Quick question: have you ever filled out a form and thought... none of these options are actually me?

I was doing a federal health survey last week. You know, trying to be a good citizen, contribute to the data, all that.

Got to demographics. Scrolled through looking for myself: South Asian, second-gen, Pakistani, descended from immigrants and refugees.

Found one box. "Asian."

That's it. That's the whole menu.

So apparently I'm in the same category as someone from Japan, Korea, the Philippines, India, Bangladesh, Vietnam, Thailand. You know, that massive continent with 60% of the world's population and roughly 2,000 distinct ethnic groups ¹.

Cool, cool, cool.

It's a small moment. But it sticks. Because you realize: this is how the system sees you. Or doesn't.

Public health loves to talk about "evidence-based" everything ². Evidence-based interventions. Evidence-based policy. Evidence-based buzzwords for grant applications.

But the evidence is based on data. And the data is based on whoever bothered to disaggregate it.

Spoiler: they often don't ³.

Right now, a lot of systems are built for convenience, not accuracy. They lump people into big categories so they can compare states, counties, trends. It's tidy. It's efficient.

It's also wildly incomplete.

When you collapse vastly different populations into one label, you hide everything that actually matters. Korean Americans have significantly higher rates of stomach cancer than other Asian subgroups ⁴. But if you're just looking at "Asian" as one blob, that detail disappears. Vietnamese health outcomes? Filipino community needs? Indian immigrant experiences? All gone, averaged into nothingness ⁵.

It's like taking the temperature of everyone in a room, adding it up, dividing by the number of people, and announcing "the average person is 98.6 degrees" while someone in the corner is literally on fire and someone else has hypothermia.

Technically accurate. Practically useless. Occasionally fatal.

Data collection isn't neutral. It's political.

And right now, the politics are pretty clear. We get broad categories that make comparisons between states easy (great for policy wonks writing reports) but tell us almost nothing about what's actually happening in specific communities (bad for, you know, the people living in these communities).

Researchers have a term for this: epistemic erasure ⁶. Basically, it means your knowledge, your lived reality, doesn't count in how the system defines truth.

No one asks your community what matters. No one builds the categories with you. So your story never makes it into the dataset in the first place.

I call it what it is: erasure with a methodology section (in other words a feature, not a bug).

Bad data doesn't just sit in a spreadsheet. It shapes decisions.

Funding. Programs. Messaging. Who gets screened. Who gets overlooked.

If the data says "Asian" is doing fine overall, that can hide real problems in specific communities. And if no one sees the problem, no one fixes it.

That's how you end up with public health solutions designed for a person who doesn't actually exist.

When health data gets lumped together, specific health challenges vanish ⁷. So do specific strengths. Community knowledge disappears. The full story, the human story, gets flattened into a deficit-based narrative that basically asks "what's wrong with these people?"

Which, fun fact, is exactly how colonial systems operate. Document what you consider problems, ignore everything else, use the documentation to justify why you're in charge.

Public health surveillance wasn't designed to do this on purpose; but it's built on systems that were. And nobody's particularly motivated to fix it because the people making decisions about data collection aren't the ones getting erased by it ⁸.

Interesting how that works…(side eye)

I'm not going to tell you that individual awareness fixes structural problems. It doesn't. You can't "be the change" (or “vote”) your way out of racist data systems.

But you can start asking better questions.

When you see a health statistic that makes a claim about "Asian Americans" or "Latinos/Latinx" or any other massive, diverse group: ask who's missing. Ask what's being hidden. Ask who benefits from keeping the data this vague.

When you can, support organizations fighting for data disaggregation ⁹. That's the practice of actually breaking down data by specific communities instead of throwing everyone into the same bucket and calling it a day.

And if you work in public health, research, policy, anywhere near data collection: push for specificity. Push for community involvement in deciding what gets counted and how. Push back on "but that's how we've always done it."

Because how we've always done it is the problem. Because “Asian” is not a health strategy.

This is exactly why I'm starting this newsletter.

Each week, I'll break down:

Not institutions. Not policy papers that nobody reads. But mutual aid. Community. The networks we build when systems fail.

No jargon. No pretending the data is neutral. Just a clearer picture of what's going on—and how we push it forward.

Current public health data practices aren't neutral. They're not objective. They're making active choices about whose experiences matter enough to count.

And right now, a lot of us aren't making the cut.

That's not an oversight. That's not a resource limitation. That's a decision about whose health is worth understanding in detail and whose can be averaged away.