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). 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.
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.)
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.
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.