Background

Obesity in children, and adults, is a rapidly growing problem in the UK and worldwide and has been increasing at accelerating rates in more recent years. It is associated with a number of co-morbidities in childhood and with increased risk of adult disease, particularly cardiovascular disease, hypertension and type 2 diabetes. Obesity related diseases account for a substantial proportion of costs of health care resources worldwide (WHO, 2004). The Select Committee Report on Obesity (2004) estimated that the total cost of treating obesity in the UK was £3.3-3.7 billion in 2002, increasing to £7 billion by 2020, and the latest estimates (Foresight report, 2007) put it at £45.5 billion by 2050.

A dietary risk factor for obesity is a high consumption of high fat, salt and sugar (HFSS) foods, and in particular "fast foods" (energy dense, nutrient poor, foods) (Reidpath et al, 2002; Mendoza et al, 2007; Procter, 2007a). The popularity of fast foods has increased over recent years and consumption by children has risen 300% over the last twenty years (St-Onge et al, 2003). It has been shown that on days when children eat fast food, then their energy and fat intake is likely to be higher, and fruit and vegetable intake lower, than normal (Bowman et al, 2004). Also children who eat fast food frequently consume more total energy, more energy per gram of food, more total fat, more total carbohydrate, more added sugars, and less fibre, less milk, fewer fruit and vegetables than children who eat fast food infrequently (Bowman et al, 2004; Speiser et al, 2005). Accordingly, it may not be the consumption of fast food, per se, that leads to obesity (as both lean and obese people consume fast food), but the fact that overweight consumers of fast food are less likely to adjust their daily energy intake to take account of an energy dense fast food meal than their lean counterparts (Ebbeling et al, 2004). This "passive over-consumption" is due to the weak innate ability of humans to identify energy dense foods and thus do not correspondingly reduce the amount of food eaten to achieve energy balance (Prentice & Jebb, 2003).

Household income has been shown to be a significant predictor of obesity (inverse relationship) (Strauss & Knight, 1999; Stamatakis et al, 2005), as has deprivation (Kinra et al, 2000; Kinra et al, 2005) and low socio-economic status (SES) (Parsons et al, 1999; Hardy et al, 2000; Okasha et al, 2003; Monden et al, 2006). The increased prevalence of obesity from more deprived backgrounds could be due to a multitude of factors: dietary differences are often apparent; lack of opportunity / funds for activities, so TV viewing is the primary leisure activity by default; constraints on calories per pound, which focuses purchases on energy dense foods. Also the association between SES and obesity may be due to SES acting as a proxy for the effect of multiple adverse circumstances, which are then manifesting as obesity in the long term (Power & Parsons, 2000). For example, it has been shown that there is a higher density of fast food outlets in poorer areas, which may (partially) explain the phenomenon (Reidpath et al, 2002). Obesity and deprivation may be connected due to the routine consumption of a high energy dense, low cost diet (Drewnoski, 2003). Energy dense diets are associated with lower diet quality and lower costs, and vice versa (Cade et al, 1999; Darmon et al 2004; Andrieu et al, 2006; Drewnoski et al, 2007). Research shows that low income households are associated with a high energy dense diet (Mendoza et al, 2006).

There is a large literature contending that the environment, particularly that of our place of residence or school/work, impacts on health related behaviour and therefore health outcomes (Macintyre et al, 2002; Mohan et al, 2005). This increased interest in the effect of place on health seems to stem from the publication of the Black Report some twenty-five years ago (Black et al, 1980). Since then many authors have shown that deprivation is related to mortality as well as to specific health outcomes. An important debate within health geography is that of whether the environment has compositional or contextual effects on health. That is, the issue of whether individual or area effects on health predominate. Accordingly, the compositional school of thought is that individuals have risks of ill health, therefore an area's ill health is reflective of that of the individuals who live (or work, as appropriate) there. For example, do obese people congregate in similar locations? Conversely, the contextual theory is that living (or working) in an area imposes ill health on that area's residents. For example, do certain attributes of places cause its inhabitants to become obese? This study seeks to address the question of fast food and obesity from both contextual (the physical environment of fast food outlets) and compositional (individual consumption) perspectives

Objectives and hypothesis of project

To describe, measure and map obesity, fast food outlet density and fast food consumption in Bristol, UK(and Leeds,UK)

To investigate the relationship between density of outlets and area measures of deprivation, and to examine whether there is a dose-response relationship between deprivation and outlet density (e.g. as deprivation rises, outlet density rises).

To assess the relationship between obesity and energy dense, low nutrient ("fast") foods: availability and consumption.

To consider the policy implications of the results.

Hypothesis: Fast food outlet density and consumption will be related to the patterns of obesity. Families with the lowest incomes will be more influenced by or susceptible to these factors than higher income households.

Methods

Stage 1 - Describing and mapping obesity

The first stage in the study of spatial variations in obesity will be to build a geographic information system (GIS). The GIS will contain all the census data for Bristol/Avon Region at 'output area' (the new small area census unit), as well as the Index of Deprivation (Communities and Local Government, 2004) at 'super output area' level. This will allow us to identify areas of both low and high social deprivation. The GIS will also contain obesity data from the ALSPAC dataset.

Spatial microsimulation modelling will then be used for estimating or predicting small area levels of obesity. Specifically, we will build on the SimObesity model (a deterministic, re-weighting, spatial microsimulation model developed in the School of Geography, University of Leeds), which combines individual micro-data from national level surveys, such as the Health Survey for England (HSE), which only have location data at the scale of large areas, with census statistics for lower Super Output Areas (SOAs) to create synthetic micro-data estimates for SOAs in Leeds. The new, synthesised, micro dataset includes all the attributes from both the survey and the census datasets. The key benefits of using spatial microsimulation are: to add more attributes to the population under analysis by adding census data to the survey data thereby creating a richer dataset; to get data to a smaller geographical scale in order to identify 'hot spots' of problem areas; and it is cheaper and quicker than commissioning a survey of the local area. There is significant experience of spatial microsimulation modelling in the University of Leeds (Clarke, 1996; Ballas et al, 2005; Procter, 2007b).

Significant clusters of obesity (from both children and adults) will be identified using Spatial Scan Statistics (such as SaTScan, Kulldorff, 1997). Temporal (serial cross sectional) as well as geographic analysis will be undertaken.

Stage 2 - Relationship between fast food and obesity

Once the patterns and spatial/temporal variations of obesity have been understood, the next exercise is to consider the relationship with fast food consumption. There are two components to this analysis.

Firstly we wish to examine the relationship between fast food outlet density and obesity. This stage of the project will involve sourcing (e.g. from the council or yellow pages) the location of fast food restaurants in the study area and transposing this data into a GIS. Ground truthing of the data will also be required (i.e. physically checking the locations exist). Then fast food outlet density for output areas can be calculated and the relationship with obesity considered. The association with an area measure of deprivation will also be considered.

The next stage is to take this further, and to consider the relationship between actual fast food consumption and obesity. This Fast Food consumption data regarding diet is available from the ALSPAC cohort and will be added to the previously described GIS model. This data may be used to simulate fastfood consumption in children in other geographical areas using spatial microsimulation modelling.

Analysis of these data will involve the use of multi-level modelling and geographically weighted regression, in order that the special properties of spatial data (e.g. spatial autocorrelation) can be accounted for.

Stage 3 - Policy implications

The final stage in the project will be to consider how the results from the previous two stages can be utilised to influence policy and to help slow down the rising rates of obesity. The spatial microsimulation model will be used to undertake "what if" scenario analysis to theoretically evaluate the potential impact of policy/intervention suggestions on the prevalence of obesity in say 5, 10 or 20 years time, which is cheaper and much quicker than running a pilot study. Further, we will work with key local stakeholders (from both public and private sector, as both have a responsibility to endorse public health (Stafford et al, 2007)) to enable suggestions to be rated for validity, relevance and potential for change. This is important, as small individual programmes are unlikely to make a difference to the obesity epidemic. For maximum benefit an obesity prevention policy needs to take a coordinated, multi-component, multi-sectoral public health approach and overall policy unity and coherence is required, with buy-in of all stakeholders.

How will the research be useful and to whom

This research will be useful to the PCTs for health planning as it will identify any hot spots of problem areas of obesity. It will also elucidate further on the impact of aspects of the environment and diet on obesity and whether these issues can be addressed using public health policies, and if so, the likely future impact of such changes.

Interdisciplinary nature of the research

This project is clearly interdisciplinary. It combines the quantitative geographic techniques (such as spatial microsimulation, GIS) with those from medical research, using data from local studies as well as national cross sectional surveys. Training will be given for use of the spatial techniques (e.g. SimObesity, ArcGIS, geographically weighted regression). There is extensive experience of using these techniques within the University of Leeds, plus some external courses are available.

Timetable

Year 1/2: Develop literature review; gather information for mapping; learn techniques; carry out stage 1

Year 3/4: Carry out stage 2

Year 5/6: Carry out stage 3 and write up thesis

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