Introduction:
Poor diet and physical inactivity is the second leading cause of mortality in the US after smoking. Cross-sectional, ecologic studies have associated specific obesogenic food environments (OFE examples: smaller distance to fast food restaurants, higher counts of fast food per population, larger distance to grocery stores, lower counts of grocery stores per population) to higher rates of poor diet or higher body mass index (BMI). OFEs are more prevalent in some low-income and racial/ethnic minority neighborhoods potentially contributing to widening health disparities. Recent analyses of two longitudinal cohorts (CARDIA; Framingham Offspring Cohort), found no associations between ecologic measures of OFEs and poor diet or BMI, possibly because they do not capture the characteristics of the OFEs associated with poor diet or BMI.
Hypothesis:
We assessed the hypothesis that current ecologic OFE measures do not capture the link between food environments and BMI because they ignore variability in food store types and actual distance traveled to purchase food. Populations defined by store type or distance may better describe the potential causal link.
Methods:
The Los Angeles Family and Neighborhood Survey (LAFANS) is a longitudinal cohort of 2619 households in Los Angeles County. In 2001-2, households were asked where they shopped for groceries (store name/location) and self-reported BMI. A six-category food environment measure based on store name and frequency was developed: high-frequency (HF) English-language named stores (“major chain”), discount stores (“less”, “value”, etc. in the name), HF Spanish-language stores, English-language specialty stores, multi-purpose or bulk purchase stores, other HF stores, and other low frequency stores of any language. We analyzed associations of this food environment measure with self-reported BMI, controlling for individual, household, and neighborhood characteristics.
Results:
In LAFANS households, 2297 (88%) reported both BMI and a valid store name. Of these, 37% of households shop at the nearest grocery store and only 13% shop in their home census tract. In adjusted models, discount store shoppers have substantially higher BMI than the referent group, major chain store shoppers in low disadvantage neighborhoods (BMI difference 1.40 points, (95% CI 0.62 - 2.18, p = 0.004), equivalent to a weight difference of 8.4 lbs. for an individual of median height and weight (5’5”,160 lbs.).
Conclusions:
In conclusion, distinguishing between store types may better describe the causal link between individuals, stores and BMI than ecologic measures. In L.A. County, discount stores, found almost exclusively in high disadvantage and racial/ethnic minority neighborhoods are associated with individual differences in BMI. Further research should assess whether the association between discount stores and BMI is related to unmeasured elements of store content or individual characteristics. Current policy efforts focused on modifying small markets or building major chain stores in high disadvantage neighborhoods may inadequately address food environment based racial/ethnic and income based health disparities in BMI.
Bikes or Bust? Analyzing the Impact of Bicycle Infrastructure on Business Performance in San Francisco
