Interplay of socioeconomic status and supermarket distance is associated with excess obesity risk: a UK cross-sectional study

US policy initiatives have sought to improve health through attracting neighborhood supermarket investment. Little evidence exists to suggest these policies will be effective, in particular where there are socioeconomic barriers to healthy eating. We measured the independent associations and combined interplay of supermarket access and socioeconomic status with obesity. Using data on 9,702 UK adults, we employed adjusted regression analyses to estimate measured BMI (kg/m2), overweight (25≥BMI<30) and obesity (≥30), across participants’ highest educational attainment (three groups) and tertiles of street network distance (km) from home location to nearest supermarket. Jointly-classified models estimated combined associations of education and supermarket distance, and relative excess risk due to interaction (RERI). Participants farthest away from their nearest supermarket had higher odds of obesity (OR, 95% CI: 1.33, 1.11-1.58), relative to those living closest. Lower education was also associated with higher odds of obesity. Those least-educated and living farthest away had 3.39 (2.46-4.65) times the odds of being obese of those highest-educated and living closest, with an excess obesity risk (RERI=0.09); results were similar for overweight. Our results suggest that public health can be improved through planning better access to supermarkets, in combination with interventions to address socioeconomic barriers.This work was supported by the Centre for Diet and Activity Research (CEDAR), a UK Clinical Research Collaboration (UKCRC) Public Health Research Centre of Excellence. Funding from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council, the National Institute for Health Research (grant number ES/G007462/1), and the Wellcome Trust (grant number 087636/Z/08/Z), under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged. The Fenland Study is funded by the MRC and the study PIs acknowledge support from MC_UU_12015/1 and MC_UU_12015/5. Pablo Monsivais also received support from the Health Equity Research Collaborative, a Grand Challenge Research Initiative of Washington State University

Accessibility and Affordability of Supermarkets: Associations With the DASH Diet

$\textbf{Introduction:}$ It is unknown whether there is an interplay of affordability (economic accessibility) and proximity (geographic accessibility) of supermarkets in relation to having a Dietary Approaches to Stop Hypertension (DASH)-accordant diet. $\textbf{Methods:}$ Data (collected: 2005–2015, analyzed: 2016) were from the cross-sectional, population-based Fenland Study cohort: 9,274 adults aged 29–64 years, living in Cambridgeshire, United Kingdom. Dietary quality was evaluated using an index of DASH dietary accordance, based on recorded consumption of foods and beverages in a validated 130-item, semi-quantitative food frequency questionnaire. DASH accordance was defined as a DASH score in the top quintile. Dietary costs (£/day) were estimated by attributing a food price variable to the foods consumed according to the questionnaire. Individuals were classified as having low-, medium-, or high-cost diets. Supermarket affordability was determined based on the cost of a 101-item market basket. Distances between home address to the nearest supermarket (geographic accessibility) and nearest economically-appropriate supermarket (economic accessibility) were divided into tertiles. $\textbf{Results:}$ Higher-cost diets were more likely to be DASH-accordant. After adjustment for key demographics and exposure to other food outlets, individuals with lowest economic accessibility to supermarkets had lower odds of being DASH-accordant (OR=0.59, 95% CI=0.52, 0.68) than individuals with greatest economic accessibility. This association was stronger than with geographic accessibility alone (OR=0.85, 95% CI=0.74, 0.98). $\textbf{Conclusions:}$ Results suggest that geographic and economic access to food should be taken into account when considering approaches to promote adherence to healthy diets for the prevention of cardiovascular diseases and other chronic disease.This work was undertaken by the Centre for Diet and Activity Research, a UK Clinical Research Collaboration Public Health Research Centre of Excellence. Funding from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council, National Institute for Health Research, and Wellcome Trust, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged. Core MRC Epidemiology Unit support through programs MC_UU_12015/1 and MC_UU_12015/5 is acknowledged

Neighbourhood social capital: measurement issues and associations with health outcomes.

We compared ecometric neighbourhood scores of social capital (contextual variation) to mean neighbourhood scores (individual and contextual variation), using several health-related outcomes (i.e. self-rated health, weight status and obesity-related behaviours). Data were analysed from 5,900 participants in the European SPOTLIGHT survey. Factor analysis of the 13-item social capital scale revealed two social capital constructs: social networks and social cohesion. The associations of ecometric and mean neighbourhood-level scores of these constructs with self-rated health, weight status and obesity-related behaviours were analysed using multilevel regression analyses, adjusted for key covariates. Analyses using ecometric and mean neighbourhood scores, but not mean neighbourhood scores adjusted for individual scores, yielded similar regression coefficients. Higher levels of social network and social cohesion were not only associated with better self-rated health, lower odds of obesity and higher fruit consumption, but also with prolonged sitting and less transport-related physical activity. Only associations with transport-related physical activity and sedentary behaviours were associated with mean neighbourhood scores adjusted for individual scores. As analyses using ecometric scores generated the same results as using mean neighbourhood scores, but different results when using mean neighbourhood scores adjusted for individual scores, this suggests that the theoretical advantage of the ecometric approach (i.e. teasing out individual and contextual variation) may not be achieved in practice. The different operationalisations of social network and social cohesion were associated with several health outcomes, but the constructs that appeared to represent the contextual variation best were only associated with two of the outcomes

The relation between sleep duration and sedentary behaviours in European adults.

Too much sitting, and both short and long sleep duration are associated with obesity, but little is known on the nature of the relations between these behaviours. We therefore examined the associations between sleep duration and time spent sitting in adults across five urban regions in Europe. We used cross-sectional survey data from 6,037 adults (mean age 51.9 years (SD 16.4), 44.0% men) to assess the association between self-reported short (8 h per night) sleep duration with self-report total time spent sitting, time spent sitting at work, during transport, during leisure and while watching screens. The multivariable multilevel linear regression models were tested for moderation by urban region, age, gender, education and weight status. Because short sleepers have more awake time to be sedentary, we also used the percentage of awake time spent sedentary as an outcome. Short sleepers had 26.5 min day(-1) more sedentary screen time, compared with normal sleepers (CI 5.2; 47.8). No statistically significant associations were found with total or other domains of sedentary behaviour, and there was no evidence for effect modification. Long sleepers spent 3.2% higher proportion of their awake time sedentary compared with normal sleepers. Shorter sleep was associated with increased screen time in a sample of European adults, irrespective of urban region, gender, age, educational level and weight status. Experimental studies are needed to assess the prospective relation between sedentary (screen) time and sleep duration

Exploring why residents of socioeconomically deprived neighbourhoods have less favourable perceptions of their neighbourhood environment than residents of wealthy neighbourhoods.

Residents of socioeconomically deprived areas perceive their neighbourhood as less conducive to healthy behaviours than residents of more affluent areas. Whether these unfavourable perceptions are based on objective neighbourhood features or other factors is poorly understood. We examined individual and contextual correlates of socioeconomic inequalities in neighbourhood perceptions across five urban regions in Europe. Data were analysed from 5205 participants of the SPOTLIGHT survey. Participants reported perceptions of their neighbourhood environment with regard to aesthetics, safety, the presence of destinations and functionality of the neighbourhood, which were summed into an overall neighbourhood perceptions score. Multivariable multilevel regression analyses were conducted to investigate whether the following factors were associated with socioeconomic inequalities in neighbourhood perceptions: objectively observed neighbourhood features, neighbourhood social capital, exposure to the neighbourhood, self-rated health and lifestyle behaviours. Objectively observed traffic safety, aesthetics and the presence of destinations in the neighbourhood explained around 15% of differences in neighbourhood perceptions between residents of high and low neighbourhoods; levels of neighbourhood social cohesion explained around 52%. Exposure to the neighbourhood, self-rated health and lifestyle behaviours were significant correlates of neighbourhood perceptions but did not contribute to socioeconomic differences. This cross-European study provided evidence that socioeconomic differences in neighbourhood perceptions are not only associated with objective neighbourhood features but also with social cohesion. Levels of physical activity, sleep duration, self-rated health, happiness and neighbourhood preference were also associated with neighbourhood perceptions

Neighbourhood typology based on virtual audit of environmental obesogenic characteristics.

Virtual audit (using tools such as Google Street View) can help assess multiple characteristics of the physical environment. This exposure assessment can then be associated with health outcomes such as obesity. Strengths of virtual audit include collection of large amount of data, from various geographical contexts, following standard protocols. Using data from a virtual audit of obesity-related features carried out in five urban European regions, the current study aimed to (i) describe this international virtual audit dataset and (ii) identify neighbourhood patterns that can synthesize the complexity of such data and compare patterns across regions. Data were obtained from 4,486 street segments across urban regions in Belgium, France, Hungary, the Netherlands and the UK. We used multiple factor analysis and hierarchical clustering on principal components to build a typology of neighbourhoods and to identify similar/dissimilar neighbourhoods, regardless of region. Four neighbourhood clusters emerged, which differed in terms of food environment, recreational facilities and active mobility features, i.e. the three indicators derived from factor analysis. Clusters were unequally distributed across urban regions. Neighbourhoods mostly characterized by a high level of outdoor recreational facilities were predominantly located in Greater London, whereas neighbourhoods characterized by high urban density and large amounts of food outlets were mostly located in Paris. Neighbourhoods in the Randstad conurbation, Ghent and Budapest appeared to be very similar, characterized by relatively lower residential densities, greener areas and a very low percentage of streets offering food and recreational facility items. These results provide multidimensional constructs of obesogenic characteristics that may help target at-risk neighbourhoods more effectively than isolated features

Mismatch between perceived and objectively measured environmental obesogenic features in European neighbourhoods.

Findings from research on the association between the built environment and obesity remain equivocal but may be partly explained by differences in approaches used to characterize the built environment. Findings obtained using subjective measures may differ substantially from those measured objectively. We investigated the agreement between perceived and objectively measured obesogenic environmental features to assess (1) the extent of agreement between individual perceptions and observable characteristics of the environment and (2) the agreement between aggregated perceptions and observable characteristics, and whether this varied by type of characteristic, region or neighbourhood. Cross-sectional data from the SPOTLIGHT project (n = 6037 participants from 60 neighbourhoods in five European urban regions) were used. Residents' perceptions were self-reported, and objectively measured environmental features were obtained by a virtual audit using Google Street View. Percent agreement and Kappa statistics were calculated. The mismatch was quantified at neighbourhood level by a distance metric derived from a factor map. The extent to which the mismatch metric varied by region and neighbourhood was examined using linear regression models. Overall, agreement was moderate (agreement < 82%, kappa < 0.3) and varied by obesogenic environmental feature, region and neighbourhood. Highest agreement was found for food outlets and outdoor recreational facilities, and lowest agreement was obtained for aesthetics. In general, a better match was observed in high-residential density neighbourhoods characterized by a high density of food outlets and recreational facilities. Future studies should combine perceived and objectively measured built environment qualities to better understand the potential impact of the built environment on health, particularly in low residential density neighbourhoods

Self-defined residential neighbourhoods: size variations and correlates across five European urban regions.

The neighbourhood is recognized as an important unit of analysis in research on the relation between obesogenic environments and development of obesity. One important challenge is to define the limits of the residential neighbourhood, as perceived by study participants themselves, in order to improve our understanding of the interaction between contextual features and patterns of obesity. An innovative tool was developed in the framework of the SPOTLIGHT project to identify the boundaries of neighbourhoods as defined by participants in five European urban regions. The aims of this study were (i) to describe self-defined neighbourhood (size and overlap with predefined residential area) according to the characteristics of the sampling administrative neighbourhoods (residential density and socioeconomic status) within the five study regions and (ii) to determine which individual or/and environmental factors are associated with variations in size of self-defined neighbourhoods. Self-defined neighbourhood size varies according to both individual factors (age, educational level, length of residence and attachment to neighbourhood) and contextual factors. These findings have consequences for how residential neighbourhoods are defined and operationalized and can inform how self-defined neighbourhoods may be used in research on associations between contextual characteristics and health outcomes such as obesity

Exploring absolute and relative measures of exposure to food environments in relation to dietary patterns among European adults.

OBJECTIVE: To explore the associations of absolute and relative measures of exposure to food retailers with dietary patterns, using simpler and more complex measures. DESIGN: Cross-sectional survey. SETTING: Urban regions in Belgium, France, Hungary, the Netherlands and the UK.ParticipantsEuropean adults (n 4942). Supermarkets and local food shops were classified as 'food retailers providing healthier options'; fast-food/takeaway restaurants, cafés/bars and convenience/liquor stores as 'food retailers providing less healthy options'. Simpler exposure measures used were density of healthy and density of less healthy food retailers. More complex exposure measures used were: spatial access (combination of density and proximity) to healthy and less healthy food retailers; density of healthier food retailers relative to all food retailers; and a ratio of spatial access scores to healthier and less healthy food retailers. Outcome measures were a healthy or less healthy dietary pattern derived from a principal component analysis (based on consumption of fruits, vegetables, fish, fast foods, sweets and sweetened beverages). RESULTS: Only the highest density of less healthy food retailers was significantly associated with the less healthy dietary pattern (β = -129·6; 95 % CI -224·3, -34·8). None of the other absolute density measures nor any of the relative measures of exposures were associated with dietary patterns. CONCLUSIONS: More complex measures of exposure to food retailers did not produce stronger associations with dietary patterns. We had some indication that absolute and relative measures of exposure assess different aspects of the food environment. However, given the lack of significant findings, this needs to be further explored

Place-of-residence errors on death certificates for two contiguous U. S. counties

BACKGROUND: Based on death certificate data, the Texas Department of Health Bureau of Vital Statistics calculates age adjusted all-cause mortality rates for each Texas county yearly. In 1998 the calculated rates for two adjacent Texas counties was disparate. These counties contain one city (Amarillo) and are identical in size. This study examined the accuracy of recorded county of residence for deaths in the two counties in 1998. In our jurisdiction, the county of residence is assigned by funeral homes. METHODS: A random sample of 20% of death certificates was selected. The accuracy of the county of residence was verified by using a large area map, Tax Appraisal District records, and U.S. Census Bureau databases. Inaccuracies in recording the county or zip code of residence was recorded. RESULTS: Eighteen of 354 (5.4%) death certificates recorded the incorrect county and 21 of 354 (5.9%) of death certificates recorded the zip code improperly. There was a 14.4% county recording error rate for one county compared to a 0.82% for the other county. The zip code error rate was similar for the two counties (5.9% vs. 5.8%). Of the county errors, 83% occurred for addresses within a zip code that contained addresses in both counties. CONCLUSION: This study demonstrated a large error rate (14%) in recording county of residence for deaths in one county. A similar rate was not seen in an adjacent county. This led to significant miscalculation of mortality rates for two counties. We believe that errors may have arisen in part from use of internet programs by funeral homes to assign the county of residence. With some of these programs, the county is determined by zip code, and when a zip code straddles two counties, the program automatically assigns the county whose name appears first in the alphabet. This type of error could be avoided if funeral homes determined the county of residence from Tax Appraisal District or Census Bureau records, both of which are available on the internet. This type of error could also be avoided if vital statistics offices verified the county and zip code of residence using official sources