Data from over 200 countries collected since 1980 has revealed steady increases in obesity in every region of the world, including in most low- and middle-income countries. The international medical journal The Lancet recently published a series of papers highlighting the latest data about what’s driving global obesity, and what we can do about it. In this post, we review key findings from the articles and look at several questions addressed by them: what is the size and nature of the problem, what is driving its global increase, what will the future obesity burden be under a business-as-usual scenario, and what action is needed to reverse the trend?
The epidemiology of big bones
In the 1970s and 1980s, most high-income countries encountered a simultaneous rise in the prevalence of obesity. Since then, the subsequent rise of obesity in low- and middle-income countries has been varied. Once thought to be a “disease of affluence”, obesity seems to be arising in a variety of settings in a manner that isn’t sufficiently explained by GDP. In most cases, urban and wealthier groups were the first to become fat, but as the trend developed, the distribution of obesity has been shifting towards the rural and poor. The highest burden currently seems to be among middle-aged women (ages 40 to 60). But even among the rest of the population, obesity has become so common that it has overtaken tobacco as the largest preventable cause of disease in several regions.
As shown in the figure above, there’s still a lot of variation in how much obesity is found between countries, which doesn’t strictly correspond to a country’s wealth (countries at similar levels of wealth have widely varying levels of obesity). It’s not simply inevitable that economic development will result in obesity. In some uniformly low-income countries, such as Pacific Island nations, obesity prevalence is very high; conversely, many countries that still have a substantial burden of undernutrition also have a large or emerging burden of obesity among low-income groups whose income hasn’t changed substantially during the period of obesity development. (Admittedly, people of different ethnicities often have differences in their frame size, so BMI alone can be misleading; someone with a BMI of 30 in China would be far fatter in essence than a Tongan with a BMI of 30, because the height correction in the BMI equation doesn’t fully account for frame size.) The wide variability between countries indicates that some other factors besides higher income are affecting the differential gain in weight around the world, and that a few countries have found a way to shield themselves from becoming as fat as their neighbors.
Drivers of the epidemic
Professor Boyd Swinburn has summarized the epidemiological opinion about the rise of obesity in this statement: “obesity is similar to rising greenhouse gases and environmental degradation as yet another detrimental effect of individual and corporate overconsumption.“ In Swinburn’s opinion, obesity is an example of “market failure”: when prices and the quantities bought and sold are no longer indicative of their costs and benefits to society. Food markets have certainly failed where people cannot perceive or anticipate the downstream costs of unhealthy purchases; the additional fact that educational interventions have at best a modest impact also suggests that economic incentives (cheapness) and physiological incentives (taste) have driven people towards purchasing unsafe foods over safer alternatives even when the benefit of the alternatives is clear. Should the state intervene in markets to protect consumers? It’s generally accepted that it should when there are substantial externalities caused by market failure: in this case, the higher cost to taxpayers if the state doesn’t intervene and we all have to pay for downstream consequences of obesity, whether in medical bills or lost economic productivity. The latest estimates from the US and UK suggest that if current trends continue, 65 million more adults will become obese in the US and 11 million more in the UK by 2030, accruing an additional 6—8·5 million cases of diabetes, 5·7—7·3 million cases of heart disease and stroke, 492 000—669 000 additional cases of cancer, and 26—55 million quality-adjusted life years forgone for the US and UK combined. This amounts to increasing medical costs by $48—66 billion/year in the US and by £1·9—2 billion/year in the UK by 2030.
But in identifying the major drivers of the epidemic, can we really blame the rise in obesity on food consumption when there are so many other factors—like the built environment (not having enough park space to exercise, for example)—that contribute to obesity? At an epidemiological level, we can: because the built environment hasn’t changed much, it can’t really be responsible for the dramatic rise we see in obesity rates over the past four decades. By contrast, the major correlate to the change in obesity rates has been the change in the food system: the increased supply of cheap, energy-dense foods; improved distribution systems to make food much more accessible and available in comparison to alternatives; and more persuasive and pervasive food marketing that’s shown dramatic impact on consumption patterns. Indeed, a series of studies has shown that the rise of calorie supply from changes in the food system statistically explains the rise in obesity in the USA from the 1970s, and most of the weight increase in the UK since the 1980s.
One hypothesis for what’s happened is the creation of an energy balance “flipping point” (not to be confused with Malcolm Gladwell’s “tipping points”, hilariously spoofed on this website). That is, as life became more sedentary due to technology (the adoption of the automobile, the increase in sedentary jobs) in the early 20th century, energy expenditure reduced, but somehow people didn’t start to get fat until the 1970s. It appears that in the early 20th century, people reduced their energy intake in association with the lower amount of energy they needed to survive (see the graph below, based on data from the US Department of Agriculture). But with the advent of mass food production, cheap food was readily accessible, and seems to have “pushed up” energy consumption as people ate food that was easy to access even if they weren’t hungry. Hence, it wasn’t just increased sedentariness that produced obesity; it took a change in food supply to generate widely-available cheap calories, increasing the average person’s food consumption and driving up obesity rates starting in the 1970s.
That doesn’t mean that exercise or the built environment or other factors are unimportant; they just don’t appear to have changed as substantially and as powerfully as the food system has, to account for the dramatic rise in obesity rates at a statistical level. But factors like the built environment may be useful for reducing obesity once it’s started, or contributing to the environment conducive to obesity. The most comprehensive compilation of determinants of obesity and their modifiers is probably the UK government’s Obesity System Map, introduced in 2007. The problem with this map is that it’s so comprehensive that it’s too complex to use as a tool for understanding fundamental relationships between suggested policies and their likely outcomes; this is why we turn to mathematical models, which help simplify reality and test relationships under various assumptions, so that we can tease apart discrete processes in a manner that can help us assess the intended and unanticipated consequences of policy proposals.
Modeling the waistline
Perhaps the most impressive mathematical models of obesity, and how it might be reduced, have been produced by Dr. Kevin Hall and his colleagues at the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK, part of the National Institutes of Health). In a remarkable display of scientific chivalry, Hall has not only spent years carefully validating models of how energy intake and expenditure can help predict obesity and its associated policy implications, but has also made the model code free-available to anyone with an Internet connection—displaying the true spirit of “open source” for those in the field of public health for whom hoarding data has been considered a prerequisite for publication-based success.
We won’t go into the full mathematical details of Hall’s model, which are available here, but we will briefly summarize what the model does: it looks at how energy intake of various forms (fats, proteins, etc.) are metabolized, stored or expended by the body, conditional on how much energy is used, and taking into account various non-linear relationships in how our bodies work (e.g., people with higher initial adiposity partition a greater proportion of a net energy imbalance towards gain or loss of body fat versus lean tissue than do people with low initial adiposity). The model then builds up from this metabolism-level simulation of calorie processing to individual-level weights, and from individual-level weights to population-level obesity.
Why bother with all of these layers? Because they offer some novel insights into much a given intervention is likely to accomplish at both a personal and a community level. For example, physical activity increases energy expenditure and can therefore cause weight loss, assuming no compensatory changes in energy intake. But does an increase of physical activity necessarily lead to the same weight loss as an energy-equivalent decrease of food intake? The food industry has argued that if people simply exercised more, they could burn off what they eat in spite of the high energy-density foods being marketing (calories in = calories out). But Hall’s model reveals that the dynamics of metabolism produce a different scenario: a relatively modest increase of physical activity expenditure results in slightly more rapid and greater predicted weight loss compared with an energy-equivalent reduction of food intake…at first. But then the body hits a point when diet leads to greater weight loss than does physical activity. Therefore, by contrast with the assumption that “a calorie is a calorie”, the model shows that energy-equivalent initial changes of physical activity versus food intake can lead to differences in weight change. The model also helps explain why the same diet has substantially different effects on different individuals (yes, you can use the model online to simulate yourself…that’s simulate, not stimulate).
Perhaps more importantly, the model also helps address policy proposals. In the past, medical students were taught an old rule that losing about 3500 kcals will drop a person’s weight by about a pound, resulting in a slow and steady weight loss. We now know that such slow and steady weight loss doesn’t really happen. But to evaluate the impact of a policy such as a tax on sugar-sweetened beverages (see our previous post), people have extrapolated this rule to the whole country. The dynamic model from Hall predicts very different outcomes than the rule of thumb: that a lot of weight would be expected to be lost in the first few years of the intervention, then a tapering effect would take place, requiring other interventions to sustain enough impact to address the obesity epidemic. The model similarly shows that a small, chronic daily gap between energy consumed and energy expended has caused the obesity epidemic in most countries. While we could prevent further average excess weight gain through small changes (on the order of tens of kilocalories per day), the weight of the overall population has been accumulating for decades, so while only a little energy loss is needed to stop gaining weight, the loss needed to lose existing excess weight (the maintenance energy gap) is much larger.
Approaches to intervention
While modeling can help gain a better sense of how much a given policy might work, we still have to propose and pass these policies to have any impact. So what policies should we put forth? Motivating behavioral change is important at the individual level (through weight loss counseling, education and social marketing, for example), but there are some fundamental problems with this approach: their limited sustainability and affordability, even when they’re effective (which often requires applying them to whole communities).
As a result, policies intending to reduce obesity will likely have to address the environment that contributes to the development of the problem. And there is reason to be hopeful about this prevention-oriented strategy: even in the US, few children are being born already obese. It appears that infant risk factors for obesity do not drive most adult obesity, and that early childhood obesity still usually disappears as people age. Therefore, every new cohort of children is a potentially “fresh” group for long-term prevention of obesity. When models like Hall’s have been applied to these prevention-oriented policies, a few key interventions have stuck out as being particularly effective, especially at a reasonable cost.
Among the interventions, there are only small benefits noted among obese adults through dieting, exercising, and prescription medications, although better results are achieved with bariatric surgery, which remains out of reach for most of the world. Some more encouraging interventions exist to prevent obesity among children, particularly reducing time in front of computer and television screens. But most encouraging are reports from countries such as Sweden, Switzerland, France, and Australia that the rate of obesity among children might be leveling-off or even decreasing. What have these countries done to achieve this result, and can others follow?
A summary of the key interventions are presented in the table; they’re a collection of twenty interventions which were analyzed through a detailed procedure to weigh the strength of supporting data, affordability, feasibility, sustainability and cost-effectiveness of each strategy (because of the host for these studies, the calculated results are presented in terms of Australian dollars, A$).
Eight of the 20 interventions were found to be both health-improving and cost saving (so-called “dominant” interventions; the first eight listed in the table). Of note, the top three money-saving interventions (the first three listed in the table) are interventions that address the environment of obesity, not just education, behavioral modifications or medical treatments. The results have been so compelling that, of all groups, the Organization of Economic Cooperation and Development (OECD) has argued that these regulatory interventions are the only ones likely to pay for themselves if implemented widely, by saving so much in health expenditures that the savings would outweigh the costs of implementation.
Following the path of Mayor Bloomberg’s salt reduction program, we may need to carefully document how each regulatory intervention can be applied to the existing food markets in each country, how much benefit we expect for each intervention among different groups around the world, and what the political status of each affected country is in terms of its legislative pathway to implementation. But at least the recommendations and evidence base are consistent; the opportunities for intervention exist, and now the politics will be our key challenge for obesity reduction.