Consult the CDC’s 2019 report of Racial and Ethnic Disparities in Breastfeeding Initiation in the United States. You’ll notice that breastfeeding initiation among Asian dyads is quite high. In fact, data depict high overall breastfeeding rates among Asian mothers.
But Breastfeeding Champion To-Wen Tseng clarifies that there is great diversity within this lumped-together ethnic group and that looking at the data alone can be terribly misleading.
First, she says, many Asian Pacific Islander (API) communities are missing entirely in the data.
In Los Angeles specifically, where most API families are first generation immigrants, they suffer from the lowest in-hospital breastfeeding rates– 52.9 percent of Asian families breastfeed in the hospital while 79.5 percent of white parents are breastfeeding in the hospital, she shares.
We are drawn to quantitative data in an effort to cut through bias and human error, but Heather Krause, PStat, the founder of We All Count— a project to increase equity in data science– points out that quantitative data is not neutral. All data have someone’s lived experience centered in it, she says.
In so many bodies of research, you may notice that the experience of being white is centered. Other identities are usually “in comparison to,” where white is the standard.
Earlier this year, the United States Breastfeeding Committee (USBC) hosted We All Count Data Equity Framework Session where Krause presented an introduction to the foundations of data equity.
Because the team at We All Count believes that equity is a process and not a binary state, they pursue independent research, studying their own on-the-ground data work, partner with experts, and consult with communities to improve and update their understanding of what ‘equity’ means in data science in order to create the Data Equity Framework, “a living, feedback-responsive system for addressing data project equity which is continually updated, added to, and refined.” [https://weallcount.com/about-us/]
The Data Equity Framework breaks up projects into seven stages. That process can be found here. Krause explains that the framework is not a recipe. Instead, it works holistically and individually and should be viewed more like a tool kit, she says.
An important feature of quantitative data are the choices involved that shape the numbers: Who is represented? What is measured? What metrics are used? Who defines “success”? How is the data presented? And so on.
“Every choice reflects a worldview,” Krause puts it. “Even the smallest choices can have huge impacts.”
She refers to the work of Abigail Echo-Hawk, chief research officer of the Seattle Indian Health Board, who said “We are a small population because of genocide, no other reason. If you eliminate us in the data, we don’t exist, we don’t exist for the allocation of resources.”
In a piece by Manola Secaira on Crosscut, Echo-Hawk is quoted further explaining this concept:
“When we think about data, and how it’s been gathered, is that, from marginalized communities, it was never gathered to help or serve us. It was primarily done to show the deficits in our communities, to show where there are gaps. And it’s always done from a deficit-based framework. They talk about how our communities have the highest rates of obesity, have the highest rates of diabetes, highest rates of infant mortality. How our people may be experiencing high rates of opiate misuse.
What they don’t talk about is the strengths of our community. What we know, particularly for indigenous people, is that there was a genocide and assimilation policies and termination policies that were perpetuated against us. If they had worked, we wouldn’t be here. And so we were always strength-based people, who passed on and continued knowledge systems regardless of people who tried to destroy us.”
Krause offers a set of actions that we can do right now to advance equity in data:
- Recognize that we all are making subjective, human choices in our data work.
- Recognize the choices. Figure out what your choices are.
- Make choices around data so they reflect the equality we are trying to align with.
- Expand the group of people who get to make meaningful choices about data.
- Talk about our data choices, be honest and stand by them.
- Be ready to try and make even better choices next time. Equity is not a static place that we can arrive at. Our data reflects how we see the world, but that’s a good thing. It means we can choose equity.
Continuing to dive into this work, author of Decolonizing Data: Unsettling Conversations about Social Research Methods Jacqueline M. Quinless suggests that “….Researchers should support community-driven initiatives and work in partnership with Indigenous people, communities and/or organizations in such a way as to avoid misinterpretations and misrepresentations in the knowledge inquiry process.”
The Coalition of Communities of Color (CCC) is another organization striving for research and data justice. CCC’s approach to research justice is informed by community-based participatory action research (CBPAR) which challenges the belief that researchers know best because of the degrees they hold and the position of power they occupy.
Instead, the CBPAR strategy is driven by the ideas that community members are experts, BIPOC communities are positioned as researchers rather than the objects of research and inquiry and already have the capacity to conduct critical and systemic inquiry into their own lived experiences, and that BIPOC knowledge and expertise can counter dominant cultural narratives that center deficit models rather than strengths-based models. [https://www.coalitioncommunitiescolor.org/ccc-researchdatajustice]
We All Count offers several learning opportunities that you can learn about on their website. Krause actively teaches medical students about data equity and says chuckling that she is “trying to infiltrate every course at the graduate level” too. We All Count also works with organizations that influence social identity categories.