Micro‐level analysis of childhood obesity, diet, physical activity, residential socioeconomic and social capital variables: where are the obesogenic environments in Leeds?

Procter, Kimberley L; Clarke, Graham; Ransley, Joan K; Cade, Janet

doi:10.1111/j.1475-4762.2008.00822.x

Cited by 63 publications

(45 citation statements)

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“…However, public health researchers have begun to challenge the stationarity assumption. Using spatial regression modeling, such as geographically weighted regression (GWR), a technique that allows for spatial variations in parameter estimates (19,20), investigators have highlighted variations in associations across space between a range of environmental exposures and outcomes such as diet (21), obesity (22)(23)(24)(25)(26)(27), active transportation (28), and birth weight (29). Fraser et al (21) have observed marked spatial variations, in both magnitude and nature, in the relationship between residential exposure to fast-food outlets and fast-food consumption among adolescents living in Bristol, United Kingdom.…”

mentioning

confidence: 99%

The Local Food Environment and Fruit and Vegetable Intake: A Geographically Weighted Regression Approach in the ORiEL Study

Clary¹,

Lewis²,

Flint³

et al. 2016

Am. J. Epidemiol.

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Studies that explore associations between the local food environment and diet routinely use global regression models, which assume that relationships are invariant across space, yet such stationarity assumptions have been little tested. We used global and geographically weighted regression models to explore associations between the residential food environment and fruit and vegetable intake. Analyses were performed in 4 boroughs of London, United Kingdom, using data collected between April 2012 and July 2012 from 969 adults in the Olympic Regeneration in East London Study. Exposures were assessed both as absolute densities of healthy and unhealthy outlets, taken separately, and as a relative measure (proportion of total outlets classified as healthy). Overall, local models performed better than global models (lower Akaike information criterion). Locally estimated coefficients varied across space, regardless of the type of exposure measure, although changes of sign were observed only when absolute measures were used. Despite findings from global models showing significant associations between the relative measure and fruit and vegetable intake (β = 0.022; P < 0.01) only, geographically weighted regression models using absolute measures outperformed models using relative measures. This study suggests that greater attention should be given to nonstationary relationships between the food environment and diet. It further challenges the idea that a single measure of exposure, whether relative or absolute, can reflect the many ways the food environment may shape health behaviors. absolute exposure; fruit and vegetable intake; geographically weighted regression; local food environment; nonstationarity; relative exposure Abbreviations: AICc, corrected Akaike information criterion; GWR, geographically weighted regression; ORiEL, Olympic Regeneration in East London.Over the last decade, an extensive body of research has investigated how the local food environment may be related to dietary behaviors (1). A large majority of studies have used global regression to model the association between exposure to either healthy or unhealthy food environments and diet-related outcomes (2, 3). However, reviews have highlighted the lack of consistency in findings (2, 3), with associations for the same type of relationship being positive (4-10), negative (11), or nonexistent (12-18).By using global regression models, researchers have implicitly relied on the assumption of a stationary relationship; that is, parameter estimates describe what is assumed to be an invariant relationship across space. However, public health researchers have begun to challenge the stationarity assumption. Using spatial regression modeling, such as geographically weighted regression (GWR), a technique that allows for spatial variations in parameter estimates (19,20), investigators have highlighted variations in associations across space between a range of environmental exposures and outcomes such as diet (21), obesity (22-27), active transportation (28), and bi...

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mentioning

confidence: 99%

The Local Food Environment and Fruit and Vegetable Intake: A Geographically Weighted Regression Approach in the ORiEL Study

Clary¹,

Lewis²,

Flint³

et al. 2016

Am. J. Epidemiol.

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“…This proportion is displayed for the census dataset, the simulated dataset, and the HSE sub-sample dataset for the Yorkshire and Humber GOR, followed by the differences in these proportions between the different datasets. For example, 48.5% of the people living in the study area according to the Census dataset are male (1) and (2) Differences between (1) and (3) Differences between (2) and (3) Obese n/a 20.8 28.7 n/a n/a 7.9 authors are seeing both effects (Cummins et al 2007;Procter et al 2008). It would be informative to investigate the areas with particularly high (and low) relative risks to seek to determine whether there is something about these areas that impacts the health behaviours of individuals or otherwise results in obesity.…”

Section: Discussionmentioning

confidence: 96%

“…These models have been used to model health data (for example, see Morrissey et al 2008;Procter et al 2008;Tomintz et al 2008). Spatial microsimulation models can provide small area estimates that could be used in a practical way to help to influence policy.…”

Section: Introductionmentioning

confidence: 99%

“…crime, street connectivity, access to recreational facilities, and deprivation (Nelson et al 2006). Further, examination of micro-area data enables tailored programmes based on locally relevant evidence to be designed and implemented, thereby responding to local differences in the prevalence of health related behaviours and obesity (Procter et al 2008). These bespoke programmes are likely to be more effective at preventing obesity.…”

Section: Introductionmentioning

confidence: 99%

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Internal and External Validation of Spatial Microsimulation Models: Small Area Estimates of Adult Obesity

Edwards

Clarke

Thomas

et al. 2010

Appl. Spatial Analysis

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Spatial microsimulation models can be used to estimate previously unknown data at the micro-level, although validation of these models can be challenging. This paper seeks to describe an approach to validation of these models. Obesity data in adults were estimated at the small area level using a static, deterministic, spatial microsimulation model called SimObesity. This model utilised both Census 2001 data and the Health Survey for England for 2004-2006. Regression analysis was used to identify the covariates that were the strongest predictors of obesity and these were used as the model input variables. The model was calibrated using regression and equal variance t-tests. Two methods of external validation were undertaken; aggregating obesity data to a coarser geographical level at which obesity data was available, and secondly using small area level cancer data for tumour sites known to be correlated to obesity. The output obesity data were mapped and statistically significant hot (cold) spots of high (low) prevalence of obesity identified. Both internal and external validation showed low errors, suggesting this was a satisfactory simulation. Statistically significant hot and cold spots of (simulated) obesity prevalence existed, even after adjusting for age. This paper emphasises three steps to validation of spatial microsimulation models: 1. Accurate simulations require strong correlations between the input and output variables; 2. It is essential to internally validate the models; 3. Use all means possible to externally validate the model.

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“…The essence of the problem is that many health and lifestyle conditions exhibit demographic profiles which are strongly skewed and richly characterised within surveys like the Health Survey for England and British Household Panel Survey, but none of these provides the spatial detail required for the planning of local services. Through the combination of synthetic individual demographic profiles for small areas, which may be generated from an abundance of Census Area Statistics (Birkin & Clarke 1988;Ballas et al 2005), with health profiles of real but anonymised individuals from survey sources, researchers have been able to estimate local distributions of conditions as diverse as diabetes (Smith 2007), obesity (Procter et al 2008) and smoking (Tomintz et al 2008). In addition to their use as a backdrop for service evaluation (see below), simulated distributions can provide valuable estimates of 'missing data' in their own right.…”

Section: Local Policy Issuesmentioning

confidence: 99%

The achievements and future potential of applied quantitative geography: A case study

Birkin¹,

Clarke²,

Clarke³

et al. 2014

Geogr. Pol.

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There has been much debate about the usefulness of human geography for public and private sector planning. In this paper we make the case in terms of quantitative analysis in geography. We provide a wealth of applications of applied research from the perspective of one team of quantitative geographers -based in the School of Geography at the University of Leeds. This research is rooted in spatial interaction modelling, microsimulation, spatial optimisation and geodemographics. A number of applications are explored and their benefits articulated -for end-users. i.e. planners in the broadest sense of the word, the University and the School, for students and, more broadly, for the research environment within applied spatial analysis.

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Micro‐level analysis of childhood obesity, diet, physical activity, residential socioeconomic and social capital variables: where are the obesogenic environments in Leeds?

Cited by 63 publications

References 50 publications

The Local Food Environment and Fruit and Vegetable Intake: A Geographically Weighted Regression Approach in the ORiEL Study

The Local Food Environment and Fruit and Vegetable Intake: A Geographically Weighted Regression Approach in the ORiEL Study

Internal and External Validation of Spatial Microsimulation Models: Small Area Estimates of Adult Obesity

The achievements and future potential of applied quantitative geography: A case study

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