Despite recent advancements in regulatory requirements around disaggregating sex and gender variables, hormone variability and the impacts of different reproductive life events are still largely not considered in the analytical process (in part due to related data points not being collected in the first place). This lack of representation in the analytical process perpetuates disparities in equitable health outcomes, such as those seen in conditions like cardiovascular disease and Alzheimer’s in spite of an increase in inclusion of women and female-identifying individuals in trials.
In reaction to this persistent equity gap, the White House recently committed to investing $100M in research into the ways in which women and female-identifying individuals are impacted disproportionately, differently, or uniquely by illness.
The process of data collection is the foundation of generating unbiased research findings and thus equitable health outcomes across sex and gender.
So let’s talk about it.
The sex and gender data gap.
“What’s wrong with the data we already have?”
We have more data on human health now than ever before, however; the 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. means that we have an incomplete data set (ie. a data gap). This gap in the data creates biases and errors when we try to retroactively use it for understanding why differences exist across sex and gender.
In order to leverage the data we have today, we must first build a data set that is collected for the population underserved by the current data set. Only after we understand what the results can be when we account for sex and gender can we look back at the data we have and retrospectively pull insights from existing data.
“And why hasn’t the gap already been addressed?”
Frankly, because it’s hard.
Between a lack of training and expertise on the realities of variabilities across sex and gender to the tools we use and the ways by which we collect that data disrupting the current system has been so far, for many researchers and innovators, too big of a lift for the pay off that comes from inclusion and representativeness. From logistical burden to participant burden to financial burden so far it just hasn’t been tenable to measure women and female identifying people accurately.
My Normative makes it tenable. My Normative makes it easy. My Normative makes it so that you don’t have to hire a whole new team of experts and interns to process pee sticks and salivary swabs to do a good job of accounting for sex and gender variability.
Join us over the next few weeks as we talk shop on the “what,” “so what,” and “now what,” of equitable data collection in the life sciences.
At the end of this month we’ll be running a webinar to talk all things about the "Future of Data Equity" and we’d love to have you join us.
Want to go straight to the source and talk about all things data capture for an upcoming project? Book a meeting with Allison.
