Kimberley L. Edwards

The association between the geography of fast food outlets and childhood obesity rates in Leeds, UK.

OBJECTIVE To analyse the association between childhood overweight and obesity and the density and proximity of fast food outlets in relation to the childs residential postcode. DESIGN This was an observational study using individual level height/weight data and geographic information systems methodology. SETTING Leeds in West Yorkshire, UK. This area consists of 476 lower super-output areas. PARTICIPANTS Children aged 3-14 years who lived within the Leeds metropolitan boundaries (n=33,594). MAIN OUTCOME MEASURES The number of fast food outlets per area and the distance to the nearest fast food outlet from the childs home address. The weight status of the child: overweight, obese or neither. RESULTS 27.1% of the children were overweight or obese with 12.6% classified as obese. There is a significant positive correlation (p<0.001) between density of fast food outlets and higher deprivation. A higher density of fast food outlets was significantly associated (p=0.02) with the child being obese (or overweight/obese) in the generalised estimating equation model which also included sex, age and deprivation. No significant association between distance to the nearest fast food outlet and overweight or obese status was found. CONCLUSIONS There is a positive relationship between the density of fast food outlets per area and the obesity status of children in Leeds. There is also a significant association between fast food outlet density and areas of higher deprivation.

The neighbourhood matters: studying exposures relevant to childhood obesity and the policy implications in Leeds, UK

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.

The design and validation of a spatial microsimulation model of obesogenic environments for children in Leeds, UK: SimObesity☆☆☆

Obesogenic environments are a major explanation for the rapidly increasing prevalence in obesity. Investigating the relationship between obesity and obesogenic variables at the micro-level will increase our understanding about local differences in risk factors for obesity. SimObesity is a spatial microsimulation model designed to create micro-level estimates of obesogenic environment variables in the city of Leeds in the UK: consisting of a plethora of health, environment, and socio-economic variables. It combines individual micro-data from two national surveys with a coarse geography, with geographically finer scaled data from the 2001 UK Census, using a reweighting deterministic algorithm. This creates a synthetic population of individuals/households in Leeds with attributes from both the survey and census datasets. Logistic regression analyses identify suitable constraint variables to use. The model is validated using linear regression and equal variance t-tests. Height, weight, age, gender, and residential postcode data were collected on children aged 3-13 years in the Leeds metropolitan area, and obesity described as above the 98th centile for the British reference dataset. Geographically weighted regression is used to investigate the relationship between different obesogenic environments and childhood obesity. Validation shows that the small-area estimates were robust. The different obesogenic environments, as well as the parameter estimates from the corresponding local regression analyses, are mapped, all of which demonstrate non-stationary relationships. These results show that social capital and poverty are strongly associated with childhood obesity. This paper demonstrates a methodology to estimate health variables at the small-area level. The key to this technique is the choice of the models input variables, which must be predictors for the output variables; this factor has not been stressed in other spatial microsimulation work. It also provides further evidence for the existence of obesogenic environments for children.

Joint disease mapping using six cancers in the Yorkshire region of England

ObjectivesThe aims of this study were to model jointly the incidence rates of six smoking related cancers in the Yorkshire region of England, to explore the patterns of spatial correlation amongst them, and to estimate the relative weight of smoking and other shared risk factors for the relevant disease sites, both before and after adjustment for socioeconomic background (SEB).MethodsData on the incidence of oesophagus, stomach, pancreas, lung, kidney, and bladder cancers between 1983 and 2003 were extracted from the Northern & Yorkshire Cancer Registry database for the 532 electoral wards in the Yorkshire region. Using postcode of residence, each case was assigned an area-based measure of SEB using the Townsend index. Standardised incidence ratios (SIRs) were calculated for each cancer site and their correlations investigated. The joint analysis of the spatial variation in incidence used a Bayesian shared-component model. Three components were included to represent differences in smoking (for all six sites), bodyweight/obesity (for oesophagus, pancreas and kidney cancers) and diet/alcohol consumption (for oesophagus and stomach cancers).ResultsThe incidence of cancers of the oesophagus, pancreas, kidney, and bladder was relatively evenly distributed across the region. The incidence of stomach and lung cancers was more clustered around the urban areas in the south of the region, and these two cancers were significantly associated with higher levels of area deprivation. The incidence of lung cancer was most impacted by adjustment for SEB, with the rural/urban split becoming less apparent. The component representing smoking had a larger effect on cancer incidence in the eastern part of the region. The effects of the other two components were small and disappeared after adjustment for SEB.ConclusionThis study demonstrates the feasibility of joint disease modelling using data from six cancer sites. Incidence estimates are more precise than those obtained without smoothing. This methodology may be an important tool to help authorities evaluate healthcare system performance and the impact of policies.

What is the cost of a healthy diet? Using diet data from the UK Women's Cohort Study

Background A healthy diet is important to promote health and well-being while preventing chronic disease. However, the monetary cost of consuming such a diet can be a perceived barrier. This study will investigate the cost of consuming a range of dietary patterns. Methods A cross-sectional analysis, where cost of diet was assigned to dietary intakes recorded using a Food Frequency Questionnaire. A mean daily diet cost was calculated for seven data-driven dietary patterns. These dietary patterns were given a healthiness score according to how well they comply with the UK Department of Healths Eatwell Plate guidelines. This study involved ∼35 000 women recruited in the 1990s into the UK Womens Cohort Study. Results A significant positive association was observed between diet cost and healthiness of the diet (p for trend >0.001). The healthiest dietary pattern was double the price of the least healthy, £6.63/day and £3.29/day, respectively. Dietary diversity, described by the patterns, was also shown to be associated with increased cost. Those with higher education and a professional or managerial occupation were more likely to consume a healthier diet. Conclusions A healthy diet is more expensive to the consumer than a less healthy one. In order to promote health through diet and reduce potential inequalities in health, it seems sensible that healthier food choices should be made more accessible to all.

Running and knee osteoarthritis: a systematic review and meta-analysis

Background: Osteoarthritis (OA) is a chronic condition characterized by pain, impaired function, and reduced quality of life. A number of risk factors for knee OA have been identified, such as obesity, occupation, and injury. The association between knee OA and physical activity or particular sports such as running is less clear. Previous reviews, and the evidence that informs them, present contradictory or inconclusive findings. Purpose: This systematic review aimed to determine the association between running and the development of knee OA. Study Design: Systematic review and meta-analysis. Methods: Four electronic databases were searched, along with citations in eligible articles and reviews and the contents of recent journal issues. Two reviewers independently screened the titles and abstracts using prespecified eligibility criteria. Full-text articles were also independently assessed for eligibility. Eligible studies were those in which running or running-related sports (eg, triathlon or orienteering) were assessed as a risk factor for the onset or progression of knee OA in adults. Relevant outcomes included (1) diagnosis of knee OA, (2) radiographic markers of knee OA, (3) knee joint surgery for OA, (4) knee pain, and (5) knee-associated disability. Risk of bias was judged by use of the Newcastle-Ottawa scale. A random-effects meta-analysis was performed with case-control studies investigating arthroplasty. Results: After de-duplication, the search returned 1322 records. Of these, 153 full-text articles were assessed; 25 were eligible, describing 15 studies: 11 cohort (6 retrospective) and 4 case-control studies. Findings of studies with a diagnostic OA outcome were mixed. Some radiographic differences were observed in runners, but only at baseline within some subgroups. Meta-analysis suggested a protective effect of running against surgery due to OA: pooled odds ratio 0.46 (95% CI, 0.30-0.71). The I2 was 0% (95% CI, 0%-73%). Evidence relating to symptomatic outcomes was sparse and inconclusive. Conclusion: With this evidence, it is not possible to determine the role of running in knee OA. Moderate- to low-quality evidence suggests no association with OA diagnosis, a positive association with OA diagnosis, and a negative association with knee OA surgery. Conflicting results may reflect methodological heterogeneity. More evidence from well-designed, prospective studies is needed to clarify the contradictions.

Validation of Spatial Microsimulation Models

Spatial microsimulation models, both static and dynamic, are a useful means to estimate area-level data, whatever these data are regarding, be it health, socio-economic status or income/finance. However, in order for planners and government to be able to use and rely on these data, it is essential that the modellers can show that the estimates are an accurate presentation of the real world and are reliable. Generally, to verify the integrity of any model, it is necessary to validate the model outputs, using both internal and external validation methods. However, for spatial microsimulation models, validation is a massive challenge. This is because, generally, these models are used to estimate data that does not otherwise exist, perhaps due to confidentiality reasons (e.g. income or medical data for individuals) and/or because it would be expensive and time consuming to try to collect a large sample of data for the population in question (particularly as, in many countries, national sample datasets already exist, thus it would also be a duplication of both time and money).

Childhood consumption of fruit and vegetables across England: a study of 2306 6–7-year-olds in 2007

The School Fruit and Vegetable Scheme (SFVS) provides children in government-run schools in England with a free piece of fruit or a vegetable each school day for the first 3 years of school. The present study examines the impact of the SFVS, in terms of its contribution towards the total daily intake of fruit and vegetables by children across England. Quantitative dietary data were collected from 2306 children in their third year of school, from 128 schools, using a 24 h food diary. The data were examined at different spatial scales, and variations in the impact of the scheme across areas with different socio-economic characteristics were analysed using a deprivation index and a geodemographic classification. The uptake of the SFVS and the total intake of fruit and vegetables by children varied across different parts of England. Participation in the SFVS was positively associated with fruit and vegetable consumption. That is, in any one area, those children who participated in the SFVS consumed more fruit and vegetables. However, children living in deprived areas still consumed less fruit and vegetables than children living in more advantaged areas: the mean daily frequency of fruit and vegetables consumed, and rates of consumption of fruit or vegetables five times or more per d, decreased as deprivation increased (r -0.860; P = 0.001; r -0.842; P = 0.002). So the SFVS does not eliminate the socio-economic gradient in fruit and vegetable consumption, but it does help to increase fruit and vegetable consumption in deprived (and affluent) areas.

A cross-sectional study examining the pattern of childhood obesity in Leeds: affluence is not protective

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.

SimObesity: Combinatorial Optimisation (Deterministic) Model

This chapter details a deterministic method of spatial microsimulation modelling, which uses a set of algorithms based on combinatorial optimisation. This model, called SimObesity, was developed within the School of Geography, University of Leeds. An application of this model to estimate adult obesity prevalence is demonstrated. The chapter discusses the value of adopting a spatial microsimulation procedure and briefly debates the pros and cons of probabilistic and deterministic techniques for data imputation. Having chosen the latter, the chapter discusses the data and methodology used to estimate small-area prevalence of obesity in northern England. The results are discussed both in terms of the reliability of the model outputs (validation) and in terms of the spatial variation in estimated patterns.