Faculty Bibliography

Research has examined how the food environment affects the risk of cardiovascular disease (CVD). Many studies have focused on residential neighbourhoods, neglecting the activity spaces of individuals. The objective of this study was to investigate whether food environments in both residential and global positioning system (GPS)-defined activity space buffers are associated with body mass index (BMI) and blood pressure (BP) among low-income adults. Data came from the New York City Low Income Housing, Neighborhoods and Health Study, including BMI and BP data (n=102, age=39.3±14.1 years), and one week of GPS data. Five food environment variables around residential and GPS buffers included: fast-food restaurants, wait-service restaurants, corner stores, grocery stores, and supermarkets. We examined associations between food environments and BMI, systolic and diastolic BP, controlling for individual- and neighbourhood-level sociodemographics and population density. Within residential buffers, a higher grocery store density was associated with lower BMI (β=- 0.20 kg/m2, P<0.05), and systolic and diastolic BP (β =-1.16 mm Hg; and β=-1.02 mm Hg, P<0.01, respectively). In contrast, a higher supermarket density was associated with higher systolic and diastolic BP (β=1.74 mm Hg, P<0.05; and β=1.68, P<0.01, respectively) within residential buffers. In GPS neighbourhoods, no associations were documented. Examining how food environments are associated with CVD risk and how differences in relationships vary by buffer types have the potential to shed light on determinants of CVD risk. Further research is needed to investigate these relationships, including refined measures of spatial accessibility/exposure, considering individual's mobility.

The purpose of this research was to examine whether the local food environment, specifically the distance to the nearest sugar sweetened beverage (SSB) vendor, a measure of SSB availability and accessibility, was correlated with the likelihood of self-reported SSB consumption among a sample of fast food consumers. As part of a broader SSB behavior study in 2013-2014, respondents were surveyed outside of major chain fast food restaurants in New York City (NYC). Respondents were asked for the intersection closest to their home and how frequently they consume SSBs. Comprehensive, administrative food outlet databases were used to geo-locate the SSB vendor closest to the respondents' home intersections. We then used a logistic regression model to estimate the association between the distance to the nearest SSB vendor (overall and by type) and the likelihood of daily SSB consumption. Our results show that proximity to the nearest SSB vendor was not statistically significantly associated with the likelihood of daily SSB consumption, regardless of type of vendor. Our results are robust to alternative model specifications, including replacing the linear minimum distance measure with count of the total number of SSB vendors or presence of a SSB vendor within a buffer around respondents' home intersections. We conclude that there is not a strong relationship between proximity to nearest SSB vendor, or proximity to a specific type of SSB vendor, and frequency of self-reported SSB consumption among fast food consumers in NYC. This suggests that policymakers focus on alternative strategies to curtail SSB consumption, such as improving the within-store food environment or taxing SSBs.

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.

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.

INTRODUCTION/BACKGROUND:Understanding what influences where food outlets locate is important for mitigating disparities in access to healthy food outlets. However, few studies have examined how neighborhood characteristics influence the neighborhood food environment over time, and whether these relationships differ by neighborhood-level income. METHODS:(density) of food outlets within real estate-derived neighborhoods. Associations were examined with lagged neighborhood-level sociodemographics and lagged density of food outlets, with interaction terms for neighborhood-level income. Data were analyzed in 2016. RESULTS:Neighborhood-level income at earlier years was negatively associated with the current density of convenience stores (β= -0.27, 95% CI= -0.16, -0.38, p<0.001). The percentage of neighborhood white population was negatively associated with fast food restaurant density in low-income neighborhoods (10th percentile of income: β= -0.17, 95% CI= -0.34, -0.002, p=0.05), and the density of smaller grocery stores across all income levels (β= -0.27, 95% CI= -0.45, -0.09, p=0.003). There was a lack of policy-relevant associations between the pre-existing food environment and the current density of food outlet types, including supermarkets. CONCLUSIONS:Socioeconomically disadvantaged and minority populations may attract "unhealthy" food outlets over time. To support equal access to healthy food outlets, the availability of "less healthy" food outlets types may be relatively more important than the potential lack of supermarkets or full-service restaurants.

BACKGROUND:Little is known about how diet-related and activity-related amenities relate to residential location behaviour. Understanding these relationships is essential for addressing residential self-selection bias. METHODS:Using 25 years (6 examinations) of data from the Coronary Artery Risk Development in Young Adults (CARDIA) study (n=11 013 observations) and linked neighbourhood-level data from the 4 CARDIA baseline cities (Birmingham, Alabama; Chicago, Illinois; Minneapolis, Minnesota; Oakland, California, USA), we characterised participants' neighbourhoods as having low, average or high road connectivity and amenities using non-hierarchical cluster analysis. We then used repeated measures multinomial logistic regression with random effects to examine the associations between individual-level sociodemographics and neighbourhood-level characteristics with residential neighbourhood types over the 25-year period, and whether these associations differed by individual-level income. RESULTS:Being female was positively associated with living in neighbourhoods with low (vs high) road connectivity and activity-related and diet-related amenities among high-income individuals only. At all income levels, a higher percentage of neighbourhood white population and neighbourhood population <18 years were associated with living in neighbourhoods with low (vs high) connectivity and amenities. Individual-level race; age; and educational attainment, neighbourhood socioeconomic status and housing prices did not influence residential location behaviour related to neighbourhood connectivity and amenities at any income level. CONCLUSIONS:Neighbourhood-level factors appeared to play a comparatively greater role in shaping residential location behaviour than individual-level sociodemographics. Our study is an important step in understanding how residential locational behaviour relates to amenities and physical activity opportunities, and may help mitigate residential self-selection bias in built environment studies.

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.

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.

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.