The attack on red meat consumption takes an amusing twist
Below is a recent report subject to all the usual criticisms of observational studies and self-reported food intake. But even if you take their data seriously, you find some interesting stuff.
Red Meat Consumption and Mortality: Results From 2 Prospective Cohort Studies
But a careful reanalyisis of their data shows that red meat is actually GOOD for you
The statistics below are quite esoteric and I would analyse the data a little differently (I prefer Pearsonian rs to beta weights as the best indicator of a relationship) but are quite defensible
I have highlighted in red the key points
As we have seen in an earlier post on the China Study data (1), which explored relationships hinted at by Denise Minger’s previous and highly perceptive analysis (2), one can use a multivariate analysis tool like WarpPLS (3) to explore relationships based on data reported by others. This is true even when the dataset available is fairly small.
So I entered the data reported in the most recent (published online in March 2012) study looking at the relationship between red meat consumption and mortality into WarpPLS to do some exploratory analyses. I discussed the study in my previous post; it was conducted by Pan et al. (Frank B. Hu is the senior author) and published in the prestigious Archives of Internal Medicine (4). The data I used is from Table 1 of the article; it reports figures on several variables along 5 quintiles, based on separate analyses of two samples, called “Health Professionals” and “Nurses Health” samples. The Health Professionals sample comprised males; the Nurses Health sample, females.
Below is an interesting exploratory model, with results. It includes a number of hypotheses, represented by arrows, which seem to make sense. This is helpful, because a model incorporating hypotheses that make sense allows for easy identification of nonsense results, and thus rejection of the model or the data. (Refutability is one of the most important characteristics of good theoretical models.) Keep in mind that the sample size here is very small (N=10), as the authors of the study reported data along 5 quintiles for the Health Professionals sample, together with 5 quintiles for the Nurses Health sample. In a sense, this is somewhat helpful, because a small sample tends to be “unstable”, leading nonsense results and other signs of problems to show up easily – one example would be multivariate coefficients of association (the beta coefficients reported near the arrows) greater than 1 due to collinearity (5).
So what does the model above tell us? It tells us that smoking (Smokng) is associated with reduced physical activity (PhysAct); beta = -0.92. It tells us that smoking (Smokng) is associated with reduced food intake (FoodInt); beta = -0.36. It tells us that physical activity (PhysAct) is associated with reduced incidence of diabetes (Diabetes); beta = -0.25. It tells us that increased food intake (FoodInt) is associated with increased incidence of diabetes (Diabetes); beta = 0.93. It tells us that increased food intake (FoodInt) is associated with increased red meat intake (RedMeat); beta = 0.60. It tells us that increased incidence of diabetes (Diabetes) is associated with increased mortality (Mort); beta = 0.61. It tells us that being female (SexM1F2) is associated with reduced mortality (Mort); beta = -0.67.
Some of these betas are a bit too high (e.g., 0.93), due to the level of collinearity caused by such a small sample. Due to being quite high, they are statistically significant even in a small sample. Betas greater than 0.20 tend to become statistically significant when the sample size is 100 or greater; so all of the coefficients above would be statistically significant with a larger sample size. What is the common denominator of all of the associations above? The common denominator is that all of them make sense, qualitatively speaking; there is not a single case where the sign is the opposite of what we would expect. There is one association that is shown on the graph and that is missing from my summary of associations above; and it also makes sense, at least to me. The model also tells us that increased red meat intake (RedMeat) is associated with reduced mortality (Mort); beta = -0.25. More technically, it tells us that, when we control for biological sex (SexM1F2) and incidence of diabetes (Diabetes), increased red meat intake (RedMeat) is associated with reduced mortality (Mort).
How do we roughly estimate this effect in terms of amounts of red meat consumed? The -0.25 means that, for each standard deviation in the amount of red meat consumed, there is a corresponding 0.25 standard deviation reduction of mortality. (This interpretation is possible because I used WarpPLS’ linear analysis algorithm; a nonlinear algorithm would lead to a more complex interpretation.) The standard deviation for red meat consumption is 0.897 servings. Each serving has about 84 g. And the highest number of servings in the dataset is 3.1 servings, or 260 g/d (calculated as: 3.1*84). To stay a bit shy of this extreme, let us consider a slightly lower intake amount, which is 3.1 standard deviations, or 234 g/d (calculated as: 3.1*0.897*84). Since the standard deviation for mortality is 0.3 percentage points, we can conclude that an extra 234 g of red meat per day is associated with a reduction in mortality of approximately 23 percent (calculated as: 3.1*0.25*0.3).
Let me repeat for emphasis: the data reported by the authors suggests that, when we control for biological sex and incidence of diabetes, an extra 234 g of red meat per day is associated with a reduction in mortality of approximately 23 percent. This is exactly the opposite, qualitatively speaking, of what was reported by the authors in the article. I should note that this is also a minute effect, like the effect reported by the authors. (The mortality rates in the article are expressed as percentages, with the lowest being around 1 percent. So this 23 percent is a percentage of a percentage.) If you were to compare a group of 100 people who ate little red meat with another group of the same size that ate 234 g more of red meat every day, over a period of more than 20 years, you would not find a single additional death in either group. If you were to compare matched groups of 1,000 individuals, you would find only 2 additional deaths among the folks who ate little red meat.
At the same time, we can also see that excessive food intake is associated with increased mortality via its effect on diabetes. The product beta coefficient for the mediated effect FoodInt --> Diabetes --> Mort is 0.57. This means that, for each standard deviation of food intake in grams, there is a corresponding 0.57 standard deviation increase in mortality, via an increase in the incidence of diabetes. This is very likely at levels of food consumption where significantly more calories are consumed than spent, ultimately leading to many people becoming obese. The standard deviation for food intake is 355 calories. The highest daily food intake quintile reported in the article is 2,396 calories, which happens to be associated with the highest mortality (and is probably an underestimation); the lowest is 1,202 (also probably underestimated).
So, in summary, the data suggests that, for the particular sample studied (made up of two subsamples): (a) red meat intake is protective in terms of overall mortality, through a direct effect; and (b) the deleterious effect of overeating on mortality is stronger than the protective effect of red meat intake. These conclusions are consistent with those of my previous post on the same study (6). The difference is that the previous post suggested a possible moderating protective effect; this post suggests a possible direct protective effect. Both effects are small, as was the negative effect reported by the authors of the study. Neither is statistically significant, due to sample size limitations (secondary data from an article; N=10). And all of this is based on a study that categorized various types of processed meat as red meat, and that did not distinguish grass-fed from non-grass-fed meat.
By the way, in discussions of red meat intake’s effect on health, often iron overload is mentioned. What many people don’t seem to realize is that iron overload is caused primarily by hereditary haemochromatosis. Another cause is “blood doping” to improve athletic performance (7). Hereditary haemochromatosis is a very rare genetic disorder; rare enough to be statistically “invisible” in any study that does not specifically target people with this disorder.
Sugar Taxes Are Unfair And Unhealthy
If the regulatory discussion about sugar is going to be based on science, rather than science fiction, it needs to move beyond kicking the soda can.
Conventional wisdom says draconian regulation—specifically, a high tax—on sugary drinks and snacks reduces unhealthy consumption, and thereby improves public health. There are many reasons, however, why high sugar taxes are at best unsuccessful, and at worst economically and socially harmful.
Research finds that higher prices don't reduce soda consumption, for example. No scientific studies demonstrate a difference either in aggregate soda consumption or in child and adolescent Body Mass Index between the two thirds of states with soda taxes and those without such taxes.
Patrick Basham is a Cato Institute adjunct scholar. He coauthored (with John Luik) the Democracy Institute book, Gambling: A Healthy Bet.
More by Patrick Basham
The study that did find taxes might lead to a moderate reduction in soda consumption also found this had no effect on adolescent obesity, as the reduction was completely offset by increases in consumption of other calorific drinks.
Economic research finds sugar taxes are a futile instrument in influencing the behavior and habits of the overweight and the obese. Why do sugar taxes fail? Those consumers who strongly prefer unhealthy foods continue to eat and drink according to their individual preferences until such time as it becomes prohibitively expensive to do so.
Demand for food is largely insensitive to price. A 10 percent increase in price reduces consumption by less than 1 percent. Applied to soda, this means that to reduce consumption by 10 percent, the tax rate on sugary drinks would need to be 100 percent!
A sugar tax also has undesirable social and economic consequences. This tax is economically regressive, as a disproportionate share of the tax is paid by low earners, who pay a higher proportion of their incomes in sales tax and also consume a disproportionate share of sugary snacks and drinks.
Such taxes also have perverse, unintended consequences. Taxes on sugary snacks lead many consumers to replace the taxed food with equally unhealthy foods. Poorer consumers react to higher food prices not by changing their diets but by consuming even fewer healthy foods, such as fruits and vegetables, and eating more processed foods. For instance, taxes levied specifically on sugar content increase saturated fat consumption.
Sugar taxes have failed where they've been tried, and are unfair and unhealthy. Given that there's no compelling evidence they'll improve public health, we can't justify using the tax code to shape the sweetness of our dietary choices.