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4^th-year graduate student Hannah Zadeh, also UIowa ’21, is completing their PhD studies in ����vlog�����ۿ� this summer—but they are not going far. This fall, Hannah will enroll in the Carver College of Medicine to pursue an MD degree. Hannah’s research, which was awarded a National Science Foundation Graduate Research Fellowship, focuses on the implications of the growing role of predictive statistics in medicine: an area that is rapidly expanding with the field’s increasing use of artificial intelligence. 

Hannah’s research explains “how political economic structures affect the production of medical knowledge, including the way that certain kinds of medical knowledge can exacerbate racial and economic inequalities.”

Hannah’s path to sociology began in our Intro to Sociology course taught by Professor Jenn Haylett and TA’d by Katie Linder, which Hannah took in their first year as a pre-med student. Hannah explains, “it was really interesting to read across classes, the different ways that they were talking about race specifically and sex/gender. And so I got really interested in the way that race gets materialized in medicine that way.” 

Hannah ultimately majored in sociology and continued at the U of Iowa for graduate work. Hannah’s masters’ thesis, on the origins of the race coefficient in an algorithm for measuring kidney function, was inspired in part by Hannah’s experiences shadowing doctors as a premed student. That work continued as the basis for a PhD dissertation, which has broadened into an analysis of statistics in medicine. 

The kidney function algorithmic tool might be familiar to people who have recently visited the doctor for blood work: “You might see it in your laboratory results as eGFR [estimated Glomerular Filtration Rate], but it basically estimates your kidney function. And until a couple years ago it had a race coefficient, which basically just means that if your doctor categorizes you as Black or African American, your kidney function score would be estimated to be about 20% higher.”

The “race coefficient” in this kidney function algorithm was recently removed because it was “contributing to the myth that race is biological.” As critics pointed out, African American patients tend to experience lower kidney function because of structural inequality, not because of innate differences between racial groups. Adjusting their scores upward would be the equivalent of saying this systematically lower kidney function was “normal” for African Americans, just because it was more common. 

And the stakes are high. Hannah explains, “The tool is used to decide whether to refer you to a specialist. It gets used to decide whether you're eligible for the kidney transplant registry. It's used in and outside medicine for a lot of important decisions. So it's important that we understand how those estimations are being produced.”

Hannah traces the development of this race coefficient to the larger turn to predictive statistics in medicine—that is, using average performance on a given factor, especially broken out by subgroup, to predict an individual patient’s outcomes. To do this work, Hannah conducted archival research, analyzed instructional texts and kidney function model components, and interviewed practicing nephrologists. 

Hannah explains:

“A lot of sociologists have already argued in general with racialized medicine that it can exacerbate racial inequalities. But also I think, in general, relying on statistical knowledge above and over any other kind of knowledge, especially for applications to the individual and the clinic, potentially privileges priorities other than the care of the patient, such as reducing costs or building the simplest model, even if that is not what is ideal for understanding what's going on with the patient.”

Hannah attributes their success in graduate school in part to funding from the National Science Foundation: “That has been a great honor, and I hope that [the program] continues to be funded. I feel like it's what research should be for everyone: protected time, a wage that matches cost of living.” Hannah also highlighted their participation in the graduate student union, COGS UE 896, which Hannah led for two years: “I think that there are a lot of pressures facing the university, especially right now. The union is fighting for better conditions for graduate workers to work and teach and do research under. And it's also a place for finding out in a very sociological sense that your personal troubles are public issues.”

We wish Hannah good luck as they bring that sociological standpoint across the river to Carver!