Faculty Bibliography

Background/UNASSIGNED:Findings in the observational retail food environment and obesity literature are inconsistent, potentially due to a lack of adjustment for residual confounding. Methods/UNASSIGNED:Using data from the CARDIA study (n = 12 174 person-observations; 6 examinations; 1985-2011) across four US cities (Birmingham, AL; Chicago, IL; Minneapolis, MN; Oakland, CA), we used instrumental-variables (IV) regression to obtain causal estimates of the longitudinal associations between the percentage of neighbourhood food stores or restaurants (per total food outlets within 1 km network distance of respondent residence) with body mass index (BMI), adjusting for individual-level socio-demographics, health behaviours, city, year, total food outlets and market-level prices. To determine the presence and extent of bias, we compared the magnitude and direction of results with ordinary least squares (OLS) and random effects (RE) regression, which do not control for residual confounding, and with fixed effects (FE) regression, which does not control for time-varying residual confounding. Results/UNASSIGNED:Relative to neighbourhood supermarkets (which tend to be larger and have healthier options than grocery stores), a higher percentage of grocery stores [mean = 53.4%; standard deviation (SD) = 31.8%] was positively associated with BMI [β = 0.05; 95% confidence interval (CI) = 0.01, 0.10] using IV regression. However, associations were negligible or null using OLS (β = -0.001; 95% CI = -0.01, 0.01), RE (β = -0.003; 95% CI = -0.01, 0.0001) and FE (β = -0.003; 95% CI = -0.01, 0.0002) regression. Neighbourhood convenience stores and fast-food restaurants were not associated with BMI in any model. Conclusions/UNASSIGNED:Longitudinal associations between neighbourhood food outlets and BMI were greater in magnitude using a causal model, suggesting that weak findings in the literature may be due to residual confounding.

BACKGROUND: The relationship between food environment exposures and diet behaviours is unclear, possibly because the majority of studies ignore potential residual confounding. METHODS: We used 20 years (1985-1986, 1992-1993 2005-2006) of data from the Coronary Artery Risk Development in Young Adults (CARDIA) study across four US cities (Birmingham, Alabama; Chicago, Illinois; Minneapolis, Minnesota; Oakland, California) and instrumental variables (IV) regression to obtain causal estimates of longitudinal associations between the percentage of neighbourhood food outlets (per total food outlets within 1 km network distance of respondent residence) and an a priori diet quality score, with higher scores indicating higher diet quality. To assess the presence and magnitude of bias related to residual confounding, we compared results from causal models (IV regression) to non-causal models, including ordinary least squares regression, which does not account for residual confounding at all and fixed-effects regression, which only controls for time-invariant unmeasured characteristics. RESULTS: The mean diet quality score across follow-up was 63.4 (SD=12.7). A 10% increase in fast food restaurants (relative to full-service restaurants) was associated with a lower diet quality score over time using IV regression (beta=-1.01, 95% CI -1.99 to -0.04); estimates were attenuated using non-causal models. The percentage of neighbourhood convenience and grocery stores (relative to supermarkets) was not associated with diet quality in any model, but estimates from non-causal models were similarly attenuated compared with causal models. CONCLUSION: Ignoring residual confounding may generate biased estimated effects of neighbourhood food outlets on diet outcomes and may have contributed to weak findings in the food environment literature.

OBJECTIVES/OBJECTIVE:We sought to examine characteristics of neighborhoods with changing older adult populations. METHODS:We used 30 years (1980-2011) of data from four U.S. cities (n=392 neighborhoods; Birmingham, AL; Chicago, IL; Minneapolis, MN; Oakland, CA) and finite mixture modeling to identify trajectory classes: neighborhoods with "stable", declining, or increasing older adult populations (≥65 years). We then compared mean baseline and change in their characteristics. RESULTS:Neighborhoods with increasing (vs. "stable") percentage of older adult populations had lower initial poverty and greater increases in education and income, with lower increases in road connectivity, population density, and housing prices/debt over time. The same was true for neighborhoods with declining older adult populations, with the exception of having higher increases in housing prices/debt. We observed few significant differences in neighborhood amenities or parks across classes. CONCLUSIONS:Our results emphasize the need to consider built and social environments when planning communities for older adults.

Little research has addressed whether neighborhood context influences associations between fast food price, diet, and cardiometabolic health. We investigated these associations using 25 years of Coronary Artery Risk Development in Young Adults (CARDIA) study data (n=4,469, observations=21,134). We found a negative association between fast food price and consumption, with stronger inverse associations in more (vs. less) deprived neighborhoods [3rd tertile: beta=-0.68 (95% CI: (-0.85, -0.51); 1st tertile: beta=-0.22 (95% CI: -0.42, -0.02); p-interaction-0.002], and a similar association for BMI [3rd tertile: beta=-1.34 (95% CI: -1.54, -1.14); 1st tertile: beta=-0.45 (95% CI: -0.66, -0.25); p-interaction<0.001], but not insulin resistance [3rd tertile: beta=-0.07 (95% CI: -0.24, 0.09); 1st tertile: beta=0.09 (95% CI: -0.08, 0.26); p-interaction=0.40]. We observed no modification of fast food price by fast food availability. Future research on obesity disparities should consider potential differences in the association between fast food prices and health outcomes across neighborhood socioeconomic levels.

OBJECTIVE: Obtaining valid, reliable measures of food environments that serve Latino communities is important for understanding barriers to healthy eating in this at-risk population. DESIGN: The primary aim of the study was to examine agreement between retail food outlet data from two commercial databases, Nielsen TDLinx (TDLinx) for food stores and Dun & Bradstreet (D&B) for food stores and restaurants, relative to field observations of food stores and restaurants in thirty-one census tracts in Durham County, NC, USA. We also examined differences by proportion of Hispanic population (/=23.4 % Hispanic population) in the census tract and for outlets classified in the field as 'Latino' on the basis of signage and use of Spanish language. SETTING: One hundred and seventy-four food stores and 337 restaurants in Durham County, NC, USA. RESULTS: We found that overall sensitivity of food store listings in TDLinx was higher (64 %) than listings in D&B (55 %). Twenty-five food stores were characterized by auditors as Latino food stores, with 20 % identified in TDLinx, 52 % in D&B and 56 % in both sources. Overall sensitivity of restaurants (68 %) was higher than sensitivity of Latino restaurants (38 %) listed in D&B. Sensitivity did not differ substantially by Hispanic composition of neighbourhoods. CONCLUSIONS: Our findings suggest that while TDLinx and D&B commercial data sources perform well for total food stores, they perform less well in identifying small and independent food outlets, including many Latino food stores and restaurants.

OBJECTIVES: We examined the association between neighborhood convenience stores and diet outcomes for 20 years of the Coronary Artery Risk Development in Young Adults study. METHODS: We used dietary data from the Coronary Artery Risk Development in Young Adults study years 1985-1986, 1992-1993, and 2005-2006 (n = 3299; Birmingham, AL; Chicago, IL; Minneapolis, MN; and Oakland, CA) and geographically and temporally matched neighborhood-level food resource and US Census data. We used random effects repeated measures regression to estimate associations between availability of neighborhood convenience stores with diet outcomes and whether these associations differed by individual-level income. RESULTS: In multivariable-adjusted analyses, greater availability of neighborhood convenience stores was associated with lower diet quality (mean score = 66.3; SD = 13.0) for participants with lower individual-level income (b = -2.40; 95% CI = -3.30, -1.51); associations at higher individual-level income were weaker. We observed similar associations with whole grain consumption across time but no statistically significant associations with consumption of sugar-sweetened beverages, artificially sweetened beverages, snacks, processed meats, fruits, or vegetables. CONCLUSIONS: The presence of neighborhood convenience stores may be associated with lower quality diets. Low-income individuals may be most sensitive to convenience store availability.

The literature on food stores, neighborhood poverty, and race/ethnicity is mixed and lacks methods of accounting for complex spatial and temporal clustering of food resources. We used quarterly data on supermarket and convenience store locations from Nielsen TDLinx (Nielsen Holdings N.V., New York, New York) spanning 7 years (2006-2012) and census tract-based neighborhood sociodemographic data from the American Community Survey (2006-2010) to assess associations between neighborhood sociodemographic characteristics and food store distributions in the Metropolitan Statistical Areas (MSAs) of 4 US cities (Birmingham, Alabama; Chicago, Illinois; Minneapolis, Minnesota; and San Francisco, California). We fitted a space-time Poisson regression model that accounted for the complex spatial-temporal correlation structure of store locations by introducing space-time random effects in an intrinsic conditionally autoregressive model within a Bayesian framework. After accounting for census tract-level area, population, their interaction, and spatial and temporal variability, census tract poverty was significantly and positively associated with increasing expected numbers of supermarkets among tracts in all 4 MSAs. A similar positive association was observed for convenience stores in Birmingham, Minneapolis, and San Francisco; in Chicago, a positive association was observed only for predominantly white and predominantly black tracts. Our findings suggest a positive association between greater numbers of food stores and higher neighborhood poverty, with implications for policy approaches related to food store access by neighborhood poverty.