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Objective The early use of automated external defibrillators (AED) improves outcomes in out-of-hospital cardiac arrest (OOHCA). We investigated AED access Great Britain (GB) according to socioeconomic deprivation. Methods Cross-sectional observational study using AED location data from the British Heart Foundation Circuit registry. We calculated street network distances between all 1,677,466 postcodes in GB and the nearest AED, and used a multilevel linear mixed regression model to investigate associations between the distances from each postcode to the nearest AED and Index of Multiple Deprivation, stratified by country and according to 24-hours-7-days-a-week (24/7) access. Results 78,425 AED locations were included. Across GB, the median distance from the centre of a postcode to an AED was 726m (England: 739m, Scotland: 743m, Wales: 512m). For 24/7-access AEDs the median distances were further (991m, 994m, 570m). In Wales, the average distance to the nearest AED and 24/7 AED was shorter for the most deprived communities. In England, the average distance to the nearest AED was also shorter in the most deprived areas. There was no association between deprivation and average distance to the nearest AED in Scotland. However, the distance to the nearest 24/7 AED was greater with increased deprivation in England and Scotland. On average, a 24/7 AED is respectively 192.1m and 317.1m further in the most-deprived than least-deprived communities. Conclusions In England and Scotland there are differences in distances to the nearest 24/7-accessible AED between the most and least deprived communities. Equitable access to ‘out-of-hours’-accessible AEDs may improve outcomes for people with OOHCA.

Background Previous studies demonstrated a relation between takeaway outlet exposure and health outcomes. Individual characteristics, such as eating behaviour traits, could make some people more susceptible to the influence of the food environment. Few studies have investigated this topic. We aimed to investigate the moderating role of eating behaviour traits (cognitive restraint, uncontrolled eating and emotional eating) in the association between neighbourhood exposure to hot food takeaway outlets (hereafter referred to as takeaway outlets), and takeaway food consumption and adiposity. Methods We used cross-sectional data from a cohort in Cambridgeshire, UK (The Fenland study). Takeaway outlet exposure was derived using participants’ residential address and data from local authorities and divided into quarters. The Three Factor Eating questionnaire (TFEQ-R18) was used to measure eating behaviour traits. Primary outcomes were consumption of takeaway-like foods (derived from food frequency questionnaire), and body fat percentage (measured using dual-energy X-ray absorptiometry). Results Mean age of participants (n = 4791) was 51.0 (SD = 7.2) and 53.9% were female. Higher exposure to takeaway outlets in the neighbourhood and higher eating behaviour trait scores were independently associated with greater takeaway consumption and body fat percentage. Uncontrolled eating did not moderate the associations between takeaway outlet exposure and takeaway consumption or body fat percentage. The association between takeaway outlet exposure and takeaway consumption was slightly stronger in those with higher cognitive restraint scores, and the association between takeaway outlet exposure and body fat percentage was slightly stronger in those with lower emotional eating scores. Conclusion Eating behaviour traits and exposure to takeaway outlets were associated with greater takeaway consumption and body fat, but evidence that individuals with certain traits are more susceptible to takeaway outlets was weak. The findings indicate that interventions at both the individual and environmental levels are needed to comprehensively address unhealthy diets. Trial registry ISRCTN72077169

Background Hand transcribing nutrient composition data from websites requires extensive human resources and is prone to error. As a result, there are limited nutrient composition data on food prepared out of the home in the United Kingdom. Such data are crucial for understanding and monitoring the out-of-home food environment, which aids policy making. Automated data collection from publicly available sources offers a potential low-resource solution to address this gap. Objective In this paper, we describe the first UK longitudinal nutritional database of food prepared out of the home, MenuTracker. As large chains will be required to display calorie information on their UK menus from April 2022, we also aimed to identify which chains reported their nutritional information online in November 2021. In a case study to demonstrate the utility of MenuTracker, we estimated the proportions of menu items exceeding recommended energy and nutrient intake (eg, >600 kcal per meal). Methods We have collated nutrient composition data of menu items sold by large chain restaurants quarterly since March 2021. Large chains were defined as those with 250 employees or more (those covered by the new calorie labeling policy) or belonging to the top 100 restaurants based on sales volume. We developed scripts in Python to automate the data collection process from business websites. Various techniques were used to harvest web data and extract data from nutritional tables in PDF format. Results Automated Python programs reduced approximately 85% of manual work, totaling 500 hours saved for each wave of data collection. As of January 2022, MenuTracker has 76,405 records from 88 large out-of-home food chains at 4 different time points (ie, March, June, September, and December) in 2021. In constructing the database, we found that one-quarter (24.5%, 256/1043) of large chains, which are likely to be subject to the United Kingdom’s calorie menu labeling regulations, provided their nutritional information online in November 2021. Across these chains, 24.7% (16,391/66,295) of menu items exceeded the UK government’s recommendation of a maximum of 600 kcal for a single meal. Comparable figures were 46.4% (29,411/63,416) for saturated fat, 34.7% (21,964/63,388) for total fat, 17.6% (11,260/64,051) for carbohydrates, 17.8% (11,434/64,059) for sugar, and 35.2% (22,588/64,086) for salt. Furthermore, 0.7% to 7.1% of the menu items exceeded the maximum daily recommended intake for these nutrients. Conclusions MenuTracker is a valuable resource that harnesses the power of data science techniques to use publicly available data online. Researchers, policy makers, and consumers can use MenuTracker to understand and assess foods available from out-of-home food outlets. The methods used in development are available online and can be used to establish similar databases elsewhere.

OBJECTIVE: To quantify the sector-wide energy and nutritional differences of both adult and children's restaurant menu items in the UK and the USA in 2018. DESIGN: Cross-sectional study. SETTING: Energy and nutritional information provided on restaurant websites. PARTICIPANTS: Menu items (n 40 902) served by forty-two large UK chains and ninety-six large USA chains. RESULTS: Mean absolute energy, fat and saturated fat values were higher in USA menu items. For example, the mean adjusted per-item differences of adult menu items between the USA and the UK were 45·6 kcal for energy and 3·2 g for fat. Comparable figures for children's menu items were 43·7 kcal and 4 g. Compared with UK menu items, USA adult menu items also had higher sugar content (3·2 g, 95 % CI (0·5, 6)), and children's menu items had higher Na content (181·1 mg, 95 % CI (108·4, 253·7)). Overall, 96·8 % of UK and 95·8 % of USA menu items exceeded recommended levels for at least one of Na, fat, saturated fat or sugars. CONCLUSIONS: Menu items served by large chain restaurants had higher mean absolute levels of energy, fat and saturated fat in the USA compared with the UK. UK adult menu items were also lower in sugars compared with the USA ones and children's items lower in Na. As more than 95 % of all items were considered to have high levels of at least one nutrient of public health concern in the USA and the UK, improvements in restaurant menu items are needed in both countries.

BACKGROUND: The objective of this study was to create the Canadian Food Environment Dataset (Can-FED) and to demonstrate its validity. DATA AND METHODS: Food outlet data were extracted from Statistics Canada's Business Register (BR) in 2018. Retail food environment access measures (both absolute and relative measures) were calculated using network buffers around the centroid of 56,589 dissemination areas in Canada. A k-medians clustering approach was used to create categorical food environment variables that were easy to use and amenable to dissemination. Validity of the measures was assessed by comparing the food environment measures from Can-FED with measures created using Enhanced Points of Interest data by DMTI Spatial Inc. and data from a municipal health inspection list. Validity was also assessed by calculating the geographic variability in food environments across census metropolitan areas (CMAs) and assessing associations between CMA-level food environments and CMA-level health indicators. RESULTS: Two versions of Can-FED were created: a researcher file that must be accessed within a secure Statistics Canada environment and a general-use file available online. Agreement between Can-FED food environment measures and those derived from a proprietary dataset and a municipal health inspection list ranged from rs=0.28 for convenience store density and rs=0.53 for restaurant density. At the CMA level, there is wide geographic variation in the food environment with evidence of patterning by health indicators. INTERPRETATION: Can-FED is a valid and accessible dataset of pan-Canadian food environment measures that was created from the BR, a data source that has not been explored fully for health research.

Funder: Health Data Research UK, Funder: British Heart Foundation, Funder: Cancer Research UK, Funder: Economic and Social Research Council; funder-id: http://dx.doi.org/10.13039/501100000269, Funder: Programme Grants for Applied Research; funder-id: http://dx.doi.org/10.13039/501100007602, BACKGROUND: Restricting the advertisement of products with high fat, salt, and sugar (HFSS) content has been recommended as a policy tool to improve diet and tackle obesity, but the impact on HFSS purchasing is unknown. This study aimed to evaluate the impact of HFSS advertising restrictions, implemented across the London (UK) transport network in February 2019, on HFSS purchases. METHODS AND FINDINGS: Over 5 million take-home food and drink purchases were recorded by 1,970 households (London [intervention], n = 977; North of England [control], n = 993) randomly selected from the Kantar Fast Moving Consumer Goods panel. The intervention and control samples were similar in household characteristics but had small differences in main food shopper sex, socioeconomic position, and body mass index. Using a controlled interrupted time series design, we estimated average weekly household purchases of energy and nutrients from HFSS products in the post-intervention period (44 weeks) compared to a counterfactual constructed from the control and pre-intervention (36 weeks) series. Energy purchased from HFSS products was 6.7% (1,001.0 kcal, 95% CI 456.0 to 1,546.0) lower among intervention households compared to the counterfactual. Relative reductions in purchases of fat (57.9 g, 95% CI 22.1 to 93.7), saturated fat (26.4 g, 95% CI 12.4 to 40.4), and sugar (80.7 g, 95% CI 41.4 to 120.1) from HFSS products were also observed. Energy from chocolate and confectionery purchases was 19.4% (317.9 kcal, 95% CI 200.0 to 435.8) lower among intervention households than for the counterfactual, with corresponding relative reductions in fat (13.1 g, 95% CI 7.5 to 18.8), saturated fat (8.7 g, 95% CI 5.7 to 11.7), sugar (41.4 g, 95% CI 27.4 to 55.4), and salt (0.2 g, 95% CI 0.1 to 0.2) purchased from chocolate and confectionery. Relative reductions are in the context of secular increases in HFSS purchases in both the intervention and control areas, so the policy was associated with attenuated growth of HFSS purchases rather than absolute reduction in HFSS purchases. Study limitations include the lack of out-of-home purchases in our analyses and not being able to assess the sustainability of observed changes beyond 44 weeks. CONCLUSIONS: This study finds an association between the implementation of restrictions on outdoor HFSS advertising and relative reductions in energy, sugar, and fat purchased from HFSS products. These findings provide support for policies that restrict HFSS advertising as a tool to reduce purchases of HFSS products.

BackgroundThe study aimed to evaluate the validity and spatial accuracy of the Food Standards Agency Food Hygiene Rating online data through a field audit.MethodsA field audit was conducted in five Lower Layer Super Output Areas (LSOAs) in the North East of England. LSOAs were purposively selected from the top and bottom quintiles of the Index of Multiple Deprivation and from urban and rural areas. The FHRS data validity against the field data was measured as Positive Predictive Values (PPV) and sensitivity. Spatial accuracy was evaluated via mean difference in straight line distances between the FHRS coordinates and the field coordinates.ResultsIn all, 182 premises were present in the field, of which 162 were in the FHRS data giving a sensitivity of 89%. Eight outlets recorded in the FHRS data were absent in the field, giving a PPV of 95%.The mean difference in the geographical coordinates of the field audit compared to the FHRS was 110 m, and ConclusionsAfter an evaluation of the validity and spatial accuracy of the FHRS data, the results suggest that it is a useful dataset for surveillance of the food environment and for intervention evaluation.

Background and purpose Neighbourhood exposure to takeaway (‘fast’-) food outlets selling different cuisines may be differentially associated with diet, obesity and related disease, and contributing to population health inequalities. However research studies have not disaggregated takeaways by cuisine type. This is partly due to the substantial resource challenge of de novo manual classification of unclassified takeaway outlets at scale. We describe the development of a new model to automatically classify takeaway food outlets, by 10 major cuisine types, based on business name alone. Material and methods We used machine (deep) learning, and specifically a Long Short Term Memory variant of a Recurrent Neural Network, to develop a predictive model trained on labelled outlets (n=14,145), from an online takeaway food ordering platform. We validated the accuracy of predictions on unseen labelled outlets (n=4000) from the same source. Results Although accuracy of prediction varied by cuisine type, overall the model (or ‘classifier’) made a correct prediction approximately three out of four times. We demonstrated the potential of the classifier to public health researchers and for surveillance to support decision-making, through using it to characterise nearly 55,000 takeaway food outlets in England by cuisine type, for the first time. Conclusions Although imperfect, we successfully developed a model to classify takeaway food outlets, by 10 major cuisine types, from business name alone, using innovative data science methods. We have made the model available for use elsewhere by others, including in other contexts and to characterise other types of food outlets, and for further development., This study is funded by the National Institute of Health Research (NIHR) School of Public Health Research (Grant Reference Number PD-SPH-2015). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This work was also supported by the MRC Epidemiology Unit, University of Cambridge (Grant Reference Number MC/UU/00006/7). TBu is funded 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 of Health Research, and the Wellcome Trust (Grant Reference Number MR/K023187/1), under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged. These funders played no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

Funder: Gates Cambridge Trust; FundRef: http://dx.doi.org/10.13039/501100005370, Funder: Public Health Research Programme; FundRef: http://dx.doi.org/10.13039/501100001921, Funder: Centre for Diet and Activity Research; FundRef: http://dx.doi.org/10.13039/501100011032, Funder: Wellcome Trust; FundRef: http://dx.doi.org/10.13039/100010269, Objective: The objective of this study was to evaluate the change in energy and nutrient content of menu items sold in large UK chain restaurants (e.g., fast-food, full-service) from 2018 to 2020. Design: Observational study Setting: Energy and nutritional information of menu items served by 29 large UK chain restaurants that consistently provided this information online in all three years. Data were collected in 2018 (March – April), 2019 (April), and 2020 (October – November) from restaurant websites. Primary and secondary outcome measures: The per-item energy and nutrient (saturated fat, sugar, and salt) changes in all items available on menus (“all menu items”) and recurring items that were consistently available on menus in all three years (“core menu items”). Results: Our study included 7,770, 9,213, and 6,928 menu items from 29 chain restaurants in 2018, 2019, and 2020, respectively. Our results showed that sugar content declined from 2018 to 2020 among all menu items (per-item: -0.43g/year, 95%CI= -0.66, -0.21). This reduction in sugar was evident in beverages, sandwiches and desserts. Among core menu items (N=1,855), sugar content reduced significantly from 2018 to 2020 (per-item: -0.31g/year, 95%CI= -0.45, -0.17), especially in beverages. Energy, salt, and saturated fat content in menu items remained constant overall, in both all menu items and core menu items. Fewer food categories had significant changes in sugar, salt, and saturated fat content among core menu items, compared to all menu items. Conclusions: From 2018 to 2020, sugar content declined in restaurant menu items, which may reflect a response to the Sugar Reduction Strategy and the effects of the Soft Drinks Industry Levy. In contrast, there was little change in other nutrients. Future policies addressing the overall nutritional quality of restaurant foods, rather than single nutrients, may help the restaurant sector move towards offering healthier foods.

Background Online food delivery services facilitate ‘online’ access to food outlets that typically sell energy-dense nutrient-poor food. Greater online food outlet access might be related to the use of this purchasing format and living with excess bodyweight, however, this is not known. We aimed to investigate the association between aspects of online food outlet access and online food delivery service use, and differences according to customer sociodemographic characteristics, as well as the association between the number of food outlets accessible online and bodyweight. Methods In 2019, we used an automated data collection method to collect data on all food outlets in the UK registered with the leading online food delivery service Just Eat (n = 33,204). We linked this with contemporaneous data on food purchasing, bodyweight, and sociodemographic information collected through the International Food Policy Study (analytic sample n = 3067). We used adjusted binomial logistic, linear, and multinomial logistic regression models to examine associations. Results Adults in the UK had online access to a median of 85 food outlets (IQR: 34–181) and 85 unique types of cuisine (IQR: 64–108), and 15.1% reported online food delivery service use in the previous week. Those with the greatest number of accessible food outlets (quarter four, 182–879) had 71% greater odds of online food delivery service use (OR: 1.71; 95% CI: 1.09, 2.68) compared to those with the least (quarter one, 0–34). This pattern was evident amongst adults with a university degree (OR: 2.11; 95% CI: 1.15, 3.85), adults aged between 18 and 29 years (OR: 3.27, 95% CI: 1.59, 6.72), those living with children (OR: 1.94; 95% CI: 1.01; 3.75), and females at each level of increased exposure. We found no association between the number of unique types of cuisine accessible online and online food delivery service use, or between the number of food outlets accessible online and bodyweight. Conclusions The number of food outlets accessible online is positively associated with online food delivery service use. Adults with the highest education, younger adults, those living with children, and females, were particularly susceptible to the greatest online food outlet access. Further research is required to investigate the possible health implications of online food delivery service use.

Background Advertising of less healthy foods and drinks is hypothesised to be associated with obesity in adults and children. In February 2019, Transport for London implemented restrictions on advertisements for foods and beverages high in fat, salt or sugar across its network as part of a city-wide strategy to tackle childhood obesity. The policy was extensively debated in the press. This paper identifies arguments for and against the restrictions. Focusing on arguments against the restrictions, it then goes on to deconstruct the discursive strategies underpinning them. Methods A qualitative thematic content analysis of media coverage of the restrictions (the ‘ban’) in UK newspapers and trade press was followed by a document analysis of arguments against the ban. A search period of March 1, 2018 to May 31, 2019 covered: (i) the launch of the public consultation on the ban in May 2018; (ii) the announcement of the ban in November 2018; and (iii) its implementation in February 2019. A systematic search of printed and online publications in English distributed in the UK or published on UK-specific websites identified 152 articles. Results Arguments in favour of the ban focused on inequalities and childhood obesity. Arguments against the ban centred on two claims: that childhood obesity was not the ‘right’ priority; and that an advertising ban was not an effective way to address childhood obesity. These claims were justified via three discursive approaches: (i) claiming more ‘important’ priorities for action; (ii) disputing the science behind the ban; (iii) emphasising potential financial costs of the ban. Conclusion The discursive tactics used in media sources to argue against the ban draw on frames widely used by unhealthy commodities industries in response to structural public health interventions. Our analyses highlight the need for interventions to be framed in ways that can pre-emptively counter common criticisms., Highlights • The first analysis of news coverage of regulation of food advertisements on public transport. • Arguments against the ban claimed childhood obesity was not a valid priority and restricting advertising was ill advised. • Public health interventions need to be framed in ways that can pre-emptively counter opposition.

The distribution of food outlets within towns and cities and the provision of active travel infrastructure have been associated with health behaviours that can contribute to obesity risk. Decision-makers describe a lack of local data and research evidence as a barrier to policy adoption to improve the public's health. Online spatial data visualisation tools created by researchers can help to bridge this gap. We explored stakeholder experiences of using such tools for decision-support, with a focus on facilitators and barriers to use. We conducted 16 qualitative interviews with Public Health, Planning and Transport Planning professionals, who had used two recently-developed tools. Participants described the importance of tools being open access; their use in “story-telling”, particularly to non-experts; and more broadly their use even when imperfect. They expressed that ‘robustness’ of underpinning data was important, however this was not easily defined. Participants employed personal heuristics, principally based on endorsement and developer credibility, to determine suitability for use. We present key learning points for future developers to maximise engagement and impact., Highlights • We conducted 16 interviews with Planners, Transport planners & Public Health professionals in England. • These stakeholders were users of online spatial data visualisation tools, who deemed them acceptable. • Robustness of underpinning data was an important precursor to use but was not easily defined. • Participants assessed developer endorsement and credibility as a marker of quality, which affected use. • Barriers and facilitators to use should be considered by developers to increase engagement & impact.

Online food delivery services facilitate ‘online’ access to food outlets selling food prepared away-from-home. Online food outlet access has not previously been investigated in England or across an entire country. Systematic differences in online food outlet access could exacerbate existing health inequalities, which is a public health concern. However, this is not known. Across postcode districts in England (n = 2118), we identified and described the number of food outlets and unique cuisine types accessible online from the market leader (Just Eat). We investigated associations with area-level deprivation using adjusted negative binomial regression models. We also compared the number of food outlets accessible online with the number physically accessible in the neighbourhood (1600m Euclidean buffers of postcode district geographic centroids) and investigated associations with deprivation using an adjusted general linear model. For each outcome, we predicted means and 95% confidence intervals. In November 2019, 29,232 food outlets were registered to accept orders online. Overall, the median number of food outlets accessible online per postcode district was 63.5 (IQR; 16.0–156.0). For the number of food outlets accessible online as a percentage of the number accessible within the neighbourhood, the median was 63.4% (IQR; 35.6–96.5). Analysis using negative binomial regression showed that online food outlet access was highest in the most deprived postcode districts (n = 106.1; 95% CI: 91.9, 120.3). The number of food outlets accessible online as a percentage of those accessible within the neighbourhood was highest in the least deprived postcode districts (n = 86.2%; 95% CI: 78.6, 93.7). In England, online food outlet access is socioeconomically patterned. Further research is required to understand how online food outlet access is related to using online food delivery services., Highlights • Online food delivery services provide access to food prepared away-from-home. • We describe ‘online’ access to outlets selling this food across England. • Around one-third of food outlets were registered to accept orders online. • Online food outlet access increases existing neighbourhood food outlet access. • Online food outlet access was greatest in the most deprived areas.

Funder: NNIHR Biomedical Research Centre Cambridge: Nutrition, Diet, and Lifestyle Research Theme; Grant(s): IS-BRC-1215-20014, Background: Characteristics of the built environment, such as neighbourhood fast-food outlet exposure, are increasingly recognised as risk factors for unhealthy diet and obesity. Obesity also has a genetic component, with common genetic variants explaining a substantial proportion of population-level obesity susceptibility. However, it is not known whether and to what extent associations between fast-food outlet exposure and body weight are modified by genetic predisposition to obesity. Methods: We used data from the Fenland Study, a population-based sample of 12,435 UK adults (mean age 48.6 years). We derived a genetic risk score associated with BMI (BMI-GRS) from 96 BMI-associated single nucleotide polymorphisms. Neighbourhood fast-food exposure was defined as quartiles of counts of outlets around the home address. We used multivariable regression models to estimate the associations of each exposure, independently and in combination, with measured BMI, overweight and obesity, and investigated interactions. Results: We found independent associations between BMI-GRS and risk of overweight (RR = 1.34, 95% CI 1.23–1.47) and obesity (RR = 1.73, 95% CI 1.55–1.93), and between fast-food outlet exposure and risk of obesity (highest vs lowest quartile RR = 1.58, 95% CI 1.21–2.05). There was no evidence of an interaction of fast-food outlet exposure and genetic risk on BMI (P = 0.09), risk of overweight (P = 0.51), or risk of obesity (P = 0.27). The combination of higher BMI-GRS and highest fast-food outlet exposure was associated with 2.70 (95% CI 1.99–3.66) times greater risk of obesity. Conclusions: Our study demonstrated independent associations of both genetic obesity risk and neighbourhood fast-food outlet exposure with adiposity. These important drivers of the obesity epidemic have to date been studied in isolation. Neighbourhood fast-food outlet exposure remains a potential target of policy intervention to prevent obesity and promote the public’s health.

Online food delivery services like Just Eat and Grubhub facilitate online ordering and home delivery of food prepared away-from-home. It is poorly understood how these services are used and by whom. This study investigated the prevalence of online food delivery service use and sociodemographic characteristics of customers, in and across Australia, Canada, Mexico, the UK, and the USA. We analyzed online survey data (n = 19,378) from the International Food Policy Study, conducted in 2018. We identified respondents who reported any online food delivery service use in the past 7 days and calculated the frequency of use and number of meals ordered. We investigated whether odds of any online food delivery service use in the past 7 days differed by sociodemographic characteristics using adjusted logistic regression. Overall, 15% of respondents (n = 2929) reported online food delivery service use, with the greatest prevalence amongst respondents in Mexico (n = 839 (26%)). Online food delivery services had most frequently been used once and the median number of meals purchased through this mode of order was two. Odds of any online food delivery service use were lower per additional year of age (OR: 0.95, 95% CI: 0.94, 0.95) and greater for respondents who were male (OR: 1.50, 95% CI: 1.35, 1.66), that identified with an ethnic minority (OR: 1.57, 95% CI: 1.38, 1.78), were highly educated (OR: 1.66, 95% CI: 1.46, 1.90), or living with children (OR: 2.71, 95% CI: 2.44, 3.01). Further research is required to explore how online food delivery services may influence diet and health.

Takeaway food outlets offer limited seating and sell hot food to be consumed away from their premises. They typically serve energy-dense, nutrient-poor food. National planning guidelines in England offer the potential for local planning policies to promote healthier food environments through regulation of takeaway food outlets. Around half of English local government areas use this approach, but little is known about the process of adoption. We aimed to explore experiences and perceived success of planning policy adoption. In 2018 we recruited Planning and Public Health professionals from 16 local government areas in England and completed 26 telephone interviews. We analysed data with a thematic analysis approach. Participants felt that planning policy adoption was appropriate and can successfully regulate takeaway food outlets with the intention to improve health. They identified several facilitators and barriers towards adoption. Facilitators included internal co-operation between Planning and Public Health departments, and precedent for planning policy adoption set elsewhere. Barriers included "nanny-state" criticism, and difficulty demonstrating planning policy effectiveness. These could be considered in future guidelines to support widespread planning policy adoption.

This study is funded by the NIHR School for Public Health Research (SPHR) (Grant Reference NumberPD-SPH-2015). The National Institute for Health Research (NIHR) School for Public Health Research is a partnership between the Universities of Sheffield; Bristol; Cambridge; Imperial; and University College London; The London School for Hygiene and Tropical Medicine (LSHTM); LiLaC –a collaboration between the Universities of Liverpool and Lancaster; and Fuse -The Centre for Translational Research in Public Health, a collaboration between Newcastle, Durham, Northumbria, Sunderland and Teesside Universities. CT is funded by the National Institute for Health Research (NIHR) Applied Research Collaboration East of England. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. JA, MW and TB are supported by the MRC Epidemiology Unit, University of Cambridge [grant number MC/UU/12015/6] and Centre for Diet and Activity Research (CEDAR), a UK Clinical Research Collaboration (UKCRC) Public Health Research Centre of Excellence. Funding for CEDAR from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council, the National Institute for Health Research [grant numbers ES/G007462/1 and MR/K023187/1], and the Wellcome Trust [grant number 087636/Z/08/Z], under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged. FdV is partly funded by National Institute for Health Research Applied Research Collaboration West (NIHR ARC West) at University Hospitals Bristol NHS Foundation Trust. AAL is a member of Fuse, the Centre for Translational Research in Public Health (www.fuse.ac.uk). Fuse is a Public Health Research Centre of Excellence funded by the five North East Universities of Durham, Newcastle, Northumbria, Sunderland and Teesside. SC is funded by Health Data Research UK (HDR-UK). HDR-UK is an initiative funded by the UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities. The views expressed are those of the authors and do not necessarily represent those of any of the above named funders. The funders had no role in the design of the study, or collection, analysis and interpretation of the data, or in the decision to publish, or in writing the manuscript.

BACKGROUND: We assessed manager perceptions of food security and obesity in young children attending nurseries across England, assessing spatial differences by area-level deprivation. METHODS: We conducted an adjusted multinomial logistic regression and an adjusted geographically weighted logistic regression examining the odds of a manager perceiving obesity, food insecurity, or both as a problem among children in care measured via a mailed survey. RESULTS: 851 (54.3%) managers returned the survey. A nursery being in the highest tertile of area-level deprivation was associated with a 1.89 (95% CI 1.00, 3.57) greater odds of perceiving obesity as a problem, a 3.06 (95% CI 1.94, 4.84) greater odds of perceiving food insecurity as a problem, and a 8.39 (95% CI 4.36, 16.15) greater odds of perceiving both as a problem, compared with the lowest tertile. CONCLUSIONS: We observed differences in manager perception by area-level deprivation, but the relationship was especially pronounced for food insecurity.

Takeaway food outlets typically sell hot food, ordered and paid for at the till, for consumption off the premises due to limited seating provision. Growing numbers of these outlets has raised concerns about their impact on diet and weight gain. This has led to proposals to regulate their proliferation through urban planning. We conducted a census of local government areas in England with planning power (n = 325) to identify planning policies specifically addressing takeaway food outlets, with a ‘health’, and ‘non-health’ focus. We reviewed planning policies using content analysis, and developed a typology. One hundred and sixty-four (50.5%) local government areas had a policy specifically targeting takeaway food outlets; of these, 56 (34.1%) focused on health. Our typology revealed two main foci: ‘Place’ with five targeted locations and ‘Strategy’ with four categories of approach. The most common health-focused approach was describing exclusion zones around places for children and families (n = 33). Non-health focused approaches primarily involved minimising negative impacts associated with takeaway food outlets within a local government area boundary (n = 146). To our knowledge, this is the first census of planning policies explicitly focused on takeaway food outlets in England. Further work is required to determine why different approaches are adopted in different places and their acceptability and impact., Highlights • We conducted a census of English local government areas (n = 325) to locate planning policies regulating takeaway food outlets. • 164 (50.5%) local government areas had a policy specifically targeting takeaway food outlets, 56 (34.1%) focused on health. • In a typology of approaches, we present two foci: ‘Place’ with five targeted locations and ‘Strategy’ with four categories. • The most common health-focused approach was exclusion zones around places for children and families (n = 33). • We found a range of current planning practice for regulating takeaway food outlets in England, more than previously estimated.

Eating away from home is a risk factor for poor diet quality and obesity. With an ever-increasing proportion of household food spend directed toward eating out, the proliferation of these food establishments may contribute to their use, a potential precursor to less healthy food choices and low overall diet quality. However few studies are conducted at the national level and across a range of away from home food sources. The purpose of this study was to examine the association between the density of away from home food establishments (e.g., restaurants, fast food outlets and caf&eacute, s) and household spend on away from home food within a nationally representative sample for England, UK. A cross-sectional analysis of data from Wave 1 of the UK Household Longitudinal Survey (n = 24,047 adults aged &ge, 19y) was conducted. Exposure was characterised as the density of away from home food establishments to all other food sources within 1 mile of the home, divided into quintiles (Q1 as lowest exposure and Q5 as highest exposure). The primary outcome included households with a high away from home equivalised monthly food spend (&ge, 25% of total food spend). Logistic regression was used to estimate associations between away from home food establishment exposure and high away from home food spend. Away from home food establishment density was significantly associated with a greater odds of high monthly food spend (Q3: OR = 1.18, 95% CI = 1.07, 1.30, Q4: OR = 1.30, 95% CI = 1.18, 1.43, and Q5: OR = 1.52, 95% CI = 1.37, 1.68) with attenuation after controlling for known socioeconomic confounders (Q4: OR = 1.13, 95% CI = 1.02, 1.25, and Q5: OR = 1.16, 95% CI = 1.04, 1.30) compared to those least exposed (Q1). Those most exposed to away from home food establishments had a 16% greater odds of allocating more than 25% of household food spend on away from home food sources. This study provides one of the first analyses at the national level to examine the role of the local food environment in relation to household food spend, a potential precursor to diet quality and health.

Data visualisation is becoming an established way to drive discovery and develop theory and hypotheses among researchers. Data visualisations can also serve as tools for knowledge translation with policy makers, who are increasingly using data and evidence to inform and implement policy. For obesity policy, data visualisation tools can help policy makers and other professionals understand the socio-spatial distribution of risk factors and quantify social and environmental conditions that are recognised upstream determinants of diet, activity and obesity. The demand for and use of data visualisation tools can be driven by an identified policy need, which can be met by researchers and data scientists. Alternatively, researchers are developing and testing data visualisations, which may be subsequently adapted for, and adopted by policy users. Two recently-released interactive data visualisation tools in the UK illustrate these points. The Propensity to Cycle Tool (PCT) was developed with funding from the UK government to inform the investment of cycling infrastructure in England. The Food environment assessment tool (Feat) evolved as a translational output from a programme of epidemiological research. This article uses PCT and Feat as case studies, drawing parallels and contrasts between them. We discuss these two tools from policy context and scientific underpinnings, to product launch and evaluation. We review challenges inherent in the development and dissemination of data tools for policy, including the need for technical expertise, feedback integration, long-term sustainability, and provision of training and user support. Finally, we attempt to derive learning points that may help overcome challenges associated with the creation, dissemination and sustaining of data tools for policy. We contend that, despite a number of challenges, data tools provide a novel gateway between researchers and a range of stakeholders, who are seeking ways of accessing and using evidence to inform obesity programs and policies.

Background Household income (as a marker of socioeconomic position) and neighbourhood fast-food outlet exposure may be related to diet and body weight, which are key risk factors for non-communicable diseases. However, the research evidence is equivocal. Moreover, understanding the double burden of these factors is a matter of public health importance. The purpose of this study was to test associations of neighbourhood fast-food outlet exposure and household income, in relation to frequency of consumption of processed meat and multiple measures of adiposity, and to examine possible interactions. Methods We employed an observational, cross-sectional study design. In a cohort of 51,361 adults aged 38–72 years in Greater London, UK, we jointly classified participants based on household income (£/year, four groups) and GIS-derived neighbourhood fast-food outlet proportion (counts of fast-food outlets as a percentage of all food outlets, quartiles). Multivariable regression models estimated main effects and interactions (additive and multiplicative) of household income and fast-food outlet proportion on odds of self-reported frequent processed meat consumption (> 1/week), measured BMI (kg/m2), body fat (%), and odds of obesity (BMI ≥ 30). Results Income and fast-food proportion were independently, systematically associated with BMI, body fat, obesity and frequent processed meat consumption. Odds of obesity were greater for lowest income participants compared to highest (OR = 1.54, 95% CI: 1.41, 1.69) and for those most-exposed to fast-food outlets compared to least-exposed (OR = 1.51, 95% CI: 1.40, 1.64). In jointly classified models, lowest income and highest fast-food outlet proportion in combination were associated with greater odds of obesity (OR = 2.43, 95% CI: 2.09, 2.84), with relative excess risk due to interaction (RERI = 0.03). Results were similar for frequent processed meat consumption models. There was no evidence of interaction on a multiplicative scale between fast-food outlet proportion and household income on each of BMI (P = 0.230), obesity (P = 0.054) and frequent processed meat consumption (P = 0.725). Conclusions Our study demonstrated independent associations of neighbourhood fast-food outlet exposure and household income, in relation to diet and multiple objective measures of adiposity, in a large sample of UK adults. Moreover, we provide evidence of the double burden of low income and an unhealthy neighbourhood food environment, furthering our understanding of how these factors contribute jointly to social inequalities in health. Electronic supplementary material The online version of this article (10.1186/s12966-018-0699-8) contains supplementary material, which is available to authorized users.

Background Household income (as a marker of socioeconomic position) and neighbourhood fast-food outlet exposure may be related to diet and body weight, which are key risk factors for non-communicable diseases. However, the research evidence is equivocal. Moreover, understanding the double burden of these factors is a matter of public health importance. The purpose of this study was to test associations of neighbourhood fast-food outlet exposure and household income, in relation to frequency of consumption of processed meat and multiple measures of adiposity, and to examine possible interactions. Methods We employed an observational, cross-sectional study design. In a cohort of 51,361 adults aged 38–72 years in Greater London, UK, we jointly classified participants based on household income (£/year, four groups) and GIS-derived neighbourhood fast-food outlet proportion (counts of fast-food outlets as a percentage of all food outlets, quartiles). Multivariable regression models estimated main effects and interactions (additive and multiplicative) of household income and fast-food outlet proportion on odds of self-reported frequent processed meat consumption (> 1/week), measured BMI (kg/m2), body fat (%), and odds of obesity (BMI ≥ 30). Results Income and fast-food proportion were independently, systematically associated with BMI, body fat, obesity and frequent processed meat consumption. Odds of obesity were greater for lowest income participants compared to highest (OR = 1.54, 95% CI: 1.41, 1.69) and for those most-exposed to fast-food outlets compared to least-exposed (OR = 1.51, 95% CI: 1.40, 1.64). In jointly classified models, lowest income and highest fast-food outlet proportion in combination were associated with greater odds of obesity (OR = 2.43, 95% CI: 2.09, 2.84), with relative excess risk due to interaction (RERI = 0.03). Results were similar for frequent processed meat consumption models. There was no evidence of interaction on a multiplicative scale between fast-food outlet proportion and household income on each of BMI (P = 0.230), obesity (P = 0.054) and frequent processed meat consumption (P = 0.725). Conclusions Our study demonstrated independent associations of neighbourhood fast-food outlet exposure and household income, in relation to diet and multiple objective measures of adiposity, in a large sample of UK adults. Moreover, we provide evidence of the double burden of low income and an unhealthy neighbourhood food environment, furthering our understanding of how these factors contribute jointly to social inequalities in health.

Garden-based interventions may increase child intake of fruits and vegetables and offset food costs, but few have been conducted in early care and education (ECE). This study assessed whether nurseries were interested in and perceived any barriers to growing fruits and vegetables. Surveys were mailed to a cross-sectional sample of nurseries in 2012&ndash, 2013 throughout England. Nurseries were stratified based on socioeconomic status as most, middle, or least deprived areas. We fit logistic regression models to assess the odds of nurseries interested in growing fruits and vegetables and perceiving any barriers, by deprivation tertile. A total of 851 surveys were returned (54% response). Most nurseries (81%) were interested in growing fruits and vegetables. After adjustment, there was no difference in interest in the middle (OR 1.55, CI 0.84, 2.78, p = 0.16) or most (OR 1.05, CI 0.62, 1.78, p = 0.87) deprived areas, compared to the least deprived. Nurseries reported barriers to growing fruits and vegetables, including space (42%), expertise (26%), and time (16%). Those in the most deprived areas were more likely to report space as a barrier (OR 2.02, 95% CI 1.12, 3.66, p = 0.02). Nurseries in the most deprived areas may need creative solutions for growing fruits and vegetables in small spaces.

Summary Background The built environment might be associated with development of obesity and related disorders. We examined whether neighbourhood exposure to fast-food outlets and physical activity facilities were associated with adiposity in UK adults. Methods We used cross-sectional observational data from UK Biobank. Participants were aged 40–70 years and attended 21 assessment centres between 2006 and 2010. Using linked data on environments around each participant's residential address, we examined whether density of physical activity facilities and proximity to fast-food outlets were associated with waist circumference, body-mass index (BMI), and body fat percentage. We used multilevel linear regression models adjusted for potential confounders, and conducted several sensitivity analyses. Findings Complete case sample sizes were 401 917 (waist circumference models), 401 435 (BMI), and 395 640 (body fat percentage). Greater density of physical activity facilities within 1000 m of home was independently associated with smaller waist circumference and lower BMI and body fat percentage. Compared with people with no nearby facilities, those with at least six facilities close to home had 1·22 cm smaller waist circumference (95% CI −1·64 to −0·80), 0·57 kg/m2 lower BMI (−0·74 to −0·39), and 0·81 percentage points lower body fat (−1·03 to −0·59). Living further from a fast-food outlet was weakly associated with waist circumference and BMI, mostly among women. Compared with people living fewer than 500 m from a fast-food outlet, those living at least 2000 m away had 0·26 cm smaller waist circumference (−0·52 to 0·01). Interpretation This study shows strong associations between high densities of physical activity facilities and lower adiposity for adults in mid-life. We observed weaker associations for access to fast food, but these are likely to be underestimated owing to limitations of the food environment measure. Policy makers should consider interventions aimed at tackling the obesogenic built environment. Funding Commonwealth Scholarship Commission, Wellcome Trust Institutional Strategic Support Fund.

Highlights • Recent national guidelines call for improved nutrition in child care in England. • Many nurseries were not meeting national guidelines for foods and beverages served. • Comparisons were made between nurseries in deprived areas and high resource areas. • Nurseries in more deprived areas reported serving more healthy and unhealthy foods. • Just over half of providers sought nutrition guidance from national reports., Recent national guidelines call for improved nutrition within early years settings. The aim of this cross-sectional study was to describe foods and beverages served in nurseries, assess provider behaviors related to feeding, and compare these practices to national guidelines. We administered a mailed survey to a random sample of nurseries across England, stratifying by tertile of deprivation. A total of 851 nurseries returned the survey (54.3% response rate). We fitted separate multivariate logistic regression models to estimate the association of deprivation with each of the 13 food and beverage guidelines and the seven provider behavior guidelines. We also conducted a joint F-test for any deprivation effect, to evaluate the effect of the guidelines combined. After adjusting for confounders, we observed differences in the frequency of nurseries that reported serving healthier foods across the tertiles of deprivation (p = 0.02 for joint F test). These adjusted results were driven mainly by nurseries in more deprived areas serving more whole grains (OR 1.57 (95% CI 1.00, 2.46)) and legumes, pulses, and lentils (1.40 (1.01, 2.14)). We also observed differences in the frequency of nurseries reporting more provider behaviors consistent with national guidelines across the tertiles of deprivation (p = 0.01 for joint F test). Nurseries in more deprived areas were more likely to dilute juice with water (2.35 (1.48, 3.73)), allow children to select their own portions (1.09 (1.06, 1.58)), and sit with children during meals (1.84 (1.07, 3.15)). While nurseries in the most deprived areas reported serving more healthy foods, a large percentage were still not meeting national guidelines. Policy and intervention efforts may increase compliance with national guidelines in nurseries in more deprived areas, and across England.

$\textbf{Introduction:}$ Eating meals away from home has been associated with the consumption of unhealthy foods and increased body weight. However, more rigorous assessment of the contribution of different types of away-from-home food establishments to overall diet quality and obesity is minimal. This study examined usage of these food establishments, accordance to the Dietary Approaches to Stop Hypertension (DASH) dietary pattern and obesity status in a nationally representative sample of adults in the United Kingdom. $\textbf{Methods:}$ A cross-sectional analysis of data from a national survey (N=2,083 aged ≥19 years, from 2008 to 2012) with dietary intake measured using a 4-day food diary, and height and weight measured objectively. Exposures included usage of (i.e., by proportion of energy) all away-from-home food establishments combined, and fast-food outlets, restaurants, and cafés separately. Outcomes included accordance with the DASH diet, and obesity status. Multivariable logistic regressions were conducted in 2016 to estimate associations between food establishments, diet quality, and obesity. $\textbf{Results:}$ People consuming a higher proportion of energy from any away-from-home food establishment had lower odds of DASH accordance (OR=0.45, 95% CI=0.31, 0.67) and increased odds of obesity (OR=1.48, 95% CI=1.10, 1.99). After adjustment, only use of fast-food outlets was significantly associated with lower odds of DASH accordance (OR=0.48, 95%=0.33, 0.69) and higher odds of obesity (OR=1.30, 95% CI=1.01, 1.69). $\textbf{Conclusions:}$ Although a greater reliance on eating away-from-home is associated with less-healthy diets and obesity, dietary public health interventions that target these food establishments may be most effective if they focus on modifying the use of fast-food outlets., The 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, the National Institute for Health Research, and the Wellcome Trust, under the auspices of the UK Clinical Research Collaboration [RG72218], is gratefully acknowledged. In addition, Dr. Penney’s PhD studentship is generously supported by the Cambridge International Scholarship, a scheme funded by the Cambridge Commonwealth, European, and International Trust.

U.S. policy initiatives have sought to improve health through attracting neighborhood supermarket investment. Little evidence exists to suggest that 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 9702 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 1.33, 95% CI: 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, compared to 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.

With 796,500 places available for children in England, pre-school nurseries could serve as an important setting for population-wide dietary intervention. It is critical to understand the determinants of healthy food provision in this setting, which may include access to food stores. This study examined the association between objective, GIS-derived supermarket proximity and fruit and vegetable serving frequency, using data from 623 English nurseries. Overall, 116 (18%) nurseries served fruits and vegetables infrequently (, Highlights • We examined the relationship between nursery supermarket access and the provision of fruits and vegetables to children. • We used data from a representative sample of 623 English nurseries, alongside objectively measured supermarket proximity. • 18% of nurseries served fruits and vegetables infrequently, but this differed systematically by supermarket proximity. • Nurseries farthest from their nearest supermarket were significantly less likely to serve fruits and vegetables frequently. • Supermarket access may be important for nurseries in meeting guidelines for the provision of fruits and vegetables.

$\textbf{Introduction:}$ 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.

Policies supporting breastfeeding vary by state, but little is known about the geographical aspects of this variation. This study describes state breastfeeding licensing and administrative regulations targeting child care settings, compares regulations with national standards, and examines the spatial patterning and clustering of these regulations throughout the United States (US). We compared regulations for child care centers (centers) and family child care homes (homes) with national standards for: (1) general breastfeeding support; (2) designated place for breastfeeding; (3) no solids before infants are four months of age; and (4) no formula for breastfed infants without parent permission. We scored state regulations as 0 = standard not addressed, 1 = standard partially addressed, and 2 = standard fully addressed. We considered each regulation individually, and also summed scores to provide an overall rating of regulations by state. We mapped regulations using geographic information systems technology, and explored overall and local spatial autocorrelation using global and local variants of Moran's I. Five states had regulations for centers and two for homes that addressed all four standards. Mean regulation scores were 0.35, 0.20, 0.98, 0.74 for centers, and 0.17, 0.15, 0.79, 0.58 for homes. Local Moran's I revealed that New York and Pennsylvania had substantially stronger regulations than their adjacent states, while Florida had weaker regulations than its neighbors. Overall, few states had regulations that met breastfeeding standards. We identified some patterns of spatial correlation, suggesting avenues for future research to better understand distributions of regulations across the US.

BACKGROUND: Greater exposures to fast-food outlets and lower levels of education are independently associated with less healthy diets and obesity. Little is known about the interplay between these environmental and individual factors. OBJECTIVE: The purpose of this study was to test whether observed differences in fast-food consumption and obesity by fast-food outlet exposure are moderated by educational attainment. DESIGN: In a population-based cohort of 5958 adults aged 29-62 y in Cambridgeshire, United Kingdom, we used educational attainment-stratified regression models to estimate the food-frequency questionnaire-derived consumption of energy-dense "fast foods" (g/d) typically sold in fast-food restaurants and measured body mass index (BMI; in kg/m(2)) across geographic information system-derived home and work fast-food exposure quartiles. We used logistic regression to estimate the odds of obesity (BMI ≥30) and calculated relative excess risk due to interaction (RERI) on an additive scale. Participant data were collected during 2005-2013 and analyzed in 2015. RESULTS: Greater fast-food consumption, BMI, and odds of obesity were associated with greater fast-food outlet exposure and a lower educational level. Fast-food consumption and BMI were significantly different across education groups at all levels of fast-food outlet exposure (P < 0.05). High fast-food outlet exposure amplified differences in fast-food consumption across levels of education. The relation between fast-food outlet exposure and obesity was only significant among those who were least educated (OR: 2.05; 95% CI: 1.08, 3.87; RERI = 0.88), which suggested a positive additive interaction between education and fast-food outlet exposure. CONCLUSION: These findings suggest that efforts to improve diets and health through neighborhood-level fast-food outlet regulation might be effective across socioeconomic groups and may serve to reduce observed socioeconomic inequalities in diet and obesity.

OBJECTIVES: To examine the association between environmental exposure to takeaway food outlets, takeaway food consumption, and body weight, while accounting for home, work place, and commuting route environments. DESIGN: Population based, cross sectional study, using data on individual participants' diet and weight, and objective metrics of food environment exposure. PARTICIPANTS: Working adults participating in the Fenland Study, Cambridgeshire, UK (n = 5442, aged 29-62 years), who provided home and work addresses and commuting preferences. Takeaway food outlet exposure was derived using data from local authorities for individual environmental domains (at home, at work, and along commuting routes (the shortest route between home and work)), and for exposure in all three domains combined. Exposure was divided into quarters (Q); Q1 being the least exposed and Q4 being the most exposed. MAIN OUTCOME MEASURES: Self reported consumption of takeaway type food (g/day; pizza, burgers, fried foods, and chips) using food frequency questionnaires, measured body mass index, and cut-offs for body mass index as defined by the World Health Organization. RESULTS: In multiple linear regression models, exposure to takeaway food outlets was positively associated with consumption of takeaway food. Among domains at home, at work, and along commuting routes, associations were strongest in work environments (Q4 v Q1; β coefficient = 5.3 g/day, 95% confidence interval 1.6 to 8.7; P

Background Interest in the role of food environments in shaping food consumption behaviours has grown in recent years. However, commonly used secondary food environment data sources have not yet been fully evaluated for completeness and systematic biases. This paper assessed the accuracy of UK Points of Interest (POI) data, compared to local council food outlet data for the county of Cambridgeshire. Methods Percentage agreement, positive predictive values (PPVs) and sensitivities were calculated for all food outlets across the study area, by outlet type, and across urban/rural/SES divisions. Results Percentage agreement by outlet type (29.7-63.5%) differed significantly to overall percentage agreement (49%), differed significantly in rural areas (43%) compared to urban (52.8%), and by SES quintiles. POI data had an overall PPV of 74.9%, differing significantly for Convenience Stores (57.9%), Specialist Stores (68.3%), and Restaurants (82.6%). POI showed an overall ‘moderate’ sensitivity, although this varied significantly by outlet type. Whilst sensitivies by urban/rural/SES divides varied significantly from urban and least deprived reference categories, values remained ‘moderate’. Conclusions Results suggest POI is a viable alternative to council data, particularly in terms of PPVs, which remain robust across urban/rural and SES divides. Most variation in completeness was by outlet type; lowest levels were for Convenience Stores, which are commonly cited as ‘obesogenic’.

Socioeconomic disparities in the food environment are known to exist but with little understanding of change over time. This study investigated the density of takeaway food outlets and presence of supermarkets in Norfolk, UK between 1990 and 2008. Data on food retail outlet locations were collected from telephone directories and aggregated within electoral wards. Supermarket presence was not associated with area deprivation over time. Takeaway food outlet density increased overall, and was significantly higher in more deprived areas at all time points; furthermore, socioeconomic disparities in takeaway food outlet density increased across the study period. These findings add to existing evidence and help assess the need for environmental interventions to reduce disparities in the prevalence of unhealthy food outlets.

Background Retail food environments (foodscapes) are a recognised determinant of eating behaviours and may contribute to inequalities in diet. However, findings from studies measuring socioeconomic inequality in the foodscape have been mixed, which may be due to methodological differences. The aim of this cross-sectional study was to compare exposure to the foodscape by socioeconomic position using different measures, to test whether the presence, direction or amplitude of differences was sensitive to the choice of foodscape metric or socioeconomic indicator. Methods A sample of 10,429 adults aged 30–64 years with valid home address data were obtained from the Fenland Study, UK. Of this sample, 7270 participants also had valid work location data. The sample was linked to data on food outlets obtained from local government records. Foodscape metrics included count, density and proximity of takeaway outlets and supermarkets, and the percentage of takeaway outlets relative to all food outlets. Exposure metrics were area-based (lower super output areas), and person-centred (proximity to nearest; Euclidean and Network buffers at 800 m, 1 km, and 1 mile). Person-centred buffers were constructed using home and work locations. Socioeconomic status was measured at the area-level (2010 Index of Multiple Deprivation) and the individual-level (highest educational attainment; equivalised household income). Participants were classified into socioeconomic groups and average exposures estimated. Results were analysed using the statistical and percent differences between the highest and lowest socioeconomic groups. Results In area-based measures, the most deprived areas contained higher takeaway outlet densities (p

BACKGROUND: Socio-ecological models of behaviour suggest that dietary behaviours are potentially shaped by exposure to the food environment ('foodscape'). Research on associations between the foodscape and diet and health has largely focussed on foodscapes around the home, despite recognition that non-home environments are likely to be important in a more complete assessment of foodscape exposure. This paper characterises and describes foodscape exposure of different types, at home, at work, and along commuting routes for a sample of working adults in Cambridgeshire, UK. METHODS: Home and work locations, and transport habits for 2,696 adults aged 29-60 were drawn from the Fenland Study, UK. Food outlet locations were obtained from local councils and classified by type - we focus on convenience stores, restaurants, supermarkets and takeaway food outlets. Density of and proximity to food outlets was characterised at home and work. Commuting routes were modelled based on the shortest street network distance between home and work, with exposure (counts of food outlets) that accounted for travel mode and frequency. We describe these three domains of food environment exposure using descriptive and inferential statistics. RESULTS: For all types of food outlet, we found very different foodscapes around homes and workplaces (with overall outlet exposure at work 125% higher), as well as a potentially substantial exposure contribution from commuting routes. On average, work and commuting environments each contributed to foodscape exposure at least equally to residential neighbourhoods, which only accounted for roughly 30% of total exposure. Furthermore, for participants with highest overall exposure to takeaway food outlets, workplaces accounted for most of the exposure. Levels of relative exposure between home, work and commuting environments were poorly correlated. CONCLUSIONS: Relying solely on residential neighbourhood characterisation greatly underestimated total foodscape exposure in this sample, with levels of home exposure unrelated to levels of away from home exposure. Such mis-estimation is likely to be expressed in analyses as attenuated parameter estimates, suggesting a minimal 'environmental' contribution to outcomes of interest. Future work should aim to assess exposure more completely through characterising environments beyond the residential neighbourhood, where behaviours related to food consumption are likely to occur.

BACKGROUND: This study examined whether objective measures of food, physical activity and built environment exposures, in home and non-home settings, contribute to children's body weight. Further, comparing GPS and GIS measures of environmental exposures along routes to and from school, we tested for evidence of selective daily mobility bias when using GPS data. METHODS: This study is a cross-sectional analysis, using objective assessments of body weight in relation to multiple environmental exposures. Data presented are from a sample of 94 school-aged children, aged 5-11 years. Children's heights and weights were measured by trained researchers, and used to calculate BMI z-scores. Participants wore a GPS device for one full week. Environmental exposures were estimated within home and school neighbourhoods, and along GIS (modelled) and GPS (actual) routes from home to school. We directly compared associations between BMI and GIS-modelled versus GPS-derived environmental exposures. The study was conducted in Mebane and Mount Airy, North Carolina, USA, in 2011. RESULTS: In adjusted regression models, greater school walkability was associated with significantly lower mean BMI. Greater home walkability was associated with increased BMI, as was greater school access to green space. Adjusted associations between BMI and route exposure characteristics were null. The use of GPS-actual route exposures did not appear to confound associations between environmental exposures and BMI in this sample. CONCLUSIONS: This study found few associations between environmental exposures in home, school and commuting domains and body weight in children. However, walkability of the school neighbourhood may be important. Of the other significant associations observed, some were in unexpected directions. Importantly, we found no evidence of selective daily mobility bias in this sample, although our study design is in need of replication in a free-living adult sample.

Background In recent years, alongside the exponential increase in the prevalence of overweight and obesity, there has been a change in the food environment (foodscape). This research focuses on methods used to measure and classify the foodscape. This paper describes the foodscape across urban/rural and socio-economic divides. It examines the validity of a database of food outlets obtained from Local Authority sources (secondary level & desk based), across urban/rural and socio-economic divides by conducting fieldwork (ground-truthing). Additionally this paper tests the efficacy of using a desk based classification system to describe food outlets, compared with ground-truthing. Methods Six geographically defined study areas were purposively selected within North East England consisting of two Lower Super Output Areas (LSOAs; a small administrative geography) each. Lists of food outlets were obtained from relevant Local Authorities (secondary level & desk based) and fieldwork (ground-truthing) was conducted. Food outlets were classified using an existing tool. Positive predictive values (PPVs) and sensitivity analysis was conducted to explore validation of secondary data sources. Agreement between 'desk' and 'field' based classifications of food outlets were assessed. Results There were 438 food outlets within all study areas; the urban low socio-economic status (SES) area had the highest number of total outlets (n = 210) and the rural high SES area had the least (n = 19). Differences in the types of outlets across areas were observed. Comparing the Local Authority list to fieldwork across the geographical areas resulted in a range of PPV values obtained; with the highest in urban low SES areas (87%) and the lowest in Rural mixed SES (79%). While sensitivity ranged from 95% in the rural mixed SES area to 60% in the rural low SES area. There were no significant associations between field/desk percentage agreements across any of the divides. Conclusion Despite the relatively small number of areas, this work furthers our understanding of the validity of using secondary data sources to identify and classify the foodscape in a variety of geographical settings. While classification of the foodscape using secondary Local Authority food outlet data with information obtained from the internet, is not without its difficulties, desk based classification would be an acceptable alternative to fieldwork, although it should be used with caution.