Does a Good Diet Guarantee Good Health?

To be sure, dietary factors contribute to bad health at least some of the time for some people.  But, how large a role does diet play?  Stated differently: even if you eat well all the time, are you guaranteed to be free of cancer, heart disease, and diabetes?  Far from it according to two recent studies.  

The first was published Friday in Science by Tomasetti, Li, and Vogelstein, who investigated cancer causes.  When discussing the things that can cause cancer, causes normally fall into one of two broad categories: nature (environmental factors) or nurture (inherited genetic factors).  These authors, however, point to a third factor: as we grow, our cells naturally replicate themselves, and in the process, unavoidable DNA replication errors occur which ultimately lead to cancer.  The authors calculate that these replication errors or  

mutations are responsible for two-thirds of the mutations in human cancers.

Secondly, I ran across this interesting paper published a couple weeks ago in the Journal of the American Medical Association.  The authors attempted to ferret out how many deaths from heart disease, stroke, and type 2 diabetes (what the authors call "cardiometabolic deaths") that result each year annually come about from over- or under-consumption of certain types of foods.  As this critic pointed out, it is important to note that the authors estimates are associations/correlations NOT causation.  As such, I'd suggest caution in placing too much interpretation on the impacts from different types of food.  Nonetheless, there were a couple of other less-well-publicized results which I found interesting.

First, the authors found:

In 2012, suboptimal intake of dietary factors was associated with an estimated 318 656 cardiometabolic deaths, representing 45.4% of cardiometabolic deaths.

Stated differently, 54.6% of deaths from heart disease, stroke, and type 2 diabetes seems to be caused by something other than diet.   

The other result that I found interesting from this study is that there has been a big decline in so-called cardiometabolic deaths.  The authors write:

Between 2002 and 2012, population-adjusted US cardiometabolic deaths per year decreased by 26.5%.

Some of this decline, they argue, is due to reduced sugar consumption and increased nut/seed consumption from 2002 to 2012.

Why does all this matter?  Because these statistics help us understand the impacts of dietary and lifestyle changes.  To illustrate, let's take the above cancer statistic: 66.7% of cancers are caused by unavoidable replication errors. That leaves 33.3% of cancers, some of which are diet and lifestyle related and some of which are caused by inherited genetic factors.  For sake of simplicity, lets say you have zero risk from inherited genetic factors. Also note that the National Cancer Institute suggests that the chances of getting a new cancer in a given year are 454.8 per 100,000 people (or a 0.45% chance).  

Putting it all together, your chance of getting cancer from random errors in DNA replication is 0.667*0.45%=0.30%, and your chance of getting cancer from diet and lifestyle factors (assuming no inherited risks) is 0.333*0.45%=0.15%.  So, even if you could completely eliminate the cancer risk from diet and lifestyle factors, you'd go from a 0.45% chance of getting a new cancer to a 0.30% chance, a reduction of 0.15 percentage points.

What do school children want to eat?

In the past I have, at times, been somewhat critical of the National School Lunch Program (NSLP) guidelines destined to make school lunches healthier by reducing calories, sodium content, saturated fat, etc.  It's not not that I'm against healthy kids!  Rather, I bristled at the idea of a bunch of nutritionists, policy makers, etc. setting rules and guidelines for how they think kids should eat without considering how the children would respond to the rules.  Nutritional content is but one of the components we care about when eating - don't we also care about how the food tastes, how much it costs, whether it leaves feeling full, whether it is safe to eat, etc. etc.  In short, the guidelines were established with limited understanding of what children want to eat, and as such we knew very little about whether the rules might increase food waste, increase the frequency of home lunches, cause unintended substitution patterns, and so on.  

In an interesting paper in the most recent edition of the American Journal of Agricultural Economics, a team of six researchers sought to do what should have been done prior to implementing nutritional guidelines.  In particular, the authors studied almost 280,000 school lunch choices of about 5,500 elementary age children in a suburban South Carolina school district.  The authors know the precise foods available at each lunch offering, the nutritional characteristics of the foods, which foods the child selected (or whether the child brought a lunch from home - note that lunch menus were published well in advance), and some of the characteristics of the child who made the choice such as their grade, gender, race, and whether they received free or reduced price lunch.

The authors are able to take all this data to estimate demand curves associated with different food offerings.  Their demand models let them answer questions like the following:

  • If the sodium content of a pizza offering were lowered, how would that change the number of children who select it?  
  • If a low fat pizza is paired with a peanut butter sandwich, which would most people choose?
  • If the caloric content were unilaterally lowered on all offerings, how many more children would bring their lunches from home?       

Here's what the authors find:

If the protein content of Entrée 1 is increased by 3.2grams (one standard deviation of all entree offerings over the course of study), students are, on average, 2.8 percentage points more likely to select that offering. Increasing the fat content of Entrée 1 by one standard deviation (3.9grams) has a similar effect, though smaller in magnitude; students are only 0.2 percentage points more likely to select Entrée 1 because of this increase in its fat content. Increasing the carbohydrate content has the opposite effect; the average probability of choosing Entrée 1 over the alternatives decreases by 3 percentage points if the carbohydrate content increased by 6.8grams (one standard deviation). Thus, the first row of table 3 reveals that students prefer more fat and protein but dislike additional carbohydrates. While the results for sodium are positive, the effect is not statistically significant.

There are important differences across children:

While an increase in the fat content of Entrée 1 increases the average probability that a student receiving free lunch will select it, the same increase in fat reduces the likelihood a student who pays full-price will select Entrée 1. The results also suggest that students who pay full-price are more likely to select offerings with more protein than students receiving free or reduced-price lunches (Bonferroni p-value <0.0001), and those who received free lunches are more likely to reject entrées with additional sodium relative to students who pay full-price or students who received reduced-price lunches (Bonferroni p-value =0.0044).

The authors use their results to suggest how "schools can increase the healthfulness of their students’ meals by replacing unhealthy options with relatively healthy options that are already popular amongst the students."  One things the authors didn't do (but which is possible given their estimates) is to ask: are the children better or worse off (at least as measured by their own preferences revealed by their short run choice behavior) with the new nutritional standards?  Which types of children are now happier or sadder?  Because there is no price variation in the dataset, the authors can't provide a monetary measure of the loss (or gain) in student happiness, but they could covert it to some other unit they measure - such as grams of protein or calories.  

Nonetheless, this is a really interesting study, and it has a number of important findings.  Here's some from the conclusions:

Nationwide between school year 2010–11 and 2012–13, the number of students receiving free lunches increased while the number of students purchasing full-price lunches decreased, leading to an overall reduction in participation by 3.7% (Government Accounting Office 2014). The results of our analyses suggest that the underlying preferences for offerings with higher levels of fat and lower levels of carbohydrates may be driving the decline in NSLP participation. Full-price participants are most likely to respond to changes in the nutritional content of the offered entrées by opting out of purchasing a school lunch altogether. Our findings have particularly important implications for the NSLP’s stated goal of reducing childhood obesity as they indicate that children are likely to reject those entrées that are most compatible with this particular aim. However, our results do suggest that the future guidelines reducing sodium levels may not trigger additional participation declines.

Value of Nutritional Information

There is a general sense that nutritional information on food products is "good" and "valuable."  But, just how valuable is it?  Are the benefits greater than the costs?

There have been a large number of studies that have attempted to address this question and all have significant shortcomings.  Some studies just ask people survey questions about whether they use or look at labels.  Other studies have tried to look at how the addition of labels changes purchase behavior - but the focus here is typically limited to only a handful of products. As noted in an important early paper on this topic, by Mario Teisl, Nancy Bockstael, and Alan Levy, nutritional labels don't have to cause people to choose healthier foods to be valuable.  Here is one example they give:

consider the individual who suffers from hypertension, has reduced his sodium intake according to medical advice, and believes his current sodium intake is satisfactory. If this individual were to learn that certain brands of popcorn were low in salt, then he may switch to these brands and allow himself more of some other high sodium food that he enjoys. Better nutritional information will cause changes in demand for products and increases in welfare even though it may not always cause a backwards shift in all risk increasing foods nor even a positive change in health status.

This is why it is important to consider a large number of foods and food choices when trying to figure out the value of nutritional labels.  And that's exactly what we did in a new paper just published in the journal Food Policy.  One of my Ph.D. students, Jisung Jo, used some data from an experiment conducted by Laurent Muller and Bernard Ruffieux in France to estimate consumers' demands for 173 different food items in an environment where shoppers made an entire day's worth of food choices.  This lets us calculate the value of nutritional information per day (not just per product).  

The nutritional information we studied relies on two simple nutritional indices created by French researchers.  They are something akin to a NuVal label system or a traffic light system.  We first asked people where they thought each of the 173 foods fell on the nutritional indices (and we also asked how tasty or untasty each of the foods were), and then after making a day's worth of (non-hypothetical) food choices, we told them were each food actually fell.   Here's a bit more detail.  

The initial “day 1” food choices were based on the individuals’ subjective (and implicit) health beliefs. Between days 1 and 2, we sought to measure those subjective health beliefs and also to provide objective information about each of the 173 foods. The beliefs were measured by asking respondents to pick the quadrant in the SAIN (Nutrient Adequacy Score for Individual foods) and LIM (for Limited Nutrient) table (Fig. 2) that best described where they thought each food fit. The SAIN and LIM are nutrient profiling models and indices introduced by the French Food Safety Agency. The SAIN score is a measure of “good” nutrients calculated as an un-weighted arithmetic mean of the percentage adequacy for five positive nutrients: protein, fiber, ascorbic acid, calcium, and iron. The LIM score is a measure of “bad” nutrients calculated as the mean percentage of the maximum recommended values for three nutrients: sodium, added sugar, and saturated fatty acid.2 Since indices help reduce search costs, displaying the information in the form of an index is a way to make the information available in an objective way but also allows consumers to better compare the many alternative products in their choice set.

Here are the key results:

In this study, we found that nutrient information conveyed through simple indices influences consumers’ grocery choices. Nutrient information increases willingness-to-pay (WTP) for healthy food and decreases WTP for unhealthy food. The added certainty provided by objective nutrient information increased the marginal WTP for healthy food. Moreover, there is a sort of loss aversion at play in that WTP for healthy vs. neutral food is lower than WTP for neutral vs. unhealthy food, and this loss aversion increases with information. . . . This study estimated the value of the nutrient index information at €0.98/family/day. The advantage of our approach is that the value of information reflects choices over a larger number of possible foods and represents an aggregate value over the whole day.

I should also note that people valued the taste of their food as well.  We found consumers were willing to pay 4.33 eruos/kg more for a one-unit increase in on the -5 to +5 taste scale.  To put this number in perspective, let's take a closer look at the average taste rating given to all 173 food items. Most items had a mean rating above zero. The highest rated items on average were items like tomatoes (+4.1), green salad (+4), and zucchini (+3.9). The lowest rated items on average included cheese spread ( 0.2) and Orangina light ( 1.9). [remember: these were French consumers] Moving from one of the lower to higher rated items would induce a four-point change in the taste scale associated with a change in economic value of 4.33 ⁄ 4 = 17.32 euros/kg.”

Are Farm Subsidies Making Us Fat?

In the past couple weeks, there have been a number of popular press articles suggesting that farm subsidies are a big part of the reason Americans eat unhealthy and are overweight. Here's the title from the New York Times: "How the Government Supports Your Junk Food Habit", and Fox News: "Government heavily subsidizes junk food, report suggests", and NPR: "Does Subsidizing Crops We're Told To Eat Less Of Fatten Us Up?". All the hubbub seems to stem from this article by some CDC researchers in JAMA Internal Medicine, which shows people who are more overweight tend to get more of their calories from foods that happen to be subsidized.

But, as we should all know by now, correlation is not causation.  Here's Tracie McMillian in a piece for National Geographic:

But what the study does not show is the degree to which subsidies—and, in particular, the ones that are currently in place—actually persuade people to eat more of those foods. The researchers, by the way, admit this: “We cannot say [the link between subsidies and consumption] is causal from this study,” says K.M. Vankat Narayan, a lead author.

So while there’s an accepted correlation between low prices and increased purchases, nobody really knows how much farm subsidies matter when it comes to which foods people buy—and eat.

She's right on the first part and wrong on the second.  There are actual lots of people who know how much farm subsidies contribute to food consumption, and they're called agricultural economists (in fact, McMillian goes on to then cite two prominent food and agricultural economists on the issue: Parke Wilde and David Just).  My view is in line with Wilde's and Just's:

Indeed, in contemporary America, “the potential impact of the agricultural subsidies on consumption right now is inconsequential,” argues David Just, an agricultural and behavioral economist at Cornell University. Subsidies for farmers are unlikely to have much impact on consumer prices, adds Wilde, because farmers’ share of what we pay at the store is so little.

Let me pause right here and say that the question of the causal relationship between farm policy and unhealthy food consumption is an empirical, positive question, not a normative one.  There are a variety of reasons one may think we should or should not have farm subsidies (I generally find myself in the latter camp for reasons I won't go into here), but for the moment let's set the "should" question aside and ask what the evidence actually says on the link between farm subsides and unhealthy eating.  

Here's what I wrote on the issue in a recent Mercatus paper (which came out well before all the JAMA paper and the resulting news stories):

Despite popular claims to the contrary, research suggests that farm subsidies have likely had little to no effect on obesity rates. First, although such policies may have had some effect on farm commodity prices, these inputs account for only a small share of the overall retail cost of food. For example, in 2013, only 7 percent of the retail price of bread was a result of the farm-gate price of wheat and other agricultural commodities. Even the enormous price swing that took wheat from around $3 per bushel in 2006 to almost $12 per bushel in February 2008 (a 300 percent increase) would be expected to increase the price of bread by only about 14 percent. Second, agricultural policies are mixed, and some policies (such as those for sugar, ethanol promotion, and the Conservation Reserve Program, or CRP) push the prices of agricultural commodities up rather than down. Third, despite the widely varying agricultural policies across countries and over time (see figures 14–16), those policies do not correlate well with differences in food prices and obesity rates across countries or with changes in obesity rates over time.

In the model I used for the forthcoming paper I wrote on the distributional impacts of crop insurance subsidies, I find that the complete removal of crop insurance subsidies to farmers would only increase the price of cereal and bakery products by 0.09% and increase the price of meat by 0.5%, and would also increase the price of fruits ad vegetables by 0.7%.  So, while these policies may be inefficient, regressive, and promote regulatory over-reach, their effects on food prices are tiny, and depending on which policy we're talking about, could push prices and consumption  up or down.  

For those truly interested, here's a small list of academic papers by economists on the relationship between farm policy and obesity/health (for links to the actual papers, just do a quick googlescholar search).

Alston, Julian M., Daniel A. Sumner, and Stephen A. Vosti, “Farm Subsidies and Obesity in the United States: National Evidence and International Comparisons,” Food Policy 33, no. 6 (2008): 470–79. 

Balagtas, J.V., Krissoff, B., Lei, L. and Rickard, B.J., 2014. How Has US Farm Policy Influenced Fruit and Vegetable Production?. Applied Economic Perspectives and Policy, 36(2), pp.265-286.

Beghin, John C., and Helen H. Jensen. "Farm policies and added sugars in US diets." Food Policy 33, no. 6 (2008): 480-488.

Miller,J. Coreyand Keith H. Coble, “Cheap Food Policy: Fact or Rhetoric?” Food Policy 32, no. 1 (2007): 98–111. 

Okrent, Abigail M.  and Julian M. Alston, “The Effects of Farm Commodity and Retail Food Policies on Obesity and Economic Welfare in the United States,” American Journal of Agricultural Economics 94, no. 3 (2012): 611–46.

Rickard, B.J., Okrent, A.M. and Alston, J.M., 2013. How have agricultural policies influenced caloric consumption in the United States?. Health Economics, 22(3), pp.316-339.

Zilberman, D., Hochman, G., Rajagopal, D., Sexton, S. and Timilsina, G., The impact of biofuels on commodity food prices: Assessment of findings. American Journal of Agricultural Economics, 95, no. 2 (2013) : 275-281.


Does Diet Coke Cause Fat Babies?

O.k., I just couldn't let this one slide.  I've seen the results of this study in JAMA Pediatrics discussed in a variety of news outlets with the claim that researchers have found a link between mothers drinking artificially sweetened beverages and the subsequent weight of their infants.

I'm going to be harsh here, but this sort of study represents everything wrong with a big chunk of the nutritional and epidemiology studies that are published and how they're covered by the media.  

First, what did the authors do?  They looked at the weight of babies one year after birth and looked at how those baby weights correlated with whether (and how much) Coke and Diet Coke the mom drank, as indicated in a survey, during pregnancy.  

The headline result is that moms who drank artificially sweetened beverages every day in pregnancy had slightly larger babies, on average, a year later than the babies from moms who didn't drink any artificially sweetened beverages at all.  Before I get to the fundamental problem with this result, it is useful to look at a few more results contained in the same study which might give us pause.

  • Mom's drinking sugar sweetened beverages (in any amount) had no effect on infants' later body weights.  So drinking a lot of sugar didn't affect babys' outcomes at all but drinking artificial sweeteners did?
  • The researchers only found an effect for moms who drank artificially sweetened beverages every day.  Compared to moms who never drink them, those who drink diet sodas less than once a week actually had lighter babies! (though the result isn't statistically significant).  Also, moms drinking artificially sweetened beverages 2-6 times per week had roughly the same weight babies as moms who never drank artificially sweetened beverages.  In short, there is no evidence of a dose-response relationship that one would expect to find if there was a causal relationship at play.  

And, that's the big issue here: causality.  The researchers have found a single statistically significant correlation in one of six comparisons they made (three levels of drinking compared to none for sugar sweetened beverages and for artificially sweetened beverages).  But, as the researchers themselves admit, this is NOT a casual link (somehow that didn't prevent the NYT editors from using the word "link" in the title of their story).  

Causality is what we want to know.  An expecting mother wants to know: if I stop drinking Diet Coke every day will that lower the weight of my baby?  That's a very different question than what the researchers actually answered: are the types of moms who drink Diet Coke every day different from moms who never drink Diet Coke in a whole host of ways, including how much their infants weigh?  

Why might this finding be only a correlation and not causation? There are a bunch of possible reasons.  For example, moms who expect their future children might have weight problems may choose to drink diet instead of regular.  If so, the the moms drinking diet have selected themselves into a group that is already likely to have heavy children.  Another possible explanation: moms who never drink Diet Cokes may be more health conscious overall.  This is an attitude that is likely to carry over to how they feed and raise their children which will affect their weight in ways that has nothing to do with artificially sweetened beverages.

Fortunately economics (at least applied microeconomics) has undergone a bit of credibility revolution.  If you attend a research seminar in virtually any economist department these days, you're almost certain to hear questions like, "what is your identification strategy?" or "how did you deal with endogeneity or selection?"  In short, the question is: how do we know the effects you're reporting are causal effects and not just correlations.  

Its high time for a credibility revolution in nutrition and epidemiology.