Credit: Free-Photos/pixabay

On cancers, false balance and the judiciary

Climate change has for long been my go-to example to illustrate how absolute objectivity can sometimes be detrimental to the reliability of a news report. Stating that A said “Climate change is real” and that B replied “No, it isn’t” isn’t helping anyone even though it has voices from both sides of the issue. Now, I have a new example: cancer due to radiation from cellphone towers. (And yes, there seems to be a pattern here: false balance becomes a bigger problem when a popular opinion is on the verge of becoming unpopular thanks new scientific discoveries.)

This post was prompted by a New York Times article published January 5, 2018. Excerpt:

From 1991 to 2015, the cancer death rate dropped about 1.5 percent a year, resulting in a total decrease of 26 percent — 2,378,600 fewer deaths than would have occurred had the rate remained at its peak. The American Cancer Society predicts that in 2018, there will be 1,735,350 new cases of cancer and 609,640 deaths. The latest report on cancer statistics appears in CA: A Cancer Journal for Clinicians. The most common cancers — in men, tumours of the prostate; in women, breast — are not the most common causes of cancer death. Although prostate cancer accounts for 19 percent of cancers in men and breast cancer for 30 percent of cancers in women, the most common cause of cancer death in both sexes is lung cancer, which accounts for one-quarter of cancer deaths in both sexes.

This is a trend I’d alluded to in an earlier post: that age-adjusted cancer death rates in the US, among both men and women, have been on a steady downward decline since at least 1990 whereas, in the same period, the number of cellphone towers has been on the rise. More generally, scientific studies continue to fail to find a link between radio-frequency emissions originating from smartphones and cancers of the human body. Source: this study and this second study.

The simplest explanation remains that these emissions are non-ionising – i.e. when they pass through matter, they can excite electrons to higher energy levels but they can’t remove them entirely. In other words, they can cause temporary disturbances in matter but they can’t change its chemical composition. Some have also argued that cellphone radiation can heat up tissues in the body enough to damage them. This is ridiculous: apart from the fact that the human body is a champion at regulating internal heat, imagine what’s happening the next time you get a fever or if you go to Delhi in May.

Those who continue to believe cellphone towers can damage our genes do so for a variety of reasons – including poor outreach and awareness efforts (although I’m told TRAI has done a lot of work on this front) and, more troublingly, the judiciary. By not ensuring that the evidence presented before them is held to higher scientific standards, Indian courts have on many occasions admitted strange arguments and thus pronounced counterproductive verdicts.

For example, in April 2017, the Supreme Court (of India) directed a BSNL cellphone tower in Gwalior be taken down after one petitioner claimed radiation from the structure had given him Hodgkin’s lymphoma. If the court was trying to err on the side of caution: what about the thousands of people now left with poorer connectivity in the area (and who are not blaming their ailments on cellphone tower radiation)?

This isn’t confined to India. In early 2017, Joel Moskowitz, a professor at the Berkeley School of Public Health, filed a suit asking for the state of California to release a clutch of documents describing cellphone safety measures. Moskowitz believes that cellphone radiation causes cancer, and that Big Telecom has allegedly been colluding with Big Government to keep this secret away from the public.

In December 2017, a state judge ruled in Moskowitz’s favour and directed the California Department of Public Health (CDPH) to release a “Guidance on How to Reduce Exposure to Radiofrequency Energy from Cell Phones” – a completely unnecessary set of precautions that, by the virtue of its existence, reinforces a gratuitous panic. By all means, let those who believe in this drivel consume this drivel, but it shouldn’t have been at the expense of making a mockery of the court nor should it have been effected by pressing the CDPH’s reputation to endorse the persistence of pseudoscience. What a waste of time and money when we have bigger and more legitimate problems on our hands.

… which brings us to climate change and the perniciousness of false balance. On December 20, 2017, Times of India published an article titled ‘Can mobile phones REALLY increase the risk of brain cancer? Or is it too far-fetched?’. It quotes studies saying ‘yes’ as well as those saying ‘no’ but it doesn’t contain any attributions, citations or hyperlinks. Sample this:

Lab studies where animals are exposed to radio frequency waves suggest that as the waves are not that strong and cannot break the DNA, they cannot cause cancer. But some other studies claim that that they can damage the cells up to some level and this can support a tumour to grow.

It also contains ill-conceived language, for example by asking how radio-frequency waves become harmful before it goes on to ‘discuss’ whether they are harmful at all, or by saying the waves are “absorbed” in the human body. But most of all, it’s the intent to remain equivocal – instead of assuming a rational position based on the information and/or knowledge available on the subject – that’s really frustrating. This is no different from what the Californian judge did or what the SC of India did: not consider evidence of better quality while trying to please everyone.

Featured image credit: Free-Photos/pixabay.

Oxygen may be a carcinogen

In inordinate amounts or forms, anything can be poison to life – even the air we breathe. But its threat seems more ominous when you think that even in small quantities, accumulated over time, the oxygen in the air can cause cancer. Two American scientists, Kamen Simeonov and Daniel Himmelstein, have concluded exactly that after analyzing cancer-incidence data compiled between 2005 and 2009 among people populating counties along the US’s west coast. Their calculation doesn’t show a dramatic drop in incidence with altitude yet the statistical methods used to refine the results suggest the relationship is definitely there: oxygen contributes to the growth of cancerous tumors. As they write in their paper,

“As a predictor of lung cancer incidence, elevation was second only to smoking prevalence in terms of significance and effect size.

A relative-importance test on R with the data, available on Himmelstein’s GitHub, attests to this (regression indices: LMG, Pratt, first and last). elevlung Additionally,

the lung cancer association was robust to varying regression models, county stratification, and population subgrouping; additionally seven environmental correlates of elevation, such as exposure to sunlight and fine particulate matter, could not capture the association.”

Simeonov and Himmelstein found that with every 1,000 m rise in elevation, lung cancer incidence decreased by 7.23% – that is, 5.18-9.29 per 100,000 individuals, which is fully 12.7% of the mean incidence (56.8 per 100,000 individuals). Overall, the duo attributes a decrease of 25.299% of lung cancer cases per 100,000 individuals to the “range of elevation of counties of the Western United States”. In other words,

Were the entire United States situated at the elevation of San Juan County, CO (3,473 m), we estimate 65.496% [46,855–84,136] fewer new lung cancer cases would arise per year.
Their paper was published in the open access journal PeerJ on January 13, 2015. The validity of the result lies in the strength of the statistical analysis backing it. Cancers are caused by a variety of agents. Respiratory cancers, in turn, are often the result of exposure to certain heavy metals, fine particulate matter, radiation, inhalation of toxic substances and genetic predisposition. To say oxygen could be one such toxic substance requires the claimants to show its relative significance with other known carcinogens and its covariance with incidence of cancer. Only statistics enables this. First, the data shows that the incidence of cancer dropped with increasing altitude.

My plot from data. The grey band represents the confidence level.
My plot from data. The grey band represents the confidence interval. Lung cancer incidence in per 100,000 individuals, elevation in 1,000s of meters.

Next, it shows that the incidence couldn’t have dropped due to anything else but the elevation. (‘Pearson’ is the Pearson correlation coefficient: the higher its absolute value is, the stronger the correlation.)

"Predictors displayed expected correlations such as a strong positive correlation between obesity and diabetes. Collinearity was moderate but pervasive. Elevation covaried with most variables including cancers indicating the need to adjust for covariates while carefully considering collinearity." Credit: http://dx.doi.org/10.7717/peerj.705
“Predictors displayed expected correlations such as a strong positive correlation between obesity and diabetes. Collinearity was moderate but pervasive. Elevation covaried with most variables including cancers indicating the need to adjust for covariates while carefully considering collinearity.” Credit: http://dx.doi.org/10.7717/peerj.705

To corroborate their results, the authors were also able to show that their statistical models were able to point out known risks – such as variation of incidence with smoking and exposure to radon. On the other hand, unlike smoking, exposure to radon also varies with altitude. The paper however does not clarify how it eliminates the resulting confounding fully.

Alternatively, Van Pelt (2003) attributed “some, but not all” of the Cohen (1995) radon association to elevation. Follow-up correspondences by each author revolved around the difficulty in assigning the effect wholly to elevation or radon when both of these highly-correlated predictors remained significant (Cohen, 2004; Van Pelt, 2004). We believe that our data quality improvements, including county-specific smoking prevalences and population-weighted elevations, were responsible for wholly attributing the effect to elevation.
In fact, this admission belies the study’s ultimate problem (and that of others like it): a profusion of influences on the final results. Cancer – lung or another – can be caused due to so many things. To assess its incidence in terms of a few variables – such as elevation, smoking and sunlight – could only be for the sake of convenience. Because, beyond a point, to think cancer could be the result of just one or two factors is to be foolishly reductionist. At the same time, this issue is typical of so many statistical investigations that it would be more productive to consider Simeonov’s and Himmelstein’s find as a springboard off which to launch more studies than to think it the final word on anything. They endorse the same thing with their final admission, that their study is still a victim of the ‘ecological fallacy’ – when studies of groups are thought to be equivalent to studies of individuals but are really not so. As this essay states,
Serious errors can result when an investigator makes the seemingly natural assumption that the inferences from an ecological analysis must pertain either to the individuals within the groups or to individuals across groups. A frequently cited early example of an ecological inference was Durkheim’s study of the correlation between suicide rates and religious denominations in Prussia in which the suicide rate was observed to be correlated with the number of Protestants. However, it could as well have been the Catholics who were committing suicide in largely Protestant provinces.