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Helen Lee Senior Research Associate, MDRC

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1 Helen Lee Senior Research Associate, MDRC
Neighborhood Food Availability, Disparities, and Childhood Obesity Risk Helen Lee Senior Research Associate, MDRC

2 Scientists Sound the Alarm on Obesity Early
“It is clear that weight control is a major public health problem” Experts at the American Public Health Association Annual Meetings declare obesity as problem #1 The year is 1952: 1 McDonald’s in the nation 6 pack of Coca Cola contains fewer ounces than one Big Gulp 10% of the nation is estimated to be obese Set context for my talk – by stepping back and asking why did public health became so focused on the issue of food deserts, and food environments … With Americans eating increasing quantities of cheap and abundant food, it was the scientific community that sounded an alarm. “It is clear that weight control is a major public health problem,” Dr. Lester Breslow, a leading researcher, warned at the annual meeting of the western branch of the American Public Health Association (APHA) meetings. At the national meeting of the APHA a few weeks later, experts called obesity “America’s No. 1 health problem.” The year was There was exactly one McDonald’s in all of America, an entire six-pack of Coca-Cola contained fewer ounces of soda than a single Super Big Gulp today, and about 10 percent of the population was obese. In the 2.5 decades that followed, the number of McDonald’s restaurants would rise to 4,000, sales of soda pop and junk food would explode — and yet, against the fears and predictions of public health experts, obesity rates hardly budged. The obesity rate was 13.5 percent in In 1980, it was 15 percent. If fast food was making us fatter, it wasn't by very much.  Then, somewhat inexplicably, obesity rates took off.

3 Despite Warnings, Obesity Rates Rise Dramatically
Childhood Obesity Prevalence Rates By 1994, the national obesity rate had hit 23 percent, and numerous studies connected obesity with rising rates of heart disease, diabetes, and other serious illnesses. By 2005, 33 percent of all adults, half of all African-American women, and 15 percent of all American children were obese. As shown in this slide, obesity rates have doubled for both very young children and adolescents since the the late 1970s. And for elementary school children, obesity rates have nearly tripled. SOURCE: National Health and Nutrition Examination Surveys (NHANES)

4 And Disparities are Large
Percent obese by race/ethnicity Percent obese by maternal education We also know that across studies and samples, racial/ethnic and SES disparities are large. As seen, using the data that I analyze in my paper, that disparities exist when children begin formal schooling, and they persist and even grow larger as children transition out of elementary school. For children, in particular, these data are concerning… Obese children are much more likely to become obese adults than their normal weight peers. Negative health trajectories are beginning earlier than before SOURCE: Early Childhood Longitudinal Study – Kindergarten Cohort (ECLS-K), 1999 and 2004

5 Concerns Are Multi-faceted, but Framing Becomes Simplified
Most research suggests increased calorie consumption explains rise in obesity (Cutler et al. 2003; Lakdawalla et al. 2005) Parallels to tobacco control drawn (e.g, “toxic” exposure) Focus efforts upstream: Obesity risk is involuntary and universal (Lawrence, 2004) “Obesogenic” environments arguably potential culprits Advertising and media exposure Supersizing of the food industry Agri-business (e.g., high fructose corn syrup) Pricing policy as to why obesity rates remained flat for so long, or why they then spiked so dramatically after 1980. While there is a broad consensus that increased consumption of calories is behind the rise of obesity, there is no consensus about what drove increased consumption. Just as the causes of America's declining crime rate against the backdrop of a large economic recession remain highly contested, scientists have identified a tangle of bio-psycho-social factors behind the rise in obesity, from cheap corn, cheap corn flakes and chips, supersized portions, to the breakdown of traditional eating rituals to the decline of smoking. More than a few pundits and intellectuals, as seen in Fast Food Nation and Supersize me, began to hone in on a seductively simple culprit: rising obesity was the result of the proliferation of fast, cheap, and unhealthy foods, of toxic food environments Drawing deliberate lessons from what has been deemed to be among the most successful of recent public health erfforts – and that is the tobacco control movement, Scholars began to talk about how the food industry was profiting by selling slickly marketed products that were dangerously addictive, particularly for the poor, who lacked grocery stores offering healthier food options.

6 Policymakers Respond Increasing discussion in policy circles of “food deserts” and their consequences for disparities Poor, minority neighborhoods more likely to lack access to healthy food (Gallagher 2006; Moore & Diez-Roux 2006; Powell et al. 2007) First Lady’s “Let’s Move” campaign Federal Healthy Food Financing Initiative Policy efforts to decrease exposure to “toxic” vendors L.A.’s fast food establishment moratorium in South Central NYC’s super-size soda ban An accumulating body of research has documented that the children and families at greatest risk of obesity -- low-income, black and Hispanic minority groups – not only lack access to healthful food stores, but they live in communities with high concentrations of fast food and convenience stores. These patterns have led some to hypothesize that food environment inequities drive obesity inequalities. Policy makers and public figures, like the First Lady, have taken note Especially the notion of food deserts, which are defined as areas, typically low-income areas, with limited or no access to healthful affordable food stores such as supermarkets The image is quite powerful: while many of us may resolve to eat healthier, children and families living in food desert areas lack that semblance of choice At the same time, extrapolating from the relative success of the tobacco control model, local level officials began to enact zoning ordinances that regulated what are labeled “toxic” food establishments and products – most notably fast food vendors and sugar sweetened soda.

7 The USDA recently came out with a food desert locator to assist health planners and local policy folks in the identification of limited access areas. By definition, a food desert =certain share of low-income residents, has at least 500 residents and lack proximate access to a grocery store, I’ve zeroed in on Philadelphia here for you, and the pink areas designate Census tracts that are food deserts. Out of the hundred (??) census tracts, note that there are only about 4 that qualify as a food desert. Largest food desert area is Fairmount Park…. What this doesn’t tell you, though is how these low-income tracts compare to other low-income tracts, nor how this relates to health outcomes of concern.

8 But Empirical Foundation and Evidence is Inconclusive…
Research Questions: Are there distinct patterns in food access by neighborhood poverty and race? Do differences in residential food availability explain obesity risk over young childhood? Do they explain disparities? The jury is still out about whether disadvantaged neighborhoods, by measures of income or minority status, are also SYSTEMATICALLY disadvantaged in terms of their food options. Many of the published studies documenting food availability inequities have been based on a neighborhoods within a city or clusters of communities. More importantly, without being able to link neighborhood food access to individual-level obesity-related outcomes, it’s hard to say whether this has an independent relationship to weight trajectories. Studies that have done so have primarily been cross-sectional, but longitudinal studies offer strong evidence on associations. And these studies have painted a mixed picture, with some finding modest associations and others finding no impact. So this brings us to my analysis, which combines two different national datasets to track kids over time and also track food outlet availability over time. First, I document the extent of limited food availability in children’s residential environments and particularly focus on understanding inequities in food access by neighborhood poverty and racial/ethnic composition 2) Second, I ask whether differences in food environments explains children’s risk of unhealthy weight gain over the course of elementary school, and whether they help us understand racial and poverty patterns.

9 Merged Individual Data on Children with Neighborhood Food Establishments
Longitudinal database of children (Early Childhood Longitudinal Study – Kindergarten Cohort (ECLS-K)) Nationally-representative study of 20,000 kindergarteners attending school in Looked at kids followed from K to 5th grade (7,730 out of ~11,000 children in full K-5 sample) Longitudinal national database of all business establishments (National Establishment Time Series Data (NETS)) Use industry codes, trade name, HQ, sales, and size to isolate food vendors To get at the question of what INDEPENDENT role food retail availability plays in predicting who becomes heavier over time, I needed information on changes in children's BMI over time and also needed to map that against changes and levels of food retail proximity, composition, and density. The child data comes from the ECLS-K: National, longitudinal study of elementary school children. Kindergarten cohort was sampled in 1998, followed til the end of 5th. BMI is objectively assessed and I have information on where children live (their census tract) For the food establishment data, I was fortunate enough to have access through another colleague to the NETS – National Establishment Time Series Database. NETS is considered one of the most comprehensive business databases available. Ideal because: Can map exact addresses of bus establishments Can go beyond broad industry categorizations (as is often done), and create finer , more precise categories . For example, important to get at supermarkets and large chain grocers, pull them out from mid-size, independent and corner grocery stores – do this by combing sales, headquarters, employee size and trade names. Longitudinal, so can look at changes in the food retail context – this helps to get at exposure and changes in exposure on child outcomes, I use the panel of students who had complete residential tract information , also had parental information over the waves, and had accurate information on the outcome variable of interest. These basic criterion dropped my analytic sample to about 7700 kids. Of course, a problem that I’m sure you are all too familiar with is sample attrition. It’s not random, I’ll return to this point later, but the results using the panel are not representative and this is an important caveat

10 Key Measures Child outcome: changes in BMI percentile
BMI is weight in kg/ height in meters squared Used BMI-sex-age specific growth charts to calculate where child falls in percentile distribution Food availability: density per sq. mile Supermarkets/large-scale grocery stores At least $2 million in sales; Appended warehouse clubs, supercenters Corner grocery stores Grocery stores operated by 3 employees or less Convenience stores Sell limited line of goods; Also includes gas stations Full-service restaurants Provide food to patrons who are served and pay after eating Fast-food restaurants Limited service, chain restaurants (based on top 100 list) Food availability can be measured in a number of ways, and I examined several. each of which I believe has different behavioral assumptions and different policy justifications. For brevity, focusing on prevalence of specific food industries adjusted for land area size in most of the graphics. This captures the likelihood that a person would encounter the food purveyor within the neighborhood’ boundaries, regardless of how many people are served by each outlet . Though less commonly examined, researchers studying liquor store concentrations have often used land area adjusted measures because it better captures the visual cues and physical prevalence of an establishment type. Note that in my models. I also control for the usual suspects that we know are correlates of obesity, including race/ethnicity, sex, SES indicators, as well as behavioral factors, including TV viewing and physical activity pattern I look at both differences across children and within children in estimating of food environment exposure, but I’ll mostly focus on the effects of changes in the food retail environment (store growth or decline) on weight gain.

11 SOURCE: NETS 2006 and Census 2000
Minority Neighborhoods Have Higher Concentrations of Various Food Vendors * * As shown above, residentially minority areas have higher concentrations of fast food establishments and convenience stores, significantly different from majority white neighborhoods This finding is consistent with much of extant literature. Note that I’ve excluded rural tracts as food availability looks very different, and access means something different, but rural tracts, no surprise do have limited access across most measures and food outlet types. That said, minority areas also have equal or slightly greater densities of other food outlet types that have not been linked to obesity risk, including large-scale grocery stores or supermarkets. * * SOURCE: NETS 2006 and Census 2000 NOTES: Based on all U.S. non-rural Census tracts, weighted by population. Similar patterns are found when tracts restricted to ECLS-K children in K-5 analytic sample. * denotes difference is significant in reference to majority white neighborhoods (p<0.05).

12 SOURCE: NETS 2006 and Census 2000
Poorer Areas Do Not Have Worse Access to Healthy Food Stores Fast food and convenience stores are more predominant in poorer neighborhoods where ECLSK children reside. At the same time, contrary to food desert portrait, there does not, on average, appear to be systematic disparities in the availability of large grocery stores or supermarkets. And here a poor neighborhood is one where at least 20% of residents live below the FPL, very poor are tracts where at least 40% of residents live below poverty. It should also be noted that corner stores are a common feature of the retail food context in low-income and minority neighborhoods. While corner stores sell healthier goods, they tend to sell them at higher prices and offer less variety. SOURCE: NETS 2006 and Census 2000 NOTES: Based on all U.S. non-rural Census tracts, weighted by population. Similar patterns are found when tracts restricted to ECLS-K children in K-5 analytic sample. * denotes difference is significant in reference to majority white neighborhoods (p<0.05).

13 How One Measures Food Environments Might Matter
Food availability measure Non-poor Poor Very poor White Black Hispanic Density per 1,000 pop Supermarkets 0.09 0.07 0.05 0.06 Corner stores 0.23 0.52 0.64 0.22 0.48 0.53 Convenience stores 0.38 0.49 0.47 0.39 0.42 0.41 Fast food 0.32 0.29 0.27 0.34 Minimum distance (miles) 1.30 1.01 0.94 1.33 0.96 1.05 0.55 0.46 1.09 0.57 0.77 0.45 0.43 0.79 1.02 0.72 0.69 1.03 0.68 0.83 Shares (% out of all food stores) 3% 2% 1% 8% 17% 21% 18% 14% 15% 10% 6% 7% There is a measurement story to be told. When I was asked by reporters why these findings went against most other studies, one of my responses was that measurement seems to matter, at least looking at the prelim results shown above, While this wasn’t the case in the ECLSK subsample of tracts, when we look at all non-rural tracts across the NATION, patterns of differences vary according to the measurement used. Per capita density measures, most commonly used in literature, suggest that supermarkets are indeed slightly more available in wealthier and white areas. But distance measures (where population weighted centroid is used to measure distance to nearest outlet type) suggest that poorer and minority neighborhoods have better access to supermarkets. Food outlet shares tell a story similar to density per square mile and distance. This suggests to me, at the very least, we need to think more about what each measure taps into , and why we care about that from a policy perspective. Are we capturing what we think we are capturing – Research 101 From a research perspective, suggests that if and when possible, this is another robustness and sensitivity check.

14 Null Findings for Food Availability and Child Weight Outcomes
Food availability (density per square mile) Coef P<value Associations with BMI percentile at baseline Supermarkets 0.37 0.38 Corner stores 0.07 0.46 Convenience stores 0.08 0.61 All other restaurants 0.01 0.73 Fast food outlets 0.16 0.44 Associations between change in food outlet exposure and change in BMI percentile 0.54 0.58 -0.48 0.68 0.93 -0.19 -0.66 0.63 When I look at whether fast food and other food outlet availability predict unhealthy weight gain over time, independent of child, familial, and other school and neighborhood level factors? The answer is no There is no statistical relationship between food outlet exposure and weight gain or loss over time. Similar results are found when I use different measures of food availability and run other sorts of sensitivity results, such as restricting my sample to only children who only children in poverty, or using the K-3 panel, where sample attrition is less problematic. LIMITATIONS Sample attrition is not random, so I may be understating the relationship between changes in food retail exposire and changes in weight. Also cannot establish a causal relationship – was an observational study and people as we know do not randomly live in neighborhoods. it should be noted that I was unable to examine aspects of food quality of these other establishments, such as grocery stores, which may indeed differ across neighborhood settings in meaningful ways. This is an area where further research could be useful. In fact more research that tries to unpack the relationships between food pricing, access and purchasing behavior would go a long way towards thinking of new policy strategies SOURCE: ECLS-K, Kindergarten to 5th grade panel, , and NETS, NOTES: First panel estimates show associations between food outlet density (stores per sq mile) and child BMI percentile at kindergarten wave, from cross-classified random-effects models adjusted for other covariates. Second panel estimates show associations between change in prevalence of food outlets (growth or decline) and change in BMI percentile over elementary school, from cross-classified random-effects models adjusted for other covariates, and time.

15 Implications How problematic are food deserts?
SSM study: Easy access to food retailers of all types, rather than lack of access, better portrays the food environments of disadvantaged communities We need to do better job at thinking through the behavioral mechanisms of our policy solutions Food access likely less important than other factors “A millionaire may enjoy breakfasting off orange juice and Ryvita biscuits; an unemployed man does not… When you are unemployed you don’t want to eat dull wholesome food. You want to eat something a little tasty. There is always some cheap pleasant thing to tempt you.” -- George Orwell, quoted in Banerjee and Duflo (Poor Economics) For policy makers, implications of dealing with an ease of access rather than lack of access story are different . True that we need to consider quality variation and price, which I was not able to do, but there’s a body of research that suggests that the main reason people give when asked why they buy what they buy – it’s taste. That’s seen in this great quote from George Orwell, that was cited in a paper by Esther Duflo in Poor Economics If you build it, they may not come. If they do, they may not buy what you want them to buy. Also, SES works in nuanced ways, and the current food access approaches may be too linear. People go out of their way, bypass the nearest store to shop at the store that they think better fits their socio-demographic profile.

16 I think it’s an understatement to say that human behavior is complicated.
Nurses and doctors smoke Even the most active of people, will take the escalator over the stairs.

17 Conclusion Tobacco control may not be the right parallel:
While overall smoking has declined, SES disparities have increased Disparities in obesity rates have narrowed, disparities in health outcomes associated with obesity grown If poverty is heart of the concern, weigh benefits and costs of other strategies to improve health Instead of food deserts, what about income deserts? Education deserts? Health care deserts? I want to end by questioning whether the tobacco control efforts of the last three decades represent a particularly promising model for reducing the disproportionate impacts of obesity on the poor. the gap in smoking rates between rich and poor grew substantially while smoking rates declined overall. Smoking among adults with less than a high school degree is 29 percent, compared with just 10 percent among college graduates While rates of obesity have risen among virtually all Americans during this period, the gap between rich and poor has actually declined over this period. But gaps in health outcomes, including mortality, associted with obesity-related diseases like diabetes have widened. In focusing not only on obesity but also on diet, the meaning of Dr. Breslow's work has gotten muddied. Dr. Lester Breslow began what would become a landmark study in Alameda County, California that would establish that there are multiple determinants of health, and would offer more holisitic view of health promotion. Fatness and diet are but two factors among many others in shaping health outcomes Why aren’t we talking about education deserts or income deserts if we’re concerned about poverty and health and mobility? Self-control and self-efficacy are not exclusive to the rich or elites – these are things that can be taught in children at an early age… How do we scale that up?

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