Abstract
This paper investigates the effect of food environments, characterized as food swamps, on adult obesity rates. Food swamps have been described as areas with a high-density of establishments selling high-calorie fast food and junk food, relative to healthier food options. This study examines multiple ways of categorizing food environments as food swamps and food deserts, including alternate versions of the Retail Food Environment Index. We merged food outlet, sociodemographic and obesity data from the United States Department of Agriculture (USDA) Food Environment Atlas, the American Community Survey, and a commercial street reference dataset. We employed an instrumental variables (IV) strategy to correct for the endogeneity of food environments (i.e., that individuals self-select into neighborhoods and may consider food availability in their decision). Our results suggest that the presence of a food swamp is a stronger predictor of obesity rates than the absence of full-service grocery stores. We found, even after controlling for food desert effects, food swamps have a positive, statistically significant effect on adult obesity rates. All three food swamp measures indicated the same positive association, but reflected different magnitudes of the food swamp effect on rates of adult obesity (p values ranged from 0.00 to 0.16). Our adjustment for reverse causality, using an IV approach, revealed a stronger effect of food swamps than would have been obtained by naïve ordinary least squares (OLS) estimates. The food swamp effect was stronger in counties with greater income inequality (p < 0.05) and where residents are less mobile (p < 0.01). Based on these findings, local government policies such as zoning laws simultaneously restricting access to unhealthy food outlets and incentivizing healthy food retailers to locate in underserved neighborhoods warrant consideration as strategies to increase health equity.
Generated Summary
This research investigates the impact of food environments, specifically food swamps, on adult obesity rates in the United States. The study employed an instrumental variables (IV) strategy to correct for the endogeneity of food environments, using data from the USDA Food Environment Atlas, the American Community Survey, and a commercial street reference dataset. The authors categorized food environments as food swamps and food deserts, including alternate versions of the Retail Food Environment Index (RFEI). The IV approach addressed the issue of reverse causality, where individuals may self-select into neighborhoods, potentially influencing food availability decisions. OLS and two-stage least squares (2SLS) regression models were used to analyze associations between food environments and obesity rates. The study aimed to determine whether food swamps are a stronger predictor of obesity rates than food deserts and to assess the effect of food access on obesity rates at the county level, facilitating policy discussions. The key hypotheses were that food swamps are a distinct phenomenon from food deserts and that food swamps have a stronger positive effect on obesity rates than food deserts. The study also aimed to examine the effect of food access on obesity rates by using a county-level unit of analysis to facilitate policy discussions.
Key Findings & Statistics
- The study utilized data from the United States Department of Agriculture (USDA) Food Environment Atlas, which includes 211 food environment indicators for all 3141 US counties.
- The Food Environment Atlas data was combined with data from the ArcGIS ESRI StreetMap North America, on the number of highway exits per county.
- The primary outcome measure was the rate of adult obesity at the county level, taken from the Food Environment Atlas, measured as the age-adjusted percentage of persons, aged 20 and older, who are obese.
- People are considered obese if they have a body mass index (BMI) greater than or equal to 30 kilograms per meter squared.
- The traditional Retail Food Environment Index (RFEI) is calculated as the ratio of fast food retailers and convenience stores to grocery stores and supermarkets.
- The study constructed two forms of “Expanded RFEI” to include additional food outlets.
- Food deserts were calculated as a continuous measure reflecting the proportion of each county’s total population identified as both low income and low access.
- The study used 2009 data from the United States Department of Agriculture (USDA) Food Environment Atlas (N = 3140).
- The median adult obesity rate was 30.5 (4.16).
- The median for the Natural Amenities Index (1 to 6) was 3.0 (1.04).
- Fast Food Restaurants had a median of 15.0 (228.60), Grocery Stores had a median of 6.0 (76.80), Supercenters had a median of 0.0 (2.64), Convenience Stores had a median of 16.0 (87.50), Specialized Food Stores had a median of 1.0 (37.95), and Farmers Market had a median of 1.0 (4.00).
- The Food Desert (% Low income and Low Access to Grocery Store) had a median of 6.2 (8.37), Food Swamp (Traditional RFEI) had a median of 3.5 (1.86), Food Swamp (Expanded RFEI_1) had a median of 3.4 (2.14), Food Swamp (Expanded RFEI_2) had a median of 3.6 (2.38), Fast food retail per 10,000 had a median of 5.8 (2.99), and Number of fast food retailers had a median of 15.0 (228.58).
- The correlations between food deserts and food swamps at the county level varied by the measure of food swamps used.
- The relationship between food deserts and food swamps showed a statistically significant negative correlation.
- The food desert variable was not statistically significantly associated with obesity.
- The food swamp measures were significantly positively correlated with the number of highway exits in a county.
- The traditional RFEI was significantly negatively associated with the number of highway exits in a county.
- The food swamp effect on obesity varied across the three measures.
- The version of the expanded RFEI 1 with supercenters categorized as healthy had a larger magnitude than the expanded RFEI 2.
- The OLS regression analyses adding total food outlets as a covariate and there were no major changes in the findings.
- The first stage results (Table 7) indicated that highway exits were significantly associated with the food swamp measures, the size of the effect was small.
- The IV coefficient estimates imply that policies, like zoning laws, could lower obesity rates by about three percent.
- The food swamp effect remained significant among counties where there was less driving or reliance on public transportation for travelling to work (βRFEI = 2.452; p < 0.05).
- The food swamp effect on obesity remained statistically significant in areas with lower income inequality (p < 0.05).
Other Important Findings
- The study found that the presence of a food swamp is a stronger predictor of obesity rates than the absence of full-service grocery stores.
- All three food swamp measures indicated the same positive association, but reflected different magnitudes of the food swamp effect on rates of adult obesity.
- The food swamp effect was stronger in counties with greater income inequality (p < 0.05) and where residents are less mobile (p < 0.01).
- The traditional RFEI had the strongest relationship with rates of adult obesity.
- The balance among fast food restaurants, convenience stores, and grocery stores is a more important determinant of aggregate obesity levels than other food outlets, including supercenters, farmers’ markets, or specialized food stores.
- The positive food swamp effect on obesity rates was stronger in counties where people were less likely to drive or use public transportation to get to work.
- In areas where the population was less mobile, living in a food swamp was more closely associated with rates of obesity.
Limitations Noted in the Document
- The study did not provide information about counties where the food environment is not associated with the number of highway exits.
- The study did not examine mechanisms linking food environments to obesity such as diet quality, food away from home expenditures, and exposure to food marketing.
- The study was limited to the county level data available, so it did not assess the impact of micro-level food environments by using census tract, neighborhood-level assessments, or individual-level BMI data.
- The cross-sectional nature of the dataset limited the ability to make causal statements.
- The obesity rates were based on self-reported height and weight, rather than measured height and weight.
- There are validity issues with secondary data sources, including the Food Environment Atlas, which categorizes food stores based on NAICS codes.
- The presence of 1672 counties with no highway exits meant the IV approach could not provide information on those areas.
- The physical activity indicators were measures of access and conduciveness to physical activity, not information about how frequently people exercise each week.
Conclusion
The study’s findings suggest that food swamps are a significant factor in predicting adult obesity rates, surpassing the influence of food deserts. The research highlights the importance of considering relative food environment measures, emphasizing the balance between healthy and unhealthy food sources. The IV approach reveals that the food swamp effect on obesity rates is underestimated in typical OLS regression analyses, with zoning policies potentially lowering obesity rates significantly. The positive association between food swamps and obesity rates is particularly pronounced in areas with high-income inequality and among residents with limited mobility. The study underscores that the composition of retail food options is a more crucial determinant of obesity levels than other factors like supercenters or farmers’ markets. The research also emphasizes the value of incorporating county-level data to inform policy, with the aim of mitigating the harmful effects of food swamps. This study underscores the importance of recognizing food swamps as a distinct phenomenon from food deserts, supporting a multi-pronged approach to urban planning and public health interventions. Specifically, it indicates that the integration of policies like zoning laws restricting access to unhealthy food outlets and incentivizing healthy food retailers in underserved areas could be an effective strategy. Policymakers can use these results to support zoning laws and policies related to the local food environment. This can lead to greater health equity by improving food access. Future research should concentrate on refining zoning policies, understanding how to define and enforce relevant terms, and identifying priority locations for intervention. This includes strategies to reduce the harm associated with food swamps, and studying how to engage community members and leaders to develop a more comprehensive approach to obesity prevention. The findings contribute to evidence-based strategies, emphasizing the need for a proactive approach in urban planning, public health, and community engagement to tackle the obesity epidemic.