In an era in which we have more data on human health than ever before, it is understandable that there is a level of disbelief around the idea of a data gap… and more specifically the need for basic and discovery focused science with prospective cohorts to address it.
So, to understand how the gap developed, it is important to understand two things:
As data increasingly inform every aspect of our lives, sex and gender discrimination and bias in the collection and application of female-based data has also risen —not improved. Data have historically been, and continue to be, primarily sourced from cis, white, males, and the solutions we design to address global problems are also primarily based trends emergent from the data collected from men, i.e. male bodies, male health experiences and disease pathways, male preferences and prototypical male life choices. This lack of sex and gender diversity and representation in the data collection process is what creates the so-called “gap.”
Scientists love a proxy, even when we have to do incredible mental gymnastics to justify them. It’s easy to look back at the Greek theories of anatomy and Galen’s heavily animalistic interpretations of the human body and critique them, and say, “Wasn’t that silly? Of course Macaques are going to be different from humans,” but, we’re still very much participating in this proxy race today due to the oversimplification of data collection caused by assumptions built into “The Reference Man.”
The Reference Man caused a historic lack of disaggregation between sex and gender in the data, and lack of inclusion of other demographic and health factors such as hormonal state, reproductive life stage, ethnicity, etc.. As such, today we have an incomplete data set (ie. a data gap)... that’s building models, diagnostic plans, and treatments based on a white male proxy.
Just like the errors are obvious when looking back at Galen and Vesalius’ work, future innovators will look at our changes today and laugh at the obvious holes in our work. But for today, some of the key ways to address the sex and gender data gap include:
Only after we understand what the results can be when we account for sex and gender in its various forms can we look back at the data we already have and retrospectively pull insights from existing data. Major medical innovation jumps have been made every time we’ve adapted our models for a more nuanced and true to form version of reality… and abandoned a proxy.
Join us on June 4th as we discuss the “Future of Data Equity” and dig into how My Normative helps researchers more easily account for sex and gender based differences. Register today!
Want to get started today? Book a meeting with Allison.
