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Building Fairness Into the Data Supply Chain

The Impact of One Lab Value

Step 2Live-room allocation: 3:00

Open in Colab — 01_act1.ipynb, opens in a new tab

Open 01_act1.ipynb and run every cell, top to bottom. The numbers on this page are already computed — the notebook lets you reproduce them.

Consider one patient: 50 years old, male, Black, with a serum creatinine of 1.5 mg/dL.

Portrait of a middle-aged Black man seated in an exam room before clinical vitals are taken.

One patient at the start of a routine clinical visit. The blood draw has not changed; what changes is the equation used to interpret it.

That creatinine is a measurement. It does not change. What changes is the equation the lab runs it through.

Under CKD-EPI 2009, which includes a coefficient for Black race, this patient’s estimated GFR is 62.0. Under CKD-EPI 2021, which does not, the same blood gives 56.4.

One patient, both equations. The gap is small; the boundary it crosses is not.

The difference is 5.7 units. On its own that is unremarkable — well inside the noise you would accept from a different assay or a different day.

But the number does not stay a number. It lands in a table of stage boundaries that somebody drew, and this patient lands on different sides of one of them: stage G2 under the 2009 equation, stage G3a under the 2021 one.

Nothing about the patient changed. Their stage did.

That is the whole shape of what follows. A change this small is invisible at the point it happens — it shows up as a slightly different float in a slightly different column. It becomes visible only downstream, at the places where somebody drew a line and attached a decision to it. The rest of this workshop follows that number to those places.