Weight moderation

Lane Kenworthy, The Good Society
July 2015

Obesity is one of America’s chief social problems. People who are obese tend to earn less and are more likely to be depressed. They are at greater risk of diabetes, heart disease, stroke, and some types of cancer, and they tend to die younger. The monetary cost of obesity is estimated to be 1% to 4% of our GDP.1

The incidence of obesity has risen dramatically in the past generation. Today, one in three American adults is obese. Why has this happened? What can we do to reverse the trend?


Obesity is defined as having a body mass index (BMI) of 30 or more.2 For a person 6 feet tall, that means a weight of more than 220 pounds. For someone 5’6″, the threshold is 185 pounds.

Reference to “obesity” or “the obese” inclines us to think of obese persons as a distinct group, different from others not just in degree but in kind. Actually, obesity is part of a continuum. Figure 1, which shows the distribution of BMI among American adults, makes this clear.

Figure 1. Distribution of body mass index in the United States
Share of American adults at each level of body mass index (BMI). BMI is calculated as weight (in kilograms) divided by height (in meters squared). Obesity is defined as body mass index of 30 or more. Source: Shiriki K. Kumanyika, Lynn Parker, and Leslie J. Sim, eds., Bridging the Evidence Gap in Obesity Prevention, Institute of Medicine, 2010, figure 1.1, using National Health and Nutrition Examination Survey (NHANES) data.


Figure 2 shows the trend in obesity among American adults since 1960, the first year for which we have good data. After holding constant at about 15% in the 1960s and 1970s, the obesity rate shot up beginning in the 1980s, reaching 37% in 2014.

Figure 2. Obesity in the United States
Adult obesity rate. Obesity is defined as body mass index of 30 or more. Data sources: Cheryl D. Fryar, Margaret D. Carroll, and Cynthia L. Ogden, “Prevalence of Overweight, Obesity, and Extreme Obesity Among Adults: United States, 1960-1962 through 2011-2012,” National Center for Health Statistics, 2014, table 2, using National Health and Nutrition Examination Survey (NHANES) data; Cynthia L. Ogden et al, “Prevalence of Obesity Among Adults and Youth: United States, 2011–2014,” NCHS Data Brief 219, 2015.

Obesity is more common in the United States than in any other rich nation, as shown in figure 3. For the US and a few other countries, the data are from actual measurements of people’s height and weight. For most, though, they are from self-reports by survey respondents, which are likely to be less accurate. For some countries data from both sources are available, and they suggest that self-reports tend to underestimate a country’s true obesity rate by 2 to 8 percentage points. While we can’t be sure of the exact position of each country, there is little doubt that the United States has the highest obesity rate.

Figure 3. Obesity in 21 rich nations
Adult obesity rate, 2010. Obesity is defined as body mass index greater than 30. “Measured” means the obesity estimate comes from actual measurements of people’s height and weight. “Self-reported” means the obesity estimate comes from surveys in which people report their height and weight to the interviewer. Data source: OECD.

Figure 4 shows the over-time patterns in these countries, with data based on measured height and weight shown with solid lines and data from self-reports shown with dashed lines. The increase in the United States was marked, but no more so than in Australia, New Zealand, and the UK. This suggests that there may be something different not just about the United States but about the English-speaking nations as a group, though it’s possible that this apparent difference is simply a function of differing data sources.

Figure 4. Obesity in 21 rich nations
Adult obesity rate. Obesity is defined as body mass index greater than 30. “Measured” means the obesity estimate comes from actual measurements of people’s height and weight. “Self-reported” means the obesity estimate comes from surveys in which people report their height and weight to the interviewer. Data source: OECD.

What caused the surge in obesity in America?


One prominent notion is that Americans are heavier because we get less exercise than we used to. But this is probably wrong. While it’s true that we’re less active now than we were half a century ago, the timing of the decline in activity doesn’t match up with the rise in obesity.

We don’t have good historical data for a comprehensive measure of activity, such as calories expended, so we have to look instead at individual components. Begin with the most-often-cited culprit: television. Figure 5 shows the trend in average time spent watching TV since 1950. The amount of time has increased, but the increase has been steady. There was no sudden rise that would account for the jump in obesity beginning in the 1980s.

Figure 5. Time spent watching TV
Average hours per day. Data source: The Nielsen Company, “Historical Daily Viewing Activity among Households.”

What about video games, the internet, and smartphones? Video games became popular in the 1980s, but mostly among the young, while obesity rates rose among adults of all ages, even the elderly.3 The internet and smartphones arrived on the scene too late to account for the rise in obesity in the 1980s and most of the 1990s.

More Americans now have sedentary jobs and drive to work. Yet as David Cutler, Edward Glaeser, and Jesse Shapiro point out, these shifts have been going on for a long time, with no acceleration in the 1980s.4

“Between 1910 and 1970, the share of people employed in jobs that are highly active like farm workers and laborers fell from 68 to 49 percent. Since then, the change has been more modest. Between 1980 and 1990, the share of the population in highly active jobs declined by a mere 3 percentage points, from 45 to 42 percent. Occupation changes are not a major cause of the recent increase in obesity.

“Changes in transportation to work are another possible source of reduced energy expenditure — driving a car instead of walking or using public transportation. Over the longer time period, cars have replaced walking and public transportation as a means of commuting. But this change had largely run its course by 1980. In 1980, 84 percent of people drove to work, 6 percent walked, and 6 percent used public transportation. In 2000, 87 percent drove to work, 3 percent walked, and 5 percent used public transportation. Changes of this minor magnitude are much too small to explain the trend in obesity.”

Another reason to doubt the importance of declining physical activity is that the elderly probably have become more active over time, not less, and yet we observe a rise in obesity among the elderly too, similar in timing and magnitude to that of younger adults.5

In short, the evidence suggests that reduced physical activity hasn’t been a key cause of the surge in obesity in America.6 This doesn’t mean physical activity plays no role in determining which persons become obese. And it doesn’t mean an increase in activity can’t help to reduce obesity’s prevalence. But it does suggest that a strategy focused on increasing activity may not get us very far.


People who smoke regularly are less likely to become obese, and Americans have been smoking less over time. Is this why obesity has increased? Probably not. According to the best available data, smoking has decreased steadily since the 1960s, which is long before obesity shot up.7


The most likely cause of rising obesity is that Americans began consuming more calories.8 Figure 6 shows the trend in eating, measured as average calories in the food supply (adjusted for loss and spoilage) according to data from the Department of Agriculture. These data are available beginning in 1970. The timing of change matches that for obesity; the level is flat in the 1970s and then rises sharply beginning in the 1980s. A data series that goes back to 1950, measuring energy consumption per capita, also shows little or no change up to 1980 and then a sharp jump.9

Figure 6. Food consumption
Average daily per capita calories of available food, adjusted for spoilage and other waste. Data source: US Department of Agriculture, Economic Research Service, “Food Availability (Per Capita) Data System.”

More support for the eating hypothesis comes from recent developments. In the early to middle 2000s, calorie consumption stopped rising. We can see this in figure 6 — and in other data sources, such as food diaries and family logs of food purchases.10 Shortly after this, the obesity rate also began to flatten, as we see in figure 2 above.


What caused the rise in eating beginning in the 1980s? The most compelling story points at changes in the supply of food. Tasty, high-calorie food became cheaper and more easily accessible in larger quantities, so we began eating more of it.

There is no simple measure that can capture this change. One partial measure, the number of restaurants per capita, correlates strongly with the prevalence of obesity over time.11

In this account, the comparatively rapid increase in obesity in the US and in the other English-speaking rich nations owes to these countries’ weak regulation of food and restaurants and to their lack of a well-entrenched healthy eating culture. Large-portion restaurants, particularly fast-food ones, proliferated more rapidly in the English-speaking countries. Junk food became available in grocery and convenience stores sooner and in larger quantities. And the shift away from home cooking and limited snacking occurred more quickly and decisively.12


In recent years some researchers have advanced an alternative hypothesis that blames rising income inequality. Inequality is said to increase status competition, which increases stress, which in turn prompts overeating.13

In the United States, income inequality has risen sharply since the late 1970s, as has obesity.14 For the timing to work, though, we need to assume little or no lag. If it takes a long while for inequality to have an impact on obesity, as we might expect, then it can’t have caused the 1980s obesity increase.

What about the trend in stress? None of the research I’ve seen looks into this, but some cite a paper published in 2000 that finds evidence of a rise in anxiety in America.15 Yet that study concluded that anxiety rose steadily from the 1950s through the early 1990s, which doesn’t match up well at all with the over-time trends in income inequality or obesity.

The chief empirical evidence cited in support of the income inequality hypothesis is the pattern across affluent countries. Figure 7 shows the cross-country association between obesity and income inequality as of the mid-to-late 2000s. I’ve used obesity rates based on self-reports for most of the countries and the true rate minus a few percentage points for the others.16 As the solid line indicates, the association is positive; nations with higher income inequality tend to have higher obesity rates.

Figure 7. Obesity by income inequality, 21 countries
2010. The solid regression line is for all 21 countries. The dashed line is calculated with Australia, New Zealand, the UK, and the US excluded. Obesity is defined as body mass index greater than 30. Income inequality is based on posttransfer-posttax income adjusted for household size. Data sources: OECD, Luxembourg Income Study.

However, the positive association is driven by the four nations in the upper-right portion of the chart: Australia, New Zealand, the United Kingdom, and the United States. If we exclude them, the association disappears entirely; there is no relationship between income inequality and obesity across the other 17 countries (dashed line). So if obesity is exceptionally high in those four countries due to something other than income inequality — to the supply of cheap high-calorie food, for example — then there is nothing left in support of the inequality hypothesis.

Some researchers have found a positive association between income inequality and obesity across the US states.17 (Obesity data for the US states are from self-reports, so we should be wary. But we can hope the degree of bias is similar in each state.) Figure 8 shows the cross-sectional pattern as of the late 2000s. The predicted positive association is there (solid line). But it is driven by eight southern states in the upper-right portion of the chart: Alabama, Arkansas, Kentucky, Louisiana, Mississippi, Oklahoma, Tennessee, and West Virginia. (Four other deep-south states — Georgia, North Carolina, South Carolina, and Texas — are close by.) Without these eight states, there is no correlation (dashed line). It’s not difficult to think of plausible alternative causes of these states’ high obesity rates, most notably diet.18

Figure 8. Obesity by income inequality, US states
Late 2000s. The solid regression line is for all 50 states. The dashed line is calculated with eight southern states excluded (Alabama, Arkansas, Kentucky, Louisiana, Mississippi, Oklahoma, Tennessee, West Virginia). Obesity is defined as body mass index of 30 or more. The obesity data are from self-reports of height and weight. Income inequality is based on posttransfer-pretax income. Data sources: Centers for Disease Control (BRFSS), Census Bureau.

Since countries and states differ in a number of ways that might affect obesity — from food preferences to education to affluence to physical activity — our best bet analytically is to compare changes over time. Figure 9 shows change in the obesity rate by change in income inequality for countries. The pattern summarized by the solid regression line suggests support for the notion that income inequality increases obesity. The dashed line excludes Australia, New Zealand, the UK, and the US — countries where the food supply hypothesis may account for much of the rise in obesity. It suggests no association between change in income inequality and change in obesity.

Figure 9. Change in obesity by change in income inequality, 18 countries
The values on the axes are for change over 28 years. The lines are linear regression lines. The solid line includes all eighteen countries; the dashed line excludes Australia, New Zealand, the UK, and the US. For Australia, Japan, New Zealand, the United Kingdom, and the United States, the obesity estimate comes from actual measurements of people’s height and weight. For other countries the obesity estimate comes from surveys in which people report their height and weight to the interviewer. Obesity is defined as body mass index greater than 30. Income inequality is based on posttransfer-posttax income adjusted for household size. Data sources: OECD; Luxembourg Income Study.

Data for the US states are available from 1995 to 2010. If income inequality is an important determinant of obesity, we would expect states in which income inequality has increased the most to have experienced the largest rise in obesity. But as figure 10 shows, they haven’t.19

Figure 10. Change in obesity by change in income inequality, US states
Change is calculated as 2009 value minus 1995 value. Obesity is defined as body mass index of 30 or more. The obesity data are from self-reports of height and weight. Income inequality is based on posttransfer-pretax income. Data sources: Centers for Disease Control (BRFSS); Census Bureau.

The bottom line on the inequality hypothesis: It’s plausible. And at first glance the over-time pattern in the US, the cross-country pattern, and the cross-state pattern seem supportive. But when we look deeper, the case for income inequality as a key determinant of obesity is weak.


A related hypothesis suggests that the problem is rising economic insecurity. Here too the mechanism is said to be stress.20

In the United States, economic insecurity has increased since the 1970s, which is consistent with the timing of obesity’s rise. I’m not sure, however, that the increase in economic insecurity has been large enough to have produced the massive jump in obesity that occurred.21

The English-speaking countries may well have more economic insecurity than other rich nations, and it could be that insecurity has increased more in those countries than elsewhere. If so, the insecurity hypothesis might be helpful in accounting for the differences in obesity across these countries. Unfortunately, we don’t have an especially valid and reliable measure of economic insecurity for cross-country analysis.22 That’s true also for the US states.

Another piece of evidence seemingly consistent with the economic insecurity hypothesis is the social gradient in obesity in the United States — the fact that obesity rates have tended to be highest among the poor, those who are least secure economically. Of course, there are reasons other than stress why poor people might be more vulnerable to obesity, such as less education, less ability to afford healthy food, and less access to such food. Here too, then, it’s helpful to examine changes over time rather than just levels.

The mid-1990s welfare reform gives us something of a natural experiment. That reform placed strict time limits on receipt of cash assistance for women with low income. If there is any change in recent decades that ought to have heightened stress among low-income women, welfare reform is it. According to the economic insecurity hypothesis, obesity rates in the period after the mid-1990s should have risen more rapidly among low-income women than among other women. But that isn’t what happened, as figure 11 shows. Obesity rates increased at roughly the same pace among middle-income and high-income women as among those with low income.

Figure 11. Obesity among American women before and after welfare reform
Adult female obesity rate. Obesity is defined as body mass index of 30 or more. The obesity data are from actual measurements of people’s height and weight. “Low income”: income below 130% of the poverty line. “Low-middle income”: income between 130% and 350% of the poverty line. “Middle and high income”: income above 350% of the poverty line. Data source: Cynthia L. Ogden et al, “Obesity and Socioeconomic Status in Adults,” NCHS Data Brief #50, 2010, figure 4, using NHANES data.

Another piece of evidence comes from Canada. A recent study examined the association between economic insecurity, measured as a reduction in household income of 25% or more from one year to the next, and obesity from 1998 to 2008. The data suggest a positive but weak association between insecurity and obesity for working-age males and no association for females. During this period the obesity rate in Canada increased even though economic insecurity decreased.23

Like income inequality, then, economic insecurity is a plausible candidate as a cause of changes in obesity, but at the moment the supportive evidence is thin.


Obesity is a significant social and economic problem, and its incidence has risen sharply in the United States in the past generation. How could we reverse that trend?

The key is to get Americans to eat fewer calories.

Why not fewer bad calories, such as those from sugars? The reason is that eating fewer calories almost by definition entails eating fewer calories from junk food. Junk food is calorie-dense, so a typical person won’t feel full from junk food unless they’re eating more than a weight-maintaining number of calories.24

One strategy is dissemination of information. Americans have been subjected to a sea of misinformation in recent decades, not just from advertising but also from nutritionists. We’ve been pushed from one fad to the next, from “eat less fat” to “eat fewer carbohydrates” to “eat more of this or that nutrient.” But there is now growing sentiment in favor of a simpler approach summed up by Michael Pollan as “Eat food [instead of processed substances]. Mostly plants. Not too much.”25

For an information-based strategy to work, people must be able to estimate how many calories they are eating. Food labeling already is mandatory for packaged foods sold at grocery and convenience stores, and beginning in December 2016 chain restaurants will be required to post calorie and nutrition information on their menus. So far, however, studies have tended to find that menu labeling has little or no impact on eating behavior.26

A second approach is to make vegetables, fruits, legumes, and whole grains more available and attractive. We could, for instance, shift our government food subsidies away from meat, dairy, and corn in favor of healthier foods. And we could reduce the number of communities that have no nearby grocery stores with fresh vegetables and fruits (“food deserts”).27

A third approach holds that to make a real dent in the prevalence of obesity we must directly discourage consumption of unhealthy foods. There are two main options here. One is to tax these foods. Some research suggests that such a tax would need to be in the neighborhood of 20% or more to have an impact on eating behavior.28 We could conceivably go even further and impose a tax similar to that for cigarettes, which is 50% to 100% in most states.

The other option is an outright ban. The best-known proposal in this vein was Mayor Michael Bloomberg’s bid to ban the sale of sugared drinks in containers of 32 ounces or more in New York City. Others recommend limiting or banning fast-food restaurants in particular areas. In a country with a very strong tradition and culture of individual liberty, this approach is, not surprisingly, quite controversial.29

  1. D.W. Haslam and W.P. James, “Obesity,” Lancet, 2005; G. Whitlock, “Body-Mass Index and Cause-Specific Mortality in 900,000 Adults,” Lancet, 2009; Shiriki K. Kumanyika, Lynn Parker, and Leslie J. Sim, eds., Bridging the Evidence Gap in Obesity Prevention, Institute of Medicine, 2010, ch. 1; Centers for Disease Control, “Overweight and Obesity: Causes and Consequences,” 2012; Richard Dobbs et al, “Overcoming Obesity: An Initial Economic Analysis,” McKinsey Global Institute, 2014; Gabriel Hogstrom, Anna Nordstrom, and Peter Nordstrom, “Aerobic Fitness in Late Adolescence and the Risk of Early Death: A Prospective Cohort Study of 1.3 Million Swedish Men,” International Journal of Epidemiology, 2015; Vida Maralani and Douglas McKee, “Obesity Is in the Eye of the Beholder: BMI and Socioeconomic Outcomes across Cohorts,” Sociological Science, 2015. 
  2. BMI is calculated as weight in kilograms divided by height in meters squared. 
  3. Youfa Wang and May A. Beydoun, “The Obesity Epidemic in the United States — Gender, Age, Socioeconomic, Racial/Ethnic, and Geographic Characteristics: A Systematic Review and Meta-Regression Analysis,” Epidemiologic Reviews, 2007, table 2. 
  4. David M. Cutler, Edward L. Glaeser and Jesse M. Shapiro, “Why Have Americans Become More Obese?” Journal of Economic Perspectives, 2003. 
  5. Wang and Beydoun, “The Obesity Epidemic in the United States,” table 2. 
  6. For more, see K.R. Westerterp and J.R. Speakman, “Physical Activity Energy Expenditure Has Not Declined Since the 1980s and Matches Energy Expenditures of Wild Mammals,” International Journal of Obesity, 2008; B. Swinburn et al, “Increased Food Energy Supply Is More Than Sufficient to Explain the US Epidemic of Obesity,” American Journal of Clinical Nutrition, 2009; S. Li et al, “How Active Are American Adolescents and Have They Become Less Active?,” Obesity Reviews, 2010; Boyd A. Swinburn et al, “The Global Obesity Pandemic: Shaped by Global Drivers and Local Environments,” The Lancet, 2011; Claudia Dreifus, “A Mathematical Challenge to Obesity,” New York Times, 2012; Aaron Carroll, “To Lose Weight, Eating Less Is Far More Important Than Exercising More,” New York Times, 2015. 
  7. Centers for Disease Control, “Cigarette Smoking among Adults — United States, 2006,” Morbidity and Mortality Monthly Review, 2007. 
  8. See also Charles J. Courtemanche, Joshua C. Pinkston, Christopher J. Ruhm, and George Wehby, “Can Changing Economic Factors Explain the Rise in Obesity?,” Working Paper 20892, National Bureau of Economic Research, 2015. 
  9. Shi-Sheng Zhou et al, “B-Vitamin Consumption and the Prevalence of Diabetes and Obesity among the US Adults: Population Based Ecological Study,” BMC Public Health, 2010, figure 6, chart F. 
  10. Margot Sanger-Katz, “Americans Are Finally Eating Less,” New York Times, 2015. 
  11. S.Y. Chou et al, “An Economic Analysis of Adult Obesity: Results from the Behavioral Risk Factor Surveillance System,” Journal of Health Economics, 2004. 
  12. R. Rosenheck, “Fast Food Consumption and Increased Caloric Intake: A Systematic Review of a Trajectory Towards Weight Gain and Obesity Risk,” Obesity Reviews, 2008. 
  13. D. Kim et al, “Is Inequality at the Heart of It? Cross-Country Associations of Income Inequality with Cardiovascular Diseases and Risk Factors,” Social Science Medicine, 2008; Richard G. Wilkinson and Kate Pickett, The Spirit Level: Why More Equal Societies Almost Always Do Better, Bloomsbury, 2009. 
  14. Lane Kenworthy, “Income Inequality,” The Good Society. 
  15. Jean M. Twenge, “The Age of Anxiety: Birth Cohort Change in Anxiety and Neuroticism, 1952-1993,” Journal of Personality and Social Psychology, 2000. 
  16. The calculation is: measured obesity rate x .79. Recall that in most of these nations the only available obesity data are from interviewees’ self-reports of their height and weight, which are likely to underestimate the true obesity rate. 
  17. Wilkinson and Pickett, The Spirit Level, ch. 7. 
  18. Jennifer Warner, “5 Dietary Patterns Most Americans Fit Into,” WebMD, 2012. 
  19. Three potential objections: First, do we need to allow a longer time lag for the effect to show up? Perhaps, but if so the case for inequality as a major contributor to the rise in obesity is, as I suggested earlier, much less compelling. Second, is there a positive association between the level of income inequality in the mid-1990s and subsequent changes in obesity? The answer again is no. Third, could it be that the strong causal effects of income inequality are visible only in the 1980s, when the obesity rise began, and patterns since then don’t shed much light? Unfortunately, there’s no way to know, because we don’t have 1980s obesity data for many of the states. 
  20. Trenton G. Smith, Christiana Stoddard, and Michael G. Barnes, “Why the Poor Get Fat: Weight Gain and Economic Insecurity,” 2007; Avner Offer, Rachel Pechey, and Stanley Ulijaszek, “Obesity Under Affluence Varies by Welfare Regimes: The Effect of Fast Food, Insecurity, and Inequality,” Economics and Human Biology, 2010; Offer, Pechey, and Ulijaszek, eds., Insecurity, Inequality, and Obesity in Affluent Societies, Oxford University Press, 2012. 
  21. Lane Kenworthy, “A Decent and Rising Income Floor,” The Good Society; Kenworthy, “Stable Income and Expenses,” The Good Society; Kenworthy, “Economic Security for the Elderly,” The Good Society. 
  22. The measure used by Offer and colleagues yields some significant anomalies in the over-time story. In the United States, this economic insecurity measure increased sharply in the 1980s but then was flat in the 1990s, yet obesity in the US increased as fast in the nineties as in the eighties. In Australia, this economic insecurity measure has held constant since 1980, yet obesity has risen just as rapidly as in the United States. The same is true of the United Kingdom since 1983. 
  23. Barry Watson, “Does Economic Insecurity Cause Weight Gain Among Canadian Labor Force Participants?,” Review of Income and Wealth, 2016. 
  24. The following totals 2,000 calories, which is the weight-maintaining number for a moderately-active woman in her thirties or forties: a muffin (450) for breakfast, a cheeseburger and medium french fries (650) along with a 16-ounce soda (180) for lunch, two slices of pizza (550) and a 16-ounce soda (180) for dinner. See also Di Dong, Marcel Bilger, Rob M. van Dam, and Eric A. Finkelstein, “Consumption of Specific Foods and Beverages and Excess Weight Gain Among Children and Adolescents,” Health Affairs, 2015; David Just and Brian Wansink, “Fast Food, Soft Drink, and Candy Intake is Unrelated to Body Mass Index for 95% of American Adults,” Obesity Science and Practice, 2015. 
  25. Michael Pollan, In Defense of Food, Penguin, 2008. 
  26. Katherine Mangu-Ward, “Five Myths About Healthy Eating,” Washington Post, 2011; Victorina Matus, “Menu Labeling: Will Calorie Counts Matter to Diners?” Washington Post, 2011; Frank Bruni, “Don’t Count on Calorie Counts,” New York Times, 2013; Aaron Carroll, “The Failure of Calorie Counts on Menus,” New York Times, 2015. 
  27. Janne Boone-Heinonen et al, “Fast Food Restaurants and Food Stores: Longitudinal Associations With Diet in Young to Middle-Aged Adults,” JAMA Internal Medicine, 2011; Michele Ver Ploeg et al, “Access to Affordable and Nutritious Food: Updated Estimates of Distance to Supermarkets Using 2010 Data,” US Department of Agriculture, Economic Research Service, 2012; Anna Fifield, “Starved of Healthy Options,” Financial Times, 2013. 
  28. Oliver T. Mytton et al, “Taxing Unhealthy Food and Drinks to Improve Health,” BMJ, 2012. 
  29. Mark Bittman, “Limit Soda for Kids’ Sake,” New York Times: The Opinion Pages, 2012; Frank Bruni, “Trimming a Fat City,” New York Times, 2012.