The global obesity pandemic has been well-documented and widely discussed by the public, the media, health officials, the food industry and academic researchers. While the problem is widely recognised, the potential solutions are far less clear. There is only limited evidence to guide decisions as to how best to manage obesity in individuals and in populations. While widely viewed as a clinical and public health problem in developed countries, it is now clear that many developing countries also have to grapple with this problem or face the crippling healthcare costs resulting from obesity-related morbidity. There is also abundant evidence that obesity is socio-economically distributed. In developed countries persons of lower socio-economic position are more likely to be affected, while in developing countries, it is often those of higher socio-economic position who are overweight or obese. The aim of this paper is to briefly review the evidence that links socio-economic position and obesity, to discuss what is known about underlying mechanisms, and to consider the role of social, physical, policy and cultural environments in explaining the relationships between socio-economic position and obesity. We introduce the concept of 'resilience' as a potential theoretical construct to guide research efforts aimed at understanding how some socio-economically disadvantaged individuals manage to avoid obesity. We conclude by considering an agenda to guide future research and programs focused on understanding and reducing obesity among those of low socio-economic position. Key Words: socio-economic factors, obesity, environment, social environment, resilience. Introduction Population obesity as a socio-economically patterned phenomenon There is a substantial body of evidence that demonstrates that obesity is associated with socio-economic position (SEP). That literature was first reviewed by Sobal and Stunkard in 1989. 1 In their now classic paper, they examined 144 mostly cross-sectional studies and concluded that, in developed countries, SEP was consistently inversely associated with obesity among women. Among men and children, however, the associations between SEP and obesity were less consistent. For example, of 66 studies including data from men, 52% found an inverse relationship between SEP and obesity, but 30% found the opposite -a direct relationship -and 17% found no association. In developing societies, Sobal and Stunkard's 1 review showed that SEP was strongly directly associated with obesity among men, women and children. In a more recent review, Ball and Crawford 2 examined the evidence regarding the associations between SEP and weight change. That review identified 34 papers published between 1980 and 2002 that reported on longitudinal studies conducted in developed countries. Based on the more rigorous of these studies, the authors concluded that occupation was inversely associated with weight gain. For example, in a sample of almost 8,000 adults participating in the Whitehall study, participants in the lowest occupational category (clerical) had 1.64 (men) or 2.16 (women) times the odds of long-term major BMI increases (>3 BMI points) compared with those in the highest occupational category (administrators). 3 The review also showed that the association between education and weight gain was less consistent, and that income was inconsistently associated with weight gain. While some previous evidence suggested that obesity may actually be an antecedent to low SEP, 4,5 results of Ball and Crawford's 2 review of longitudinal studies suggests that SEP precedes weight gain and risk for obesity, rather than the reverse. Too few studies from developing countries were identified to be included in that review, and similarly the remainder of the present paper focuses on developed countries, from where the majority of research evidence is derived. Socio-economic position and nutrition and physical activity behaviours There seems little doubt that the epidemic of obesity that we are witnessing worldwide is attributable to excess energy consumption and inadequate energy expenditure. In order words, at a population level, changes in both eating patterns and in physical activity habits are important in terms of obesity aetiology. 7-11 While the search for specific behavioural drivers of obesity continues, it is likely that these comprise both eating and physical activity-related behaviours. There is good evidence that the socio-economic distribution of eating patterns and of leisure-time physical activity are consistent with those found for obesity. Studies show that persons from low socio-economic backgrounds are less likely than those from high socio-economic backgrounds to participate in organised sport and leisure-time physical activity.

Regulating specific inputs into health and safety production functions is unlikely to be effective when optimizing consumers can compensate along other margins. This paper examines the implications of this principle in the context of economic policies targeted at reducing obesity. Well-established cross-sectional and time-series correlations between average body weight and eating out have convinced many researchers and policymakers that restaurants are a leading cause of obesity in the United States. But a basic identification problem challenges these conclusions: do more restaurants cause obesity, or do preferences for greater food consumption lead to an increase in restaurant density? To answer this question, we design a natural experiment in which we exploit exogenous variation in the effective price of restaurants and examine the impact on consumers ’ body mass. We use the presence of Interstate Highways in rural areas as an instrument for the supply of restaurants. The instrument strongly predicts restaurant access and frequency of consumption, and robustness tests support its validity. The results find no evidence of a causal link between restaurants and obesity, and the estimates are precise enough to rule out any meaningful effect. Analysis of food intake micro data suggests that although consumers eat larger meals at restaurants than at home (even after accounting for selection), they offset these calories at other times of day. We conclude that public health policies targeting restaurants are unlikely to reduce obesity but could negatively affect consumer welfare.

Abstract. This paper provides quantitative estimates of the effect of proximity to fast food restaurants and grocery stores on obesity in urban food markets. Our empirical model combined georeferenced micro data on access to fast food restaurants and grocery stores with data about salient personal characteristics, individual behaviors, and neighborhood characteristics. We defined a “local food environment ” for every individual utilizing 0.5-mile buffers around a person’s home address. Local food landscapes are potentially endogenous due to spatial sorting of the population and food outlets, and the body mass index (BMI) values for individuals living close to each other are likely to be spatially correlated because of observed and unobserved individual and neighborhood effects. The potential biases associated with endogeneity and spatial correlation were handled using spatial econometric estimation techniques. Our policy simulations for Indianapolis, Indiana, focused on the importance of reducing the density of fast food restaurants or increasing access to grocery stores. We accounted for spatial heterogeneity in both the policy instruments and individual neighborhoods, and consistently found small but statistically significant effects for the hypothesized relationships between individual BMI values

This paper employs an identification strategy based on county-level variation in the number of fast-food restaurants to investigate the effect of fast-food availability on weight outcomes by geo-graphic location, gender, and race/ethnicity. The number of interstate exits in the county of residence is employed as an instrument for restaurant location. Using the 2004–2006 Behavioral Risk Factor Surveillance System and self-collected data on the number of fast-food restaurants, I find that avail-ability does not affect weight outcomes in rural counties, but does tend to increase body mass index among females and non-Whites in medium-density counties. These results are robust to specification choices.

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Increasing research has focused on the built food environment and nutrition-related outcomes, yet what constitutes a food environment and how this environment influences individual behavior still remain unclear. This study assesses whether travel mode and distance to food shopping venues differ among individuals in varying food environments and whether individual- and household-level factors are associated with food shopping patterns. Fifty neighbors who share a traditionally defined food environment (25 in an unfavorable environment and 25 in a favorable environment) were surveyed using a mix of close- and open-ended survey questions. Food shopping patterns were mapped using Geographic Information Systems (GIS). Stores visited were beyond the 0.5-mile (805 meters) radius traditionally used to represent the extent of an individual’s food environment in an urban area. We found no significant difference in shopping frequency or motivating factor behind store choice between the groups. No differences existed between the two groups for big food shopping trips. For small trips, individuals in the favorable food environment traveled shorter distances and were more likely to walk than drive. Socioeconomic status, including car ownership, education, and income influenced distance traveled. These

Abstract Background: Forming implementation intentions (specifying when, where and how to act) has been proposed as a potentially effective and inexpensive intervention, but has mainly been studied in controlled settings for straightforward behaviors.

Background: Recent obesity prevention initiatives focus on healthy neighborhood design, but most research examines neighborhood food retail and physical activity (PA) environments in isolation. We estimated joint, interactive, and cumulative impacts of neighborhood food retail and PA environment characteristics on body mass index (BMI) throughout early adulthood.

This article examines the influence of the neighborhood environment on blood profiles, percent body fat, blood pressure and the metabolic syndrome (MetS) in adolescents. One hundred eighty-eight adolescents (10–16 yrs) agreed to have a fasting blood sample drawn in addition to measures of weight, height, percent fat and blood pressure. A MetS cluster score was derived by calculating the sum of the sample-specific z-scores from the percent body fat, fasting glucose, high density lipoprotein cholesterol (negative), triglyceride, and systolic blood pressure. Geographic Information Systems (GIS) technology was used to calculate the distance to and density of built environmental features. Spearman correlation was used to identify significant (p<0.05) relationships between the built environment and the MetS. Statistically significant correlations were added to linear regression models, adjusted for pubertal status, age and sex. Multivariate linear regression models revealed significant associations between an increased distance to convenience stores and the MetS. The results of this study suggest a role for the built environment in the development of the MetS.

Abstract Background: Current UK policy in relation to the influence of the 'food environment' on childhood obesity appears to be driven largely on assumptions or speculations because empirical evidence is lacking and findings from studies are inconsistent. The aim of this study was to investigate the number of food outlets and the proximity of food outlets in the same sample of children, without solely focusing on fast food. Methods: Cross sectional study over 3 years (n = 13,291 data aggregated). Body mass index (BMI) was calculated for each participant, overweight and obesity were defined as having a BMI >85 th (sBMI 1.04) and 95 th (sBMI 1.64) percentiles respectively (UK90 growth charts). Home and school neighbourhoods were defined as circular buffers with a 2 km Euclidean radius, centred on these locations. Commuting routes were calculated using the shortest straight line distance, with a 2 km buffer to capture varying routes. Data on food outlet locations was sourced from Leeds City Council covering the study area and mapped against postcode. Food outlets were categorised into three groups, supermarkets, takeaway and retail. Proximity to the nearest food outlet in the home and school environmental domain was also investigated. Age, gender, ethnicity and deprivation (IDACI) were included as covariates in all models. Results: There is no evidence of an association between the number of food outlets and childhood obesity in any of these environments; Home Q4 vs. Q1 OR = 1.11 (95% CI = 0.95-1.30); School Q4 vs. Q1 OR = 1.00 (95% CI 0.87 -1.16); commute Q4 vs. Q1 OR = 0.1.00 (95% CI 0.83 -1.20). Similarly there is no evidence of an association between the proximity to the nearest food outlet and childhood obesity in the home (OR = 0.77 [95% CI = 0.61 -0.98]) or the school (OR = 1.01 [95% CI 0.84 -1.23]) environment. Conclusions: This study provides little support for the notion that exposure to food outlets in the home, school and commuting neighbourhoods increase the risk of obesity in children. It seems that the evidence is not well placed to support Governmental interventions/recommendations currently being proposed and that policy makers should approach policies designed to limit food outlets with caution.