The National Center for Health Statistics (NCHS)/AcademyHealth Data Visualization Challenge provided an opportunity to collaborate with colleagues in different sectors to develop a data visualization titled, “Sound the Alarm: Data Show Large Inequities in Rates of Gun Deaths Among Young People.” We gathered and analyzed data from multiple data sources, including Health, United States (US); the National Vital Statistics System; and the National Hospital Ambulatory Medical Care Survey.

NCHS makes these data readily available to researchers and the public in several ways. For example, the mortality data we used from the National Vital Statistics System can be tabulated through a series of drop-down menus on CDC WONDER by specifying the population group and cause(s) of death that are of interest. In addition to gun deaths, data are available for drug overdose deaths, maternal mortality, COVID-19, and leading causes of death, among other causes. The provisional mortality data, while subject to change, are available in as little as 2-3 months. The remaining data we used are available from the Health, US, Tables of Summary Health Statistics, which includes hundreds of tabulated measures at the national level and by various population groups.

For our submission, we utilized Tableau to develop an interactive storybook visualization to communicate the disproportionate impact that guns have on young Black men, particularly those in urban areas. A major appeal of developing a data visualization in Tableau was that we could aim to engage with diverse audiences. In Tableau, patterns and trends can be assessed using the visuals we built to demonstrate the impacts of gun deaths on various communities. However, for those wanting to go beyond the visuals to either view or use more quantitative information, they can click on components of our visual for discrete values represented by each bar on the graph. Further, those interacting with our visualization could access our findings through a high-level overview of gun deaths by observing the initial data visualization on each page. Users can then drill down to look at gun deaths by a particular factor, such as age, sex, race/ethnicity, and rurality.

As challenge winners, we were afforded the opportunity to present at AcademyHealth’s 2022 Health Datapalooza and the National Health Policy Conference this past February. We had the chance to engage with other researchers and practitioners about ways to utilize and disseminate these data. Our poster garnered the attention of many attendees representing a wide range of fields, including physicians, researchers, and public health practitioners. Our goal was to showcase the data that we had compiled to communicate the disproportionate impact that guns have on young Black men to lay and expert audiences. This knowledge drives us to address the inequitable distribution of social supports and discriminatory policies and practices that influence the high number of gun deaths among this population.

Now, you might be wondering why we are so enthusiastic about participating in this work and submitting our visualization for the challenge. Well, our concern is relatively simple – data die in darkness. They are collected to shed light on a problem and guide the design of solutions. They are pointless unless applied. Professional analysts often focus on a specific problem that can be addressed with data and do not imagine the countless ways citizen analysts can learn from data. But citizen analysts may benefit from having some fundamental understanding of data limitations (e.g, preliminary v. final data, time lags, etc.).

Data visualization affords a practical balance between the narrow findings professionals typically report and the broad expanse of analyses lay persons may want to attempt. Data visualization allows citizen analysts to explore data while simultaneously providing structure to steer users away from over-interpreting the data or drawing conclusions that are not fully evidence based.

If you are interested in collaborating with a multi-disciplinary team to learn how to analyze and apply data in creative and interactive ways, apply for the 2022 NCHS/AcademyHealth Data Visualization Challenge now open through November 14, 2022. Apply today for a unique opportunity to explain an important trend or disparity in public health.


Lauren E. Russell, M.P.P.

M.P.H Candidate - Johns Hopkins Bloomberg School of Public Health 2021 NCHS Data Visualization Challenge Team Winner

Lauren Russell is currently a member of the Johns Hopkins Bloomberg School of Public Master of Public Health (... Read Bio


Sumin Jeong

Research Volunteer - Veterans Health Administration Office for Health Equity 2021 NCHS Data Visualization Challenge Team Winner

Sumin Jeong graduated in 2019 with a Bachelors of Science in Nursing from Case Western Reserve University.  Read Bio


Sirin Yaemsiri, Ph.D., M.S.P.H.

Senior Statistician - U.S. Government Accountability Office 2021 NCHS Data Visualization Challenge Team Winner

Sirin Yaemsiri is a senior statistician at the U.S. Government Accountability Office. Read Bio

Moy headshot

Ernest Moy, M.D., M.P.H.

Executive Director - Department of Veterans Affairs, Office of Health Equity

Ernest Moy, M.D., M.P.H., is a medical officer in the Center for Quality Improvement and Patient Safety at the... Read Bio

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