The Emperor of All Maladies: A Biography of Cancer (43 page)

This, too, then, was knowing the enemy.

We must learn to count the living
with that same particular attention with which we number the dead.

—Audre Lorde

Counting is the religion of this generation
. It is its hope and its salvation.

—Gertrude Stein

In November 1985, with oncology caught at a pivotal crossroads between the sobering realities of the present and the hype of past promises, a Harvard biologist named John Cairns resurrected the task of measuring progress in the War on Cancer.

The word
resurrection
implies a burial, and since the
Fortune
article of 1937, composite assessments of the War on Cancer had virtually been buried—oddly, in an overwhelming excess of information. Every minor footfall and every infinitesimal step had been so obsessively reported in the media that it had become nearly impossible to discern the trajectory of the field as a whole. In part, Cairns was reacting to the
overgranularity
of the view from the prior decade. He wanted to pull away from the details and offer a bird’s-eye view. Were patients with cancer surviving longer in general? Had the enormous investments in the War on Cancer since 1971 translated into tangible clinical achievements?

To quantify “progress,” an admittedly hazy metric, Cairns began by revitalizing a fusty old record that had existed since World War II, the cancer registry, a state-by-state statistical record of cancer-related deaths subclassified by the type of cancer involved. “
These registries,” Cairns wrote in an article
in
Scientific American
, “yield a rather precise picture of the natural history of cancer, and that is a necessary starting point for any discussion of treatment.” By poring through that record, he hoped to draw
a portrait of cancer over time—not over days or weeks, but over decades.

Cairns began by using the cancer registry to estimate the number of lives saved by the therapeutic advances in oncology since the 1950s. (Since surgery and radiation therapy preceded the 1950s, these were excluded; Cairns was more interested in advances that had emerged from the brisk expansion in biomedical research since the fifties.) He divided these therapeutic advances into various categories, then made numerical conjectures about their relative effects on cancer mortality.

The first of these categories was “curative” chemotherapy—the approach championed by Frei and Freireich at the NCI and by Einhorn and his colleagues at Indiana. Assuming relatively generous cure rates of about 80 or 90 percent for the subtypes of cancer curable by chemotherapy, Cairns estimated that between 2,000 and 3,000 lives were being saved overall every year—700 children with acute lymphoblastic leukemia, about 1,000 men and women with Hodgkin’s disease, 300 men with advanced testicular cancer, and 20 to 30 women with choriocarcinoma. (Variants of non-Hodgkin’s lymphomas, which were curable with polychemotherapy by 1986, would have added another 2,000 lives, bringing the total up to about 5,000, but Cairns did not include these cures in his initial metric.)

“Adjuvant” chemotherapy—chemotherapy given after surgery, as in the Bonadonna and Fisher breast cancer trials—contributed to another 10,000 to 20,000 lives saved annually. Finally, Cairns factored in screening strategies such as Pap smears and mammograms that detected cancer in its early stages. These, he estimated loosely, saved an additional 10,000 to 15,000 cancer-related deaths per year. The grand tally, generously speaking, amounted to about 35,000 to 40,000 lives per year.

That number was to be contrasted with the annual incidence of cancer in 1985—448 new cancer cases diagnosed for every 100,000 Americans, or about 1 million every year—and the mortality from cancer in 1985—211 deaths for every 100,000, or 500,000 deaths every year. In short, even with relatively liberal estimates about lives saved, less than one in twenty patients diagnosed with cancer in America, and less than one in ten of the total number of patients who would die of cancer, had benefited from the advances in therapy and screening.

Cairns wasn’t surprised by the modesty of that number; in fact, he claimed, no self-respecting epidemiologist should be. In the history of medicine, no significant disease had ever been eradicated by a treatment-related program alone. If one plotted the decline in deaths from tubercu
losis, for instance, the decline predated the arrival of new antibiotics by several decades. Far more potently than any miracle medicine, relatively uncelebrated shifts in civic arrangements—better nutrition, housing, and sanitation, improved sewage systems and ventilation—had driven TB mortality down in Europe and America. Polio and smallpox had also dwindled as a result of vaccinations. Cairns wrote, “The death rates from malaria, cholera, typhus, tuberculosis, scurvy, pellagra and other scourges of the past have dwindled in the US because humankind has learned how to
prevent
these diseases. . . . To put most of the effort into treatment is to deny all precedent.”

Cairns’s article was widely influential in policy circles, but it still lacked a statistical punch line. What it needed was some measure of the
comparative
trends in cancer mortality over the years—whether more or less people were dying of cancer in 1985 as compared to 1975. In May 1986, less than a year after Cairns’s article, two of his colleagues from Harvard, John Bailar and Elaine Smith, provided precisely such an analysis in the
New England Journal of Medicine
.

To understand the Bailar-Smith analysis, we need to begin by understanding what it was not. Right from the outset, Bailar rejected the metric most familiar to patients: changes in survival rates over time. A five-year survival rate is a measure of the fraction of patients diagnosed with a particular kind of cancer who are alive at five years after diagnosis. But a crucial pitfall of survival-rate analysis is that it can be sensitive to biases.

To understand these biases, imagine two neighboring villages that have identical populations and identical death rates from cancer. On average, cancer is diagnosed at age seventy in both villages. Patients survive for ten years after diagnosis and die at age eighty.

Imagine now that in one of those villages, a new, highly specific test for cancer is introduced—say the level of a protein Preventin in the blood as a marker for cancer. Suppose Preventin is a perfect detection test. Preventin “positive” men and women are thus immediately counted among those who have cancer.

Preventin, let us further suppose, is an exquisitely sensitive test and reveals very early cancer. Soon after its introduction, the average age of cancer
diagnosis
in village 1 thus shifts from seventy years to sixty years, because earlier and earlier cancer is being caught by this incredible new
test. However, since no therapeutic intervention is available even after the introduction of Preventin tests, the average age of death remains identical in both villages.

To a naive observer, the scenario might produce a strange effect. In village 1, where Preventin screening is active, cancer is now detected at age sixty and patients die at age eighty—i.e., there is a twenty-year survival. In village 2, without Preventin screening, cancer is detected at age seventy and patients die at age eighty—i.e., a ten-year survival. Yet the “increased” survival cannot be real. How can Preventin, by its mere existence, have increased survival without any therapeutic intervention?

The answer is immediately obvious: the increase in survival is, of course, an artifact. Survival rates seem to increase, although what has really increased is the
time from diagnosis to death
because of a screening test.

A simple way to avoid this bias is to not measure survival rates, but overall mortality. (In the example above, mortality remains unchanged, even after the introduction of the test for earlier diagnosis.)

But here, too, there are profound methodological glitches. “Cancer-related death” is a raw number in a cancer registry, a statistic that arises from the diagnosis entered by a physician when pronouncing a patient dead. The problem with comparing that raw number over long stretches of time is that the American population (like any) is gradually aging overall, and the rate of cancer-related mortality naturally increases with it. Old age inevitably drags cancer with it, like flotsam on a tide. A nation with a larger fraction of older citizens will seem more cancer-ridden than a nation with younger citizens, even if actual cancer mortality has not changed.

To compare samples over time, some means is needed to
normalize
two populations to the same standard—in effect, by statistically “shrinking” one into another. This brings us to the crux of the innovation in Bailar’s analysis: to achieve this scaling, he used a particularly effective form of normalization called age-adjustment.

To understand age-adjustment, imagine two very different populations. One population is markedly skewed toward young men and women. The second population is skewed toward older men and women. If one mea
sures the “raw” cancer deaths, the older-skewed population obviously has more cancer deaths.

Now imagine normalizing the second population such that this age skew is eliminated. The first population is kept as a reference. The second population is adjusted: the age-skew is eliminated and the death rate shrunk proportionally as well. Both populations now contain identical age-adjusted populations of older and younger men, and the death rate, adjusted accordingly, yields identical cancer-specific death rates. Bailar performed this exercise repeatedly over dozens of years: he divided the population for every year into age cohorts—20–29 years, 30–39 years, 40–49, and so forth—then used the population distribution from 1980 (chosen arbitrarily as a standard) to convert the population distributions for all other years into the same distribution. Cancer rates were adjusted accordingly. Once all the distributions were fitted into the same standard demographic, the populations could be studied and compared over time.

Bailar and Smith published their article in May 1986—and it shook the world of oncology by its roots. Even the moderately pessimistic Cairns had expected at least a small decrease in cancer-related mortality over time. Bailar and Smith found that even Cairns had been overgenerous: between 1962 and 1985, cancer-related deaths had
increased
by 8.7 percent. That increase reflected many factors—most potently, an increase in smoking rates in the 1950s that had resulted in an increase in lung cancer.

One thing was frightfully obvious:
cancer mortality was not declining
in the United States.
There is “no evidence
,” Bailar and Smith wrote darkly, “that some thirty-five years of intense and growing efforts to improve the treatment of cancer have had much overall effect on the most fundamental measure of clinical outcome—death.” They continued, “We are losing the war against cancer notwithstanding progress against several uncommon forms of the disease [such as childhood leukemia and Hodgkin’s disease], improvements in palliation and extension of productive years of life. . . . Some thirty-five years of intense effort focused largely on improving treatment must be judged a qualified failure.”

That phrase, “qualified failure,” with its mincing academic ring, was deliberately chosen. In using it, Bailar was declaring his own war—against the cancer establishment, against the NCI, against a billion-dollar cancer-treatment industry. One reporter described him as “
a thorn in the side of the National Cancer Institute
.” Doctors railed against Bailar’s analysis, describing him as a naysayer, a hector, a nihilist, a defeatist, a crank.

Predictably, a torrent of responses appeared in medical journals. One camp of critics contended that the Bailar-Smith analysis appeared dismal not because cancer treatment was ineffective, but because it was not being implemented aggressively enough. Delivering chemotherapy, these critics argued, was a vastly more complex process than Bailar and Smith had surmised—so complex that even most oncologists often blanched at the prospect of full-dose therapy.
As evidence, they pointed to a survey
from 1985 that had estimated that only one-third of cancer doctors were using the most effective combination regimen for breast cancer. “I estimate that 10,000 lives could be saved by the early aggressive use of polychemotherapy in breast cancer, as compared with the negligible number of lives, perhaps several thousand, now being saved,” one prominent critic wrote.