Frequent consumption of takeaway meals has been found to be negatively associated with the diet quality of British adolescents. The Food Environment Policy Index believes that strengthening planning policies to discourage unhealthy fast food is a priority and will have a significant influence on mitigating diet-related diseases and obesity. Planning policies for limiting the clustering of hot food takeaways (HFTs) around schools exists in the UK. However, long-term effectiveness and their impact on health should be studied and explored as few studies have investigated the longitudinal associations with health outcomes among adolescents attending secondary school particularly in the UK. Therefore, this study aimed to investigate the relationships between the density, proximity and accessibility of takeaway outlets and the BMI and body fat percentage of UK adolescents from the Avon Longitudinal Study of Parents and Children study conducted between 2005 and 2011. In total, 52 state-funded schools with 1382 participants (44.5% male) were included in this study. A Geographical Information System was used to locate all schools and takeaways in the region and to measure the density within 800and 1000metres and proximity scores, applying the road network method. In addition, the Hansen Index was used to measure the accessibility score of each school to all takeaways in the region. The statistical analysis tests, including linear and logistic regression tests, were conducted to explore the associations between availability, proximity, and accessibility of HFTs at baseline (2007) when the adolescents were 13 years and BMI z–score and body fat percentage status at 15 and17 years in 2009 and 2011; using Stata software, Version 15.0. Adjusted linear regression showed non-significant associations between availability of HFTs and BMI z–score and body fat percentage at 15 or 17 years when using either an 800or 1000-metre buffer. An adjusted logistic regression showed non-significant associations between availability of HFTs within 800and 1000metres and risk of being obese at 15 years. However, the adjusted logistic analysis showed protective effects between the availability of HFTs within 800and 1000metres and the risk of being obese, particularly at 17 years, which was the opposite effect expected. For example, the odds of being obese and attending schools with HFTs was 0.56 (95% CI; 0.41, 0.76) at age 17 years. The proximity of HFTs showed no associations with BMI z–score. Accessibility of HFTs showed small negative but significant associations with BMI z–score and attenuated results with body fatness. Overall results showed conflicting findings, and further exploration is still needed. An intensive understanding of the effect of the food environment, particularly around secondary schools, is needed, especially using more recent data for both the exposure and health outcomes.

The UK government plans to limit price-based and location-based promotions for products high in saturated fat, salt and sugars. The 2004/2005 UK Nutrient Profiling Model (NPM) is the proposed legislative basis, but may be superseded by the draft 2018 NPM. This study develops an algorithm to apply both NPMs to a large food composition database (FCDB), and assesses implementation challenges. UK NPMs were applied algorithmically to the myfood24 FCDB, representing similar to 45 000 retail products. Pass rates - indicating free or restricted promotions - and micronutrient compositions were compared. Challenges were assessed, and recommendations addressed the legislation's public consultation questions. For products in scope (75% of total), 6% fewer passed the 2018 NPM (36%, P < 0.001) compared with the 2004/2005 NPM (42%). Beverages showed the greatest reduction in pass rate (75%). Under both models, micronutrient contents (per 100 g of product) were generally lower for products that passed; except folate, vitamin C and vitamin D were no different for passed and failed products. Compared with products passing the 2004/2005 NPM, products passing the 2018 NPM on average had marginally higher amounts of iron (0.05 mg, 95% CI: 0.02, 0.08, P < 0.001) and magnesium (1.00 mg, 95% CI: 0.00, 1.17, P = 0.029), but marginally lower levels of calcium (-0.42 mg, 95% CI: -2.00, -0.40, P = 0.025). Missing ingredient information and heterogeneous product categories were challenges for both NPMs. Free sugars calculation further complicated 2018 NPM application. To balance feasibility and public health benefit, the proposed legislative basis may not be appropriate.

Background Obesity prevalence of epidemic proportions continues to be a major public health problem globally. Better understanding of the spatial and social variation in obesity is essential in order to support changes to policy or our environment to reduce obesity prevalence. This paper uses a geodemographic classification – which combines demographic characteristics with a small area geographic unit – to profile weight status and estimate small-area obesity prevalence in Australia and the US. Methods This study is a cross sectional analysis of two large studies; the Australian Longitudinal Study on Women’s Health (ALSWH) and the Seattle Obesity Study (SOS1). Descriptive statistics, chi2 and Kruskal Wallis test for difference, linear and multinomial logistic regression were carried out using Stata 12 statistical software. ArcMap10 was used to: (1) match the study participants to a CAMEO geodemographic identifier, using the longitude and latitude of their home address and (2) to visualise the obesity estimates for Newcastle (Australia) and Seattle (US). CAMEO is a commercially available geodemographic classification. Results Both ALSWH and SOS1 had under and over-representation in certain CAMEO groups compared to national representation, but each group contains high numbers of individuals suggesting results are robust. Demographic characteristics of the study participants are in line with those expected in the corresponding CAMEO groups. In both studies significant differences in body mass index across CAMEO groups exists (p < 0.001). In Australia, the Diverse Low Income Urban Communities had twice the odds of being obese than the Affluent Urban Professionals (OR = 2.24 (95% CI 1.55 to 3.23)). In the US, compared to the American Aristocracy, the Enterprising Households (OR 1.97 (95% CI 1.25 to 3.09)), Comfortable Communities (OR 2.01 (95% CI 1.25 to 3.22)) and Dynamic Neighbourhoods (2.09 (1.30 to 3.36)) also had twice the odds of being obese. Comprehensive obesity maps for Newcastle and Seattle at a small area geographical resolution were produced, identifying neighbourhoods with likely high prevalence of obesity. Conclusion Geodemographic classifications, such as CAMEO, combined with survey data offer promising solutions for profiling obesity outcomes worldwide which could facilitate effective targeting of potential public health interventions at a neighbourhood geography scale.

BACKGROUND:Attitudes towards physical activity are largely developed during childhood meaning that school physical education classes can have a strong influence.METHODS:National level data of school pupils (n = 21 515) in England were analysed to examine the association between school provision of physical education with sex, age, geographic and socioeconomic factors.RESULTS:Children attending independent schools had more scheduled physical education time (P < 0.001; 95% confidence interval (CI) 18 to 30 extra min per week). This association was true for males (P = 0.024); schools located in the South (P < 0.001; 95% CI 2 to 3) and rural areas (P < 0.001; 95% CI 3 to 5); or with a higher percentage of pupils eligible for free school meals (P < 0.001; 95% CI 3 to 4). Schools in more affluent areas (P < 0.001; 95% CI -1 to -2) and those with lower percentages of pupils from ethnic minorities (P < 0.001; 95% CI -1 to -2) also had higher minutes of physical education provision per week. Regarding age, 93% of schools met the guidelines in Years 1-9; only 45% did in Years 10-13.CONCLUSION:Differences in physical education were found in relation to school type, socioeconomic status and geographical factors. Age-related differences in compliance with guidelines are of concern; ways to increase provision for older children should be investigated.

Diet cost could influence dietary patterns, with potential health consequences. Assigning a monetary cost to diet is challenging, and there are contrasting methods in the literature. This study compares two methods—a food cost database linked to 4-day diet diaries and an individual cost calculated from household till receipts. The Diet and Nutrition Tool for Evaluation (DANTE) had supermarket prices (cost per 100 g) added to its food composition table. Agreement between diet costs calculated using DANTE from food diaries and expenditure recorded using food purchase till receipts for 325 individuals was assessed using correlation and Bland Altman (BA) plots. The mean difference between the methods' estimates was £0.10. The BA showed 95% limits of agreement of £2.88 and -£3.08. Excluding the highest 5% of diet cost values from each collection method reduced the mean difference to £0.02, with limits of agreement ranging from £2.31 to -£2.35. Agreement between the methods was stronger for males and for adults. Diet cost estimates using a food price database with 4-day food diaries are comparable to recorded expenditure from household till receipts at the population or group level. At the individual level, however, estimates differed by as much as £3.00 per day. The methods agreed less when estimating diet costs of children, females or those with more expensive diets.

The obesogenic environment model would suggest that increased availability or access to energy dense foods which are high in saturated fat may be related to obesity. The association between food outlet location, deprivation, weight status and ethnicity was analysed using individual level data on a sample of 1198 pregnant women in the UK Born in Bradford cohort using geographic information systems (GIS) methodology. In the non South Asian group 24% were obese as were 17% of the South Asian group (BMI > 30). Food outlet identification methods revealed 886 outlets that were allocated into 5 categories of food shops. More than 95% of all participants lived within 500 m of a fast food outlet. Women in higher areas of deprivation had greater access to fast food outlets and to other forms of food shops. Contrary to hypotheses, there was a negative association between BMI and fast food outlet density in close (250 m) proximity in the South Asian group. Overall, these women had greater access to all food stores including fast food outlets compared to the non South Asian group. The stronger association between area level deprivation and fast food density than with area level deprivation and obesity argues for more detailed accounts of the obesogenic environment that include measures of individual behaviour.

To assess the association between the consumption of fast food (FF) and body mass index (BMI) of teenagers in a large UK birth cohort. A structural equation modelling (SEM) approach was chosen to allow direct statistical testing of a theoretical model. SEM is a combination of confirmatory factor and path analysis, which allows for the inclusion of latent (unmeasured) variables. This approach was used to build two models: the effect of FF outlet visits and food choices and the effect of FF exposure on consumption and BMI. A total of 3620 participants had data for height and weight from the age 13 clinic and the frequency of FF outlet visits, and so were included in these analyses. This SEM model of food choices showed that increased frequency of eating at FF outlets is positively associated with higher consumption of unhealthy foods (β=0.29, P<0.001) and negatively associated with the consumption of healthy foods (β=−1.02, P<0.001). The SEM model of FF exposure and BMI showed that higher exposure to FF increases the frequency of visits to FF outlets (β=0.61, P<0.001), which is associated with higher body mass index standard deviation score (BMISDS; β=0.08, P<0.001). Deprivation was the largest contributing variable to the exposure (β=9.2, P<0.001). The teenagers who ate at FF restaurants consumed more unhealthy foods and were more likely to have higher BMISDS than those teenagers who did not eat frequently at FF restaurants. Teenagers who were exposed to more takeaway foods at home ate more frequently at FF restaurants and eating at FF restaurants was also associated with lower intakes of vegetables and raw fruit in this cohort.

BACKGROUND:Reducing childhood obesity is a key UK government target. Obesogenic environments are one of the major explanations for the rising prevalence and thus a constructive focus for preventive strategies. Spatial analysis techniques are used to provide more information about obesity at the neighbourhood level in order to help to shape local obesity-prevention policies.METHODS:Childhood obesity was defined by body mass index, using cross-sectional height and weight data for children aged 3-13 years (obesity>98th centile; British reference dataset). Relationships between childhood obesity and 12 simulated obesogenic variables were assessed using geographically weighted regression. These results were applied to three wards with different socio-economic backgrounds, tailoring local obesity-prevention policy.RESULTS:The spatial distribution of childhood obesity varied, with high prevalence in deprived and affluent areas. Key local covariates strongly associated with childhood obesity differed: in the affluent ward, they were perceived neighbourhood safety and fruit and vegetable consumption; in the deprived ward, expenditure on food, purchasing school meals, multiple television ownership and internet access; in all wards, perceived access to supermarkets and leisure facilities. Accordingly, different interventions/strategies may be more appropriate/effective in different areas.CONCLUSIONS:These analyses identify the covariates with the strongest local relationships with obesity and suggest how policy can be tailored to the specific needs of each micro-area: solutions need to be tailored to the locality to be most effective. This paper demonstrates the importance of small-area analysis in order to provide health planners with detailed information that may help them to prioritise interventions for maximum benefit.

BACKGROUND:The aim of this paper was to investigate variations in childhood obesity globally and spatially at the micro-level across Leeds.METHODS:Body mass index data from three sources were used. Children were aged 3-13 years. Obesity was defined as above the 98th centile (British reference dataset). The data were analysed by age group and gender, then tested for significant micro-level hot spots of childhood obesity using a spatial scan statistic and a two-level multilevel model.RESULTS:Older children (13 years) were 2.5 times (95% CI 2.1 to 3.1) more likely to be obese than younger children (3 years). Childhood obesity was significantly associated with deprived and affluent areas. 'Blue collar communities,' 'Constrained by circumstances' and 'Multicultural' had significantly higher (relative risk (RR): 1.1, 1.2, 1.2; 95% CI 1.0 to 1.2, 1.1 to 1.2, 1.1 to 1.3, respectively) obesity levels, and 'Typical traits' and 'Prospering suburbs' had significantly lower (RR: 0.9, 0.8; 95% CI 0.8 to 1.0, 0.7 to 0.9, respectively) obesity levels. In the unadjusted model, obesity 'hot spots' were found in deprived (RR 1.5) and affluent (RR 6.1) areas. After adjusting for demographic covariates, hot spots were found only in affluent areas (RR 1.6 to 1.9), and cold spots in affluent (RR 1.3 to 4.4) and deprived (RR up to 1.1) areas.CONCLUSION:These results suggest there is either a spread of obesity across socio-economic groups and/or something special about the high-/low-prevalence areas that affects the likelihood of obesity. The microlevel spatial analyses displayed the variations in obesity across Leeds thoroughly, identifying high-risk populations.

This paper describes global (whole of Leeds) and local (super output area) analyses of the relationship between childhood obesity and many 'obesogenic environment' variables, such as deprivation, urbanisation, access to local amenities, and perceived local safety, as well as dietary and physical activity behaviours. The analyses identify the covariates with the strongest relationships with obesity, and highlight variation in these relationships across Leeds, thus identifying 'at-risk' populations. This paper seeks to demonstrate the importance of analysis at the micro-level in order to provide health planners with additional information with which to tailor interventions and health policies to prevent childhood obesity.

This Working Paper is a part of PhD thesis 'Modelling Crime: A Spatial Microsimulation Approach' which aims to investigate the potential of spatial microsimulation for modelling crime. This Working Paper presents SimCrime, a static spatial microsimulation model for crime in Leeds. It is designed to estimate the likelihood of being a victim of crime and crime rates at the small area level in Leeds and to answer what-if questions about the effects of changes in the demographic and socio-economic characteristics of the future population. The model is based on individual microdata. Specifically, SimCrime combines individual microdata from the British Crime Survey (BCS) for which location data is only at the scale of large areas, with census statistics for smaller areas to create synthetic microdata estimates for output areas ?(OAs) in Leeds using a simulated annealing method. The new microdata dataset includes all the attributes from the original datasets. This allows variables such as crime victimisation from the BCS to be directly estimated for OAs.

The aim of this paper is to outline a research agenda for the estimation of household wealth in the USA. The authors argue that, although much progress has been made with the estimation of household income, such research has concentrated on wage and benefit income. A dynamic microsimulation model called CORSIM is used to estimate new indicators of wealth. These include household income derived from stocks and shares, retirement accounts, and financial gains made from house sales and inheritances. New indicators of debts include all household debts, especially mortgage debts. Although these indicators have been calculated at national levels in the past, it is argued that considerable benefits will accrue to our understanding of social problems and changing consumer lifestyles if these indicators can be estimated at regional and subregional levels of resolution.

The estimation of water demand is fundamental to effective water resource management. Water supply is measured at district level but true demand is not, and therefore studies of water‐pricing relations are limited and mass‐balance based assessment of leakage, illegal use, meter inaccuracies etc., are compromised. This paper describes the value and limitations of existing geodemographic methods, and an alternative technique widely used in other fields, microsimulation, is proposed. It is shown that geographic stability in demand relations is not found in all consumer commodities and cannot be assumed for water. Sampled data for Leeds, West Yorkshire, are used to construct a microsimulation model, and the results of that model are applied to the city of Leeds at ward level. Applicability is also demonstrated at enumeration district level.

The authors set out the principles and applications of spatial modelling and GIS and demonstrate its use in location decisions and strategic planning. It demonstrated how intelligent GIS systems can be used to analyze the large amounts of data available to industry in order for informed decisions to be made. The book: demonstrates the importance of geographical thinking in business; considers the type of data available and how it can be used; shows how performance indicators can provide management information; shows how the geographical modelling processes can aid policy-making; illustrates the use of these methods in the retailing, financial services, health care, and education sectors; and provides examples drawn from academic research and real life case studies from around the world.

There has been a considerable upsurge of interest in spatial statistics in soil science over thepast decade. This reflects a growing need by the scientific community to take into account the non-random nature of the distributions of variables measured at different locations. One tool which can meet many of these needs is kriging. The kriging technique allows values of a given, spatiallydependent, variate to be predicted at points where no measurements were made. It is then possible to construct a contour map for that variate. Based on the theoretically developed equations, computer programs were written to carry out the predictions. The six developed computer programs wereapplied to data collected in the experiments. A 50-by 100-m field was sampled on two occasions at a total of 60 locations and at five to 11 depths to study the spatial variability of pH, Ca, Mg, K, P, organic matter, texture, bulk density, soil water retention, and saturated hydraulic conductivity.The programs were written in such a manner that data sets obtained at two different times could be combined into one for prediction purposes. The equations developed in the theoretical sections are the same as those used in the computer programs presented, with most of the notation remainingconsistent throughout