Methods of measuring associations between the Retail Food Environment and weight status: Importance of classifications and metrics

Wilkins, Emma; Morris, Michelle; Radley, Duncan; Griffiths, Claire

doi:10.1016/j.ssmph.2019.100404

Cited by 30 publications

(52 citation statements)

References 62 publications

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“…Conclusion: There is some indication that neighbourhood greenspace is related to physical activity. However, in agreement with other literature 26,27 , this study shows different definitions of environment can produce different results. Future work must use measures that are relevant, consistent and transferable.…”

Section: Methods and Resultssupporting

confidence: 91%

Evidence from big data in obesity research: international case studies

et al. 2020

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Background/Objective: Obesity is thought to be the product of over 100 different factors, interacting as a complex system over multiple levels. Understanding the drivers of obesity requires considerable data, which are challenging, costly and timeconsuming to collect through traditional means. Use of 'big data' presents a potential solution to this challenge. Big data is defined by Delphi consensus as: "always digital, has a large sample size, and a large volume or variety or velocity of variables that require additional computing power 1. 'Additional computing power' introduces the concept of Big Data Analytics. "The aim of this paper is to showcase international research case studies presented during a seminar series held by the Economic and Social Research Council (ESRC) Strategic Network for Obesity in the UK. These are intended to provide an in-depth view of how big data can be used in obesity research, and the specific benefits, limitations and challenges encountered. Methods and results: Three case studies are presented. The first investigated the influence of the built environment on physical activity. It used spatial data on green spaces and exercise facilities alongside individual-level data on physical activity and swipe card entry to leisure centres, collected as part of a local authority exercise class initiative. The second used a variety of linked electronic health datasets to investigate associations between obesity surgery and the risk of developing cancer. The third used data on tax parcel values alongside data from the Seattle Obesity Study to investigate sociodemographic determinants of obesity in Seattle. Conclusions: The case studies demonstrated how big data could be used to augment traditional data to capture a broader range of variables in the obesity system. They also showed that big data can present improvements over traditional data in relation

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Section: Methods and Resultssupporting

confidence: 91%

Evidence from big data in obesity research: international case studies

et al. 2020

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“…Although a recent Australian study noted “ratio-based measures of healthy to unhealthy food stores have been rarely investigated” ( [ 48 ] p.103), it appears that the use of ratio and proportion measures are in fact prominent in the food environment field with a full systematic search of the literature likely to reveal even more studies than those already cited in this debate. Whilst it is fully acknowledged that measures are needed that capture the mix of food retail stores available, recent calls to advocate for ratio and proportion measures may be misguided [ 7 , 26 , 41 , 51 , 52 ].…”

Section: Discussionmentioning

confidence: 99%

“…It is recognised that some (not all) of the issues raised hold true for absolute quantity measures. However, one of the unique points about ratio and proportion measures is that they are advocated for as an advance on absolute quantity measures and often viewed as being more sophisticated [ 26 , 51 , 52 ]. As demonstrated, this is potentially not the case and thus this debate paper makes an important contribution to the literature by highlighting these issues with the aim of redirecting the collective focus of this field.…”

Section: Discussionmentioning

confidence: 99%

“…For example, if an individual is exposed to three unhealthy food stores and one healthy food store, the ratio of unhealthy food stores to healthy food stores for that individual is 3 (i.e., unhealthy/ healthy = ratio of 3/1). Commonly used proportion indicators measure the proportion of all food stores classified as either healthy [ 29 – 34 ] or unhealthy [ 7 , 34 , 35 ] or the proportion of all restaurants that are fast food restaurants [ 25 , 26 , 32 , 36 – 39 ]. Again, if an individual is exposed to three unhealthy food stores and one healthy food store, the proportion of healthy stores is 0.25 (i.e., a healthy/total number of stores = 1/(1 + 3) = 1/4).…”

Section: Introductionmentioning

confidence: 99%

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The use and misuse of ratio and proportion exposure measures in food environment research

Thornton

Lamb

White

2020

Int J Behav Nutr Phys Act

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Background The food stores within residential environments are increasingly investigated as a possible mechanism driving food behaviours and health outcomes. Whilst increased emphasis is being placed on the type of study designs used and how we measure the outcomes, surprisingly little attention gets diverted to the measures of the food environment beyond calls for standardised approaches for food store coding and geographic scales of exposure. Food environments are a challenging concept to measure and model and the use of ratio and proportion measures are becoming more common in food environment research. Whilst these are seemingly an advance on single store type indicators, such as simply counting the number of supermarkets or fast food restaurants present, they have several limitations that do not appear to have been fully considered. Main body In this article we report on five issues related to the use of ratio and proportion food environment measures: 1) binary categorisation of food stores; 2) whether they truly reflect a more or less healthy food environment; 3) issues with these measures not reflecting the quantity of food stores; 4) difficulties when no stores are present; and 5) complications in statistical treatment and interpretation of ratio and proportion measures. Each of these issues are underappreciated in the literature to date and highlight that ratio and proportion measures need to be treated with caution. Conclusion Calls for the broader adoption of relative food environment measures may be misguided. Whilst we should continue to search for better ways to represent the complexity of food environments, ratio and proportion measures are unlikely to be the answer.

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“…For example, it was shown that people do not shop for food in their immediate neighborhood but will instead travel great distances to the supermarket or fast food restaurant of choice . Third, the choice of BE variables can influence the magnitude and direction of the observed association between BE and health; not all such metrics are predictive of health . Relatedly, investigators should be more transparent regarding the variables and methods used to quantify the BE .…”

Section: Evolving Measures Of Be Exposurementioning

confidence: 99%

Obesity and the Built Environment: A Reappraisal

Drewnowski

Buszkiewicz

Aggarwal

et al. 2019

Obesity

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The built environment (BE) has been viewed as an important determinant of health. Numerous studies have linked BE exposure, captured using a variety of methods, to diet quality and to area prevalence of obesity, diabetes, and cardiovascular disease. First‐generation studies defined the neighborhood BE as the area around the home. Second‐generation studies turned from home‐centric to person‐centric BE measures, capturing an individual’s movements in space and time. Those studies made effective uses of global positioning system tracking devices and mobile phones, sometimes coupled with accelerometers and remote sensors. Activity space metrics explored travel paths, modes, and destinations to assess BE exposure that was both person and context specific. However, as measures of the contextual exposome have become ever more fine‐grained and increasingly complex, connections to long‐term chronic diseases with complex etiologies, such as obesity, are in danger of being lost. Furthermore, few studies on obesity and the BE have included intermediate energy balance behaviors, such as diet and physical activity, or explored the potential roles of social interactions or psychosocial pathways. Emerging survey‐based applications that identify habitual destinations and associated travel patterns may become the third generation of tools to capture health‐relevant BE exposures in the long term.

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Methods of measuring associations between the Retail Food Environment and weight status: Importance of classifications and metrics

Cited by 30 publications

References 62 publications

Evidence from big data in obesity research: international case studies

Evidence from big data in obesity research: international case studies

The use and misuse of ratio and proportion exposure measures in food environment research

Obesity and the Built Environment: A Reappraisal

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