1996 Science in Society Journalism Awards

Magazine article

Gary Taubes

“Epidemiology faces its limits"
(Originally published in Science.)

Reprinted with permission of Gary Taubes

The news about health risks comes thick and fast these days, and it seems almost constitutionally contradictory.

In January of last year, for instance, a Swedish study found a significant association between residential radon exposure and lung cancer.

A Canadian study did not.

Three months later, it was pesticide residues.

The Journal of the National Cancer Institute published a study in April reporting — contrary to previous, less powerful studies — that the presence of DDT metabolites in the bloodstream seemed to have no effect on the risk of breast cancer.

In October, it was abortions and breast cancer.

Maybe yes.

Maybe no.

In January of this year it was electromagnetic fields (EMF) from power lines.

This time a study of electric utility workers in the United States suggested a possible link between EMF and brain cancer but — contrary to a study a year ago in Canada and France — no link between EMF and leukemia.

These are not isolated examples of the conflicting nature of epidemiologic studies; they’re just the latest to hit the newspapers.

Over the years, such studies have come up with a mind-numbing array of potential disease-causing agents, from hair dyes (lymphomas, myelomas, and leukemia) to coffee (pancreatic cancer and heart disease) to oral contraceptives and other hormone treatments (virtually every disorder known to woman).

The pendulum swings back and forth, subjecting the public to an "epidemic of anxiety," as Lewis Thomas put it over a decade ago.

Indeed, last July, the New England Journal of Medicine (NEJM) published an editorial by editors Marcia Angell and Jerome Kassirer asking the pithy question, "What Should the Public Believe?" Health-conscious Americans, wrote Angell and Kassirer, "increasingly find themselves beset by contradictory advice.

No sooner do they learn the results of one research study than they hear of one with the opposite message." Kassirer and Angell place responsibility on the press for its reporting of epidemiology, and even on the public "for its unrealistic expectations" of what modern medical research can do for their health.

But many epidemiologists interviewed by Science say the problem also lies with the very nature of epidemiologic studies — in particular those that try to isolate causes of noninfectious disease, known variously as "observational" or "risk-factor" or "environmental" epidemiology.

The predicament of these studies is a simple one: Over the past 50 years, epidemiologists have succeeded in identifying the more conspicuous determinants of noninfectious diseases — smoking, for instance, which can increase the risk of developing lung cancer by as much as 3000%.

Now they are left to search for subtler links between diseases and environmental causes or lifestyles.

And that leads to the Catch-22 of modern epidemiology.

On the one hand, these subtle risks — say, the 30% increase in the risk of breast cancer from alcohol consumption that some studies suggest — may affect such a large segment of the population that they have potentially huge impacts on public health.

On the other, many epidemiologists concede that their studies are so plagued with biases, uncertainties, and methodological weaknesses that they may be inherently incapable of accurately discerning such weak associations.

As Michael Thun, the director of analytic epidemiology for the American Cancer Society, puts it, "With epidemiology you can tell a little thing from a big thing.

What’s very hard to do is to tell a little thing from nothing at all." Agrees Ken Rothman, editor of the journal Epidemiology: "We’re pushing the edge of what can be done with epidemiology." With epidemiology stretched to its limits or beyond, says Dimitrios Trichopoulos, head of the epidemiology department at the Harvard School of Public Health, studies will inevitably generate false positive and false negative results "with disturbing frequency." Most epidemiologists are aware of the problem, he adds, "and tend to avoid causal inferences on the basis of isolated studies or even groups of studies in the absence of compelling biomedical evidence.

However, exceptions do occur, and their frequency appears to be increasing." As Trichopoulos explains, "Objectively the problems are not more than they used to be, but the pressure is greater on the profession, and the number who practice it is greater." As a result, journals today are full of studies suggesting that a little risk is not nothing at all.

The findings are often touted in press releases by the journals that publish them or by the researchers’ institutions, and newspapers and other media often report the claims uncritically (see box on p. 166).

And so the anxiety pendulum swings at an ever more dizzying rate.

"We are fast becoming a nuisance to society," says Trichopoulos.

"People don’t take us seriously anymore, and when they do take us seriously, we may unintentionally do more harm than good." As a solution, epidemiologists interviewed by Science could suggest only that the press become more skeptical of epidemiologic findings, that epidemiologists become more skeptical about their own findings — or both.

An observational science What drives the epidemiologic quest for risk factors is the strong circumstantial evidence that what we eat, drink, breathe, and so on are major factors in many devastating illnesses.

Rates of heart disease, for example, have changed much faster over recent decades than can be explained by genetic changes, implicating dietary and environmental causes.

And the fact that no single cancer affects every population at the same rate suggests that factors external to the human body cause 70% to 90% of all cancers.

In other words, says Richard Peto, an Oxford University epidemiologist, "there are ways in which human beings can live whereby those cancers would not arise." Only a few of these environmental factors are known — cigarette smoke for lung cancer, for example, or sunlight for skin cancer — and epidemiology seems to provide the best shot at identifying the others.

The most powerful tool for doing so is the randomized trial, which is the standard for studies of new drugs and other medical research: Assign subjects at random to test and control groups, alter the exposure of the test group to the suspected risk factor, and follow both groups to learn the outcome.

Often, both the experimenters and the subjects are "blinded" — unaware who is in the test group and who is a control.

But randomized trials would be prohibitively slow and expensive for most risk factors, because they can take years or decades to show an effect and hundreds of thousands of individuals may need to be followed to detect enough cases of the disease for the results to be significant.

And randomly subjecting thousands of healthy people to pollutants or other possible carcinogens raises obvious ethical problems.

Because the experimental approach is off limits for much of epidemiology, researchers resort to observational approaches.

In case-control studies, for example, they select a group of individuals afflicted with a particular disorder, then identify a control group free of the disorder and compare the two, looking for differences in lifestyle, diet, or some environmental factor.

Potentially more reliable, but also much more costly, are cohort studies, in which researchers take a large population — as many as 100,000 — and question the subjects in detail about their habits and environment.

They then follow the entire population for years or decades to see who gets sick and who doesn’t, what diseases they suffer from, and what factors might be different between them.

Either way, risk-factor epidemiology is "a much duller scalpel" than randomized trials, says Scott Zeger, a biostatistician at the Johns Hopkins School of Mental and Public Health.

What blunts its edge are systematic errors, known in the lingo as biases and confounding factors.

"Bias and confounders are the plague upon the house of epidemiology," says Philip Cole, chair of epidemiology at the University of Alabama.

They represent anything that might lead an epidemiologic study to come up with the wrong answer, to postulate the existence of a causal association that does not exist or vice versa.

Confounding factors are the hidden variables in the populations being studied, which can easily generate an association that may be real but is not what the epidemiologist thinks it is.

A ubiquitous example is cigarette smoking, which can confound any study looking, for instance, at the effects of alcohol on cancer.

"It just so happens," explains Trichopoulos, "that people who drink also tend to smoke," boosting their risk of cancer.

As a result, epidemiologists face the possibility that any apparent cancer-alcohol link may be spurious.

Smoking may also have confounded a study Trichopoulos himself co-authored linking coffee-drinking and pancreatic cancer — a finding that has not been replicated.

The study, published over a decade ago, corrected for smoking, which often accompanies heavy coffee drinking — but only for smoking during the 5 years before the cancer was diagnosed.

Trichopoulos now says that he and his colleagues might have done better to ask about smoking habits a full 20 years before diagnosis.

Biases are problems within study designs themselves.

The process of choosing an appropriate population of controls in a case-control study, for instance, can easily lead to an apparent difference between cases and controls that has nothing to do with what caused the disease.

"It’s often not even theoretically clear who the right comparison group is," says Harvard epidemiologist Walter Willett.

"And sometimes, even if you can design the study so that you have the theoretically correct comparison group, you usually don’t get everybody willing to participate, and the people who do participate in your study will be different from the people who don’t, often in health-related ways." For example, Charles Poole of Boston University has spent several years analyzing the results and methodology of a 1988 study of EMF and cancer, which found that exposure to relatively high EMF from power lines appeared to increase the risk of leukemia and brain cancer in children.

David Savitz of the University of North Carolina, the study’s author, selected controls for that study with a common technique known as random digit dialing: Researchers take the phone numbers of their cases and randomly change the last four digits until they find a suitable control.

Random digit dialing, however, seems to create "a pronounced bias toward the control group being deficient in persons of very low socioeconomic status," says Poole.

Poor people, it seems, are either less likely to be home during the day to answer the phone, less likely to want to take part in a study, or less likely to have an answering machine and call the researchers back.

Indeed, the North Carolina researchers reported that their data showed that the risk of leukemia and brain cancer rises not just with exposure to EMF but also with higher levels of breast-feeding, maternal smoking, and traffic density, all of which are markers for poverty.

This suggests, says Poole, that the study group was poorer than the controls, and that some poverty-associated factor other than EMF could have resulted in the apparent increase in cancer risk.

Nonetheless, the study is still cited as supporting the hypothesis that EMF causes childhood cancer, although even Savitz concedes that the random dig-it dialing problem is "a legitimate source of uncertainty." Even when such biases can be identified, their magnitude — and sometimes even their direction — can be nearly impossible to assess.

David Thomas, for example, an epidemiologist at the Fred Hutchinson Cancer Research Center in Seattle, points to studies analyzing the effect of Breast Self-Examination (BSE) on breast cancer mortality rates, which, he says, have yielded some "modest suggestion that there might be a beneficial effect" from BSE.

"You have to ask what motivates a woman to practice BSE," says Thomas.

"Maybe she has a strong family history of breast cancer.

If so, she’s more likely to get breast cancer.

That would be an obvious bias," which could make BSE look less useful than it is.

"Or maybe a woman with a strong family history of breast cancer would be afraid to practice BSE.

You have no way of predicting the direction of the bias.

So it would be very difficult to interpret your results.

You have to go to a randomized study to get a reliable answer." Tricks of memory Of all the biases that plague the epidemiologic study of risk factors, the most pernicious is the difficulty of assessing exposure to a particular risk factor.

Rothman, for instance, calls it "a towering obstacle." When exposure can be measured reliably, a subtle association may be credible — as it is in the case of early childbirth and a lower risk of breast cancer.

The reason is that both cause and effect can be measured with some certainty, says Harvard epidemiologist Jamie Robins.

"It’s easy to know which people got breast cancer, and it’s easy to know at what age they had kids," he says, adding that virtually every study on the subject comes to the same conclusion: Early childbirth reduces the risk by about 30%.

But epidemiologists are quick to list risk factors for which accurate exposure measurements are virtually impossible.

Joe Fraumeni, director of the epidemiology and biostatistics program at the National Cancer Institute (NCI), points to radon: "When you’re studying smoking," he says, "that’s easy.

Just count the number of cigarettes and duration and packs per day.

But something like radon, how do you measure exposure, particularly biologically relevant exposure that has taken place in the past?" Equally uncertain are those risk factors recorded only in human memory, such as consumption of coffee or dietary fat.

Ross Prentice of the University of Washington notes, for example, that underweight individuals tend to over report fat intake on questionnaires or in interviews and obese subjects tend to underreport it.

Such recall bias is known to be especially strong, as Willett points out, among patients diagnosed with the disease in question or among their next of kin.

In studies of a possible relationship between fat intake and breast cancer, for instance, says Willett, "people may recall their past intake of fat differently if they have just been diagnosed with breast cancer than if you pluck them out of a random sample, call them up out of the blue over the phone, and ask them what their past diet was." Recall bias, for instance, apparently accounts for the conflicting findings about oral contraceptive use and breast cancer.

Many studies have looked for this association over the years, both case-control studies and cohort studies.

Trichopoulos notes that case-control studies have tended to show an association between oral contraceptives and breast cancer, while cohort studies have not.

Epidemiologists who have done cohort studies say the problem is in case-control studies, which are thrown off by recall bias — women who are diagnosed with breast cancer are more likely to give complete information about contraceptive use than women who don’t.

Those who did case-control studies say the bias is in the cohort studies.

Cohort studies have to rely on impersonal questionnaires because they are so much larger than case-control studies, and women are less likely to give complete and honest information than they are in the more intimate interviews possible in case-control studies.

"The point," says Trichopoulos, "is which do we believe." It’s not just the subjects of studies who are prone to bias; epidemiologic studies can be plagued by interviewer bias as well.

The interviewers are rarely blinded to cases and controls, after all, and questionnaires, the traditional measuring instrument of epidemiology, are neither peer-reviewed nor published with the eventual papers.

"In the laboratory," as Yale University clinical epidemiologist Alvin Feinstein puts it, "you have all kinds of procedures for calibrating equipment and standardizing measurement procedures. In epidemiology ... it’s all immensely prey to both the vicissitudes of human memory and the biases of the interview." Salvation from statistics? With confounders, biases, and measurement errors virtually inevitable, many epidemiologists interviewed by Science say that risk-factor epidemiology is increasingly straying beyond the limits of the possible no matter how carefully the studies are done.

"I have trouble imagining a system involving a human habit over a prolonged period of time that could give you reliable estimates of [risk] increases that are of the order of tens of percent," says Harvard epidemiologist Alex Walker.

Even the sophisticated statistical techniques that have entered epidemiologic research over the past 20 years — tools for teasing out subtle effects, calculating the theoretical effect of biases, correcting for possible confounders, and so on — can’t compensate for the limitations of the data, says biostatistician Norman Breslow of the University of Washington, Seattle.

"In the past 30 years," he says, "the methodology has changed a lot.

Today people are doing much more in the way of mathematical modeling of the results of their study, fitting of regression equations, regression analysis.

But the question remains: What is the fundamental quality of the data, and to what extent are there biases in the data that cannot be controlled by statistical analysis? One of the dangers of having all these fancy mathematical techniques is people will think they have been able to control for things that are inherently not controllable." Breslow adds that epidemiologists will commonly report that they have unveiled a possible causal association between a risk factor and a disease because the association is "statistically significant," meaning that the error bars — the limits of a 95% confidence interval — do not include the null result, which is the absence of an effect.

But, as Breslow explains, such statistical "confidence" means considerably less than it seems to.

The calculation of confidence limits only takes into consideration random variation in the data.

It ignores the systematic errors, the biases and confounders, that will almost invariably overwhelm the statistical variation.

University of California, Los Angeles (UCLA) epidemiologist Sander Greenland says most of his colleagues fail to understand this simple point.

"What people want to do when they see a 95% confidence [interval]," he says, "is say ’I bet there’s a 95% chance the true value is in there.’ Even if they deny it, you see them behaving and discussing their study result as though that’s exactly what it means.

There are certain conditions under which it’s not far from the truth, but those conditions are generally not satisfied in an epidemiologic study." What to believe? So what does it take to make a study worth taking seriously? Over the years, epidemiologists have offered up a variety of criteria, the most important of which are a very strong association between disease and risk factor and a highly plausible biological mechanism.

The epidemiologists interviewed by Science say they prefer to see both before believing the latest study, or even the latest group of studies.

Many respected epidemiologists have published erroneous results in the past and say it is so easy to be fooled that it is almost impossible to believe less-than-stunning results.

Sir Richard Doll of Oxford University, who once co-authored a study erroneously suggesting that women who took the anti-hypertension medication reserpine had up to a fourfold increase in their risk of breast cancer, suggests that no single epidemiologic study is persuasive by itself unless the lower limit of its 95% confidence level falls above a threefold increased risk.

Other researchers, such as Harvard’s Trichopoulos, opt for a fourfold risk increase as the lower limit.

Trichopoulos’s ill-fated paper on coffee consumption and pancreatic cancer had reported a 2.5-fold increased risk.

"As a general rule of thumb," says Angell of the New England Journal, "we are looking for a relative risk of three or more [before accepting a paper for publication!, particularly if it is biologically implausible or if it’s a brand-new finding." Robert Temple, director of drug evaluation at the Food and Drug Administration, puts it bluntly: "My basic rule is if the relative risk isn’t at least three or four, forget it." But as John Bailar, an epidemiologist at McGill University and former statistical consultant for the NEJM, points out, there is no reliable way of identifying the dividing line.

"If you see a 10-fold relative risk and it’s replicated and it’s a good study with biological backup, like we have with cigarettes and lung cancer, you can draw a strong inference," he says.

"If it’s a 1.5 relative risk, and it’s only one study and even a very good one, you scratch your chin and say maybe." Some epidemiologists say that an association with an increased risk of tens of percent might be believed if it shows up consistently in many different studies.

That’s the rationale for meta-analysis — a technique for combining many ambiguous studies to see whether they tend in the same direction (Science, 3 August 1990, p. 476).

But when Science asked epidemiologists to identify weak associations that are now considered convincing because they show up repeatedly, opinions were divided — consistently.

Take the question of alcohol and breast cancer.

More than 50 studies have been done, and more than 30 have reported that women who drink alcohol have a 50% increased risk of breast cancer.

Willett, whose Nurse’s Health Study was among those that showed a positive association, calls it "highly probable" that alcohol increases the risk of breast cancer.

Among other compelling factors, he says, the finding has been "reproduced in many countries with many investigators controlling for lots of confounding variables, and the association keeps coming up." But Greenland isn’t so sure.

"I’d bet right now there isn’t a consensus.

I do know just from talking to people that some hold it’s a risk factor and others deny it." Another Boston-based epidemiologist, who prefers to remain anonymous, says nobody is convinced of the breast cancer-alcohol connection "except Walt Willett." Another example is long-term oral contraceptive use and breast cancer, a link that has been studied for a quarter of a century.

Thomas of the Fred Hutchinson Cancer Research Center says he did a meta-analysis in 1991 and found a dozen studies showing a believable association in younger women who were long-time users of oral contraceptives.

"The bottom line," he says, "is it’s taken us over 20 years of studies before some consistency starts to emerge.

Now it’s fairly clear there’s a modest risk." But Noel Weiss of the University of Washington says he did a similar review of the data that left him unconvinced.

"We don’t know yet," he says.

"There is a small increased risk associated [with oral contraceptive use], but what that represents is unclear." Mary Charleson, a Cornell Medical Center epidemiologist, calls the association "questionable." Marcia Angell calls it "still controversial." Consistency has a catch, after all, explains David Sackett of Oxford University: It is persuasive only if the studies use different architectures, methodologies, and subject groups and still come up with the same results.

If the studies have the same design and "if there’s an inherent bias," he explains, "it wouldn’t make any difference how many times it’s replicated.

Bias times 12 is still bias." What’s more, the epidemiologists interviewed by Science point out that an apparently consistent body of published reports showing a positive association between a risk factor and a disease may leave out other, negative findings that never saw the light of day.

"Authors and investigators are worried that there’s a bias against negative studies," and that they will not be able to get them published in the better journals, if at all, says Angell of the NEJM.

"And so they’ll try very hard to convert what is essentially a negative study into a positive study by hanging on to very, very small risks or seizing on one positive aspect of a study that is by and large negative." Or, as one National Institute of Environmental Health Sciences researcher puts it, asking for anonymity, "Investigators who find an effect get support, and investigators who don’t find an effect don’t get support.

When times are tough it becomes extremely difficult for investigators to be objective." When asked why they so willingly publish inconclusive research, epidemiologists say they have an obligation to make the data public and justify the years of work.

They also argue that if the link is real, the public health effect may be so dramatic that it would be irresponsible not to publish it.

The University of North Carolina’s Savitz, for instance, who recently claimed a possible link between EMF exposure and a tens of percent increase in the risk of breast cancer, says: "This is minute ... But you could make an argument that even if this evidence is 1000-fold less than for [an EMF-leukemia link], it is still more important, because the disease is 1000-fold more prevalent." One of the more pervasive arguments for publishing weak effects, Rothman adds, is that any real effect may be stronger than the reported one.

Any mismeasurement of exposure, so the argument goes, will only serve to reduce the observed size of the association.

Once researchers learn how to measure exposure correctly, in other words, the actual association will turn out to be bigger — and thus more critical to public health.

That was the case in studies of steelworkers and lung cancer decades ago, says Robins.

Early studies saw only a weak association, but once researchers homed in on coke-oven workers, the group most exposed to the carcinogens, the relative risk shot up.

None of the epidemiologists who spoke to Science could recall any more recent parallels, however.

An unholy alliance There would be few drawbacks to publishing weak, uncertain associations if epidemiologists operated in a vacuum, wrote Brian Mac-Mahon, professor emeritus of epidemiology at Harvard, in an April 1994 editorial in the Journal of the National Cancer Institute.

But they do not, he said.

"And, however cautiously the investigator may report his conclusions and stress the need for further evaluation," he added, "much of the press will pay little heed to such cautions ... By the time the information reaches the public mind, via print or screen, the tentative suggestion is likely to be interpreted as a fact." This is what one epidemiologist calls the "unholy alliance" between epidemiology, the journals, and the lay press.

The first one or two papers about a suspected association "spring into the general public consciousness in way that does not happen in any other field of scientific endeavor," says Harvard’s Walker.

And once a possible link is in the public eye, it can be virtually impossible to discredit.

As far as scientists were concerned, for instance, a 1981 epidemiologic study put to rest a suggestion that saccharine can cause bladder cancer — one of the few cases in which epidemiology had managed to put an end to a suspected association.

Yet 14 years later, television advertisements for Nutra-Sweet, which contains the artificial sweetener aspartame, still tout it as the sweetener that does not have saccharine.

Epidemiologists themselves are at a loss as to how to curb the "anxiety of the week" syndrome.

Many, like Rothman, simply argue that risk factor epidemiology is a young science that will take time to mature.

Others, like Robins, suggest that barring a major breakthrough in the methodological tools of epidemiology, maturity will be hard to come by.

The pressures to publish inconclusive results and the eagerness of the press to publicize them, he and others say, mean that the anxiety pendulum, like Foucault’s, will continue to swing indefinitely (see box on p. 165).

The FDA’s Temple does make one positive suggestion: Although risk-factor epidemiology will never be as sharp a tool as randomized clinical trials, epidemiologists could still benefit by adopting some of the scientific practices of those studies.

"The great thing about a clinical control trial," he says, "is that, within limits, you don’t have to believe anybody or trust anybody.

The planning for a clinical control trial is prospective; they’ve written the protocol before they’ve done the study, and any deviation that you introduce later is completely visible." While agencies like the NCI do insist on seeing study protocols in risk-factor epidemiology prospectively, this is still not standard procedure throughout the field.

Without it, says Temple, "you always wonder how many ways they cut the data.

It’s very hard to be reassured, because there are no rules for doing it." In the meantime, UCLA’s Greenland has one piece of advice to offer what he calls his "most sensible, level-headed, estimable colleagues." Remember, he says, "there is nothing sinful about going out and getting evidence, like asking people how much do you drink and checking breast cancer records.

There’s nothing sinful about seeing if that evidence correlates.

There’s nothing sinful about checking for confounding variables.

The sin comes in believing a causal hypothesis is true because your study came up with a positive result, or believing the opposite because your study was negative."

 

RELATED ARTICLE: Sizing Up the Cancer Risks In the history of epidemiology, only a dozen or so environmental agents have ever been repeatedly and strongly linked to human cancer, says University of Alabama epidemiologist Philip Cole.

Among them are cigarette smoke, alcohol, ionizing radiation, a few drugs, a handful of occupational carcinogens, such as asbestos, and perhaps three viruses — hepatitis-B virus, human T cell leukemia virus, and human papillomavirus.

But every year, epidemiologic papers are published by the journal-load, many of them reporting new potential causes of cancer in the environment.

Most are the product of observational epidemiology, in which researchers try to compare the lives of people suffering from a disease with those of healthy controls.

Even its practitioners admit this effort is plagued by biases and confounding factors (see main text).

As a result, most epidemiologists interviewed by Science said they would not take seriously a single study reporting a new potential cause of cancer unless it reported that exposure to the agent in question increased a person’s risk by at least a factor of 3 — which is to say it carries a risk ratio of 3.

Even then, they say, skepticism is in order unless the study was very large and extremely well done and biological data support the hypothesized link.

Sander Greenland, a University of California, Los Angeles, epidemiologist, says a study reporting a twofold increased risk might then be worth taking seriously — "but not that seriously." Few of the entries in the following list of potential cancer risks, reported in the journals and picked up in the popular press over the past 8 years, have come close to fulfilling those criteria.

Are these dangers real? As the saying goes, you be the judge.

• High-cholesterol diet — risk ratio (rr) 1.65 for rectal cancer in men (January 1987)
• Eating yogurt at least once a month — rr 2 for ovarian cancer (July 1989)
• Smoking more than 100 cigarettes in a lifetime — rr 1.2 for breast cancer (February 1990)
• High-fat diet — rr 2 for breast cancer (August 1990)
• Lengthy occupational exposure to dioxin — rr 1.5 for all cancers (January 1991)
• Douching once a week — rr 4 for cervical cancer (March 1991)
• Regular use of high-alcohol mouthwash — rr 1.5 for mouth cancer (June 1991)
• Use of phenoxy herbicides on lawns — rr 1.3 for malignant lymphoma in dogs (September 1991) Weighing 3.6 kilograms or more at birth — rr 1.3 for breast cancer (October 1992)
• Vasectomy — rr 1.6 for prostate cancer (February 1993)
• Pesticide exposure, indicated by high residues in blood — rr 4 for breast cancer (April 1993); contradicted 1 year later in a larger study with one of the same authors.
• Drinking more than 3.3 liters of fluid (particularly chlorinated tap water) a day — rr 2 — 4 for bladder cancer (July 1993)
• Experiencing psychological stress in the workplace — rr 5.5 for colorectal cancer (September 1993)
• Diet high in saturated fat — rr 6 for lung cancer in nonsmoking women (December 1993)
• Eating more than 20 grams of processed meats (i.e., bologna) a day — rr 1.72 for colon cancer (February 1994)
• Eating red meat five or more times a week — rr 2.5 for colon cancer (February 1994)
• Occupational exposure to electromagnetic fields — rr 1.38 for breast cancer (June 1994)
• Smoking two packs of cigarettes a day — rr 1.74 for fatal breast cancer (July 1994)
• Eating red meat twice a day — rr 2 for breast cancer (July 1994)
• Regular cigarette smoking — rr 1.7 for pancreatic cancer (October 1994)
• Ever having used a sun lamp — rr 1.3 for melanoma (November 1994)
• Abortion — rr 1.5 for breast cancer (November 1994)
• Having shorter or longer than average menstrual cycles — rr 2 for breast cancer (December 1994)
• Obesity in men (the heaviest 25% of those in the study) — rr 3 for esophageal cancer (January 1995) Consuming olive oil only once a day or less — rr 1.25 for breast cancer (January 1995)

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