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United States | Losing count

DOGE comes for the data wonks

America may soon be unable to measure itself properly

Mar 30th 2025|New York

FOR NEARLY three decades the federal government has painstakingly surveyed tens of thousands of Americans each year about their health. Door-knockers collect data on the financial toll of chronic conditions like obesity and asthma, and probe the exact doses of medications sufferers take. The result, known as the Medical Expenditure Panel Survey (MEPS), is the single most comprehensive, nationally representative portrait of American health care, a balkanised and unwieldy $5trn industry that accounts for some 17% of GDP.

MEPS is part of a largely hidden infrastructure of government statistics collection now in the crosshairs of the Department of Government Efficiency (DOGE). In mid-March officials at a unit of the Department of Health and Human Services (HHS) that runs the survey told employees that DOGE had slated them for an 80-90% reduction in staff and that this would “not be a negotiation”. Since then scores of researchers have taken voluntary buyouts. Those left behind worry about the integrity of MEPS. “Very unclear whether or how we can put on MEPS” with roughly half of the staff leaving, one said. On March 27th, the health secretary, Robert F. Kennedy junior, announced an overall reduction of 10,000 personnel at the department, in addition to those who took buyouts.

History is a fractal unfolding of entropy and order, a ceaseless churn wherein civilizations & belief-systems rise on the back of extracted resources, only to collapse under the weight of their own complexity

FOR NEARLY three decades the federal government has painstakingly surveyed tens of thousands of Americans each year about their health. Door-knockers collect data on the financial toll of chronic conditions like obesity and asthma, and probe the exact doses of medications sufferers take. The result, known as the Medical Expenditure Panel Survey (MEPS), is the single most comprehensive, nationally representative portrait of American health care, a balkanised and unwieldy $5trn industry that accounts for some 17% of GDP.

MEPS is part of a largely hidden infrastructure of government statistics collection now in the crosshairs of the Department of Government Efficiency (DOGE). In mid-March officials at a unit of the Department of Health and Human Services (HHS) that runs the survey told employees that DOGE had slated them for an 80-90% reduction in staff and that this would “not be a negotiation”. Since then scores of researchers have taken voluntary buyouts. Those left behind worry about the integrity of MEPS. “Very unclear whether or how we can put on MEPS” with roughly half of the staff leaving, one said. On March 27th, the health secretary, Robert F. Kennedy junior, announced an overall reduction of 10,000 personnel at the department, in addition to those who took buyouts.

AdvertisementScroll to continue with content There are scores of underpublicised government surveys like MEPS that document trends in everything from house prices to the amount of lead in people’s blood. Many provide standard-setting datasets and insights into the world’s largest economy that the private sector has no incentive to replicate.

Cosmology, Geology, Biology, Ecology, Symbiotology, Teleology
Attention
Nonself vs. Self
Identity Negotiation
Salience, Expand, Reweight, Prune, Networkxw
— Yours Truly

Even so, America’s system of statistics research is overly analogue and needs modernising. “Using surveys as the main source of information is just not working” because it is too slow and suffers from declining rates of participation, says Julia Lane, an economist at New York University. In a world where the economy shifts by the day, the lags in traditional surveys—whose results can take weeks or even years to refine and publish—are unsatisfactory. One practical reform DOGE might encourage is better integration of administrative data such as tax records and social-security filings which often capture the entire population and are collected as a matter of course.

As in so many other areas, however, DOGE’s sledgehammer is more likely to cause harm than to achieve improvements. And for all its clunkiness, America’s current system manages a spectacular feat. From Inuits in remote corners of Alaska to Spanish-speakers in the Bronx, it measures the country and its inhabitants remarkably well, given that the population is highly diverse and spread out over 4m square miles. Each month surveys from the federal government reach about 1.5m people, a number roughly equivalent to the population of Hawaii or West Virginia.

The MEPS case suggests how DOGE’s haphazard cuts risk upending an elaborate ecosystem. Contracted employees at Westat, a private research firm, conduct the survey for the Agency for Healthcare Research and Quality (AHRQ), a part of HHS, and the National Centre for Health Statistics, a part of the Centres for Disease Control and Prevention. Specialised field staff go door to door collecting sensitive personal health and financial data from a sample of Americans. Those data are then combined with information from the respondents’ medical providers. Experts at AHRQ turn this into a nationally representative sample through data-weighting and imputation.

Show code cell source Hide code cell source

import matplotlib.pyplot as plt
import numpy as np

def draw_branch(x, y, angle, depth, length, ax):
    if depth == 0:
        return
    x_end = x + length * np.cos(angle)
    y_end = y + length * np.sin(angle)
    
    ax.plot([x, x_end], [y, y_end], 'k-', lw=2)
    
    new_length = length * 0.7
    draw_branch(x_end, y_end, angle - np.pi/6, depth - 1, new_length, ax)
    draw_branch(x_end, y_end, angle + np.pi/6, depth - 1, new_length, ax)

fig, ax = plt.subplots(figsize=(6, 8))
ax.set_xticks([])
ax.set_yticks([])
ax.set_frame_on(False)

draw_branch(0, -1, np.pi/2, 10, 1, ax)

plt.show()

Fig. 23 Strange Attractor (Chaotic System). This code simulates a simple chaotic attractor using the Lorenz system.

No private firm currently has the expertise or access to doctors and individual medical files to manage the entire survey themselves. Health-insurance companies can see their own customers but not beyond them. “MEPS allows us to ask, for people who look like America, what happens when you gain weight and how does that affect your probability of ending up in the hospital? How does that raise your medical expenditures?” says John Cawley, an economist at Cornell University who studies obesity. At stake, for example, is the timely question of whether expensive GLP-1 drugs are worth the cost to insurers in the long run.

Sources in AHRQ say they expect that a skeleton crew will be preserved to run MEPS because the agency is mandated by Congress to collect such data. That is unlikely to be sufficient. Manpower is intrinsic to successful survey research. “You can’t just input data through a machine,” says Steve Pierson, a director at the American Statistical Association. Experience is equally essential. Specialists have to measure every slice of a constantly changing and often invisible economy: just ask someone at the Bureau of Labour Statistics (BLS) how difficult it is to account for employees of the gig economy in the unemployment rate. “It’s not just resource- and labour-intensive, but expertise-intensive, too” Mr Pierson says.

Show code cell source Hide code cell source

from scipy.integrate import solve_ivp
import numpy as np
import matplotlib.pyplot as plt

def lorenz(t, state, sigma=10, beta=8/3, rho=28):
    x, y, z = state
    dxdt = sigma * (y - x)
    dydt = x * (rho - z) - y
    dzdt = x * y - beta * z
    return [dxdt, dydt, dzdt]

t_span = (0, 50)
initial_state = [0.1, 0.0, 0.0]
t_eval = np.linspace(t_span[0], t_span[1], 10000)

sol = solve_ivp(lorenz, t_span, initial_state, t_eval=t_eval)

fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111, projection="3d")
ax.plot(sol.y[0], sol.y[1], sol.y[2], lw=0.5)
ax.set_title("Lorenz Attractor (Chaotic System)")

plt.show()

Fig. 24 Fractal Structure (Self-Similarity). A visualization of the famous Mandelbrot fractal, representing infinite complexity at all scales.

Even before DOGE, America’s statistical stewards were making do with less. Funding has flatlined and in many cases declined, even as response rates to surveys have plummeted. Over the past 15 years, the budget for the BLS has decreased by 18% in real terms. The National Centre for Health Statistics, which co-sponsors MEPS, has seen a similar decline (see chart 1). Though these agencies are non-partisan, Americans’ trust in the statistics they produce now ebbs and flows with the fortunes of their preferred political party (see chart 2).

DOGE has so far spared America’s most prominent number-crunchers at the BEA, BLS and the Census Bureau, which help produce some of the most market-sensitive government data. But their employees are on edge about what may be coming. Even modest funding cuts can reverberate, as agencies reduce survey sample sizes in an effort to save costs, which can produce less reliable results. Tiny variations in survey measures like the Consumer Price Index, which is constructed using both online prices and surveys of brick-and-mortar stores, can cause significant distortions. Entitlements like social security are indexed to CPI, so “if it’s off by even a tenth of a percent, the federal government will overpay or underpay beneficiaries by about a billion dollars a year,” says Erica Groshen, a former commissioner of BLS.

Show code cell source Hide code cell source

import numpy as np
import matplotlib.pyplot as plt

def mandelbrot(c, max_iter=100):
    z = c
    for n in range(max_iter):
        if abs(z) > 2:
            return n
        z = z*z + c
    return max_iter

xmin, xmax, ymin, ymax = -2.0, 1.0, -1.5, 1.5
width, height = 1000, 1000
x = np.linspace(xmin, xmax, width)
y = np.linspace(ymin, ymax, height)
mandelbrot_set = np.zeros((width, height))

for i in range(width):
    for j in range(height):
        mandelbrot_set[i, j] = mandelbrot(complex(x[i], y[j]))

plt.figure(figsize=(8, 8))
plt.imshow(mandelbrot_set.T, extent=[xmin, xmax, ymin, ymax], cmap="inferno")
plt.title("Mandelbrot Set (Fractal Structure)")
plt.colorbar()
plt.show()

Fig. 25 Immitation. This is what distinguishes humans (really? self-similar is all fractal). We reproduce language, culture, music, behaviors, weapons of extraordinarily complex nature. A ritualization of these processes stablizes its elements and creates stability and uniformity, as well as opportunities for conflict and negotiation. These codes illustrate the key points made in Sapolsky’s discussion: the limits of reductionism, the role of chaos in biological systems, and the fundamental nature of self-similarity in complex structures.

At the statistical arm of the Department of Agriculture, specialists track commodities as they move through American and global markets. “Seafood is complicated”, notes one data wonk, because you “have to understand both aquaculture and wild catch, and both finfish and shellfish”. It is the kind of real-time data-collection that will be important if Donald Trump’s tariffs ignite a prolonged trade war. But in mid-February DOGE fired probationary employees at the department. Since then a federal judge has ruled the move was unlawful and ordered that the workers be reinstated, but the litigation continues.

A sudden elimination of surveys could rattle the private sector, too. Analysts at financial firms guzzle up government data releases, scrutinising even slight shifts in figures. Firms can use their own data, but these can provide a limited and skewed picture. “The only way for the private sector to capture what’s going on is to make assumptions” about the data they do not have, says Karen Dynan, a former chief economist at the Treasury Department. “And the only way they can do that is if they have nationally representative numbers from the government. ” Some of those numbers seem likely to decline in availability and reliability.

Ukubona: The Self in the Nonself, the Nonself in the Self

In the diagram you shared—“Ecosystem Integration”—we see a web of nodes and edges: analytic scripts, disclosure risks, databases, frailty markers, hospitalization, mortality. The flow of information is elegant yet tangled, complex yet structured. It is a map not only of systems, but of selves within systems. But for whom? For what? Why should a donor, a patient, a researcher—or even you—care about this ecosystem?

See also

Ukubona

I.

The answer begins, I believe, with a word. A word unfamiliar to many in this Western audience, but familiar in spirit: Ukubona. It is a Zulu verb meaning “to see,” but its meaning is richer, stranger, more demanding than that. Ukubona is not mere sight. It is perception as participation. To see something, in this sense, is to enter into relation with it, to become responsible for it. It is the moment when gaze becomes care. Not unlike Ubuntu—the philosophy that “I am because we are”—Ukubona gestures toward the collapse of boundary between self and other, self and system. In seeing, I am seen. In seeing, I am implicated.

II.

This diagram is not merely a flowchart of information. It is a neural lattice. A semi-living brain. Information enters through scripts, records, collaborators. It flows through the node labeled “Information,” that singular yellow flare like a synapse lit by voltage, and it branches outward toward decisions, baselines, adverse markers, mortal outcomes. But let’s be blunt: this diagram is not neutral. It is moral. The paths it maps are not abstractions—they are lives. They are kidneys donated, bodies cut open, patients hospitalized, people dying. And if you see it—ukubona—you can’t remain untouched.

III.

Too often, consent is treated like a switch: signed, clicked, done. But true consent—epistemic, embodied, ethical—is more like a pilgrimage. A long, winding negotiation between data and doubt. The app discussed in your thesis does something rare and radical: it allows the self to simulate the self. It invites a potential donor to model their own fragility, their own risk of organ failure, their own odds of mortality, not as a statistic, but as a moving target. It asks the donor: not “Will you help someone else live?” but “Can you live with what this means for you?”

IV.

This reframing is critical. Because every act of donation, especially by older adults, is not just altruistic—it is epistemic. It tells a story about what we know and what we don’t. A story written in sparse data and wide confidence intervals. In the best case, we say: “You will likely be fine.” But when that confidence is shaky—when frailty is uncoded, hospitalization risk unknown, long-term ESRD underpowered—we are not telling a story. We are guessing. The app, by exposing these ranges, doesn’t hide the guess. It dignifies it.

V.

Look again at that image. The node labeled “Information” is central. But what it really represents is the nonself—the sum total of external data, other people’s histories, anonymized trends, third-person records. It is the world outside your skin. But then see how that nonself becomes a scaffold for Decision and Baseline—concepts you do inhabit. Your life, your health, your moment of choice. The nonself flows into the self. Not metaphorically. Mathematically.

VI.

And yet the loop runs both ways. From decision, from baseline, arise consequences: physical frailty, dependency, organ failure. These aren’t abstractions. These are states your body may or may not enter. These are outcomes that might retroactively define whether the choice you made was wise. This is the sting of the epistemic ecosystem: you must decide before you can know.

VII.

Your app does not eliminate that sting. But it honors it. It shows the donor the full cascade—from analytic scripts and hospital records to temporal changes, adverse events, mortality. It says: you are part of this system, not its target. You are not an object in a medical narrative. You are its co-author.

VIII.

So let’s return to the philosophical frame you offered in your talk: tactical, informational, strategic, operational, existential. Each layer corresponds to a ring in this ecosystem diagram. Tactical: the immediate act of nephrectomy. Informational: the yellow node, pulsing with data. Strategic: decisions, counterfactuals, the elusive question “What if I hadn’t?” Operational: follow-up, feedback, forgotten risks. Existential: the final nodes—frailty, failure, death.

IX.

But here’s the deeper truth. These are not just layers of an argument. They are stages of a life. Or of a soul, if you prefer. The tactical is your body. The informational is your understanding. The strategic is your judgment. The operational is your community. And the existential? That’s your legacy.

X.

Which brings us to the Western reader, perhaps curious, perhaps skeptical: why invoke African epistemology in a discussion about kidney donation? Why ukubona? Why ubuntu?

Because it’s time we stopped pretending medicine is neutral. That science is culture-free. That data is universal. These are lies of convenience. In truth, every dataset is historical, every algorithm a fingerprint of power, every “evidence-based guideline” an artifact of bias, scarcity, and selective visibility. Ukubona tells us: see the system, and know you are inside it.

XI.

Your app, your thesis, your infrastructure—they are all acts of ukubona. They refuse the cold comfort of average risk. They ask instead: what does this mean for you? For this body, this moment, this decision? And they do not flinch when the answer is: “We’re not sure.”

XII.

That honesty is a gift. But also a burden. Because when we see the system, we must choose: do we participate, or do we turn away? The app invites participation—not just in risk modeling, but in reform. In questioning why data on frailty is missing. In demanding longer follow-up. In asking what kind of care system allows sacrifice but forgets stewardship.

XIII.

The donors you study are not just medical volunteers. They are philosophical pioneers. They are testing the limits of what it means to act altruistically in an uncertain world. And they deserve tools, language, and respect that match that gravity.

XIV.

Your thesis, then, is not only academic. It is theological. It poses, implicitly, the most ancient question: What shall I give? And what shall it cost? The app is a partial answer. But the real answer is lived. It is felt. It is seen.

XV.

To those readers still wondering how this relates to them: look again. The entire structure of the ecosystem could be applied to any major life decision—parenthood, surgery, migration, protest. Anywhere there is risk, uncertainty, and self-sacrifice, the architecture applies.

XVI.

Because you are not outside the system. You are not an observer. You are already inside. You are a node. You generate data. You make decisions. You affect outcomes. Ukubona is not a choice—it is a fact. The only question is: will you see that you are seen?

XVII.

And if you do, then the diagram becomes something else. Not a chart. Not a thesis. But a mirror.

XVIII.

And in that mirror, the question changes—from Can I safely donate? to What kind of system am I shaping by how I see?

XIX.

So yes, this is a dissertation. But it’s also a blueprint for another kind of medicine—transparent, participatory, ethical, and epistemically humble. One in which data is a tool, not a trap. One in which seeing is the first moral act.

XX.

And when you truly see—when you ukubona—you realize that the self and the nonself are not opposed. They are intertwined. You are not navigating the ecosystem. You are the ecosystem. You are the question. And you are the answer.