Neighborhood Physical Environment and Changes in Body Mass Index: Results From the Multi-Ethnic Study of Atherosclerosis.

Longitudinal associations between neighborhood characteristics and body mass index (BMI; weight (kg)/height (m)2) were assessed from 2000 to 2011 among 5,919 participants in the Multi-Ethnic Study of Atherosclerosis. The perceived availability of healthy food and walking environment were assessed via surveys, and 1-mile (1.6-km) densities of supermarkets, fruit-and-vegetable stores, and recreational facilities were obtained through a commercial database. Econometric fixed-effects models were used to estimate the association between within-person changes in neighborhood characteristics and within-person change in BMI. In fully adjusted models, a 1-standard-deviation increase in the healthy food environment index was associated with a 0.16-kg/m2 decrease in BMI (95% confidence interval (CI): -0.27, -0.06) among participants with obesity at baseline. A 1-standard-deviation increase in the physical activity environment index was associated with 0.13-kg/m2 (95% CI: -0.24, -0.02) and 0.14-kg/m2 (95% CI: -0.27, -0.01) decreases in BMI for participants who were overweight and obese at baseline, respectively. Paradoxically, increases in the physical activity index were associated with BMI increases in persons who were normal-weight at baseline. This study provides preliminary longitudinal evidence that favorable changes in neighborhood physical environments are related to BMI reductions in obese persons, who comprise a substantial proportion of the US population.

Spatially Modelling the Association Between Access to Recreational Facilities and Exercise: The 'Multi-Ethnic Study of Atherosclerosis'.

Numerous studies have investigated the relationship between the built environment and physical activity. However these studies assume that these relationships are invariant over space. In this study, we introduce a novel method to analyze the association between access to recreational facilities and exercise allowing for spatial heterogeneity. In addition, this association is studied before and after controlling for crime, a variable that could explain spatial heterogeneity of associations. We use data from the Chicago site of the Multi-Ethnic Study of Atherosclerosis of 781 adults aged 46 years and over. A spatially varying coefficient Tobit regression model is implemented in the Bayesian setting to allow for the association of interest to vary over space. The relationship is shown to vary over Chicago, being positive in the south but negative or null in the north. Controlling for crime weakens the association in the south with little change observed in northern Chicago. The results of this study indicate that spatial heterogeneity in associations of environmental factors with health may vary over space and deserve further exploration.

A common spatial factor analysis model for measured neighborhood-level characteristics: The Multi-Ethnic Study of Atherosclerosis.

The purpose of this study was to reduce the dimensionality of a set of neighborhood-level variables collected on participants in the Multi-Ethnic Study of Atherosclerosis (MESA) while appropriately accounting for the spatial structure of the data. A common spatial factor analysis model in the Bayesian setting was utilized in order to properly characterize dependencies in the data. Results suggest that use of the spatial factor model can result in more precise estimation of factor scores, improved insight into the spatial patterns in the data, and the ability to more accurately assess associations between the neighborhood environment and health outcomes.

Improving retrospective characterization of the food environment for a large region in the United States during a historic time period.

Access to healthy foods has received increasing attention due to growing prevalence of obesity and diet-related health conditions yet there are major obstacles in characterizing the local food environment. This study developed a method to retrospectively characterize supermarkets for a single historic year, 2005, in 19 counties in 6 states in the USA using a supermarket chain-name list and two business databases. Data preparation, merging, overlaps, added-value amongst various approaches and differences by census tract area-level socio-demographic characteristics are described. Agreement between two food store databases was modest: 63%. Only 55% of the final list of supermarkets were identified by a single business database and selection criteria that included industry classification codes and sales revenue ≥$2 million. The added-value of using a supermarket chain-name list and second business database was identification of an additional 14% and 30% of supermarkets, respectively. These methods are particularly useful to retrospectively characterize access to supermarkets during a historic period and when field observations are not feasible and business databases are used.