Introduction

For this lab, I chose to analyze suicide rates across various demographic groups in the United States, focusing on the impact of age, race, and gender. The dataset, sourced from the National Center for Health Statistics (NCHS), provides insight into U.S. suicide rates per 100,000 residents for the year 2018. Growing up in the digital age, when the term “suicide” peaked on Google Search in 2018, I found this topic rather interpersonal. This topic is particularly relevant as it highlights disparities in mental health outcomes across different racial and gender groups, offering an important lens through which we can further explore social and systemic factors influencing suicide rates.

https://ssl.gstatic.com/trends_nrtr/4031_RC01/embed_loader.js trends.embed.renderExploreWidget(“TIMESERIES”, {“comparisonItem”:[{“keyword”:”suicide”,”geo”:”US”,”time”:”2004-01-01 2025-03-27″}],”category”:0,”property”:””}, {“exploreQuery”:”date=all&geo=US&q=suicide&hl=en”,”guestPath”:”https://trends.google.com:443/trends/embed/”});

The goal of this analysis is to uncover patterns of suicide rates within racial groups segmented by gender and age. By visualizing these trends, I wanted to better understand which populations are at the highest risk and consider how societal and structural issues may contribute to these disparities. The data attained directly from the NCHS website, where I then scrubbed the data to filter down the relevant information needed for this visualization.

Visualizations

The visualizations aim to highlight differences in suicide rates by age, race, and gender, using stacked area charts to reveal the relative proportions of suicide rates within each group. When experimenting on Tableau, my goal was portray the visualization with the least amount of context and cognitive load.

The first visualization is a series of stacked area charts that compare suicide rates across racial groups, with gender (male and female) represented by distinct colors that are traditionally associated with each gender. Each chart represents a different racial group: American Indian/Alaskan Native, Asian/Pacific Islander, Black/African, Hispanic/Latino, and White. The data is segmented by four age groups: 15-24 years, 25-44 years, 45-64 years, and 65 years and over. The chart reveals that, across all racial groups, males consistently have higher suicide rates than females. However, the disparity is most prominent within the American Indian/Alaskan Native and White populations, particularly in the 15-24 and 25-44 age brackets. The rates among these groups decline with age, although they remain significant throughout. Meanwhile, the suicide rates for females across all racial groups are relatively low in comparison, with slight variations but no extreme peaks like those observed in their male counterparts.

The second visualization presents a more holistic comparison by aggregating the suicide rates for all racial groups under two broader categories: Male and Female. This stacked area chart allows for a clearer understanding of how racial differences contribute to the overall suicide rates within each gender category. The male section of the chart demonstrates a strikingly high peak in the 15-24 age range, predominantly driven by the suicide rates among American Indian/Alaskan Native and White males. The rates decrease with age but remain notably higher than their female counterparts. On the female side, the suicide rates are much lower and distributed more evenly across all racial groups and age categories, with only slight increases visible in younger adulthood.

While this visualization excels in highlighting the difference between the gender distribution of this suicide data, it diminishes the data regarding the racial group distribution. Within a class presentation, I received feedback that the addition of data labels would reduce said cognitive load and improve clarity on the meaning behind each color segment within the stacked area chart. In addition, if I were to make a second rendition of this visualization, I would also create a greater distinction between the female gender by using shades of red to portray the area to refine the visualization.

Reflection

This visualization brings to light critical disparities in suicide rates across racial and gender lines. The higher rates among American Indian / Alaskan Native and White males suggest that systemic issues such as economic stress, historical trauma, social isolation, and lack of mental health resources may be contributing factors. Moreover, the consistently lower suicide rates among females across all racial groups highlight potential differences in how mental health issues are experienced and addressed between genders.

If I delved deeper into future iterations of this project, I’d be interested in exploring social and economic factors contributing to these trends. For instance, comparing this data with socioeconomic indicators, access to mental health care, and regional disparities could reveal underlying causes of the observed differences. Additionally, further exploration could include analyzing data from subsequent years to identify trends over time and how interventions or changes in policy may have impacted suicide rates.

https://trends.google.com/trends/explore?date=all&geo=US&q=suicide&hl=en

https://data.cdc.gov/NCHS/Death-rates-for-suicide-by-sex-race-Hispanic-origi/9j2v-jamp/about_data