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Abstract:
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Data equity refers to the considerations, through an equity lens, of the ways in which data are collected, analyzed, interpreted, and distributed. It is a practice important for all public health researchers and practitioners. Given that Biostatistics plays an essential role in virtually every step of the “data life cycle”, teaching these concepts to students is a critical component for an antiracist and inclusive biostatistics curriculum. However, data equity is a challenging topic to infuse throughout a Biostatistics curriculum. We will present an inter-professional experience course on data equity we are developing for biostatistics, public health and other students. The course covers topics including principles for advancing equitable data practice and how to incorporate an equity lens in each step of the research data cycle. Additional topics include data disaggregation, equity awareness in data visualization, and data equity in data science. We will also outline the core biostatistics curriculum and identify plans to incorporate data equity. We will discuss activities to fill the gaps, which will cultivate critical thinking skills on data equity.
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