One of my classmates whose primary interest is not health policy posted this graph on Facebook, saying "This is stunning... so much so in fact that I'm a bit skeptical of its accuracy." The graph compares obesity rates by state in 1994 vs. 2008, and unfortunately it is both terrifying and accurate. (I can't find the original source of this particular infographic, but the data is the same as on this CDC page.)
I think those of who study or work in public health have seen variations on these graphs so many times that they've lost some of their shock value. But this truly is an incredible shift in population health in a frighteningly short period of time. In 1994 every state had an adult population that was less than 20% obese, and many were less than 15% obese. A mere 14 years later, Colorado is the only state under 20%, and quite a few have rates over 30% -- these were completely unheard of before.
I did a quick literature search, trying to understand what causal factors might be responsible for such a rapid shift. It's a huge and challenging question, so maybe it should be unsurprising that I didn't find an article that really stood out as the best. Still, here are three articles that I found helpful:
1. Specifically looking at childhood obesity in the US (which is different from the rates highlighted in the map above, but related): "Childhood Obesity: Trends and Potential Causes" by Anderson and Butcher (JStor PDF, ungated PDF). Their intro:
The increase in childhood obesity over the past several decades, together with the associated health problems and costs, is raising grave concern among health care professionals, policy experts, children's advocates, and parents. Patricia Anderson and Kristin Butcher document trends in children's obesity and examine the possible underlying causes of the obesity epidemic.
They begin by reviewing research on energy intake, energy expenditure, and "energy balance," noting that children who eat more "empty calories" and expend fewer calories through physical activity are more likely to be obese than other children. Next they ask what has changed in children's environment over the past three decades to upset this energy balance equation. In particular, they examine changes in the food market, in the built environment, in schools and child care settings, and in the role of parents-paying attention to the timing of these changes.
Among the changes that affect children'se nergy intake are the increasing availability of energy dense, high-calorie foods and drinkst hroughs chools. Changes in the family, particularly increasing dual-career or single-parent working families, may also have increased demand for food away from home or pre-prepared foods. A host of factors have also contributed to reductions in energy expenditure. In particular, children today seem less likely to walk to school and to be traveling more in cars than they were during the early 1970s, perhaps because of changes in the built environment. Finally, children spend more time viewing television and using computers.
Anderson and Butcher find no one factor that has led to increases in children's obesity. Rather, many complementary changes have simultaneously increased children's energy intake and decreased their energy expenditure. The challenge in formulating policies to address children's obesity is to learn how best to change the environment that affects children's energy balance.
2. On global trends: "The global obesity pandemic: shaped by global drivers and local environments" by Swinburn et al. (Here's the PDF from Science Direct and an ungated PDF for those not at universities.) Summary:
The simultaneous increases in obesity in almost all countries seem to be driven mainly by changes in the global food system, which is producing more processed, affordable, and effectively marketed food than ever before. This passive overconsumption of energy leading to obesity is a predictable outcome of market economies predicated on consumption-based growth. The global food system drivers interact with local environmental factors to create a wide variation in obesity prevalence between populations.
Within populations, the interactions between environmental and individual factors, including genetic makeup, explain variability in body size between individuals. However, even with this individual variation, the epidemic has predictable patterns in subpopulations. In low-income countries, obesity mostly affects middle-aged adults (especially women) from wealthy, urban environments; whereas in high-income countries it affects both sexes and all ages, but is disproportionately greater in disadvantaged groups.
Unlike other major causes of preventable death and disability, such as tobacco use, injuries, and infectious diseases, there are no exemplar populations in which the obesity epidemic has been reversed by public health measures. This absence increases the urgency for evidence-creating policy action, with a priority on reduction of the supply-side drivers.
3. Finally, on methodological differences and where the trends are heading: "Obesity Prevalence in the United States — Up, Down, or Sideways?" (NEJM, ungated PDF). Evidently there's some debate over whether rates are going up or have stabilized in the last few years, because different data sources say different things. Generally the NHANES data (in which people are actually measured, rather than reporting their height and weight) is the best available (and that's what the maps above are made from). An excerpt:
One key reason for discrepancies among the estimates is a simple difference in data-collection methods. The most frequently quoted data sources are the NHANES studies of adults and children, the BRFSS for adults, and the CDC's Youth Risk Behavior Survey (YRBS)4 for high- school students. Although sampling strategies, response rates, age discrepancies, and the wording of survey questions may account for some variability, a major factor is that in calculating the BMI, the BRFSS and YRBS rely on respondents' self-reported heights and weights, whereas the NHANES collects measured (i.e., actual) heights and weights each year, albeit from a considerably smaller sample of the population. Since people often claim to be taller than they are and to weigh less than they actually do, we should not be surprised that obesity prevalence figures based on self-reported heights and weights are considerably lower than those based on measured data.
I would greatly appreciate any suggestions for what to read in the comments, especially links to work that tries to rigorously assess (rather than just hypothesize on) the relative import of various drivers of the increase in adult obesity.