Many years ago, when I started blogging about health issues, I noticed a couple of interesting patterns. The first pattern is that prominent health “gurus” often talk about having had serious health problems in their past, which they describe as having motivated them to do research on health issues – and thus become health gurus. Frequently these problems pop up before 45 years of age; this is a threshold beyond which there is a clearly noticeable increase in severity of health problems.
In fact, I remember being somewhat surprised by one such “guru” (I will not name him), who would regularly write posts saying something to the effect that “… finally, my health is now on the right track …” In other words, every few months or so this person had to deal with serious health problems, always coming up with reasonable knowledge-based solutions. The knowledge seemed to be of good quality, but this guy’s health was poor to say the least.
The second pattern, related to the above, is that prominent health gurus seem to have a below average life expectancy. The life expectancy for the general population is around 79 years of age in the USA at the time of this writing, according to the World Health Organization (). Anthony Colpo has written an interesting post about this below average life expectancy pattern among health gurus ().
To better understand and illustrate this situation to our blog’s readers, I created a dataset with 100 records, corresponding to 100 health gurus, with various variables interacting in ways that reflect the above observations. The observations are summarized as assumptions, listed later. The following variables are on a scale from 1 to 7; in real life they would have been measured retrospectively, looking back at a guru’s entire life:
- The guru's health before age 45 (BEF45).
- The guru's knowledge about health issues (KNOWL).
- The guru's health after age 45 (AFT45).
- The guru's prominence (GPROM).
Finally, the variable below is on a continuous scale of years, with an average of 79 and a standard deviation of 10. As mentioned earlier, 79 is the life expectancy for someone living in the USA at the time of this writing. The standard deviation of 10, which approximates that figure in the USA, means that approximately 68 percent of the individuals in the simulated dataset will have a life expectancy between 69 and 89. That is 79-10 and 79+10, respectively.
- The guru's age at the time of death (GAGED).
This experimental exercise with simulated data can be seen as a simulation “game”, where various variables interact to generate results that are not obvious. A widely used process to create data is known as the Monte Carlo method (), which is what we used here. I also made the following assumptions in the data creation process:
- That the poorer is the guru's health before age 45 (BEF45), the greater is the guru's knowledge about health issues (KNOWL). The reason for this is that poor health compels the person to study about health issues.
- That the poorer is the guru's health before age 45 (BEF45), the poorer is the guru's health after age 45 (AFT45). This assumes that the person has an underlying condition that causes the poor health in the first place, and that can be exacerbated by a poor diet and lifestyle.
- That the greater is the guru's knowledge about health issues (KNOWL), the better is the guru's health after age 45 (AFT45). This counteracts the effect above, and assumes that the knowledge is put to good use and contributes to improving the person’s health.
- That the greater is the guru's knowledge about health issues (KNOWL), the greater is also the guru's prominence (GPROM). In other words, a guru’s status among followers is enhanced by the guru’s knowledge.
- That the better is the guru's health after age 45 (AFT45), the higher is the guru's age at the time of death (GAGED).
A final assumption made is that the causal relationships laid out above have a small effect size (more technically, that they are associated with f-squared coefficients slightly below 0.1), meaning that random influences are not only present but also play a big role in what happens in the simulation. The causality links are summarized in the graph below, created with WarpPLS (). We also used this software to analyze the data.
Note that in our simulated data the guru's prominence (GPROM) does not directly influence the guru's age at the time of death (GAGED). Stated differently, there is no causality link between GPROM and GAGED, one way or the other, even though these two variables are likely to be correlated due to the network of causality links in which they exist. Nevertheless, it is by looking at the relationship between these two variables, GPROM and GAGED, that we can answer the question in the title of this post: Do prominent health gurus live longer?
And the answer appears to be “no” in our simulation. The plot below shows the relationship between a guru's prominence (GPROM), on the horizontal axis, and the guru's age at the time of death (GAGED), on the vertical axis. Each data point refers to a guru. On average, the greater a guru's prominence, the lower seems to be the guru’s life expectancy. Each one-point increase in prominence is associated, on average, with approximately a one-year decrease in life expectancy.
Note that there is one very prominent guru whose age at the time of death was around 95; the data point at the top-right corner (GPROM=7, GAGED~95). This happened largely by chance in our data. Nevertheless, assuming that our data somewhat reflects what could happen in real life, the followers of the guru would probably point at that longevity as being caused by the guru’s knowledge about health issues. They would likely be wrong.
Our dataset also allows us to estimate the probability that a fairly prominent guru (GPROM greater than 4, on a 1-7 scale) would have a below average life expectancy (GAGED lower than 79). That conditional probability would be approximately 60 percent.
Why didn't you include Jack LaLanne?
ReplyDeleteHi Tucker. The post is based on simulated data, created through the Monte Carlo method. Interestingly, from the data emerges a JL-like guru, the one whose age at the time of death was approx. 95.
ReplyDeleteOK, duh. I see that on second reading. OK, so you included simulated Jack LaLanne. I get it. :)
ReplyDeleteNed, in some discussion of this post on Panda Whale it has been pointed out that, contrary to the normal trend of higher status people living longer, those in the "spotlight" (i.e. who attain fame) tend to live for shorter periods.
ReplyDeletehttp://pandawhale.com/post/69824/do-prominent-health-gurus-live-longer
Would this be something worth building into your model?
Thanks Anon. I guess you are referring primarily to findings like the one of the study linked below, right? If yes, how do you suggest they are included in the model? I summarized the main results and implications below the link.
ReplyDeletehttp://www.theatlantic.com/health/archive/2013/04/study-people-who-are-famous-and-successful-have-shorter-lives/275078/
RESULTS: People who were both successful and famous died earliest. The average age at death of performers and athletes, 77.2 years, wasn't exactly young, but it was younger than those who had achieved success in other fields. Businesspeople and their ilk lived longest. In fact, their average age at death, 83 years, was higher than the national average for 2010 of 78.7 years.
IMPLICATIONS: The biggest difference between the performers/athletes and the others, from the study's perspective, was that they were more publicly recognized for the contributions they made to their field. The authors cite studies showing how drug use and other risky behavior is associated with fame (including later in life, once fame had faded), and question whether that, along with performance-enhancing behavior, might have played a role in the reduced life spans seen here.
Ned, thanks for your thoughts. I suppose you could model it as a direct relationship between prominence and age at death... maybe that would be like begging the questions, with respect to this model.
ReplyDeleteThe problem with the idea that being in the spotlight leads to health problems, presumably due to stress, is the finding that: “Businesspeople and their ilk lived longest.” Prominent business leaders tend to face a lot of criticism from the public.
ReplyDeleteThe situation with performers and athletes is different.
The prevalence of mental disorders such as manic depression, which cause health problems, is high among people engaged in creative and artistic activities:
http://healthcorrelator.blogspot.com/search/label/manic-depressive%20disorder
As for modern athletes, to reach prominence while young they usually have to push their bodies so hard that they acquire serious health problems later on in life. This one deserves its own post.
As long as guru's such as Jimmy Moore exist, who will inevitably succumb at a relatively young age, things don't look good for gurus!
ReplyDeleteI guess your data didn't include entertainers :)
ReplyDeleteGreat information thank you for the post. I like how you included the statistics as well. Very informative.
ReplyDeleteIt is abnormal to think too much about how to became healthier for a normal person. One should be thinking about living a life, not about living the longest. I guess many who get very preoccupied with health issues do not have a good health to begin with, if they became gurus for living, it adds a lot of stress to their lives . Not a good combination. Even I know about different health- related topics way more than an average person with average health mostly because I am dealing with different health problems since my childhood. At the moment I can brag about feeling better than normal 50+ person, but it is possible, that the initial deffect in my health will catch up with me anyway. We will see.
ReplyDeleteAbout JM - he didn't look like a potential longevity example before his diets.