In the comments section of my previous post on cancer rates in the US states () my friend Aaron Blaisdell noted that: …comparing states that are roughly comparable in terms of number of seniors per 1000 individuals, latitude appears to have the largest effect on rates of cancer.
Good point, so I collected data on the latitudes of US states, built a more complex model (with several multivariate controls), and analyzed it with WarpPLS 4.0 ().
The coefficient of association for the effect of latitude on cancer rates (path coefficient) turned out to be 0.35. Its P value was lower than 0.001, meaning that the probability that this is a false positive is less than a tenth of a percent, or that we can be 99.9 percent confident that this is not a false positive.
This was calculated controlling for the: (a) proportion of seniors in the population (population age); (b) proportion of obese individuals in the population (obesity rates); and (c) the possible moderating effect of latitude on the effect of population age on cancer rates. The graph below shows this multivariate-adjusted association.
What is cool about a multivariate analysis is that you can control for certain effects. For example, since we are controlling for proportion of seniors in the population (population age), the fact that we have a state with a very low proportion of seniors (Alaska) does not tilt the effect toward that outlier as much as it would if we had not controlled for the proportion of seniors. This is a mathematical property that is difficult to grasp, but that makes multivariate adjustment such a powerful technique.
I should note that the 99.9 percent confidence mentioned above refers to the coefficient of association. That is, we are quite confident that the coefficient of association is not zero; that is it. The P value does not support the hypothesized direction of causality (latitude -> cancer) or exclude the possibility of a major confounder causing the effect.
Nonetheless, among the newest features of WarpPLS 4.0 (still a beta version) are several causality assessment coefficients: path-correlation signs, R-squared contributions, path-correlation ratios, path-correlation differences, Warp2 bivariate causal direction ratios, Warp2 bivariate causal direction differences, Warp3 bivariate causal direction ratios, and Warp3 bivariate causal direction differences. Without going into a lot of technical detail, which you can get from the User Manual () without even having to install the software, I can tell you that all of these causality assessment coefficients support the hypothesized direction of causality.
Also, while we cannot exclude the possibility of a major confounder causing the effect, we included two possible confounders in the analysis and controlled for their effects. They were the proportion of seniors in the population (population age) and the proportion of obese individuals in the population (obesity rates).
Having said all of the above, I should also say that the effect is similar in magnitude to the effect of population age on cancer rates, which I discussed in the previous post linked above. That is, it is not the type of effect that would be clearly noticeable in a person’s normal life.
Sunlight exposure? Maybe.
We do know that our body naturally produces as much as 10,000 IU of vitamin D based on a few minutes of sun exposure when the sun is high (). Getting that much vitamin D from dietary sources is very difficult, even after “fortification”.
19 comments:
Hiya Ned!
Vitamin D might be not the only mechanism that makes sun exposure healthy.
Sunlight's wavelengths 600-1000nm might also have a direct positive effect on tissues and circulation: http://valtsus.blogspot.fi/2013/11/the-therapeutic-effects-of-red-and-near.html
City Sunlight/Cloudiness data might be interesting, too. Different cities in same state sometimes have large differences.
Hi Ned.
Forgive me for not completely understanding the main point of this article/calculation.
Shortly, can we say "vitamin D from sun exposure is helpful in cancer prevention or not"?
Thank you so much.
Thanks Valtsu, interesting.
Hi Ugur. There is a lot of evidence pointing at vit. D from sun exposure, plus other compounds and effects, having the potential to reduce cancer incidence. Vit. D has been shown to have anti-cancer properties in vitro.
Florida is interesting. I would hypothesize that it is bcs of so many transplants from more northern/colder climates.
Might be interesting if data could be obtained factoring in years of residence within that state.
Another factor might be sulfur: Stephanie Seneff has correlated various diseases (that she believes to be sulfur deficiency states) with lack of sun exposure. Sunlight on skin fixes cholesterol sulfate (I think on to vitamin D and cholesterol) and allows it to be utilized in various reactions throughout the body. Her power points from last weekends presentations at Weston Price conference can be found here http://people.csail.mit.edu/seneff/.
Interesting analysis, but I'd like to see other potentially-confounding variables be looked at as well besides obesity and age. E.g., consumption rates of refined foods (especially "junk food"), refined sugar consumption, types of cancer (is there more of one type of cancer in one place as opposed to others?), and amount of exercise.
Seems to be a correlation with longitude as well. Higher on east coast, lower on west
Florida’s position as an outlier becomes clearer if you look at the graph in my previous post:
http://bit.ly/16E17vE
It is like Alaska, but in reverse. Florida has the largest proportion of seniors.
Indeed, there is an apparent longitude trend as well. Not sure if it is significant though.
Thanks Deborah. Very interesting.
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"Interesting analysis, but I'd like to see other potentially-confounding variables be looked at...E.g., consumption rates of refined foods (especially "junk food"), refined sugar consumption, types of cancer (is there more of one type of cancer in one place as opposed to others?), and amount of exercise."
Do you have that data, Anonymous? All broken down by state?
I see the vitamin D correlation...however that isn't applicable along the bible belt. Maybe the North east needs sun exposure and maybe the Bible Belt(MS AL LA) needs to change their eating if they will be exposed to the sun as often as they are...
Hi, Ned--
Carlcraw is right, though going to a city level might be further than you want to carry this.
Average days of sunshine by state is readily available. It was Utah that drew my attention on your chart. Utah is fairly far north--but it is also #8 in the nation in terms of days of sunshine (right after Florida).
I’m not sure that days of sunshine would capture the phenomenon that we are discussing here, as factors associated with the distance from the equator (e.g., the inclination with which the sun rays hit the earth and even the overall temperature) significantly affect production of vitamin D:
http://www.ingentaconnect.com/content/mjl/adv/2011/00000091/00000002/art00001
Great post!
Rather than just longitude. How about altitude?
Of the Mountain West states, all are below the line except Montana.
Altitude would increase sunlight exposure due to less atmosphere, and, thus, the person would effectively be a lower latitude.
Also, a thinner atmosphere would mean less oxygen and relatively more carbon dioxide.
I could help run the numbers if you like.
I agree average days of sunshine isn't enough, but neither is latitude. Available radiant energy is a combination of the two.
As Anonymous mentions, altitude is also a major player--though altitude is also a major factor in overexposure to UV...
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