The Invisible Epidemic

Yoganayaki Mullai Ravi

1. What dataset did you use?

I used a "Mental Health Crisis" dataset covering 50 countries from 2000 to 2024. The dataset included the following key attributes:

• Depression prevalence
• Anxiety prevalence
• Suicide rate per 100,000
• DALY burden measures
• Treatment access and treatment gap
• Social media usage
• Academic pressure index
• Age group and gender breakdown
• Country, region and income group

The dataset combines mental health outcome indicators, treatment-related measures and social or academic pressure variables.

2. How did you analyze or prepare the data?

The data was cleaned and joined using common fields. After integrating the datasets, I created calculated measures to highlight the most meaningful patterns.

The analysis focused on:

• Long-term trends in depression, anxiety, and suicide rates
• Demographic differences across age and gender
• Treatment inequality across income groups
• Regional and country-level burden comparison
• Pressure factors such as social media use and academic pressure

3. Who is the intended audience for your visualization?

My primary audience is public health policy makers, government health ministers and international health organizations who need to understand where the mental health crisis is most severe and where the most urgent intervention is needed.

The dashboard is designed for users who need to understand:

• who is most affected
• where the burden is highest
• what factors may be driving the issue
• where support and treatment access are most needed

4. What is your visualization about, and what question does it address?

The dashboard tells a clear story of how mental health burden has grown over time, where it is most concentrated, and who is most affected. It highlights geographic and age-based burden, the gender gap in outcomes, the influence of social and academic pressures, and the deep inequality in treatment access. Overall, it shows that the crisis is not only rising, but that the global response remains inadequate and uneven.

The dashboard answers questions such as:

1. How have depression and anxiety changed over time?
2. Which countries and regions carry the highest burden?
3. Which age group is most affected?
4. How do mental health outcomes differ by gender?
5. How do social media usage and academic pressure relate to mental health burden?
6. Why does treatment access remain unequal across income groups?

5.Did you use any Oracle Analytics AI features when building your visualization? If so, please describe how they were used.

Yes, the AI Agent feature has been leveraged and integrated alongside the workbook to enhance the overall usability of the dashboard.

Agent Name: Mental Health Companion – Support and Guidance Assistant

The agent enables users to quickly access meaningful mental health guidance including:

• Identification of common emotional and behavioral patterns such as anxiety, low mood, burnout, poor sleep, grief, and emotional overload.
• Supportive next-step suggestions based on the user’s concern, including coping guidance, self-care support, exercises, therapy pathways, and when to seek professional help.
• Escalation guidance for situations involving severe distress, self-harm warning signs, suicidal thoughts or immediate safety concerns
• Practical support recommendations related to sleep, stress, emotional regulation, social withdrawal, academic pressure, and burnout
• Simple, human-centered responses that guide users toward appropriate support without diagnosing or prescribing treatment

The working of Agent is attached as a video below.

Mental Health companion Agent.mp4

The Invisible Epidemic.dva