers to care. Along this path, I use visual representation to make the data clear and engaging:
- Colors: Purple for overall, red for female, blue for male.
- Value (Color saturation): People with different levels of mental health conditions.
- Symbols and Icons: People receive health treatment.
- Length: Percentage of the reasons for unmet mental health needs.
This visual approach fits the content well by simplifying complex relationships and patterns, making the data intuitive and easy to understand.
Design Speed Dating
After conducting several rounds of design concept speed dating, I received feedback suggesting that, instead of using a bar chart, my data could be better conveyed through alternative forms of representation. As a result, I decided to pivot from a traditional bar chart to a more creative visual approach, utilizing symbols and geography to represent people in the United States with mental illnesses, those with severe conditions, and gender differences.
Additionally, I plan to use color and gradients to differentiate between various demographics and the severity of mental illnesses. Here’s what it looks like:
Dec 2 | Iterating
After receiving more feedback, I had the opportunity to revise my visual representations and strengthen my ability to tell a cohesive story. The feedback highlighted that the U.S. map in the background was distracting and that I should include legends on the side to clarify what the colors and gradients represent. Additionally, it was suggested that I explore other types of visual representation tools beyond colors, such as texture, size, and shape, to convey my data more effectively.
The main challenge I am currently facing is figuring out how to connect and transition my story from the prevalence of mental illness to the barriers people face in accessing care. Ensuring that the transitions are seamless and logical while maintaining visual and narrative coherence is proving to be a complex task.
I am eager to explore the potential of Shorthand, a new tool I am not yet familiar with, to elevate my storytelling and create a more engaging and interactive presentation. Learning how to use this tool will allow me to experiment with innovative ways of presenting data and improving the overall flow of my narrative.
My next steps include revising my current visuals to address the feedback, researching best practices for using alternative visual representation tools, and diving into Shorthand tutorials to understand how to integrate it effectively into my project. By refining these elements, I aim to create a polished, impactful story that resonates with my audience.
Reflection
This project taught me the importance of staying adaptable and letting the data shape the narrative. When my initial hypothesis didn’t align with the data, pivoting my focus led to a clearer and more accurate story. I also learned how diverse visual techniques like symbols, color coding, and gradients can simplify complex information and make insights more engaging.
Feedback played a crucial role in refining my design, showing me the value of iteration and testing ideas early. Moving forward, I plan to carry these lessons into future projects by embracing flexibility, choosing the right visual forms, and exploring new tools like Shorthand to create interactive and impactful presentations.
References
- U.S. Department of Health and Human Services. (n.d.). Mental illness. National Institute of Mental Health. https://www.nimh.nih.gov/health/statistics/mental-illness
- 2022 National Survey on Drug Use and Health (NSDUH) releases. SAMHSA.gov. (n.d.). https://www.samhsa.gov/data/release/2022-national-survey-drug-use-and-health-nsduh-releases
- Vankar, P. (2024, September 2). Reasons for unmet mental health needs in the U.S. in 2022. Statista. https://www.statista.com/statistics/1415240/reasons-for-unmet-mental-health-needs-in-the-us/
Bibliography
- Yau, N. (2013). Data Points: Visualization That Means Something. Germany: Wiley.
- Wurman, R. S., Leifer, L., Sume, D., Whitehouse, K. (2001). Information Anxiety 2. United Kingdom: Que.
- Dykes, B. (2019). Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals. United States: Wiley.
