Toms River (46 page)

The message in the numbers seemed clear: Linda Gillick was right, and so was Lisa Boornazian. Something unusual really
was
happening in Toms River—or at least had happened between 1979 and 1991. For Berry, there was only one remaining question, and it was an exceedingly difficult one to answer: Could it all just be due to random
variation, to a run of very bad luck? He had attended the cluster buster conference in 1989, and he knew how misleading apparent clusters could be. Now he would need to follow in the footsteps of Karl Pearson and the other biostatisticians who had confronted the same problem by performing tests of statistical significance. Berry needed to know how confident he could be that chance was not the cause of the cancer patterns he had identified in the county, the township, and especially in the heart of Toms River.

The significance test Berry employed was one of the most widely used in epidemiology: a 95 percent confidence interval, very similar to a margin of error in an opinion poll (though not quite the same). Pollsters employ margins of error because the fewer people they poll, the less confident they can be that the results accurately represent the sentiments of the larger population. To account for this uncertainty, statisticians apply a formula—its basics were first worked out by Siméon Poisson in the mid-nineteenth century—that assesses a poll’s accuracy based on the number of people polled and the size of the larger population those people are supposed to represent. Instead of expressing the results as a single number (“55 percent of voters approve of the president’s performance”), pollsters can apply the formula and express the results as both a number and a range (“55 percent approve, with a margin of error of plus or minus 3 percent”). The wider the margin of error, the less reliable the result. Usually, these ranges are based on a 95 percent confidence level, which means that if the poll were conducted the same way twenty times, the result would fall within the margin of error every time but once.

Cancer rates fluctuated by chance, just as opinion poll results did. Rates were especially wobbly in small communities and for rare diseases. For childhood brain and nervous system cancers in the Toms River core zone, for example, the incidence ratio was 3.05—three times higher than expected. But if there had been just three fewer cases over the thirteen-year study period—a variance that was quite possible for chance reasons alone—the ratio would have been only 1.25, barely an excess at all. On the other hand, just three more cases would have hiked the incidence ratio all the way up to a truly alarming
5.0. Those random fluctuations were the “noise” that made it so difficult to identify the “signal” of a nonrandom cancer cluster. By calculating 95 percent confidence intervals for each incidence ratio, Berry could assess how confident he could be about his results. The tighter the interval, the more confident he could be. And if the entire interval was above 1.0, then Berry could reasonably conclude that there really was more cancer than expected, for reasons other than random fluctuation. His result, in other words, would be statistically significant. The problem was, for a study of rare cancers in an area as small as the Toms River core, Berry would need to find a staggeringly high excess of cases to avoid an interval that was hopelessly wide and dipped below 1.0.

With all that in mind, Berry calculated 95 percent confidence intervals for each of his categories, taking special note every time that a confidence interval was entirely above 1.0. And then, one last time, he revised his results:

These results were precisely the kind that regularly drove cluster investigators nuts. Every number in the “incidence ratio” column carried the same message: Something was wrong in Toms River. But the
numbers in the next column, the one that showed the 95 percent confidence intervals, muddled that message in every possible way. All twelve confidence intervals were wide, especially in the township and the core, where the case numbers were lower. This meant that luck could be having a large influence on those ratios, each of which could easily be much higher or lower than the ratio indicated. And in all but three categories, the lower bound of the confidence interval was below 1.0, which meant that there might not be a problem at all. Could it be, for example, that if the effects of chance were eliminated, the Toms River core might have 50 percent
fewer
childhood leukemia cases than the statewide average, instead of 80 percent more, as Berry had calculated? Yes, it could, since 0.50 lay within the 95 percent confidence interval. But there was also a plausible chance that the leukemia rate among Toms River children was actually almost five times higher than expected, since the upper bound of the confidence interval was 4.61. The best that Poisson’s mathematics could confidently predict was that the true, nonrandom, actual-to-expected case ratio almost certainly lay somewhere within the gaping chasm of 0.48 to 4.61. (Actually, Poisson—and Berry—could do a little better than that, because not all values in between 0.48 and 4.61 were equally likely to be the true risk. When graphed, confidence intervals form a bell curve that peaks at the calculated ratio—in this case, 1.80. So, if forced to pick just one number, Berry’s best guess would be that leukemia risk for Toms River children in the core zone was 80 percent higher than expected. But he could not be
confident
about that guess. He could confidently predict only that the true risk for Toms River children lay somewhere between 52 percent lower than expected and 461 percent higher than expected.)

It was a distinctly unhelpful prediction, as if a weather forecaster had studied the radar, measured the temperature, humidity, and wind, and then declared that tomorrow’s weather would be either hot or snowy or—the best guess—something in between. There was no way to know whether to wear a sunhat or a parka.

The New Jersey Department of Health, like most other public health agencies, had already established a clear precedent on how to handle this kind of uncertain finding: Ignore it. A result was not credible,
and therefore not worthy of further attention, if its 95 percent confidence interval crossed under 1.0, no matter how slightly. It did not matter that an interval of, say, 0.98 to 7.11 (the range for brain cancers in Toms River children) meant that the cancer rate was
far
more likely than not to be much higher than expected.

There were good reasons for New Jersey’s conservative approach. For one thing, it helped to minimize the distorting effects of hidden multiple comparisons. There were almost two thousand census tracts in New Jersey, and therefore tens of thousands of groupings of four contiguous tracts that Berry could have studied. But Berry had counted cancer cases in only one such grouping, the four census tracts he designated as the Toms River core. If he had surveyed the entire state, the sample would have been so large that Berry would almost certainly have found many groupings of four contiguous census tracts that had high numbers of cases for no reason other than sheer chance. The 95 percent standard was a rigorous check against such a coincidence. It was a high hurdle, and almost all the confidence intervals Berry calculated failed to clear it.

But was the hurdle too high? The 95 percent significance test was designed for use when just one statistic was being analyzed in isolation from all others. So while there could easily have been other groupings of four census tracts in New Jersey that by chance could have had very high numbers of brain and central nervous system cancers, Berry’s analysis showed that the Toms River core zone was unusual in other important ways, too. It had an unusually high number not only of brain cancer cases but also of leukemias and all childhood cancers combined—and so did the township and the county. In fact, Berry found elevated cancer rates in every single category—all twelve of them—in which there was any hope of a statistically valid result. He had never been involved in a cluster study in which every single category was elevated, and this one was in a town with a notorious history of chemical pollution, as Berry was learning. Finally, three of those categories—brain and nervous system cancers in county children under age twenty, county children under age five, and “core zone” children under five—had managed to clear even the very high hurdle established by the state’s rules and were thus statistically significant.
Could
all that
be just an extremely unlucky series of coincidences?

The only way to find out would be for Berry to go beyond the state’s standard protocol for incidence analyses, something he had never done before for a residential cluster. He would have to
investigate
, not just calculate.

Over at the chemical factory, the few workers who remained were more worried than ever about cancer, as was the much larger group of retirees. Evidence was accumulating that their worries were well founded, though few employees knew it. For his doctoral dissertation, one of Elizabeth Delzell’s students at the University of Alabama at Birmingham, Fabio Barbone, undertook a more detailed analysis of her 1987 survey of cancer among long-term employees. Barbone completed his work in 1989, concluding that “six or seven” of the eleven cases of malignant central nervous system cancers Delzell found were likely caused by exposures at the factory and that workers in the azo, vat dye, and epichlorohydrin production areas all faced elevated risks. He also concluded that about seventeen of the fifty-one lung cancer cases were probably due to exposure to chlorine, anthraquinone, and epichlorohydrin.
⁸
This time, employees were not briefed on the results of the new analysis, which Barbone did not publish until 1994. Still, relatives of workers who died of cancer knew enough to sue the company. There were three lawsuits in 1995 alone.
⁹
Ciba (the company dropped the “Geigy” in 1995) settled them all out of court for undisclosed sums, without the public airing that would come at a trial.

Ciba was being just as careful in how it handled what was now its most critical issue in Toms River: the massive Superfund cleanup it was about to begin. Everything about the cleanup was gigantic, including the price tag: $165 million and rising. The old dumpsites on the factory property were believed to hold more than fifty thousand intact or crushed drums of hazardous waste and at least one hundred and fifty thousand cubic yards of severely contaminated soil. That was enough chemical-soaked dirt and sand to fill the passenger compartments of one hundred and thirty 747 jumbo jets and enough drummed waste to fill four Olympic-sized swimming pools. But those
were just guesses. The truth was, no one knew what had been buried back in the 1950s and 1960s. There were no reliable records, only the memories of longtime employees and the results of preliminary tests that had detected ninety-five industrial chemicals in the soil or groundwater—seventeen of them, including four known carcinogens, at concentrations higher than those permitted under state law.

It would take another decade to assess all the old dumps and figure out how to clean them up, but pumping up the contaminated ground-water was a more straightforward process. It was also much more urgent, since groundwater plumes were still spreading chemicals—and anxiety—beyond the factory grounds, across Cardinal Drive, and into Oak Ridge. The Lynnworths had moved away, but Sheila McVeigh and other residents wanted the cleanup started quickly. Life next to a Superfund site could be disconcerting. One day, McVeigh was sunbathing in her backyard when a truck appeared on the other side of her fence, on Ciba property, and a crew wearing full-body protective “moon suits” got out to check a groundwater well that was just a few dozen feet away from where McVeigh, clad in a swimsuit, was relaxing on a chaise longue. “Stuff like that happened all the time,” she recalled. “It could be a little scary.” By the spring of 1995, an interconnected system of forty-three recovery wells on the factory grounds and in Oak Ridge was sucking up about two million gallons of contaminated groundwater per day and sending it through three miles of piping to the company’s wastewater treatment plant, where the tainted water was treated and then reinjected into the ground elsewhere on the factory property.
¹⁰

Now that chemical manufacturing had ended, Toms River was a much happier place for Ciba. The company gave storage space in its empty buildings to the Boy Scouts, who used it for their canned food drives for the homeless. Ocean County Citizens for Clean Water, which had started the rebellion against Ciba eleven years earlier, was now its partner, using company funds to monitor the Superfund cleanup. Even the
Observer
, which had been so scathing in its coverage, now ran stories like “Ex-Pariah Ciba Gets Big Honor for Eco Policy” and editorials headlined “Ciba Success Benefits All.”
¹¹
No one in Toms River—Ciba least of all—wanted to think about any latent
consequences from forty years of toxic emissions into the air, the sandy soil, and the fragile river.

There were no citizen’s groups monitoring the cleanup at the other Superfund site in town and no newspaper articles chronicling every step of the process. As usual, Reich Farm was ignored, even though it posed a much more direct threat to many more people.