Do-It-Yourself China Study

do-it-yourself-china-study-scientist“The China Study,” by T. Colin Campbell, is the bible of veganism. It is a huge book which covers a lot of ground and makes many valuable points. The name of the book comes from a series of observational studies done in 69 counties in China called the China-Oxford-Cornell Project. Dr. Campbell himself led two of these studies, and from this project, he concludes that “plant-based foods are beneficial, and animal-based foods are not.” This conclusion has been disputed. See, for instance, Minger here, or Masterjohn here. OR see for yourself.

We found data for the 1989 study and and have cooked up an app below that allows you to quickly see the association between any of dozens of dietary, blood test, or lifestyle items versus several dozens causes of death. For example does increased consumption of long-reviled red meat (D050) appear to be associated with increased death by cancer (M023)? Observe the green trendline. If the trendline slopes upward, then death due to cancer is more frequent among those that consume more red meat. If it slopes downward, then those consuming more red meat experienced less death.

As you try out different combinations, you will see that a surprising number of them fly in the face of conventional medical wisdom. As to veganism, if anything is to be concluded at all, it would surely not be that it is healthier. Veganism may (?!) be good for the environment and please the animals, but any health benefits it might have certainly aren’t supported here.

Select a dietary (Dxxx), blood test (Pxxx), or lifestyle (Qxxx) item:

Select a mortality cause:

Ignore Null Data (recommended)

1) Notice the use of the word “associated with” rather than “causes.” Using this data, one can establish that red meat is associated with greater longevity. This doesn’t mean red meat was the cause. It could be that people who could afford red meat got better medical care. There could be other factors too. So nothing like “this causes that” is proven here.
To arrive at a conclusive answer requires more elaborate statistics, and such elaboration is subject to mischief as well.
However, we take it as a tenet that if the indications of the raw data are to be contradicted by some elaborate statistics, the argument needs to be very convincing.

2) Checking the “Discard Null Data” checkbox is recommended. It is often the case that data that is simply unavailable is recorded as zero, and using such data creates a false trend. Try it both ways.

3) Common sense. Any plot that has only a few number of points, or has the bulk of them lying on the horizontal or vertical axis is probably suspect. If the data forms s narrow vertical structure (eg. D020 & M006) the trend line is probably not meaningful.