Are You a Victim of Environmental Cancer Hype?



When is a cancer cluster the result of environmental contamination—and when is it a misleading statistical artifact? Science journalist George Johnson deals with this question in his latest article for Slate, “Cancer Cluster or Chance?”

Johnson’s skill in explaining why we often misinterpret epidemiological data can be seen in his opening paragraphs:

Lay a chessboard on a table. Then grab a handful of rice and let the grains fall and scatter where they may. They won’t spread out uniformly with the same number occupying each square. Instead there will be clusters. Now suppose that the chessboard is a map of the United States and the grains are cases of cancer.

Each year about 1.6 million cases of cancer are diagnosed in the United States, and epidemiologists regularly hear from people worried that their town has been plagued with an unusually large visitation. Time after time, the clusters have turned out to be statistical illusions—artifacts of chance.

The Erin Brockovich incident, one of the most famous, is among the many that have been debunked. Hexavalent chromium in the water supply of a small California town was blamed for causing cancer, resulting in a $333 million legal settlement and a movie starring Julia Roberts. But an epidemiological study ultimately showed that the cancer rate was no greater than that of the general population. The rate was actually slightly less.

Johnson is not the first to argue that cases of residential cancer clusters (as distinct from industrial clusters among, say, asbestos workers) appear to be wildly exaggerated. (For a skeptical look at the somewhat related issue of “environmental racism” in Mississippi, see this 2003 study from The Independent Review.) But he does not dismiss such claims out of hand: He discusses two possible examples of environmental contaminants leading to higher residential cancer rates—one in Woburn, Mass., in the 1980s and another in Toms River, N.J., in the 1990s—although he remains unconvinced about the latter.

Johnson closes with these words of wisdom about the tricky art of statistical inference:

The tools of statistics, so powerful when applied to large populations, break down with small numbers. As so often in life, we’re left wondering how to distinguish between randomness and patterns too subtle to see.

His piece is worth reading in its entirety.

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