The Moving to Health (M2H) approach to natural experiment research: A paradigm shift for studies on built environment and health.

Improving the built environment (BE) is viewed as one strategy to improve community diets and health. The present goal is to review the literature on the effects of BE on health, highlight its limitations, and explore the growing use of natural experiments in BE research, such as the advent of new supermarkets, revitalized parks, or new transportation systems. Based on recent studies on movers, a paradigm shift in built-environment health research may be imminent. Following the classic Moving to Opportunity study in the US, the present Moving to Health (M2H) strategy takes advantage of the fact that changing residential location can entail overnight changes in multiple BE variables. The necessary conditions for applying the M2H strategy to Geographic Information Systems (GIS) databases and to large longitudinal cohorts are outlined below. Also outlined are significant limitations of this approach, including the use of electronic medical records in lieu of survey data. The key research question is whether documented changes in BE exposure can be linked to changes in health outcomes in a causal manner. The use of geo-localized clinical information from regional health care systems should permit new insights into the social and environmental determinants of health.

A new method to visualize obesity prevalence in Seattle-King County at the census block level.

Objective: The aim of this study is to map obesity prevalence in Seattle King County at the census block level. Methods: Data for 1,632 adult men and women came from the Seattle Obesity Study I. Demographic, socioeconomic and anthropometric data were collected via telephone survey. Home addresses were geocoded, and tax parcel residential property values were obtained from the King County tax assessor. Multiple logistic regression tested associations between house prices and obesity rates. House prices aggregated to census blocks and split into deciles were used to generate obesity heat maps. Results: Deciles of property values for Seattle Obesity Study participants corresponded to county-wide deciles. Low residential property values were associated with high obesity rates (odds ratio, OR: 0.36; 95% confidence interval, CI [0.25, 0.51] in tertile 3 vs. tertile 1), adjusting for age, gender, race, home ownership, education, and incomes. Heat maps of obesity by census block captured differences by geographic area. Conclusion: Residential property values, an objective measure of individual and area socioeconomic status, are a useful tool for visualizing socioeconomic disparities in diet quality and health.

Validating self-reported food expenditures against food store and eating-out receipts.

BACKGROUND/OBJECTIVES: To compare objective food store and eating-out receipts with self-reported household food expenditures. SUBJECTS/METHODS: The Seattle Obesity Study II was based on a representative sample of King County adults, Washington, USA. Self-reported household food expenditures were modeled on the Flexible Consumer Behavior Survey (FCBS) Module from 2007 to 2009 National Health and Nutrition Examination Survey (NHANES). Objective food expenditure data were collected using receipts. Self-reported food expenditures for 447 participants were compared with receipts using paired t-tests, Bland-Altman plots and κ-statistics. Bias by sociodemographics was also examined. RESULTS: Self-reported expenditures closely matched with objective receipt data. Paired t-tests showed no significant differences between receipts and self-reported data on total food expenditures, expenditures at food stores or eating out. However, the highest-income strata showed weaker agreement. Bland-Altman plots confirmed no significant bias across both methods-mean difference: 6.4; agreement limits: -123.5 to 143.4 for total food expenditures, mean difference 5.7 for food stores and mean difference 1.7 for eating out. The κ-statistics showed good agreement for each (κ 0.51, 0.41 and 0.49 respectively. Households with higher education and income had significantly more number of receipts and higher food expenditures. CONCLUSIONS: Self-reported food expenditures using NHANES questions, both for food stores and eating out, serve as a decent proxy for objective household food expenditures from receipts. This method should be used with caution among high-income populations, or with high food expenditures. This is the first validation of the FCBS food expenditures question using food store and eating-out receipts.

Health Implications of Adults' Eating at and Living near Fast Food or Quick Service Restaurants.

BACKGROUND: This paper examined whether the reported health impacts of frequent eating at a fast food or quick service restaurant on health were related to having such a restaurant near home. METHODS: Logistic regressions estimated associations between frequent fast food or quick service restaurant use and health status, being overweight or obese, having a cardiovascular disease or diabetes, as binary health outcomes. In all, 2001 participants in the 2008-2009 Seattle Obesity Study survey were included in the analyses. RESULTS: Results showed eating ⩾2 times a week at a fast food or quick service restaurant was associated with perceived poor health status, overweight and obese. However, living close to such restaurants was not related to negative health outcomes. CONCLUSIONS: Frequent eating at a fast food or quick service restaurant was associated with perceived poor health status and higher body mass index, but living close to such facilities was not.

The spatial clustering of obesity: does the built environment matter?

BACKGROUND: Obesity rates in the USA show distinct geographical patterns. The present study used spatial cluster detection methods and individual-level data to locate obesity clusters and to analyse them in relation to the neighbourhood built environment. METHODS: The 2008-2009 Seattle Obesity Study provided data on the self-reported height, weight, and sociodemographic characteristics of 1602 King County adults. Home addresses were geocoded. Clusters of high or low body mass index were identified using Anselin's Local Moran's I and a spatial scan statistic with regression models that searched for unmeasured neighbourhood-level factors from residuals, adjusting for measured individual-level covariates. Spatially continuous values of objectively measured features of the local neighbourhood built environment (SmartMaps) were constructed for seven variables obtained from tax rolls and commercial databases. RESULTS: Both the Local Moran's I and a spatial scan statistic identified similar spatial concentrations of obesity. High and low obesity clusters were attenuated after adjusting for age, gender, race, education and income, and they disappeared once neighbourhood residential property values and residential density were included in the model. CONCLUSIONS: Using individual-level data to detect obesity clusters with two cluster detection methods, the present study showed that the spatial concentration of obesity was wholly explained by neighbourhood composition and socioeconomic characteristics. These characteristics may serve to more precisely locate obesity prevention and intervention programmes.

Food environment and socioeconomic status influence obesity rates in Seattle and in Paris.

OBJECTIVE: To compare the associations between food environment at the individual level, socioeconomic status (SES) and obesity rates in two cities: Seattle and Paris. METHODS: Analyses of the SOS (Seattle Obesity Study) were based on a representative sample of 1340 adults in metropolitan Seattle and King County. The RECORD (Residential Environment and Coronary Heart Disease) cohort analyses were based on 7131 adults in central Paris and suburbs. Data on sociodemographics, health and weight were obtained from a telephone survey (SOS) and from in-person interviews (RECORD). Both studies collected data on and geocoded home addresses and food shopping locations. Both studies calculated GIS (Geographic Information System) network distances between home and the supermarket that study respondents listed as their primary food source. Supermarkets were further stratified into three categories by price. Modified Poisson regression models were used to test the associations among food environment variables, SES and obesity. RESULTS: Physical distance to supermarkets was unrelated to obesity risk. By contrast, lower education and incomes, lower surrounding property values and shopping at lower-cost stores were consistently associated with higher obesity risk. CONCLUSION: Lower SES was linked to higher obesity risk in both Paris and Seattle, despite differences in urban form, the food environments and in the respective systems of health care. Cross-country comparisons can provide new insights into the social determinants of weight and health.