Originally published in the October 2023 issue of the Notes on Antiracism, Justice, and Equity newsletter.

President Biden’s Executive Order 13985 created the Equitable Data Working Group, and one of their many objectives was to create a strategy for assessing inequities that arise in federal data and policies. One of this group’s recommendations is that federal policies and programs must “require an approach to evaluation and data analysis that allows for robust assessment of compounded experiences and overlapping identities” (White House, 2022). 

Starting in summer 2024, the School of Public Health (SPH) Division of Biostatistics and Health Data Science, in collaboration with the Masonic Institute for the Developing Brain (MIDB), will launch a summer program funded by the National Science Foundation for undergraduate students called Equitable Data Science for Adolescent Development. This program will provide undergraduate students from across the nation with opportunities to carry out research in equitable data science, with a focus on adolescent development using data from the Adolescent Brain Cognitive Development (ABCD) Study, the largest ongoing longitudinal study on adolescent development in the U.S. The ABCD Study collects large amounts of information from over 11,000 children, their parents, and their environment, resulting in a massive database that allows researchers to ask questions related to a child’s social, behavioral, and biological development. Researchers have already used data from the ABCD Study to identify inequities due to racial, ethnic, and socioeconomic factors and quantified experiences of racial discrimination among adolescents. 

Equitable data science investigates how the collection and composition of data sources, the use of analysis techniques, and the interpretation and application of analysis results can reflect, reinforce, and mitigate systemic inequalities. Bias in data collection procedures, statistical and machine learning models, and the interpretation and application of those models can reinforce long standing inequities. For instance, racially biased algorithms underestimate the healthcare needs of black patients, and prior research on the association between brain coupling and cognitive performance on adolescents only replicated in children from higher socioeconomic backgrounds. Through this program, students will acquire statistical and computational skills while also learning about the causes and consequences of social inequality and how inequalities and inequities manifest themselves in data sources. Students will also analyze data from other studies, where they will create products that highlight social inequities, promote increased awareness of these inequities, and inform discussions about how to address them. In addition to developing technical skills, students will learn about community engagement and connect with the broader community on equity-related research. Altogether, this summer program will give future data scientists the ability to bring an equity lens to data-centered projects. 

Eligible undergraduate students from across the US who are interested in this program, especially students who are members of historically underrepresented groups or enrolled in an institution where research programs are limited, are encouraged to apply.

Graduate students, postdocs, and faculty engaged in equity-oriented research can contact Dr. Fiecas at mfiecas@umn.edu to get involved by, e.g., contributing data from their studies or serving as mentors.

Sincerely,

Mark Fiecas

Associate Professor, Division of Biostatistics and Health Data Science

References and additional resources:

• Equitable Data Science for Adolescent Development
• The White House. A Vision for Equitable Data: Recommendations From the Equitable Data Working Group. 2022. 
• “How Do We Make Health Data Equitable” (video in the SPH SPAR animated video series; 3m00s) 
• Mark Fiecas Agents for Change profile
• Ellwood-Lowe, Monica E., Susan Whitfield-Gabrieli, and Silvia A. Bunge. “Brain network coupling associated with cognitive performance varies as a function of a child’s environment in the ABCD study.” Nature Communications 12.1 (2021): 7183. https://doi.org/10.1038/s41467-021-27336-y. 
• Fadus, Matthew C., et al. “Racial disparities in elementary school disciplinary actions: findings from the ABCD study.” Journal of the American Academy of Child & Adolescent Psychiatry 60.8 (2021): 998-1009. https://doi.org/10.1016/j.jaac.2020.11.017. 
• Nagata, Jason M., et al. “Prevalence of perceived racism and discrimination among US children aged 10 and 11 years: The Adolescent Brain Cognitive Development (ABCD) Study.” JAMA pediatrics 175.8 (2021): 861-863. https://doi.org/10.1001/jamapediatrics.2021.1022. 
• Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342. 
• Stinson, Elizabeth A., et al. “Longitudinal impact of childhood adversity on early adolescent mental health during the COVID-19 pandemic in the ABCD study cohort: does race or ethnicity moderate findings?” Biological psychiatry global open science 1.4 (2021): 324-335. https://doi.org/10.1016/j.bpsgos.2021.08.007. 
• Yip, Sarah W., et al. “Multivariate, transgenerational associations of the COVID-19 pandemic across minoritized and marginalized communities.” JAMA psychiatry 79.4 (2022): 350-358. https://doi.org/10.1001/jamapsychiatry.2021.4331.