Saturday, July 9, 2011

The relationship between hunger and petroleum consumption-Part 3

Parts 1 and 2 looked at the relationship between the global hunger index (GHI) and per capita petroleum consumption.  Here in part 3, I describe the relationship between per capita petroleum consumption and another potential indicator of hunger—body mass index (BMI). 

Most people are familiar with BMI, not as a measure of hunger, but rather quite the opposite—an over-abundance of food leading to unhealthy levels of weight gain.   Roughly speaking, or speaking roughly, adults with a BMI of 25 of more, are considered to be fat or “overweight” while a BMI of 18.5 or less is considered thin or “underweight.”

My idea, or hope, was that there would be a broad range of BMI measurements reported for both developed and undeveloped countries, and, this would allow me to use BMI as an alternative indicator of “hunger.”  That is, if petroleum consumption is important to food production, then countries with high per capita petroleum consumption rate would have a higher percentage of the population in the “overweight” category (BMI > 25) than countries with a low per capita petroleum consumption rate.  Or, countries with a low per capita petroleum consumption rate would have a higher percentage of people in the “underweight” category (BMI< 18.5) than countries with a hign per capita petroleum consumption rate.

Data sources and my selection criterion
The best source of accumulated BMI data that I could find was at the World Health Organization (WHO), which makes available, in spreadsheet form, BMI measurements for a large number countries at various times collected over the last 30 years.  I use this data, plus per capita petroleum consumption rate (barrels per person per year, b/py) calculated as described in part 1 (using EIA and US census bureau data) for the particular year in which the BMI data was reported. 

To make the comparison between countries as uniform as possible, I limited my analysis to the latest data for only those countries that reported combined national data that included both sexes and both urban and rural areas.

To my disappointment, there was a relative lack of data reported for those countries identified in parts 1 and 2 as having a low GHI.  For instance, of the 58 countries identified as having serious hunger (i.e. GHI > 10), only 13 had reported BMI data that met my criterion.  So I lost 40 countries in an important range of interest.  I also lost another 29 countries which had moderate or low report hunger values (GHI<10) but no BMI data.

I did, however, pick up data for 32 countries in EU, NA, AP and ME for which there was BMI data but no GHI data.  Unfortunately, all of these pick-ups were developed countries with a high per capita petroleum consumption rate. 

Additionally, I found that for many countries, the WHO data base did not have BMI statistics for “underweight” and meeting my requirements (national, both sexes, rural and urban). Rather, the most common statistic was only data for “overweight” (BMI>25) and somtimes “obese” (BMI>30).   Therefore, I restricted my analysis to only considering the percentage of the population having a BMI>25. 

Another problem is that the BMI is not reported or collected in the same year for every country.  In fact, the reports of BMI that would meet my criterion, ranged anywhere from 1988 to 2009, although most of the data is from the 2000s.  Besides adding an undesired variable into the data (time), this made it difficult to calculate the per capita petroleum consumption because it meant I had to get the population and national petroluem consumption figures for a variety of different years for each country. 

Results
Figure 5 presents a plot of the percent population having a BMI>25, versus per capita petroleum consumption rate, for the 81 countries that met my selection criterion.   

Once again I used a power equation, the solid line in Figure 5, to help show the general trend in the data.  I don’t ascribe any particular meaning to the power curve or its best-fit parameter values—but it does help illustrate the general trend.  According to the trend line, at a per capita petorleum consumption of 1 b/py, the percentage of overweight people is down to 23% and that percentage doubles to 46% by the time consumption is at 9 b/py.

Of the 13 hold-over countries from Parts 1 and 2 which had a GHI of greater than 10 (serious or alarming/extreme hunger) and a reported BMI, the average percent of the population overweight was only 18%±12%.

As shown in Figure 5, there were only 13 countries for which less than 30% of the population was overweight (i.e., BMI >25).  Eleven of those countries also had a per capita consumption rate of less than 2 b/py.  Two strong outliers were Japan (JP) and Singapore (SG).  It may be that a BMI of greater than 25 is not a realistic definition of over-weight for Japan and Singapore, as these two countries have reduced their cut-off for being overweight to BMI>23 (ref 13 and 14 in Body Mass Index). 

For the 19 European countries that were added to the data base (i.e., countries that don’t have a reported GHI, but do have a reported BMI) the average per capita petroleum consumption was 13 b/py and the percentage of the population that was overweight was on average 50% (variable years). 

New Zealand had a slightly higher per capita petroleum consumption at 14 b/py and a higher percentage of overweight people at 63% (2006).  Australia’s petroleum consumption was higher still, at 16 b/py, but “only” 49% of the population was overweight (2004).

South Korea (KR) had a still higher per capita consumption of 17 b/py, but only 33 % of the population has  a BMI>25 (2007), but maybe the cut-off for overweight should be adjusted for this country, similar to Japan and Singapore (see AMA Commentary by Gallagher).

The USA (US) and Canada (CA), with per capita consumption rate of 25 and 26 b/py (2007 and 2004, respectively), had an even higher percentages of overweight people at 68 and 59%, respectively. 

One African country actually makes into the ranks of the high per capita petroleum consumers, 25 b/py, and a correspondingly high population that is overweight 61% (2004)— Seychelles (SY).

Lastly, there are 3 countries in the Middle East with high per capita petroleum consumption rates and very high percentages of the population being overweight:
Saudi Arabia (SA), consumption at 25 b/py and 72% overweight in 1997,
United Arab Emirates (UA), consumption at 37 b/py and 64% overweight in 2000, and
Kuwait (KW), consumption 51 b/py and 75% overweight in 2006.

ANOVA of three groups with different BMI percentages
I decided to omit South Korea, Japan and Singapore, because it seems that the global criterion for being overweight (BMI>25) is not applicable to these countries, and I don’t know how to make a correction for this. I divided the remaining countries into three groups:

G1: Low percentage overweight—30% or less overweight
G2: Intermediate percentage overweight—greater than 30% but less than 50% overweight
G3: High percentage overweight—greater than 50% overweight

A one-way analysis of variance (ANOVA) to test the hypothesis: are the mean per capita petroleum consumption rates of the three groups all equal (i.e., G1=G2=G3), was rejected at p<0.05.  That is, the per capita petroleum consumption rates of the three BMI groups are not all equal. 

A subsequent multiple group comparison, using the Tukey test (p=0.05), revealed that the per capita petroleum consumption rates of G1 versus G3 or G1 versus G3 were significantly different form each other, but G3 versus G2 were not significantly different.

Here is a summary of the average and standard deviation of the per capita petroleum consumption rates for the three groups:
Average and standard deviations of per capita petroleum consumption (b/py)
G1: Low percentage overweight (30<%)
0.80±.53*
G2: Intermediate percentage overweight (30>%<50)
8.9±6.6
G3: High percentage overweight (%>50
12±11

* significantly different than G2 and G3

Conclusions
I am a bit disappointed that there was not more data for countries with food and hunger problems, but, I suppose I should not be too surprised by this.  BMI was designed to quantify “rich-country” diseases associated with being too fat.  Countries with food and hunger problems are not that interested in measuring BMI.  Even moderately well-off countries don’t collect BMI statistics, it seems. 

Despite the limitations in the database, the trend is consistent with what I would expect for a petroleum-driven food production system not consuming enough petroleum to produce adequate food—a lower percentage of overweight people. 

The percentage of overweight people in a population is an inverse indicator of hunger.  Consequently, the trend line in Figure 5 is the inverse of the trend lines in Figure 1 and 2 in part 1.  The point where the percentage of fat people in a country begins to sharply decline occurs around a per capita consumption rate of about 1-2 b/py.  This is consistent with the analysis in parts 1 and 2 using the global hunger index (GHI) as my indicator of compromised food production where the GHI sharply increases when the petroleum consumption rate drops to about 1 b/py.

If there is a link between high BMI levels and increased risks of negative health consequences like atherosclerosis, heart disease, diabetes etc..., then maybe there can be a certain high rate of petroleum consumption that is actually harmful to one’s health. A population where 50 percent or more of the population is overweight doesn’t sound too healthy to me, and, this appears to be related to a consumption rate of about 11 barrels per person per year or higher.

4 comments:

  1. Being overweight is not a function of simply "too much food." Poor and low income people have the highest obesity rates in the USA and elsewhere, while more wealthy citizens can afford not only better food, but the education and health care to back it up. Even in destitute African countries, many starving children have very poor mothers who are overweight. This phenomenon was discussed in detail in Gary Taubes' intensively researched book, "Good Calories, Bad Calories." Obesity is caused by eating an excess of wheat, sugar and fructose, (in many "developed" countries including a lot of processed foods) and very little protein, good animal fats, eggs, and vegetables, all of which of course are more expensive. It is essentially a problem of malnutrition and disrupted/diseased metabolism, exacerbated by poverty, ignorance, and an agricultural industrial complex bent on profits at all cost.

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  2. Thanks Anonymous. I agree with you that there are many factors that result in a high percentage of a population being overweight. Yes, nutrition is a factor. Lack of physical exercise is another factor. Even poor and low income people in the USA, Europe and other higher petroleum consuming countries have motorized transportation and are not doing the manual labor of raising their own food, for the most part.

    But, there must first be adequate food, of any type, to eat before a population, at large, can become overweight.

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  3. For a counter-balance to Taubes's ideas, advocated by Anonymous, readers might find Meghan Johnson's recent article interesting:

    What Gary Taubes tries to do is to challenge one of the core principles of energy balance (endorsed by the USDA, the American Heart Association, the American Medical Association, American Dietetic Association and other reputable institutions) that calories consumed must nearly equal calories expended in order to maintain body weight. The USDA and Department of Health & Human Service’s 2011 Dietary Guidelines for Americans state, “Eating and physical activity patterns that are focused on consuming fewer calories, making informed food choices, and being physically active can help people attain and maintain a healthy weight, reduce their risk of chronic disease, and promote overall health.” The First Law of Thermodynamics supports this theory of energy balance, something a physicist knows all too well.

    Taking Gary Taubes’s Sugar Theory with a Grain of Salt (http://friedmansprout.wordpress.com/2011/05/01/taking-gary-taubes%E2%80%99s-sugar-theory-with-a-grain-of-salt)

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  4. Sorry, no more trolling for Taube. This is not a blog about BMI and nutrition.

    My assumption here is that the amount of food that a population has to eat will affect the percentage of people in the population with a BMI of greater than 25.

    If you want to say that there are other factors that affect BMI, then I agree, but the main factor is still the total amount of food you eat, at least on the low end.

    For instance, if I give you 20 crackers per day (~500 cal) to live on, I hypothesize that your BMI will decline below 25 after a few weeks or months, depending on how overweight you are to start with.

    If I give you 10 bacon strips to eat per day (~500 cal) to live on, I still hypothesize that your BMI will decline below 25 after a few weeks or months.

    Will your BMI decrease by the same amount? I don’t really care that much—you will die of starvation either way.

    If you want take exception to my hypothesis that’s fine, then I think we will have to agree to disagree.

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Your comments, questions and suggestions are welcome! However, comments with cursing or ad hominem attacks will be removed.